The Battle of Neighborhoods - Notebook.ipynb

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        {
            "source": "<h1><center>The Battle of Neighborhoods</center></h1>", 
            "cell_type": "markdown", 
            "metadata": {
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            "source": "This **capstone project** has been submitted as part of the requirements for completion of the **IBM Data Science Professional Certificate** on Coursera. In general, this project would be encompassing a series of Data Science techniques, including, but not limited to, Web Scraping (using BeautifulSoup and Requests), Data Cleaning, Data Wrangling and Machine Learning (K-Means clustering algorithm).", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Importing required libraries and packages", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 1, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
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                    "name": "stdout", 
                    "text": "Requirement already satisfied: geocoder in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (1.38.1)\nRequirement already satisfied: requests in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from geocoder) (2.18.4)\nRequirement already satisfied: future in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from geocoder) (0.16.0)\nRequirement already satisfied: ratelim in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from geocoder) (0.1.6)\nRequirement already satisfied: click in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from geocoder) (6.7)\nRequirement already satisfied: six in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from geocoder) (1.11.0)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->geocoder) (3.0.4)\nRequirement already satisfied: idna<2.7,>=2.5 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->geocoder) (2.6)\nRequirement already satisfied: urllib3<1.23,>=1.21.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->geocoder) (1.22)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->geocoder) (2019.3.9)\nRequirement already satisfied: decorator in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from ratelim->geocoder) (4.1.2)\n\u001b[31mtensorflow 1.3.0 requires tensorflow-tensorboard<0.2.0,>=0.1.0, which is not installed.\u001b[0m\nRequirement already satisfied: folium in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (0.9.1)\nRequirement already satisfied: jinja2>=2.9 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (2.9.6)\nRequirement already satisfied: numpy in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (1.13.3)\nRequirement already satisfied: requests in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (2.18.4)\nRequirement already satisfied: branca>=0.3.0 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (0.3.1)\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from jinja2>=2.9->folium) (1.0)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (3.0.4)\nRequirement already satisfied: idna<2.7,>=2.5 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (2.6)\nRequirement already satisfied: urllib3<1.23,>=1.21.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (1.22)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (2019.3.9)\nRequirement already satisfied: six in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from branca>=0.3.0->folium) (1.11.0)\n\u001b[31mtensorflow 1.3.0 requires tensorflow-tensorboard<0.2.0,>=0.1.0, which is not installed.\u001b[0m\nLibraries imported.\n"
                }
            ], 
            "source": "import numpy as np # library to handle data in a vectorized manner\n\nimport pandas as pd # library for data analsysis\npd.set_option(\"display.max_columns\", None)\npd.set_option(\"display.max_rows\", None)\n\nimport json # library to handle JSON files\n\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n!pip install geocoder\nimport geocoder # to get coordinates\n\nimport requests # library to handle requests\nfrom bs4 import BeautifulSoup # library to parse HTML and XML documents\n\nfrom pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n\n# Matplotlib and associated plotting modules\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n\n# import k-means from clustering stage\nfrom sklearn.cluster import KMeans\n\n!pip install folium\nimport folium # map rendering library\n\nprint(\"Libraries imported.\")"
        }, 
        {
            "source": "### Scraping web for neighborhood data of Berlin (WikiPedia)", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 2, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "data = requests.get(\"https://en.wikipedia.org/wiki/Category:Localities_of_Berlin\").text\nsoup = BeautifulSoup(data, 'lxml')\ntextList = []\nneighborhoodList = []"
        }, 
        {
            "execution_count": 3, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 3, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alt-Hohensch\u00f6nhausen</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alt-Treptow</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Altglienicke</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Baumschulenweg</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "           Neighborhood\n0             Adlershof\n1  Alt-Hohensch\u00f6nhausen\n2           Alt-Treptow\n3          Altglienicke\n4        Baumschulenweg"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# append the data into the list\nneighborhoodList.clear()\n\nfor row in soup.find_all(\"div\", class_=\"mw-category\")[0].findAll(\"li\"):\n    neighborhoodList.append(row.text)\n    \ndf = pd.DataFrame({\"Neighborhood\": neighborhoodList})\ndf1 = df.iloc[1:]\nberlin_df = df1.reset_index(drop=True)\nberlin_df.head()"
        }, 
        {
            "execution_count": 37, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "(96, 3)\n"
                }, 
                {
                    "execution_count": 37, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n      <td>52.43779</td>\n      <td>13.54778</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alt-Hohensch\u00f6nhausen</td>\n      <td>52.54706</td>\n      <td>13.50055</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alt-Treptow</td>\n      <td>52.49350</td>\n      <td>13.45711</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Altglienicke</td>\n      <td>52.42006</td>\n      <td>13.53969</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Baumschulenweg</td>\n      <td>52.46669</td>\n      <td>13.48840</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "           Neighborhood  Latitude  Longitude\n0             Adlershof  52.43779   13.54778\n1  Alt-Hohensch\u00f6nhausen  52.54706   13.50055\n2           Alt-Treptow  52.49350   13.45711\n3          Altglienicke  52.42006   13.53969\n4        Baumschulenweg  52.46669   13.48840"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Geographical coordinates of neighborhoods\n\n# define a function to get coordinates\ndef get_latlng(neighborhood):\n    # initialize your variable to None\n    lat_lng_coords = None\n    # loop until you get the coordinates\n    while(lat_lng_coords is None):\n        g = geocoder.arcgis('{}, Berlin, Germany'.format(neighborhood))\n        lat_lng_coords = g.latlng\n    return lat_lng_coords\n\ncoords = [ get_latlng(neighborhood) for neighborhood in berlin_df[\"Neighborhood\"].tolist() ]\n\ndf_coords = pd.DataFrame(coords, columns=['Latitude', 'Longitude'])\n\n# merge the coordinates into the original dataframe\nberlin_df['Latitude'] = df_coords['Latitude']\nberlin_df['Longitude'] = df_coords['Longitude']\n\n\n# check the neighborhoods and the coordinates\nprint(berlin_df.shape)\nberlin_df.head()"
        }, 
        {
            "execution_count": 5, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# save the DataFrame as CSV file\nberlin_df.to_csv(\"berlin_neighborhoods.csv\", index=False)"
        }, 
        {
            "source": "#### Visualise map with neighborhoods superimposed", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 6, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The geograpical coordinate of Berlin is 52.5170365, 13.3888599.\n"
                }
            ], 
            "source": "address = 'Berlin, Germany'\n\ngeolocator = Nominatim(user_agent=\"my-application\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinate of Berlin is {}, {}.'.format(latitude, longitude))"
        }, 
        {
            "execution_count": 7, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
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            var html_49c8261af61a4ae3ace0addcb28eae27 = $(`<div id="html_49c8261af61a4ae3ace0addcb28eae27" style="width: 100.0%; height: 100.0%;">Tiergarten, Berlin</div>`)[0];
            popup_62746699a08b49268905a89dc61f7ebc.setContent(html_49c8261af61a4ae3ace0addcb28eae27);
        

        circle_marker_83cf6bb5a8df41539e1bc80a44c1a966.bindPopup(popup_62746699a08b49268905a89dc61f7ebc)
        ;

        
    
    
            var circle_marker_49bb17f5ec764d65b5acfbf30c32dbbb = L.circleMarker(
                [52.606220000000064, 13.320650000000057],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_79d2b23bcfcd4722b47670a8408b5ec9 = L.popup({"maxWidth": "100%"});

        
            var html_fb9e21d923244259b26cdc28d1a515b1 = $(`<div id="html_fb9e21d923244259b26cdc28d1a515b1" style="width: 100.0%; height: 100.0%;">Waidmannslust</div>`)[0];
            popup_79d2b23bcfcd4722b47670a8408b5ec9.setContent(html_fb9e21d923244259b26cdc28d1a515b1);
        

        circle_marker_49bb17f5ec764d65b5acfbf30c32dbbb.bindPopup(popup_79d2b23bcfcd4722b47670a8408b5ec9)
        ;

        
    
    
            var circle_marker_fc6dfcd39618465585e5a0d6bb4a6c3e = L.circleMarker(
                [52.42045000000007, 13.167050000000074],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_8e0fcc45053b427ea046b33f015c5dff = L.popup({"maxWidth": "100%"});

        
            var html_20dea3a4d26848a2ae1078058bf9a4ff = $(`<div id="html_20dea3a4d26848a2ae1078058bf9a4ff" style="width: 100.0%; height: 100.0%;">Wannsee</div>`)[0];
            popup_8e0fcc45053b427ea046b33f015c5dff.setContent(html_20dea3a4d26848a2ae1078058bf9a4ff);
        

        circle_marker_fc6dfcd39618465585e5a0d6bb4a6c3e.bindPopup(popup_8e0fcc45053b427ea046b33f015c5dff)
        ;

        
    
    
            var circle_marker_8250a0eec4324495aa44e305ef976749 = L.circleMarker(
                [52.57503000000003, 13.517030000000034],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_f1473c4fcab04ff3bf816a9e0fd9f94d = L.popup({"maxWidth": "100%"});

        
            var html_63aa5485ff1941e8bb61286b8ddcc440 = $(`<div id="html_63aa5485ff1941e8bb61286b8ddcc440" style="width: 100.0%; height: 100.0%;">Wartenberg (Berlin)</div>`)[0];
            popup_f1473c4fcab04ff3bf816a9e0fd9f94d.setContent(html_63aa5485ff1941e8bb61286b8ddcc440);
        

        circle_marker_8250a0eec4324495aa44e305ef976749.bindPopup(popup_f1473c4fcab04ff3bf816a9e0fd9f94d)
        ;

        
    
    
            var circle_marker_fb11b829628941b0ad362ae87cda9480 = L.circleMarker(
                [52.54781000000003, 13.354730000000075],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_ac2be2aaaf0b44b1877a010529a716c4 = L.popup({"maxWidth": "100%"});

        
            var html_362f1656f97040c9974c38bce33e9d54 = $(`<div id="html_362f1656f97040c9974c38bce33e9d54" style="width: 100.0%; height: 100.0%;">Wedding (Berlin)</div>`)[0];
            popup_ac2be2aaaf0b44b1877a010529a716c4.setContent(html_362f1656f97040c9974c38bce33e9d54);
        

        circle_marker_fb11b829628941b0ad362ae87cda9480.bindPopup(popup_ac2be2aaaf0b44b1877a010529a716c4)
        ;

        
    
    
            var circle_marker_8b14b96684ee4e418bcdd3bddfd37d21 = L.circleMarker(
                [52.56185000000005, 13.468260000000043],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_013f26d20276410ea75bf69c4efd85cd = L.popup({"maxWidth": "100%"});

        
            var html_30b1aef27baa4e6a8e9dd10002593708 = $(`<div id="html_30b1aef27baa4e6a8e9dd10002593708" style="width: 100.0%; height: 100.0%;">Weissensee (Berlin)</div>`)[0];
            popup_013f26d20276410ea75bf69c4efd85cd.setContent(html_30b1aef27baa4e6a8e9dd10002593708);
        

        circle_marker_8b14b96684ee4e418bcdd3bddfd37d21.bindPopup(popup_013f26d20276410ea75bf69c4efd85cd)
        ;

        
    
    
            var circle_marker_aded7f5d185d4896b3861e312ca51686 = L.circleMarker(
                [52.516500000000065, 13.269220000000075],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_7b03898a3ee2427d9b3c3f091514d2ff = L.popup({"maxWidth": "100%"});

        
            var html_e0270c19ea554e66a368db1e919165bd = $(`<div id="html_e0270c19ea554e66a368db1e919165bd" style="width: 100.0%; height: 100.0%;">Westend (Berlin)</div>`)[0];
            popup_7b03898a3ee2427d9b3c3f091514d2ff.setContent(html_e0270c19ea554e66a368db1e919165bd);
        

        circle_marker_aded7f5d185d4896b3861e312ca51686.bindPopup(popup_7b03898a3ee2427d9b3c3f091514d2ff)
        ;

        
    
    
            var circle_marker_3160a6ff3c6a41b98a1d3d9b479e6b67 = L.circleMarker(
                [52.58887000000004, 13.369490000000042],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_8a8ebd26e7634619bdc2f4e48a84a09d = L.popup({"maxWidth": "100%"});

        
            var html_797915506f4d4daca7feff41dae57ac5 = $(`<div id="html_797915506f4d4daca7feff41dae57ac5" style="width: 100.0%; height: 100.0%;">Wilhelmsruh</div>`)[0];
            popup_8a8ebd26e7634619bdc2f4e48a84a09d.setContent(html_797915506f4d4daca7feff41dae57ac5);
        

        circle_marker_3160a6ff3c6a41b98a1d3d9b479e6b67.bindPopup(popup_8a8ebd26e7634619bdc2f4e48a84a09d)
        ;

        
    
    
            var circle_marker_84466a5bbf784ec79df91d75372fd57f = L.circleMarker(
                [52.52462000000003, 13.177070000000072],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_edc5718d53ef4eefabdd76bf3eb155ab = L.popup({"maxWidth": "100%"});

        
            var html_582d974f759a4693866f3d6a470d0119 = $(`<div id="html_582d974f759a4693866f3d6a470d0119" style="width: 100.0%; height: 100.0%;">Wilhelmstadt</div>`)[0];
            popup_edc5718d53ef4eefabdd76bf3eb155ab.setContent(html_582d974f759a4693866f3d6a470d0119);
        

        circle_marker_84466a5bbf784ec79df91d75372fd57f.bindPopup(popup_edc5718d53ef4eefabdd76bf3eb155ab)
        ;

        
    
    
            var circle_marker_43ec225c41eb4cc2a7e46e67fc12636c = L.circleMarker(
                [52.48976000000005, 13.31519000000003],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_827e57c55fe24e60a257e7ec29cde88e = L.popup({"maxWidth": "100%"});

        
            var html_2f92f1b719cb479fb64a9dee93904220 = $(`<div id="html_2f92f1b719cb479fb64a9dee93904220" style="width: 100.0%; height: 100.0%;">Wilmersdorf</div>`)[0];
            popup_827e57c55fe24e60a257e7ec29cde88e.setContent(html_2f92f1b719cb479fb64a9dee93904220);
        

        circle_marker_43ec225c41eb4cc2a7e46e67fc12636c.bindPopup(popup_827e57c55fe24e60a257e7ec29cde88e)
        ;

        
    
    
            var circle_marker_1678d029006c4dfabea16d0e5ba2a032 = L.circleMarker(
                [52.598520000000065, 13.332830000000058],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_5558af50f5aa4a4aa569f8b607884c80 = L.popup({"maxWidth": "100%"});

        
            var html_7dddd398fb794fc6abb0a4bbdab37abd = $(`<div id="html_7dddd398fb794fc6abb0a4bbdab37abd" style="width: 100.0%; height: 100.0%;">Wittenau</div>`)[0];
            popup_5558af50f5aa4a4aa569f8b607884c80.setContent(html_7dddd398fb794fc6abb0a4bbdab37abd);
        

        circle_marker_1678d029006c4dfabea16d0e5ba2a032.bindPopup(popup_5558af50f5aa4a4aa569f8b607884c80)
        ;

        
    
    
            var circle_marker_9f9eec6c484c444da1cfecb53254c6e5 = L.circleMarker(
                [52.43351000000007, 13.258090000000038],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_2e40066e558f4c5c982109ba29b862ed);
        
    
        var popup_faaa007c44a34548a2d383b716f05ca9 = L.popup({"maxWidth": "100%"});

        
            var html_492b38e248b14019800e3694d653052c = $(`<div id="html_492b38e248b14019800e3694d653052c" style="width: 100.0%; height: 100.0%;">Zehlendorf (Berlin)</div>`)[0];
            popup_faaa007c44a34548a2d383b716f05ca9.setContent(html_492b38e248b14019800e3694d653052c);
        

        circle_marker_9f9eec6c484c444da1cfecb53254c6e5.bindPopup(popup_faaa007c44a34548a2d383b716f05ca9)
        ;

        
    
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7f5a7d136fd0>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of Berlin using latitude and longitude values\nmap_berlin = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# add markers to map\nfor lat, lng, neighborhood in zip(berlin_df['Latitude'], berlin_df['Longitude'], berlin_df['Neighborhood']):\n    label = '{}'.format(neighborhood)\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker(\n        [lat, lng],\n        radius=5,\n        popup=label,\n        color='blue',\n        fill=True,\n        fill_color='#3186cc',\n        fill_opacity=0.7).add_to(map_berlin)  \n    \nmap_berlin"
        }, 
        {
            "source": "#### Using Foursquare API", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 8, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 9, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "radius = 2000\nLIMIT = 100\n\nvenues = []\nfor lat, long, neighborhood in zip(berlin_df['Latitude'], berlin_df['Longitude'], berlin_df['Neighborhood']):\n    \n    # create the API request URL\n    url = \"https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}\".format(\n        CLIENT_ID,\n        CLIENT_SECRET,\n        VERSION,\n        lat,\n        long,\n        radius, \n        LIMIT)\n    \n    # make the GET request\n    results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n    \n    # return only relevant information for each nearby venue\n    for venue in results:\n        venues.append((\n            neighborhood,\n            lat, \n            long, \n            venue['venue']['name'], \n            venue['venue']['location']['lat'], \n            venue['venue']['location']['lng'],  \n            venue['venue']['categories'][0]['name']))\n"
        }, 
        {
            "execution_count": 10, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "(5424, 7)\n"
                }, 
                {
                    "execution_count": 10, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>VenueName</th>\n      <th>VenueLatitude</th>\n      <th>VenueLongitude</th>\n      <th>VenueCategory</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n      <td>52.43779</td>\n      <td>13.54778</td>\n      <td>Mia Toscana</td>\n      <td>52.438327</td>\n      <td>13.549573</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Adlershof</td>\n      <td>52.43779</td>\n      <td>13.54778</td>\n      <td>Griechisches Restaurant Athen</td>\n      <td>52.434901</td>\n      <td>13.546680</td>\n      <td>Greek Restaurant</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Adlershof</td>\n      <td>52.43779</td>\n      <td>13.54778</td>\n      <td>Olympia Greek Food</td>\n      <td>52.433982</td>\n      <td>13.538500</td>\n      <td>Greek Restaurant</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Adlershof</td>\n      <td>52.43779</td>\n      <td>13.54778</td>\n      <td>Food - Taste of the world on the road</td>\n      <td>52.428982</td>\n      <td>13.537843</td>\n      <td>Food Truck</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Adlershof</td>\n      <td>52.43779</td>\n      <td>13.54778</td>\n      <td>Trattoria Il Mondo</td>\n      <td>52.434788</td>\n      <td>13.543176</td>\n      <td>Trattoria/Osteria</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Neighborhood  Latitude  Longitude                              VenueName  \\\n0    Adlershof  52.43779   13.54778                            Mia Toscana   \n1    Adlershof  52.43779   13.54778          Griechisches Restaurant Athen   \n2    Adlershof  52.43779   13.54778                     Olympia Greek Food   \n3    Adlershof  52.43779   13.54778  Food - Taste of the world on the road   \n4    Adlershof  52.43779   13.54778                     Trattoria Il Mondo   \n\n   VenueLatitude  VenueLongitude       VenueCategory  \n0      52.438327       13.549573  Italian Restaurant  \n1      52.434901       13.546680    Greek Restaurant  \n2      52.433982       13.538500    Greek Restaurant  \n3      52.428982       13.537843          Food Truck  \n4      52.434788       13.543176   Trattoria/Osteria  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# convert the venues list into a new DataFrame\nvenues_df = pd.DataFrame(venues)\n\n# define the column names\nvenues_df.columns = ['Neighborhood', 'Latitude', 'Longitude', 'VenueName', 'VenueLatitude', 'VenueLongitude', 'VenueCategory']\n\nprint(venues_df.shape)\nvenues_df.head()"
        }, 
        {
            "execution_count": 11, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "There are 339 uniques categories.\n"
                }
            ], 
            "source": "venues_df.groupby([\"Neighborhood\"]).count()\nprint('There are {} uniques categories.'.format(len(venues_df['VenueCategory'].unique())))"
        }, 
        {
            "execution_count": 12, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "(5424, 340)\n(96, 340)\n"
                }, 
                {
                    "execution_count": 12, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhoods</th>\n      <th>ATM</th>\n      <th>Adult Boutique</th>\n      <th>African Restaurant</th>\n      <th>American Restaurant</th>\n      <th>Animal Shelter</th>\n      <th>Aquarium</th>\n      <th>Arcade</th>\n      <th>Argentinian Restaurant</th>\n      <th>Art Gallery</th>\n      <th>Art Museum</th>\n      <th>Arts &amp; Crafts Store</th>\n      <th>Asian Restaurant</th>\n      <th>Athletics &amp; Sports</th>\n      <th>Austrian Restaurant</th>\n      <th>Auto Dealership</th>\n      <th>Auto Garage</th>\n      <th>Auto Workshop</th>\n      <th>Automotive Shop</th>\n      <th>BBQ Joint</th>\n      <th>Baby Store</th>\n      <th>Bagel Shop</th>\n      <th>Bakery</th>\n      <th>Bank</th>\n      <th>Bar</th>\n      <th>Baseball Field</th>\n      <th>Bath House</th>\n      <th>Bathing Area</th>\n      <th>Beach</th>\n      <th>Beach Bar</th>\n      <th>Bed &amp; Breakfast</th>\n      <th>Beer Bar</th>\n      <th>Beer Garden</th>\n      <th>Beer Store</th>\n      <th>Big Box Store</th>\n      <th>Bike Rental / Bike Share</th>\n      <th>Bike Shop</th>\n      <th>Bistro</th>\n      <th>Boat Rental</th>\n      <th>Boat or Ferry</th>\n      <th>Bookstore</th>\n      <th>Botanical Garden</th>\n      <th>Boutique</th>\n      <th>Bowling Alley</th>\n      <th>Brasserie</th>\n      <th>Breakfast Spot</th>\n      <th>Brewery</th>\n      <th>Bridge</th>\n      <th>Building</th>\n      <th>Bulgarian Restaurant</th>\n      <th>Burger Joint</th>\n      <th>Burrito Place</th>\n      <th>Bus Stop</th>\n      <th>Business Service</th>\n      <th>Butcher</th>\n      <th>Cafeteria</th>\n      <th>Caf\u00e9</th>\n      <th>Campground</th>\n      <th>Canal</th>\n      <th>Canal Lock</th>\n      <th>Candy Store</th>\n      <th>Capitol Building</th>\n      <th>Castle</th>\n      <th>Caucasian Restaurant</th>\n      <th>Cemetery</th>\n      <th>Cheese Shop</th>\n      <th>Chinese Restaurant</th>\n      <th>Chocolate Shop</th>\n      <th>Circus</th>\n      <th>Climbing Gym</th>\n      <th>Clothing Store</th>\n      <th>Cocktail Bar</th>\n      <th>Coffee Shop</th>\n      <th>College Cafeteria</th>\n      <th>College Gym</th>\n      <th>College Rec Center</th>\n      <th>Comedy Club</th>\n      <th>Community Center</th>\n      <th>Concert Hall</th>\n      <th>Convenience Store</th>\n      <th>Cosmetics Shop</th>\n      <th>Costume Shop</th>\n      <th>Coworking Space</th>\n      <th>Credit Union</th>\n      <th>Creperie</th>\n      <th>Cuban Restaurant</th>\n      <th>Cultural Center</th>\n      <th>Cupcake Shop</th>\n      <th>Currywurst Joint</th>\n      <th>Dance Studio</th>\n      <th>Deli / Bodega</th>\n      <th>Department Store</th>\n      <th>Dessert Shop</th>\n      <th>Diner</th>\n      <th>Discount Store</th>\n      <th>Distillery</th>\n      <th>Dive Bar</th>\n      <th>Dog Run</th>\n      <th>Doner Restaurant</th>\n      <th>Donut Shop</th>\n      <th>Drugstore</th>\n      <th>Dumpling Restaurant</th>\n      <th>Eastern European Restaurant</th>\n      <th>Electronics Store</th>\n      <th>Ethiopian Restaurant</th>\n      <th>Event Service</th>\n      <th>Event Space</th>\n      <th>Fabric Shop</th>\n      <th>Falafel Restaurant</th>\n      <th>Farm</th>\n      <th>Farmers Market</th>\n      <th>Fast Food Restaurant</th>\n      <th>Field</th>\n      <th>Film Studio</th>\n      <th>Fish &amp; Chips Shop</th>\n      <th>Fish Market</th>\n      <th>Flea Market</th>\n      <th>Flower Shop</th>\n      <th>Food &amp; Drink Shop</th>\n      <th>Food Court</th>\n      <th>Food Service</th>\n      <th>Food Truck</th>\n      <th>Football Stadium</th>\n      <th>Forest</th>\n      <th>Fountain</th>\n      <th>French Restaurant</th>\n      <th>Fried Chicken Joint</th>\n      <th>Furniture / Home Store</th>\n      <th>Gaming Cafe</th>\n      <th>Garden</th>\n      <th>Garden Center</th>\n      <th>Gas Station</th>\n      <th>Gastropub</th>\n      <th>Gay Bar</th>\n      <th>General Entertainment</th>\n      <th>German Restaurant</th>\n      <th>Gift Shop</th>\n      <th>Go Kart Track</th>\n      <th>Golf Course</th>\n      <th>Gourmet Shop</th>\n      <th>Greek Restaurant</th>\n      <th>Grocery Store</th>\n      <th>Gym</th>\n      <th>Gym / Fitness Center</th>\n      <th>Halal Restaurant</th>\n      <th>Harbor / Marina</th>\n      <th>Hardware Store</th>\n      <th>Hawaiian Restaurant</th>\n      <th>Health &amp; Beauty Service</th>\n      <th>Historic Site</th>\n      <th>History Museum</th>\n      <th>Hobby Shop</th>\n      <th>Hockey Field</th>\n      <th>Hockey Rink</th>\n      <th>Hookah Bar</th>\n      <th>Hostel</th>\n      <th>Hot Dog Joint</th>\n      <th>Hotel</th>\n      <th>Hotel Bar</th>\n      <th>IT Services</th>\n      <th>Ice Cream Shop</th>\n      <th>Indian Restaurant</th>\n      <th>Indie Movie Theater</th>\n      <th>Indie Theater</th>\n      <th>Indonesian Restaurant</th>\n      <th>Indoor Play Area</th>\n      <th>Insurance Office</th>\n      <th>Intersection</th>\n      <th>Irish Pub</th>\n      <th>Israeli Restaurant</th>\n      <th>Italian Restaurant</th>\n      <th>Japanese Restaurant</th>\n      <th>Jazz Club</th>\n      <th>Juice Bar</th>\n      <th>Karaoke Bar</th>\n      <th>Kebab Restaurant</th>\n      <th>Korean Restaurant</th>\n      <th>Kumpir Restaurant</th>\n      <th>Lake</th>\n      <th>Laser Tag</th>\n      <th>Latin American Restaurant</th>\n      <th>Laundry Service</th>\n      <th>Lebanese Restaurant</th>\n      <th>Light Rail Station</th>\n      <th>Liquor Store</th>\n      <th>Lottery Retailer</th>\n      <th>Lounge</th>\n      <th>Market</th>\n      <th>Massage Studio</th>\n      <th>Mediterranean Restaurant</th>\n      <th>Memorial Site</th>\n      <th>Men's Store</th>\n      <th>Metro Station</th>\n      <th>Mexican Restaurant</th>\n      <th>Middle Eastern Restaurant</th>\n      <th>Miscellaneous Shop</th>\n      <th>Mobile Phone Shop</th>\n      <th>Modern European Restaurant</th>\n      <th>Monument / Landmark</th>\n      <th>Motel</th>\n      <th>Motorcycle Shop</th>\n      <th>Mountain</th>\n      <th>Movie Theater</th>\n      <th>Multiplex</th>\n      <th>Museum</th>\n      <th>Music Venue</th>\n      <th>Nature Preserve</th>\n      <th>Neighborhood</th>\n      <th>New American Restaurant</th>\n      <th>Nightclub</th>\n      <th>Noodle House</th>\n      <th>Nudist Beach</th>\n      <th>Office</th>\n      <th>Opera House</th>\n      <th>Optical Shop</th>\n      <th>Organic Grocery</th>\n      <th>Other Great Outdoors</th>\n      <th>Outdoor Sculpture</th>\n      <th>Outdoor Supply Store</th>\n      <th>Paintball Field</th>\n      <th>Pakistani Restaurant</th>\n      <th>Palace</th>\n      <th>Park</th>\n      <th>Pastry Shop</th>\n      <th>Pedestrian Plaza</th>\n      <th>Performing Arts Venue</th>\n      <th>Perfume Shop</th>\n      <th>Persian Restaurant</th>\n      <th>Peruvian Restaurant</th>\n      <th>Pet Caf\u00e9</th>\n      <th>Pet Service</th>\n      <th>Pet Store</th>\n      <th>Pharmacy</th>\n      <th>Piano Bar</th>\n      <th>Pide Place</th>\n      <th>Pie Shop</th>\n      <th>Pier</th>\n      <th>Piercing Parlor</th>\n      <th>Pizza Place</th>\n      <th>Planetarium</th>\n      <th>Platform</th>\n      <th>Playground</th>\n      <th>Plaza</th>\n      <th>Pool</th>\n      <th>Pool Hall</th>\n      <th>Portuguese Restaurant</th>\n      <th>Post Office</th>\n      <th>Print Shop</th>\n      <th>Pub</th>\n      <th>Record Shop</th>\n      <th>Recreation Center</th>\n      <th>Rental Car Location</th>\n      <th>Rest Area</th>\n      <th>Restaurant</th>\n      <th>River</th>\n      <th>Road</th>\n      <th>Rock Climbing Spot</th>\n      <th>Rock Club</th>\n      <th>Roof Deck</th>\n      <th>Rugby Stadium</th>\n      <th>Russian Restaurant</th>\n      <th>Salon / Barbershop</th>\n      <th>Sandwich Place</th>\n      <th>Sauna / Steam Room</th>\n      <th>Scandinavian Restaurant</th>\n      <th>Scenic Lookout</th>\n      <th>Schnitzel Restaurant</th>\n      <th>Science Museum</th>\n      <th>Sculpture Garden</th>\n      <th>Seafood Restaurant</th>\n      <th>Shipping Store</th>\n      <th>Shoe Store</th>\n      <th>Shopping Mall</th>\n      <th>Skate Park</th>\n      <th>Skating Rink</th>\n      <th>Smoke Shop</th>\n      <th>Snack Place</th>\n      <th>Soccer Field</th>\n      <th>Soccer Stadium</th>\n      <th>Soup Place</th>\n      <th>South American Restaurant</th>\n      <th>Spa</th>\n      <th>Spanish Restaurant</th>\n      <th>Speakeasy</th>\n      <th>Sporting Goods Shop</th>\n      <th>Sports Bar</th>\n      <th>Sports Club</th>\n      <th>Squash Court</th>\n      <th>Stables</th>\n      <th>Stadium</th>\n      <th>Stationery Store</th>\n      <th>Steakhouse</th>\n      <th>Street Art</th>\n      <th>Supermarket</th>\n      <th>Sushi Restaurant</th>\n      <th>Swiss Restaurant</th>\n      <th>Syrian Restaurant</th>\n      <th>Szechuan Restaurant</th>\n      <th>Taco Place</th>\n      <th>Tapas Restaurant</th>\n      <th>Taverna</th>\n      <th>Tea Room</th>\n      <th>Tennis Court</th>\n      <th>Tennis Stadium</th>\n      <th>Thai Restaurant</th>\n      <th>Theater</th>\n      <th>Theme Park</th>\n      <th>Theme Park Ride / Attraction</th>\n      <th>Thrift / Vintage Store</th>\n      <th>Toy / Game Store</th>\n      <th>Track</th>\n      <th>Track Stadium</th>\n      <th>Trail</th>\n      <th>Train</th>\n      <th>Train Station</th>\n      <th>Tram Station</th>\n      <th>Trattoria/Osteria</th>\n      <th>Tree</th>\n      <th>Tunnel</th>\n      <th>Turkish Restaurant</th>\n      <th>Vacation Rental</th>\n      <th>Vape Store</th>\n      <th>Vegetarian / Vegan Restaurant</th>\n      <th>Venezuelan Restaurant</th>\n      <th>Video Store</th>\n      <th>Vietnamese Restaurant</th>\n      <th>Volleyball Court</th>\n      <th>Warehouse Store</th>\n      <th>Waterfall</th>\n      <th>Waterfront</th>\n      <th>Whisky Bar</th>\n      <th>Windmill</th>\n      <th>Wine Bar</th>\n      <th>Wine Shop</th>\n      <th>Winery</th>\n      <th>Women's Store</th>\n      <th>Yemeni Restaurant</th>\n      <th>Yoga Studio</th>\n      <th>Zoo</th>\n      <th>Zoo Exhibit</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n  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<td>Alt-Hohensch\u00f6nhausen</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.041667</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.00</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.069444</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.041667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.027778</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.013889</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.027778</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.027778</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.027778</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.013889</td>\n      <td>0.027778</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.013889</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.041667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.027778</td>\n      <td>0.00</td>\n      <td>0.013889</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.027778</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.00</td>\n      <td>0.111111</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.013889</td>\n      <td>0.069444</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alt-Treptow</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.030000</td>\n      <td>0.0</td>\n      <td>0.050000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.02</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.010000</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.070000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.02</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.060000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.020000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.02</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.020000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.030000</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.020000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.05</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.020000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.010000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Altglienicke</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.100000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.066667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.066667</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.066667</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.133333</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Baumschulenweg</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.066667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.050000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.083333</td>\n      <td>0.00</td>\n      <td>0.016667</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.016667</td>\n      <td>0.033333</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.033333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.016667</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      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<td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.016667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "          Neighborhoods  ATM  Adult Boutique  African Restaurant  \\\n0             Adlershof  0.0             0.0                0.00   \n1  Alt-Hohensch\u00f6nhausen  0.0             0.0                0.00   \n2           Alt-Treptow  0.0             0.0                0.01   \n3          Altglienicke  0.0             0.0                0.00   \n4        Baumschulenweg  0.0             0.0                0.00   \n\n   American Restaurant  Animal Shelter  Aquarium  Arcade  \\\n0                  0.0             0.0       0.0     0.0   \n1                  0.0             0.0       0.0     0.0   \n2                  0.0             0.0       0.0     0.0   \n3                  0.0             0.0       0.0     0.0   \n4                  0.0             0.0       0.0     0.0   \n\n   Argentinian Restaurant  Art Gallery  Art Museum  Arts & Crafts Store  \\\n0                     0.0          0.0    0.000000                  0.0   \n1                     0.0          0.0    0.013889                  0.0   \n2                     0.0          0.0    0.000000                  0.0   \n3                     0.0          0.0    0.000000                  0.0   \n4                     0.0          0.0    0.000000                  0.0   \n\n   Asian Restaurant  Athletics & Sports  Austrian Restaurant  Auto Dealership  \\\n0               0.0            0.000000                  0.0         0.000000   \n1               0.0            0.013889                  0.0         0.000000   \n2               0.0            0.010000                  0.0         0.000000   \n3               0.0            0.000000                  0.0         0.033333   \n4               0.0            0.000000                  0.0         0.000000   \n\n   Auto Garage  Auto Workshop  Automotive Shop  BBQ Joint  Baby Store  \\\n0          0.0            0.0         0.021277        0.0         0.0   \n1          0.0            0.0         0.000000        0.0         0.0   \n2          0.0            0.0         0.000000        0.0         0.0   \n3          0.0            0.0         0.033333        0.0         0.0   \n4          0.0            0.0         0.000000        0.0         0.0   \n\n   Bagel Shop    Bakery  Bank       Bar  Baseball Field  Bath House  \\\n0        0.00  0.000000   0.0  0.000000             0.0         0.0   \n1        0.00  0.041667   0.0  0.013889             0.0         0.0   \n2        0.01  0.030000   0.0  0.050000             0.0         0.0   \n3        0.00  0.000000   0.0  0.000000             0.0         0.0   \n4        0.00  0.016667   0.0  0.000000             0.0         0.0   \n\n   Bathing Area     Beach  Beach Bar  Bed & Breakfast  Beer Bar  Beer Garden  \\\n0           0.0  0.000000        0.0              0.0      0.00         0.00   \n1           0.0  0.013889        0.0              0.0      0.00         0.00   \n2           0.0  0.000000        0.0              0.0      0.01         0.02   \n3           0.0  0.000000        0.0              0.0      0.00         0.00   \n4           0.0  0.016667        0.0              0.0      0.00         0.00   \n\n   Beer Store  Big Box Store  Bike Rental / Bike Share  Bike Shop  Bistro  \\\n0         0.0       0.000000                       0.0        0.0     0.0   \n1         0.0       0.000000                       0.0        0.0     0.0   \n2         0.0       0.000000                       0.0        0.0     0.0   \n3         0.0       0.000000                       0.0        0.0     0.0   \n4         0.0       0.016667                       0.0        0.0     0.0   \n\n   Boat Rental  Boat or Ferry  Bookstore  Botanical Garden  Boutique  \\\n0          0.0       0.000000       0.00               0.0       0.0   \n1          0.0       0.000000       0.00               0.0       0.0   \n2          0.0       0.000000       0.01               0.0       0.0   \n3          0.0       0.000000       0.00               0.0       0.0   \n4          0.0       0.016667       0.00               0.0       0.0   \n\n   Bowling Alley  Brasserie  Breakfast Spot   Brewery  Bridge  Building  \\\n0       0.021277        0.0            0.00  0.021277    0.00       0.0   \n1       0.000000        0.0            0.00  0.000000    0.00       0.0   \n2       0.000000        0.0            0.01  0.010000    0.01       0.0   \n3       0.033333        0.0            0.00  0.000000    0.00       0.0   \n4       0.000000        0.0            0.00  0.000000    0.00       0.0   \n\n   Bulgarian Restaurant  Burger Joint  Burrito Place  Bus Stop  \\\n0                   0.0          0.00           0.00  0.000000   \n1                   0.0          0.00           0.00  0.000000   \n2                   0.0          0.01           0.01  0.000000   \n3                   0.0          0.00           0.00  0.000000   \n4                   0.0          0.00           0.00  0.066667   \n\n   Business Service  Butcher  Cafeteria      Caf\u00e9  Campground  Canal  \\\n0               0.0      0.0   0.000000  0.106383         0.0    0.0   \n1               0.0      0.0   0.013889  0.013889         0.0    0.0   \n2               0.0      0.0   0.000000  0.070000         0.0    0.0   \n3               0.0      0.0   0.000000  0.100000         0.0    0.0   \n4               0.0      0.0   0.000000  0.050000         0.0    0.0   \n\n   Canal Lock  Candy Store  Capitol Building  Castle  Caucasian Restaurant  \\\n0         0.0          0.0               0.0     0.0                   0.0   \n1         0.0          0.0               0.0     0.0                   0.0   \n2         0.0          0.0               0.0     0.0                   0.0   \n3         0.0          0.0               0.0     0.0                   0.0   \n4         0.0          0.0               0.0     0.0                   0.0   \n\n   Cemetery  Cheese Shop  Chinese Restaurant  Chocolate Shop  Circus  \\\n0       0.0          0.0                 0.0             0.0     0.0   \n1       0.0          0.0                 0.0             0.0     0.0   \n2       0.0          0.0                 0.0             0.0     0.0   \n3       0.0          0.0                 0.0             0.0     0.0   \n4       0.0          0.0                 0.0             0.0     0.0   \n\n   Climbing Gym  Clothing Store  Cocktail Bar  Coffee Shop  College Cafeteria  \\\n0          0.00             0.0          0.00     0.021277                0.0   \n1          0.00             0.0          0.00     0.000000                0.0   \n2          0.02             0.0          0.01     0.060000                0.0   \n3          0.00             0.0          0.00     0.033333                0.0   \n4          0.00             0.0          0.00     0.000000                0.0   \n\n   College Gym  College Rec Center  Comedy Club  Community Center  \\\n0          0.0                 0.0          0.0              0.00   \n1          0.0                 0.0          0.0              0.00   \n2          0.0                 0.0          0.0              0.01   \n3          0.0                 0.0          0.0              0.00   \n4          0.0                 0.0          0.0              0.00   \n\n   Concert Hall  Convenience Store  Cosmetics Shop  Costume Shop  \\\n0           0.0                0.0             0.0           0.0   \n1           0.0                0.0             0.0           0.0   \n2           0.0                0.0             0.0           0.0   \n3           0.0                0.0             0.0           0.0   \n4           0.0                0.0             0.0           0.0   \n\n   Coworking Space  Credit Union  Creperie  Cuban Restaurant  Cultural Center  \\\n0              0.0           0.0       0.0               0.0              0.0   \n1              0.0           0.0       0.0               0.0              0.0   \n2              0.0           0.0       0.0               0.0              0.0   \n3              0.0           0.0       0.0               0.0              0.0   \n4              0.0           0.0       0.0               0.0              0.0   \n\n   Cupcake Shop  Currywurst Joint  Dance Studio  Deli / Bodega  \\\n0           0.0               0.0      0.000000           0.00   \n1           0.0               0.0      0.013889           0.00   \n2           0.0               0.0      0.000000           0.01   \n3           0.0               0.0      0.000000           0.00   \n4           0.0               0.0      0.000000           0.00   \n\n   Department Store  Dessert Shop     Diner  Discount Store  Distillery  \\\n0          0.000000           0.0  0.000000             0.0        0.00   \n1          0.013889           0.0  0.000000             0.0        0.00   \n2          0.000000           0.0  0.000000             0.0        0.01   \n3          0.000000           0.0  0.000000             0.0        0.00   \n4          0.000000           0.0  0.016667             0.0        0.00   \n\n   Dive Bar  Dog Run  Doner Restaurant  Donut Shop  Drugstore  \\\n0       0.0     0.00          0.000000         0.0   0.042553   \n1       0.0     0.00          0.000000         0.0   0.069444   \n2       0.0     0.01          0.000000         0.0   0.000000   \n3       0.0     0.00          0.000000         0.0   0.033333   \n4       0.0     0.00          0.016667         0.0   0.083333   \n\n   Dumpling Restaurant  Eastern European Restaurant  Electronics Store  \\\n0                 0.00                     0.000000           0.000000   \n1                 0.00                     0.000000           0.013889   \n2                 0.01                     0.000000           0.000000   \n3                 0.00                     0.000000           0.000000   \n4                 0.00                     0.016667           0.000000   \n\n   Ethiopian Restaurant  Event Service  Event Space  Fabric Shop  \\\n0                   0.0            0.0          0.0          0.0   \n1                   0.0            0.0          0.0          0.0   \n2                   0.0            0.0          0.0          0.0   \n3                   0.0            0.0          0.0          0.0   \n4                   0.0            0.0          0.0          0.0   \n\n   Falafel Restaurant  Farm  Farmers Market  Fast Food Restaurant  Field  \\\n0            0.000000   0.0             0.0              0.000000    0.0   \n1            0.000000   0.0             0.0              0.041667    0.0   \n2            0.020000   0.0             0.0              0.000000    0.0   \n3            0.000000   0.0             0.0              0.000000    0.0   \n4            0.033333   0.0             0.0              0.000000    0.0   \n\n   Film Studio  Fish & Chips Shop  Fish Market  Flea Market  Flower Shop  \\\n0          0.0                0.0          0.0          0.0          0.0   \n1          0.0                0.0          0.0          0.0          0.0   \n2          0.0                0.0          0.0          0.0          0.0   \n3          0.0                0.0          0.0          0.0          0.0   \n4          0.0                0.0          0.0          0.0          0.0   \n\n   Food & Drink Shop  Food Court  Food Service  Food Truck  Football Stadium  \\\n0               0.00         0.0      0.000000    0.021277               0.0   \n1               0.00         0.0      0.013889    0.000000               0.0   \n2               0.02         0.0      0.000000    0.000000               0.0   \n3               0.00         0.0      0.000000    0.033333               0.0   \n4               0.00         0.0      0.000000    0.000000               0.0   \n\n     Forest  Fountain  French Restaurant  Fried Chicken Joint  \\\n0  0.000000       0.0                0.0                  0.0   \n1  0.000000       0.0                0.0                  0.0   \n2  0.000000       0.0                0.0                  0.0   \n3  0.000000       0.0                0.0                  0.0   \n4  0.033333       0.0                0.0                  0.0   \n\n   Furniture / Home Store  Gaming Cafe    Garden  Garden Center  Gas Station  \\\n0                0.021277          0.0  0.000000       0.000000     0.000000   \n1                0.027778          0.0  0.000000       0.000000     0.013889   \n2                0.000000          0.0  0.000000       0.010000     0.000000   \n3                0.033333          0.0  0.000000       0.000000     0.000000   \n4                0.016667          0.0  0.016667       0.016667     0.033333   \n\n   Gastropub  Gay Bar  General Entertainment  German Restaurant  Gift Shop  \\\n0   0.000000      0.0                    0.0           0.063830        0.0   \n1   0.013889      0.0                    0.0           0.027778        0.0   \n2   0.000000      0.0                    0.0           0.030000        0.0   \n3   0.000000      0.0                    0.0           0.000000        0.0   \n4   0.000000      0.0                    0.0           0.016667        0.0   \n\n   Go Kart Track  Golf Course  Gourmet Shop  Greek Restaurant  Grocery Store  \\\n0       0.000000          0.0           0.0          0.042553            0.0   \n1       0.013889          0.0           0.0          0.027778            0.0   \n2       0.000000          0.0           0.0          0.000000            0.0   \n3       0.000000          0.0           0.0          0.066667            0.0   \n4       0.000000          0.0           0.0          0.000000            0.0   \n\n   Gym  Gym / Fitness Center  Halal Restaurant  Harbor / Marina  \\\n0  0.0              0.021277               0.0         0.021277   \n1  0.0              0.000000               0.0         0.000000   \n2  0.0              0.020000               0.0         0.000000   \n3  0.0              0.033333               0.0         0.000000   \n4  0.0              0.016667               0.0         0.000000   \n\n   Hardware Store  Hawaiian Restaurant  Health & Beauty Service  \\\n0        0.021277                  0.0                      0.0   \n1        0.027778                  0.0                      0.0   \n2        0.000000                  0.0                      0.0   \n3        0.033333                  0.0                      0.0   \n4        0.000000                  0.0                      0.0   \n\n   Historic Site  History Museum  Hobby Shop  Hockey Field  Hockey Rink  \\\n0       0.000000             0.0         0.0           0.0     0.000000   \n1       0.013889             0.0         0.0           0.0     0.013889   \n2       0.000000             0.0         0.0           0.0     0.000000   \n3       0.000000             0.0         0.0           0.0     0.000000   \n4       0.033333             0.0         0.0           0.0     0.000000   \n\n   Hookah Bar  Hostel  Hot Dog Joint     Hotel  Hotel Bar  IT Services  \\\n0         0.0    0.00       0.000000  0.085106       0.00          0.0   \n1         0.0    0.00       0.013889  0.027778       0.00          0.0   \n2         0.0    0.01       0.000000  0.010000       0.01          0.0   \n3         0.0    0.00       0.000000  0.066667       0.00          0.0   \n4         0.0    0.00       0.000000  0.000000       0.00          0.0   \n\n   Ice Cream Shop  Indian Restaurant  Indie Movie Theater  Indie Theater  \\\n0        0.021277           0.000000                 0.00           0.00   \n1        0.013889           0.013889                 0.00           0.00   \n2        0.030000           0.000000                 0.01           0.01   \n3        0.000000           0.000000                 0.00           0.00   \n4        0.000000           0.016667                 0.00           0.00   \n\n   Indonesian Restaurant  Indoor Play Area  Insurance Office  Intersection  \\\n0                    0.0          0.000000               0.0      0.000000   \n1                    0.0          0.013889               0.0      0.000000   \n2                    0.0          0.000000               0.0      0.000000   \n3                    0.0          0.000000               0.0      0.033333   \n4                    0.0          0.000000               0.0      0.000000   \n\n   Irish Pub  Israeli Restaurant  Italian Restaurant  Japanese Restaurant  \\\n0   0.000000                 0.0            0.021277                  0.0   \n1   0.000000                 0.0            0.000000                  0.0   \n2   0.000000                 0.0            0.020000                  0.0   \n3   0.000000                 0.0            0.000000                  0.0   \n4   0.016667                 0.0            0.000000                  0.0   \n\n   Jazz Club  Juice Bar  Karaoke Bar  Kebab Restaurant  Korean Restaurant  \\\n0        0.0        0.0         0.00               0.0           0.021277   \n1        0.0        0.0         0.00               0.0           0.000000   \n2        0.0        0.0         0.01               0.0           0.010000   \n3        0.0        0.0         0.00               0.0           0.033333   \n4        0.0        0.0         0.00               0.0           0.000000   \n\n   Kumpir Restaurant      Lake  Laser Tag  Latin American Restaurant  \\\n0                0.0  0.000000        0.0                        0.0   \n1                0.0  0.041667        0.0                        0.0   \n2                0.0  0.010000        0.0                        0.0   \n3                0.0  0.000000        0.0                        0.0   \n4                0.0  0.000000        0.0                        0.0   \n\n   Laundry Service  Lebanese Restaurant  Light Rail Station  Liquor Store  \\\n0              0.0                  0.0            0.021277      0.000000   \n1              0.0                  0.0            0.013889      0.000000   \n2              0.0                  0.0            0.000000      0.000000   \n3              0.0                  0.0            0.066667      0.000000   \n4              0.0                  0.0            0.033333      0.016667   \n\n   Lottery Retailer    Lounge  Market  Massage Studio  \\\n0               0.0  0.000000     0.0             0.0   \n1               0.0  0.000000     0.0             0.0   \n2               0.0  0.010000     0.0             0.0   \n3               0.0  0.000000     0.0             0.0   \n4               0.0  0.016667     0.0             0.0   \n\n   Mediterranean Restaurant  Memorial Site  Men's Store  Metro Station  \\\n0                       0.0       0.000000          0.0            0.0   \n1                       0.0       0.013889          0.0            0.0   \n2                       0.0       0.000000          0.0            0.0   \n3                       0.0       0.000000          0.0            0.0   \n4                       0.0       0.000000          0.0            0.0   \n\n   Mexican Restaurant  Middle Eastern Restaurant  Miscellaneous Shop  \\\n0                 0.0                        0.0                 0.0   \n1                 0.0                        0.0                 0.0   \n2                 0.0                        0.0                 0.0   \n3                 0.0                        0.0                 0.0   \n4                 0.0                        0.0                 0.0   \n\n   Mobile Phone Shop  Modern European Restaurant  Monument / Landmark  \\\n0                0.0                        0.00                 0.00   \n1                0.0                        0.00                 0.00   \n2                0.0                        0.01                 0.01   \n3                0.0                        0.00                 0.00   \n4                0.0                        0.00                 0.00   \n\n      Motel  Motorcycle Shop  Mountain  Movie Theater  Multiplex  Museum  \\\n0  0.000000              0.0       0.0       0.000000        0.0    0.00   \n1  0.000000              0.0       0.0       0.000000        0.0    0.00   \n2  0.000000              0.0       0.0       0.010000        0.0    0.01   \n3  0.000000              0.0       0.0       0.000000        0.0    0.00   \n4  0.016667              0.0       0.0       0.016667        0.0    0.00   \n\n   Music Venue  Nature Preserve  Neighborhood  New American Restaurant  \\\n0     0.000000              0.0           0.0                      0.0   \n1     0.000000              0.0           0.0                      0.0   \n2     0.000000              0.0           0.0                      0.0   \n3     0.000000              0.0           0.0                      0.0   \n4     0.033333              0.0           0.0                      0.0   \n\n   Nightclub  Noodle House  Nudist Beach  Office  Opera House  Optical Shop  \\\n0       0.00           0.0           0.0     0.0          0.0           0.0   \n1       0.00           0.0           0.0     0.0          0.0           0.0   \n2       0.05           0.0           0.0     0.0          0.0           0.0   \n3       0.00           0.0           0.0     0.0          0.0           0.0   \n4       0.00           0.0           0.0     0.0          0.0           0.0   \n\n   Organic Grocery  Other Great Outdoors  Outdoor Sculpture  \\\n0              0.0                   0.0           0.000000   \n1              0.0                   0.0           0.000000   \n2              0.0                   0.0           0.010000   \n3              0.0                   0.0           0.033333   \n4              0.0                   0.0           0.000000   \n\n   Outdoor Supply Store  Paintball Field  Pakistani Restaurant    Palace  \\\n0                   0.0              0.0                   0.0  0.021277   \n1                   0.0              0.0                   0.0  0.000000   \n2                   0.0              0.0                   0.0  0.000000   \n3                   0.0              0.0                   0.0  0.000000   \n4                   0.0              0.0                   0.0  0.000000   \n\n       Park  Pastry Shop  Pedestrian Plaza  Performing Arts Venue  \\\n0  0.042553          0.0               0.0                    0.0   \n1  0.000000          0.0               0.0                    0.0   \n2  0.020000          0.0               0.0                    0.0   \n3  0.033333          0.0               0.0                    0.0   \n4  0.000000          0.0               0.0                    0.0   \n\n   Perfume Shop  Persian Restaurant  Peruvian Restaurant  Pet Caf\u00e9  \\\n0           0.0                 0.0                  0.0       0.0   \n1           0.0                 0.0                  0.0       0.0   \n2           0.0                 0.0                  0.0       0.0   \n3           0.0                 0.0                  0.0       0.0   \n4           0.0                 0.0                  0.0       0.0   \n\n   Pet Service  Pet Store  Pharmacy  Piano Bar  Pide Place  Pie Shop  Pier  \\\n0     0.000000        0.0  0.000000        0.0         0.0      0.00   0.0   \n1     0.013889        0.0  0.013889        0.0         0.0      0.00   0.0   \n2     0.000000        0.0  0.000000        0.0         0.0      0.01   0.0   \n3     0.000000        0.0  0.000000        0.0         0.0      0.00   0.0   \n4     0.000000        0.0  0.016667        0.0         0.0      0.00   0.0   \n\n   Piercing Parlor  Pizza Place  Planetarium  Platform  Playground     Plaza  \\\n0              0.0     0.000000          0.0  0.000000    0.000000  0.021277   \n1              0.0     0.013889          0.0  0.000000    0.000000  0.000000   \n2              0.0     0.010000          0.0  0.000000    0.000000  0.010000   \n3              0.0     0.000000          0.0  0.000000    0.000000  0.000000   \n4              0.0     0.016667          0.0  0.016667    0.016667  0.000000   \n\n   Pool  Pool Hall  Portuguese Restaurant  Post Office  Print Shop   Pub  \\\n0  0.00        0.0                   0.00          0.0         0.0  0.00   \n1  0.00        0.0                   0.00          0.0         0.0  0.00   \n2  0.01        0.0                   0.01          0.0         0.0  0.01   \n3  0.00        0.0                   0.00          0.0         0.0  0.00   \n4  0.00        0.0                   0.00          0.0         0.0  0.00   \n\n   Record Shop  Recreation Center  Rental Car Location  Rest Area  Restaurant  \\\n0         0.00                0.0             0.000000        0.0    0.021277   \n1         0.00                0.0             0.000000        0.0    0.000000   \n2         0.01                0.0             0.000000        0.0    0.000000   \n3         0.00                0.0             0.000000        0.0    0.033333   \n4         0.00                0.0             0.016667        0.0    0.000000   \n\n   River  Road  Rock Climbing Spot  Rock Club  Roof Deck  Rugby Stadium  \\\n0    0.0   0.0                 0.0       0.00        0.0       0.000000   \n1    0.0   0.0                 0.0       0.00        0.0       0.013889   \n2    0.0   0.0                 0.0       0.01        0.0       0.000000   \n3    0.0   0.0                 0.0       0.00        0.0       0.000000   \n4    0.0   0.0                 0.0       0.00        0.0       0.000000   \n\n   Russian Restaurant  Salon / Barbershop  Sandwich Place  Sauna / Steam Room  \\\n0                 0.0                0.00        0.021277                 0.0   \n1                 0.0                0.00        0.000000                 0.0   \n2                 0.0                0.01        0.000000                 0.0   \n3                 0.0                0.00        0.033333                 0.0   \n4                 0.0                0.00        0.000000                 0.0   \n\n   Scandinavian Restaurant  Scenic Lookout  Schnitzel Restaurant  \\\n0                      0.0             0.0                   0.0   \n1                      0.0             0.0                   0.0   \n2                      0.0             0.0                   0.0   \n3                      0.0             0.0                   0.0   \n4                      0.0             0.0                   0.0   \n\n   Science Museum  Sculpture Garden  Seafood Restaurant  Shipping Store  \\\n0             0.0               0.0                 0.0             0.0   \n1             0.0               0.0                 0.0             0.0   \n2             0.0               0.0                 0.0             0.0   \n3             0.0               0.0                 0.0             0.0   \n4             0.0               0.0                 0.0             0.0   \n\n   Shoe Store  Shopping Mall  Skate Park  Skating Rink  Smoke Shop  \\\n0    0.000000       0.000000        0.00      0.000000        0.00   \n1    0.000000       0.027778        0.00      0.013889        0.00   \n2    0.000000       0.000000        0.01      0.000000        0.01   \n3    0.000000       0.000000        0.00      0.000000        0.00   \n4    0.016667       0.000000        0.00      0.000000        0.00   \n\n   Snack Place  Soccer Field  Soccer Stadium  Soup Place  \\\n0     0.000000      0.021277        0.000000         0.0   \n1     0.000000      0.027778        0.013889         0.0   \n2     0.010000      0.000000        0.000000         0.0   \n3     0.000000      0.000000        0.000000         0.0   \n4     0.016667      0.000000        0.000000         0.0   \n\n   South American Restaurant  Spa  Spanish Restaurant  Speakeasy  \\\n0                        0.0  0.0                0.00        0.0   \n1                        0.0  0.0                0.00        0.0   \n2                        0.0  0.0                0.01        0.0   \n3                        0.0  0.0                0.00        0.0   \n4                        0.0  0.0                0.00        0.0   \n\n   Sporting Goods Shop  Sports Bar  Sports Club  Squash Court  Stables  \\\n0             0.000000    0.000000          0.0           0.0      0.0   \n1             0.000000    0.013889          0.0           0.0      0.0   \n2             0.000000    0.000000          0.0           0.0      0.0   \n3             0.000000    0.000000          0.0           0.0      0.0   \n4             0.016667    0.000000          0.0           0.0      0.0   \n\n   Stadium  Stationery Store  Steakhouse  Street Art  Supermarket  \\\n0      0.0               0.0    0.021277        0.00     0.148936   \n1      0.0               0.0    0.013889        0.00     0.111111   \n2      0.0               0.0    0.000000        0.01     0.000000   \n3      0.0               0.0    0.000000        0.00     0.133333   \n4      0.0               0.0    0.000000        0.00     0.133333   \n\n   Sushi Restaurant  Swiss Restaurant  Syrian Restaurant  Szechuan Restaurant  \\\n0          0.042553               0.0                0.0                  0.0   \n1          0.000000               0.0                0.0                  0.0   \n2          0.000000               0.0                0.0                  0.0   \n3          0.033333               0.0                0.0                  0.0   \n4          0.000000               0.0                0.0                  0.0   \n\n   Taco Place  Tapas Restaurant  Taverna  Tea Room  Tennis Court  \\\n0        0.00              0.00      0.0      0.00           0.0   \n1        0.00              0.00      0.0      0.00           0.0   \n2        0.01              0.01      0.0      0.01           0.0   \n3        0.00              0.00      0.0      0.00           0.0   \n4        0.00              0.00      0.0      0.00           0.0   \n\n   Tennis Stadium  Thai Restaurant  Theater  Theme Park  \\\n0             0.0              0.0      0.0         0.0   \n1             0.0              0.0      0.0         0.0   \n2             0.0              0.0      0.0         0.0   \n3             0.0              0.0      0.0         0.0   \n4             0.0              0.0      0.0         0.0   \n\n   Theme Park Ride / Attraction  Thrift / Vintage Store  Toy / Game Store  \\\n0                           0.0                     0.0               0.0   \n1                           0.0                     0.0               0.0   \n2                           0.0                     0.0               0.0   \n3                           0.0                     0.0               0.0   \n4                           0.0                     0.0               0.0   \n\n      Track  Track Stadium  Trail  Train  Train Station  Tram Station  \\\n0  0.000000            0.0   0.00    0.0       0.000000      0.000000   \n1  0.013889            0.0   0.00    0.0       0.013889      0.069444   \n2  0.000000            0.0   0.01    0.0       0.000000      0.000000   \n3  0.000000            0.0   0.00    0.0       0.000000      0.000000   \n4  0.000000            0.0   0.00    0.0       0.000000      0.000000   \n\n   Trattoria/Osteria  Tree  Tunnel  Turkish Restaurant  Vacation Rental  \\\n0           0.021277   0.0     0.0                 0.0              0.0   \n1           0.000000   0.0     0.0                 0.0              0.0   \n2           0.000000   0.0     0.0                 0.0              0.0   \n3           0.033333   0.0     0.0                 0.0              0.0   \n4           0.016667   0.0     0.0                 0.0              0.0   \n\n   Vape Store  Vegetarian / Vegan Restaurant  Venezuelan Restaurant  \\\n0         0.0                           0.00                    0.0   \n1         0.0                           0.00                    0.0   \n2         0.0                           0.01                    0.0   \n3         0.0                           0.00                    0.0   \n4         0.0                           0.00                    0.0   \n\n   Video Store  Vietnamese Restaurant  Volleyball Court  Warehouse Store  \\\n0          0.0               0.000000               0.0              0.0   \n1          0.0               0.000000               0.0              0.0   \n2          0.0               0.010000               0.0              0.0   \n3          0.0               0.000000               0.0              0.0   \n4          0.0               0.016667               0.0              0.0   \n\n   Waterfall  Waterfront  Whisky Bar  Windmill  Wine Bar  Wine Shop  Winery  \\\n0        0.0         0.0         0.0       0.0      0.00       0.00     0.0   \n1        0.0         0.0         0.0       0.0      0.00       0.00     0.0   \n2        0.0         0.0         0.0       0.0      0.01       0.01     0.0   \n3        0.0         0.0         0.0       0.0      0.00       0.00     0.0   \n4        0.0         0.0         0.0       0.0      0.00       0.00     0.0   \n\n   Women's Store  Yemeni Restaurant  Yoga Studio  Zoo  Zoo Exhibit  \n0            0.0                0.0         0.00  0.0          0.0  \n1            0.0                0.0         0.00  0.0          0.0  \n2            0.0                0.0         0.01  0.0          0.0  \n3            0.0                0.0         0.00  0.0          0.0  \n4            0.0                0.0         0.00  0.0          0.0  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "#### Analyse each neighborhood\n# one hot encoding\nonehot = pd.get_dummies(venues_df[['VenueCategory']], prefix=\"\", prefix_sep=\"\")\n\n# add neighborhood column back to dataframe\nonehot['Neighborhoods'] = venues_df['Neighborhood'] \n\n# move neighborhood column to the first column\nfixed_columns = [onehot.columns[-1]] + list(onehot.columns[:-1])\nonehot = onehot[fixed_columns]\n\nprint(onehot.shape)\n\ngrouped = onehot.groupby([\"Neighborhoods\"]).mean().reset_index()\n\nprint(grouped.shape)\ngrouped.head()"
        }, 
        {
            "execution_count": 13, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 13, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhoods</th>\n      <th>Italian Restaurant</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n      <td>0.021277</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alt-Hohensch\u00f6nhausen</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alt-Treptow</td>\n      <td>0.020000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Altglienicke</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Baumschulenweg</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Biesdorf (Berlin)</td>\n      <td>0.034483</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Blankenburg (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Blankenfelde</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Bohnsdorf</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Borsigwalde</td>\n      <td>0.036585</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Britz</td>\n      <td>0.025641</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Buch (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Buckow (Berlin)</td>\n      <td>0.065217</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Charlottenburg</td>\n      <td>0.110000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Charlottenburg-Nord</td>\n      <td>0.045977</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Dahlem (Berlin)</td>\n      <td>0.075949</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Falkenberg (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Falkenhagener Feld</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Fennpfuhl</td>\n      <td>0.030000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Franz\u00f6sisch Buchholz</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Friedenau</td>\n      <td>0.070000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Friedrichsfelde</td>\n      <td>0.042857</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Friedrichshagen</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Friedrichshain</td>\n      <td>0.030000</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Frohnau</td>\n      <td>0.033333</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Gatow</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Gesundbrunnen (Berlin)</td>\n      <td>0.010000</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Gropiusstadt</td>\n      <td>0.063492</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Grunewald</td>\n      <td>0.130000</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Gr\u00fcnau (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Hakenfelde</td>\n      <td>0.030303</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Halensee</td>\n      <td>0.120000</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Hansaviertel</td>\n      <td>0.030000</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Haselhorst</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Heiligensee</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Heinersdorf</td>\n      <td>0.029851</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Hellersdorf</td>\n      <td>0.017857</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Hermsdorf (Berlin)</td>\n      <td>0.058824</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Johannisthal (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Karlshorst</td>\n      <td>0.042553</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Karow (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Kaulsdorf (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Kladow</td>\n      <td>0.071429</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Konradsh\u00f6he</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>Kreuzberg</td>\n      <td>0.090000</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>K\u00f6penick</td>\n      <td>0.016667</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>Lankwitz</td>\n      <td>0.049180</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>Lichtenberg (locality)</td>\n      <td>0.030000</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>Lichtenrade</td>\n      <td>0.062500</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>Lichterfelde (Berlin)</td>\n      <td>0.057471</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>L\u00fcbars</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>Mahlsdorf</td>\n      <td>0.068966</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Malchow (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>Mariendorf</td>\n      <td>0.054795</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>Marienfelde</td>\n      <td>0.064516</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Marzahn</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Mitte (locality)</td>\n      <td>0.060000</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Moabit</td>\n      <td>0.030000</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>M\u00e4rkisches Viertel</td>\n      <td>0.051282</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>M\u00fcggelheim</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Neu-Hohensch\u00f6nhausen</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Neuk\u00f6lln (locality)</td>\n      <td>0.020000</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Niedersch\u00f6neweide</td>\n      <td>0.015873</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>Niedersch\u00f6nhausen</td>\n      <td>0.023256</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>Nikolassee</td>\n      <td>0.065217</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>Obersch\u00f6neweide</td>\n      <td>0.013158</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Pankow (locality)</td>\n      <td>0.020000</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>Pl\u00e4nterwald</td>\n      <td>0.022727</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>Prenzlauer Berg</td>\n      <td>0.020000</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>Rahnsdorf</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Reinickendorf (locality)</td>\n      <td>0.039216</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Rosenthal (Berlin)</td>\n      <td>0.029412</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Rudow</td>\n      <td>0.090909</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Rummelsburg</td>\n      <td>0.020000</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Schmargendorf</td>\n      <td>0.145161</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>Schm\u00f6ckwitz</td>\n      <td>0.100000</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>Sch\u00f6neberg</td>\n      <td>0.030000</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>Siemensstadt</td>\n      <td>0.030303</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>Spandau (locality)</td>\n      <td>0.012195</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>Staaken</td>\n      <td>0.027778</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>Stadtrandsiedlung Malchow</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>Steglitz</td>\n      <td>0.060000</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>Tegel</td>\n      <td>0.047619</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>Tempelhof</td>\n      <td>0.050505</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Tiergarten, Berlin</td>\n      <td>0.010000</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>Waidmannslust</td>\n      <td>0.085714</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>Wannsee</td>\n      <td>0.022727</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>Wartenberg (Berlin)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>Wedding (Berlin)</td>\n      <td>0.020000</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>Weissensee (Berlin)</td>\n      <td>0.015625</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>Westend (Berlin)</td>\n      <td>0.050000</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>Wilhelmsruh</td>\n      <td>0.024390</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>Wilhelmstadt</td>\n      <td>0.014925</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>Wilmersdorf</td>\n      <td>0.080000</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>Wittenau</td>\n      <td>0.045455</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>Zehlendorf (Berlin)</td>\n      <td>0.064516</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                Neighborhoods  Italian Restaurant\n0                   Adlershof            0.021277\n1        Alt-Hohensch\u00f6nhausen            0.000000\n2                 Alt-Treptow            0.020000\n3                Altglienicke            0.000000\n4              Baumschulenweg            0.000000\n5           Biesdorf (Berlin)            0.034483\n6        Blankenburg (Berlin)            0.000000\n7                Blankenfelde            0.000000\n8                   Bohnsdorf            0.000000\n9                 Borsigwalde            0.036585\n10                      Britz            0.025641\n11              Buch (Berlin)            0.000000\n12            Buckow (Berlin)            0.065217\n13             Charlottenburg            0.110000\n14        Charlottenburg-Nord            0.045977\n15            Dahlem (Berlin)            0.075949\n16        Falkenberg (Berlin)            0.000000\n17         Falkenhagener Feld            0.000000\n18                  Fennpfuhl            0.030000\n19       Franz\u00f6sisch Buchholz            0.000000\n20                  Friedenau            0.070000\n21            Friedrichsfelde            0.042857\n22            Friedrichshagen            0.000000\n23             Friedrichshain            0.030000\n24                    Frohnau            0.033333\n25                      Gatow            0.000000\n26     Gesundbrunnen (Berlin)            0.010000\n27               Gropiusstadt            0.063492\n28                  Grunewald            0.130000\n29            Gr\u00fcnau (Berlin)            0.000000\n30                 Hakenfelde            0.030303\n31                   Halensee            0.120000\n32               Hansaviertel            0.030000\n33                 Haselhorst            0.000000\n34                Heiligensee            0.000000\n35                Heinersdorf            0.029851\n36                Hellersdorf            0.017857\n37         Hermsdorf (Berlin)            0.058824\n38      Johannisthal (Berlin)            0.000000\n39                 Karlshorst            0.042553\n40             Karow (Berlin)            0.000000\n41         Kaulsdorf (Berlin)            0.000000\n42                     Kladow            0.071429\n43                Konradsh\u00f6he            0.000000\n44                  Kreuzberg            0.090000\n45                   K\u00f6penick            0.016667\n46                   Lankwitz            0.049180\n47     Lichtenberg (locality)            0.030000\n48                Lichtenrade            0.062500\n49      Lichterfelde (Berlin)            0.057471\n50                     L\u00fcbars            0.000000\n51                  Mahlsdorf            0.068966\n52           Malchow (Berlin)            0.000000\n53                 Mariendorf            0.054795\n54                Marienfelde            0.064516\n55                    Marzahn            0.000000\n56           Mitte (locality)            0.060000\n57                     Moabit            0.030000\n58         M\u00e4rkisches Viertel            0.051282\n59                 M\u00fcggelheim            0.000000\n60       Neu-Hohensch\u00f6nhausen            0.000000\n61        Neuk\u00f6lln (locality)            0.020000\n62          Niedersch\u00f6neweide            0.015873\n63          Niedersch\u00f6nhausen            0.023256\n64                 Nikolassee            0.065217\n65            Obersch\u00f6neweide            0.013158\n66          Pankow (locality)            0.020000\n67                Pl\u00e4nterwald            0.022727\n68            Prenzlauer Berg            0.020000\n69                  Rahnsdorf            0.000000\n70   Reinickendorf (locality)            0.039216\n71         Rosenthal (Berlin)            0.029412\n72                      Rudow            0.090909\n73                Rummelsburg            0.020000\n74              Schmargendorf            0.145161\n75                Schm\u00f6ckwitz            0.100000\n76                 Sch\u00f6neberg            0.030000\n77               Siemensstadt            0.030303\n78         Spandau (locality)            0.012195\n79                    Staaken            0.027778\n80  Stadtrandsiedlung Malchow            0.000000\n81                   Steglitz            0.060000\n82                      Tegel            0.047619\n83                  Tempelhof            0.050505\n84         Tiergarten, Berlin            0.010000\n85              Waidmannslust            0.085714\n86                    Wannsee            0.022727\n87        Wartenberg (Berlin)            0.000000\n88           Wedding (Berlin)            0.020000\n89        Weissensee (Berlin)            0.015625\n90           Westend (Berlin)            0.050000\n91                Wilhelmsruh            0.024390\n92               Wilhelmstadt            0.014925\n93                Wilmersdorf            0.080000\n94                   Wittenau            0.045455\n95        Zehlendorf (Berlin)            0.064516"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "len(grouped[grouped[\"Italian Restaurant\"] > 0])\nberlin_rest = grouped[[\"Neighborhoods\",\"Italian Restaurant\"]]\nberlin_rest"
        }, 
        {
            "source": "#### Clustering", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 129, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 129, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "array([2, 0, 2, 0, 0, 2, 0, 0, 0, 2], dtype=int32)"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "kclusters = 4\n\nberlin_clustering = berlin_rest.drop([\"Neighborhoods\"], 1)\nberlin_clustering.head()\n\n# run k-means clustering\nkmeans = KMeans(init=\"k-means++\", n_clusters=kclusters, n_init=12).fit(berlin_clustering)\n\n# check cluster labels generated for each row in the dataframe\nkmeans.labels_[0:10]"
        }, 
        {
            "execution_count": 130, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 130, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Italian Restaurant</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n      <td>0.021277</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alt-Hohensch\u00f6nhausen</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alt-Treptow</td>\n      <td>0.020000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Altglienicke</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Baumschulenweg</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "           Neighborhood  Italian Restaurant  Category\n0             Adlershof            0.021277         2\n1  Alt-Hohensch\u00f6nhausen            0.000000         0\n2           Alt-Treptow            0.020000         2\n3          Altglienicke            0.000000         0\n4        Baumschulenweg            0.000000         0"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "merged = berlin_rest.copy()\n\n# add clustering labels\nmerged[\"Category\"] = kmeans.labels_\nmerged.rename(columns={\"Neighborhoods\": \"Neighborhood\"}, inplace=True)\nmerged.head()"
        }, 
        {
            "execution_count": 131, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 131, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Italian Restaurant</th>\n      <th>Category</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>48</th>\n      <td>Lichtenrade</td>\n      <td>0.062500</td>\n      <td>3</td>\n      <td>52.39159</td>\n      <td>13.41247</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Friedrichsfelde</td>\n      <td>0.042857</td>\n      <td>3</td>\n      <td>52.50527</td>\n      <td>13.51326</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Hermsdorf (Berlin)</td>\n      <td>0.058824</td>\n      <td>3</td>\n      <td>52.61488</td>\n      <td>13.31752</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Karlshorst</td>\n      <td>0.042553</td>\n      <td>3</td>\n      <td>52.48181</td>\n      <td>13.52547</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Kladow</td>\n      <td>0.071429</td>\n      <td>3</td>\n      <td>52.45301</td>\n      <td>13.14256</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>Lankwitz</td>\n      <td>0.049180</td>\n      <td>3</td>\n      <td>52.43895</td>\n      <td>13.35340</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>Lichterfelde (Berlin)</td>\n      <td>0.057471</td>\n      <td>3</td>\n      <td>52.43293</td>\n      <td>13.31894</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>Mahlsdorf</td>\n      <td>0.068966</td>\n      <td>3</td>\n      <td>52.51297</td>\n      <td>13.61185</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>Mariendorf</td>\n      <td>0.054795</td>\n      <td>3</td>\n      <td>52.43855</td>\n      <td>13.38779</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>Marienfelde</td>\n      <td>0.064516</td>\n      <td>3</td>\n      <td>52.41262</td>\n      <td>13.36732</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Mitte (locality)</td>\n      <td>0.060000</td>\n      <td>3</td>\n      <td>52.52121</td>\n      <td>13.42415</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>M\u00e4rkisches Viertel</td>\n      <td>0.051282</td>\n      <td>3</td>\n      <td>52.59680</td>\n      <td>13.35831</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>Nikolassee</td>\n      <td>0.065217</td>\n      <td>3</td>\n      <td>52.43411</td>\n      <td>13.19976</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>Steglitz</td>\n      <td>0.060000</td>\n      <td>3</td>\n      <td>52.45784</td>\n      <td>13.31808</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>Tegel</td>\n      <td>0.047619</td>\n      <td>3</td>\n      <td>52.58835</td>\n      <td>13.27969</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>Tempelhof</td>\n      <td>0.050505</td>\n      <td>3</td>\n      <td>52.46076</td>\n      <td>13.38565</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>Westend (Berlin)</td>\n      <td>0.050000</td>\n      <td>3</td>\n      <td>52.51650</td>\n      <td>13.26922</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>Wilmersdorf</td>\n      <td>0.080000</td>\n      <td>3</td>\n      <td>52.48976</td>\n      <td>13.31519</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>Wittenau</td>\n      <td>0.045455</td>\n      <td>3</td>\n      <td>52.59852</td>\n      <td>13.33283</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Gropiusstadt</td>\n      <td>0.063492</td>\n      <td>3</td>\n      <td>52.42472</td>\n      <td>13.47370</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>Zehlendorf (Berlin)</td>\n      <td>0.064516</td>\n      <td>3</td>\n      <td>52.43351</td>\n      <td>13.25809</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Friedenau</td>\n      <td>0.070000</td>\n      <td>3</td>\n      <td>52.47297</td>\n      <td>13.33269</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Buckow (Berlin)</td>\n      <td>0.065217</td>\n      <td>3</td>\n      <td>52.42163</td>\n      <td>13.43077</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Charlottenburg-Nord</td>\n      <td>0.045977</td>\n      <td>3</td>\n      <td>52.53048</td>\n      <td>13.29371</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Dahlem (Berlin)</td>\n      <td>0.075949</td>\n      <td>3</td>\n      <td>52.45874</td>\n      <td>13.28344</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>Wilhelmsruh</td>\n      <td>0.024390</td>\n      <td>2</td>\n      <td>52.58887</td>\n      <td>13.36949</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>Weissensee (Berlin)</td>\n      <td>0.015625</td>\n      <td>2</td>\n      <td>52.56185</td>\n      <td>13.46826</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alt-Treptow</td>\n      <td>0.020000</td>\n      <td>2</td>\n      <td>52.49350</td>\n      <td>13.45711</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>Wilhelmstadt</td>\n      <td>0.014925</td>\n      <td>2</td>\n      <td>52.52462</td>\n      <td>13.17707</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Britz</td>\n      <td>0.025641</td>\n      <td>2</td>\n      <td>52.44873</td>\n      <td>13.43621</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Moabit</td>\n      <td>0.030000</td>\n      <td>2</td>\n      <td>52.52570</td>\n      <td>13.34005</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Neuk\u00f6lln (locality)</td>\n      <td>0.020000</td>\n      <td>2</td>\n      <td>52.48077</td>\n      <td>13.43541</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Niedersch\u00f6neweide</td>\n      <td>0.015873</td>\n      <td>2</td>\n      <td>52.45550</td>\n      <td>13.51329</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>Niedersch\u00f6nhausen</td>\n      <td>0.023256</td>\n      <td>2</td>\n      <td>52.58265</td>\n      <td>13.40362</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Pankow (locality)</td>\n      <td>0.020000</td>\n      <td>2</td>\n      <td>52.56925</td>\n      <td>13.40248</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>Pl\u00e4nterwald</td>\n      <td>0.022727</td>\n      <td>2</td>\n      <td>52.48439</td>\n      <td>13.47957</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>Prenzlauer Berg</td>\n      <td>0.020000</td>\n      <td>2</td>\n      <td>52.54083</td>\n      <td>13.42575</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Reinickendorf (locality)</td>\n      <td>0.039216</td>\n      <td>2</td>\n      <td>52.57545</td>\n      <td>13.34970</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>Lichtenberg (locality)</td>\n      <td>0.030000</td>\n      <td>2</td>\n      <td>52.51618</td>\n      <td>13.48087</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Rummelsburg</td>\n      <td>0.020000</td>\n      <td>2</td>\n      <td>52.50131</td>\n      <td>13.49127</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>Sch\u00f6neberg</td>\n      <td>0.030000</td>\n      <td>2</td>\n      <td>52.48555</td>\n      <td>13.34293</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>Siemensstadt</td>\n      <td>0.030303</td>\n      <td>2</td>\n      <td>52.53828</td>\n      <td>13.26406</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>Staaken</td>\n      <td>0.027778</td>\n      <td>2</td>\n      <td>52.53960</td>\n      <td>13.14282</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Biesdorf (Berlin)</td>\n      <td>0.034483</td>\n      <td>2</td>\n      <td>52.50989</td>\n      <td>13.55370</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>Wannsee</td>\n      <td>0.022727</td>\n      <td>2</td>\n      <td>52.42045</td>\n      <td>13.16705</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>Wedding (Berlin)</td>\n      <td>0.020000</td>\n      <td>2</td>\n      <td>52.54781</td>\n      <td>13.35473</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Rosenthal (Berlin)</td>\n      <td>0.029412</td>\n      <td>2</td>\n      <td>52.59977</td>\n      <td>13.37776</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Borsigwalde</td>\n      <td>0.036585</td>\n      <td>2</td>\n      <td>52.58522</td>\n      <td>13.30788</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n      <td>0.021277</td>\n      <td>2</td>\n      <td>52.43779</td>\n      <td>13.54778</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Fennpfuhl</td>\n      <td>0.030000</td>\n      <td>2</td>\n      <td>52.52773</td>\n      <td>13.46654</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Friedrichshain</td>\n      <td>0.030000</td>\n      <td>2</td>\n      <td>52.51402</td>\n      <td>13.45403</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Frohnau</td>\n      <td>0.033333</td>\n      <td>2</td>\n      <td>52.63346</td>\n      <td>13.29038</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Hakenfelde</td>\n      <td>0.030303</td>\n      <td>2</td>\n      <td>52.55877</td>\n      <td>13.20831</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Hansaviertel</td>\n      <td>0.030000</td>\n      <td>2</td>\n      <td>52.51679</td>\n      <td>13.33835</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>K\u00f6penick</td>\n      <td>0.016667</td>\n      <td>2</td>\n      <td>52.44330</td>\n      <td>13.58311</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Heinersdorf</td>\n      <td>0.029851</td>\n      <td>2</td>\n      <td>52.56770</td>\n      <td>13.43980</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Hellersdorf</td>\n      <td>0.017857</td>\n      <td>2</td>\n      <td>52.52945</td>\n      <td>13.59068</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Grunewald</td>\n      <td>0.130000</td>\n      <td>1</td>\n      <td>52.49203</td>\n      <td>13.28495</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>Waidmannslust</td>\n      <td>0.085714</td>\n      <td>1</td>\n      <td>52.60622</td>\n      <td>13.32065</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Halensee</td>\n      <td>0.120000</td>\n      <td>1</td>\n      <td>52.49005</td>\n      <td>13.29602</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>Schm\u00f6ckwitz</td>\n      <td>0.100000</td>\n      <td>1</td>\n      <td>52.37584</td>\n      <td>13.64899</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Schmargendorf</td>\n      <td>0.145161</td>\n      <td>1</td>\n      <td>52.47732</td>\n      <td>13.28798</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Rudow</td>\n      <td>0.090909</td>\n      <td>1</td>\n      <td>52.41917</td>\n      <td>13.49370</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Charlottenburg</td>\n      <td>0.110000</td>\n      <td>1</td>\n      <td>52.51915</td>\n      <td>13.30639</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>Kreuzberg</td>\n      <td>0.090000</td>\n      <td>1</td>\n      <td>52.49382</td>\n      <td>13.38358</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Falkenhagener Feld</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.55191</td>\n      <td>13.16802</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>Stadtrandsiedlung Malchow</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.57038</td>\n      <td>13.46342</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>L\u00fcbars</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.62014</td>\n      <td>13.35525</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Altglienicke</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.42006</td>\n      <td>13.53969</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Gatow</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.48884</td>\n      <td>13.18070</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Baumschulenweg</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.46669</td>\n      <td>13.48840</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Gesundbrunnen (Berlin)</td>\n      <td>0.010000</td>\n      <td>0</td>\n      <td>52.55535</td>\n      <td>13.37715</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Franz\u00f6sisch Buchholz</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.60822</td>\n      <td>13.43368</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>Wartenberg (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.57503</td>\n      <td>13.51703</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Malchow (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.57842</td>\n      <td>13.48302</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Tiergarten, Berlin</td>\n      <td>0.010000</td>\n      <td>0</td>\n      <td>52.50993</td>\n      <td>13.36393</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Gr\u00fcnau (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.41416</td>\n      <td>13.57499</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Blankenburg (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.59332</td>\n      <td>13.45805</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Blankenfelde</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.61855</td>\n      <td>13.39017</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Buch (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.63424</td>\n      <td>13.49753</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Bohnsdorf</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.40091</td>\n      <td>13.55983</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>Spandau (locality)</td>\n      <td>0.012195</td>\n      <td>0</td>\n      <td>52.53487</td>\n      <td>13.20216</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Marzahn</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.54212</td>\n      <td>13.54389</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Konradsh\u00f6he</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.58520</td>\n      <td>13.22698</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Falkenberg (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.56958</td>\n      <td>13.53879</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Friedrichshagen</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.45100</td>\n      <td>13.62473</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Haselhorst</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.54336</td>\n      <td>13.24152</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>M\u00fcggelheim</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.41300</td>\n      <td>13.66050</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Heiligensee</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.60694</td>\n      <td>13.21070</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Neu-Hohensch\u00f6nhausen</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.56512</td>\n      <td>13.50828</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>Rahnsdorf</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.44093</td>\n      <td>13.68891</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Kaulsdorf (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.50302</td>\n      <td>13.58079</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Karow (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.61438</td>\n      <td>13.46932</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Johannisthal (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.44329</td>\n      <td>13.50272</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>Obersch\u00f6neweide</td>\n      <td>0.013158</td>\n      <td>0</td>\n      <td>52.46237</td>\n      <td>13.51438</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alt-Hohensch\u00f6nhausen</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>52.54706</td>\n      <td>13.50055</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                 Neighborhood  Italian Restaurant  Category  Latitude  \\\n48                Lichtenrade            0.062500         3  52.39159   \n21            Friedrichsfelde            0.042857         3  52.50527   \n37         Hermsdorf (Berlin)            0.058824         3  52.61488   \n39                 Karlshorst            0.042553         3  52.48181   \n42                     Kladow            0.071429         3  52.45301   \n46                   Lankwitz            0.049180         3  52.43895   \n49      Lichterfelde (Berlin)            0.057471         3  52.43293   \n51                  Mahlsdorf            0.068966         3  52.51297   \n53                 Mariendorf            0.054795         3  52.43855   \n54                Marienfelde            0.064516         3  52.41262   \n56           Mitte (locality)            0.060000         3  52.52121   \n58         M\u00e4rkisches Viertel            0.051282         3  52.59680   \n64                 Nikolassee            0.065217         3  52.43411   \n81                   Steglitz            0.060000         3  52.45784   \n82                      Tegel            0.047619         3  52.58835   \n83                  Tempelhof            0.050505         3  52.46076   \n90           Westend (Berlin)            0.050000         3  52.51650   \n93                Wilmersdorf            0.080000         3  52.48976   \n94                   Wittenau            0.045455         3  52.59852   \n27               Gropiusstadt            0.063492         3  52.42472   \n95        Zehlendorf (Berlin)            0.064516         3  52.43351   \n20                  Friedenau            0.070000         3  52.47297   \n12            Buckow (Berlin)            0.065217         3  52.42163   \n14        Charlottenburg-Nord            0.045977         3  52.53048   \n15            Dahlem (Berlin)            0.075949         3  52.45874   \n91                Wilhelmsruh            0.024390         2  52.58887   \n89        Weissensee (Berlin)            0.015625         2  52.56185   \n2                 Alt-Treptow            0.020000         2  52.49350   \n92               Wilhelmstadt            0.014925         2  52.52462   \n10                      Britz            0.025641         2  52.44873   \n57                     Moabit            0.030000         2  52.52570   \n61        Neuk\u00f6lln (locality)            0.020000         2  52.48077   \n62          Niedersch\u00f6neweide            0.015873         2  52.45550   \n63          Niedersch\u00f6nhausen            0.023256         2  52.58265   \n66          Pankow (locality)            0.020000         2  52.56925   \n67                Pl\u00e4nterwald            0.022727         2  52.48439   \n68            Prenzlauer Berg            0.020000         2  52.54083   \n70   Reinickendorf (locality)            0.039216         2  52.57545   \n47     Lichtenberg (locality)            0.030000         2  52.51618   \n73                Rummelsburg            0.020000         2  52.50131   \n76                 Sch\u00f6neberg            0.030000         2  52.48555   \n77               Siemensstadt            0.030303         2  52.53828   \n79                    Staaken            0.027778         2  52.53960   \n5           Biesdorf (Berlin)            0.034483         2  52.50989   \n86                    Wannsee            0.022727         2  52.42045   \n88           Wedding (Berlin)            0.020000         2  52.54781   \n71         Rosenthal (Berlin)            0.029412         2  52.59977   \n9                 Borsigwalde            0.036585         2  52.58522   \n0                   Adlershof            0.021277         2  52.43779   \n18                  Fennpfuhl            0.030000         2  52.52773   \n23             Friedrichshain            0.030000         2  52.51402   \n24                    Frohnau            0.033333         2  52.63346  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\n73   13.49127  \n76   13.34293  \n77   13.26406  \n79   13.14282  \n5    13.55370  \n86   13.16705  \n88   13.35473  \n71   13.37776  \n9    13.30788  \n0    13.54778  \n18   13.46654  \n23   13.45403  \n24   13.29038  \n30   13.20831  \n32   13.33835  \n45   13.58311  \n35   13.43980  \n36   13.59068  \n28   13.28495  \n85   13.32065  \n31   13.29602  \n75   13.64899  \n74   13.28798  \n72   13.49370  \n13   13.30639  \n44   13.38358  \n17   13.16802  \n80   13.46342  \n50   13.35525  \n3    13.53969  \n25   13.18070  \n4    13.48840  \n26   13.37715  \n19   13.43368  \n87   13.51703  \n52   13.48302  \n84   13.36393  \n29   13.57499  \n6    13.45805  \n7    13.39017  \n11   13.49753  \n8    13.55983  \n78   13.20216  \n55   13.54389  \n43   13.22698  \n16   13.53879  \n22   13.62473  \n33   13.24152  \n59   13.66050  \n34   13.21070  \n60   13.50828  \n69   13.68891  \n41   13.58079  \n40   13.46932  \n38   13.50272  \n65   13.51438  \n1    13.50055  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "dfmerged = merged.merge(berlin_df)\ndfmerged.head()\n\n#Sort\ndfmerged.sort_values([\"Category\"], inplace=True, ascending=False)\ndfmerged"
        }, 
        {
            "source": "#### Visualising Clusters", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 133, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": false
            }, 
            "outputs": [
                {
                    "execution_count": 133, 
                    "metadata": {}, 
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                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_bae9b44445a64f8f9c387b80a4fcee19 = L.popup({"maxWidth": "100%"});

        
            var html_f4bb48798895414e9c7492e9ffc40b53 = $(`<div id="html_f4bb48798895414e9c7492e9ffc40b53" style="width: 100.0%; height: 100.0%;">Mitte (locality) - Cluster 3</div>`)[0];
            popup_bae9b44445a64f8f9c387b80a4fcee19.setContent(html_f4bb48798895414e9c7492e9ffc40b53);
        

        circle_marker_59d0b02be1f24e4d9b8bf30062ca79f8.bindPopup(popup_bae9b44445a64f8f9c387b80a4fcee19)
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            var circle_marker_ce37f596aaf74c2187a2ee5d6cc3c6a3 = L.circleMarker(
                [52.59680000000003, 13.358310000000074],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_8dd2398d338d4375a8ecb07b10034950 = L.popup({"maxWidth": "100%"});

        
            var html_306a6466f404427194a72d081e95056c = $(`<div id="html_306a6466f404427194a72d081e95056c" style="width: 100.0%; height: 100.0%;">Märkisches Viertel - Cluster 3</div>`)[0];
            popup_8dd2398d338d4375a8ecb07b10034950.setContent(html_306a6466f404427194a72d081e95056c);
        

        circle_marker_ce37f596aaf74c2187a2ee5d6cc3c6a3.bindPopup(popup_8dd2398d338d4375a8ecb07b10034950)
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            var circle_marker_cc7bd2f70f5d410db1d586d72778c5ee = L.circleMarker(
                [52.43411000000003, 13.199760000000026],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_887c4e79cc7c4e7d9c3a47b61426d9c9 = L.popup({"maxWidth": "100%"});

        
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            popup_887c4e79cc7c4e7d9c3a47b61426d9c9.setContent(html_20474137a1124849b94f207d1ae81a18);
        

        circle_marker_cc7bd2f70f5d410db1d586d72778c5ee.bindPopup(popup_887c4e79cc7c4e7d9c3a47b61426d9c9)
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            var circle_marker_bf76d286216b4f73907d2aa26e4c771d = L.circleMarker(
                [52.45784000000003, 13.318080000000066],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_b450c80126dc4165b4e19448f2e0a402 = L.popup({"maxWidth": "100%"});

        
            var html_3a7e4d0a9d6142619d01a82225dd8005 = $(`<div id="html_3a7e4d0a9d6142619d01a82225dd8005" style="width: 100.0%; height: 100.0%;">Steglitz - Cluster 3</div>`)[0];
            popup_b450c80126dc4165b4e19448f2e0a402.setContent(html_3a7e4d0a9d6142619d01a82225dd8005);
        

        circle_marker_bf76d286216b4f73907d2aa26e4c771d.bindPopup(popup_b450c80126dc4165b4e19448f2e0a402)
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            var circle_marker_1cc4abd247fa45069b5e2b014a8f4b45 = L.circleMarker(
                [52.58835000000005, 13.279690000000073],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_376d0c0623154582b765b390a7b23147 = L.popup({"maxWidth": "100%"});

        
            var html_5295d51814c8426fbbe85788e2fc90bb = $(`<div id="html_5295d51814c8426fbbe85788e2fc90bb" style="width: 100.0%; height: 100.0%;">Tegel - Cluster 3</div>`)[0];
            popup_376d0c0623154582b765b390a7b23147.setContent(html_5295d51814c8426fbbe85788e2fc90bb);
        

        circle_marker_1cc4abd247fa45069b5e2b014a8f4b45.bindPopup(popup_376d0c0623154582b765b390a7b23147)
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            var circle_marker_32f8c6b7ea304510b4b4471384d8cd45 = L.circleMarker(
                [52.46076000000005, 13.385650000000055],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_eab9939e4da342a6b069df87df27b739 = L.popup({"maxWidth": "100%"});

        
            var html_f7aba9e2ee8940b8b2f623fb7bce0e9f = $(`<div id="html_f7aba9e2ee8940b8b2f623fb7bce0e9f" style="width: 100.0%; height: 100.0%;">Tempelhof - Cluster 3</div>`)[0];
            popup_eab9939e4da342a6b069df87df27b739.setContent(html_f7aba9e2ee8940b8b2f623fb7bce0e9f);
        

        circle_marker_32f8c6b7ea304510b4b4471384d8cd45.bindPopup(popup_eab9939e4da342a6b069df87df27b739)
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            var circle_marker_430456cba42e4befbe34a795266a444c = L.circleMarker(
                [52.516500000000065, 13.269220000000075],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_e5d2404672bf4b89a8dba4018e954ddd = L.popup({"maxWidth": "100%"});

        
            var html_a365de75dfc84c2382008962da74b1f0 = $(`<div id="html_a365de75dfc84c2382008962da74b1f0" style="width: 100.0%; height: 100.0%;">Westend (Berlin) - Cluster 3</div>`)[0];
            popup_e5d2404672bf4b89a8dba4018e954ddd.setContent(html_a365de75dfc84c2382008962da74b1f0);
        

        circle_marker_430456cba42e4befbe34a795266a444c.bindPopup(popup_e5d2404672bf4b89a8dba4018e954ddd)
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            var circle_marker_168b56fb2bce4a668b95fbe7192f89df = L.circleMarker(
                [52.48976000000005, 13.31519000000003],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_91d0d14256ef4e4c85e0a6a3f01c4408 = L.popup({"maxWidth": "100%"});

        
            var html_24570f0c8fc64a45b4d8309d015e4c18 = $(`<div id="html_24570f0c8fc64a45b4d8309d015e4c18" style="width: 100.0%; height: 100.0%;">Wilmersdorf - Cluster 3</div>`)[0];
            popup_91d0d14256ef4e4c85e0a6a3f01c4408.setContent(html_24570f0c8fc64a45b4d8309d015e4c18);
        

        circle_marker_168b56fb2bce4a668b95fbe7192f89df.bindPopup(popup_91d0d14256ef4e4c85e0a6a3f01c4408)
        ;

        
    
    
            var circle_marker_ef62c883680a478bb801b0bb5b62a45c = L.circleMarker(
                [52.598520000000065, 13.332830000000058],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_6d7ca284230c4fb68881acd64d244ff2 = L.popup({"maxWidth": "100%"});

        
            var html_d64182d6c6a84dd48092b2148d3078a5 = $(`<div id="html_d64182d6c6a84dd48092b2148d3078a5" style="width: 100.0%; height: 100.0%;">Wittenau - Cluster 3</div>`)[0];
            popup_6d7ca284230c4fb68881acd64d244ff2.setContent(html_d64182d6c6a84dd48092b2148d3078a5);
        

        circle_marker_ef62c883680a478bb801b0bb5b62a45c.bindPopup(popup_6d7ca284230c4fb68881acd64d244ff2)
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            var circle_marker_a9af60df407c4bf29aadcfef4f15b549 = L.circleMarker(
                [52.424720000000036, 13.473700000000065],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_8f4bb56e4b824f798a28d785cea94343 = L.popup({"maxWidth": "100%"});

        
            var html_b7a45b14f93a430bb56798aeb63e9f67 = $(`<div id="html_b7a45b14f93a430bb56798aeb63e9f67" style="width: 100.0%; height: 100.0%;">Gropiusstadt - Cluster 3</div>`)[0];
            popup_8f4bb56e4b824f798a28d785cea94343.setContent(html_b7a45b14f93a430bb56798aeb63e9f67);
        

        circle_marker_a9af60df407c4bf29aadcfef4f15b549.bindPopup(popup_8f4bb56e4b824f798a28d785cea94343)
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            var circle_marker_efb70f20d25742d6a0ddde02a48e972a = L.circleMarker(
                [52.43351000000007, 13.258090000000038],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_1874e99ea72845709f42e024e2a41ecb = L.popup({"maxWidth": "100%"});

        
            var html_fcd60d45f87e4a398d5af0c2bf255673 = $(`<div id="html_fcd60d45f87e4a398d5af0c2bf255673" style="width: 100.0%; height: 100.0%;">Zehlendorf (Berlin) - Cluster 3</div>`)[0];
            popup_1874e99ea72845709f42e024e2a41ecb.setContent(html_fcd60d45f87e4a398d5af0c2bf255673);
        

        circle_marker_efb70f20d25742d6a0ddde02a48e972a.bindPopup(popup_1874e99ea72845709f42e024e2a41ecb)
        ;

        
    
    
            var circle_marker_bc4d8842dc5a4381a413ed514edf4564 = L.circleMarker(
                [52.47297000000003, 13.33269000000007],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_e9af474ecf2c4a2d83cadc9e42fa24c6 = L.popup({"maxWidth": "100%"});

        
            var html_c5dd5cd5d1964e1e9e3e196dfaba00c0 = $(`<div id="html_c5dd5cd5d1964e1e9e3e196dfaba00c0" style="width: 100.0%; height: 100.0%;">Friedenau - Cluster 3</div>`)[0];
            popup_e9af474ecf2c4a2d83cadc9e42fa24c6.setContent(html_c5dd5cd5d1964e1e9e3e196dfaba00c0);
        

        circle_marker_bc4d8842dc5a4381a413ed514edf4564.bindPopup(popup_e9af474ecf2c4a2d83cadc9e42fa24c6)
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            var circle_marker_64ba260bd7d848a18725836671d3fcaf = L.circleMarker(
                [52.42163000000005, 13.430770000000052],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_c1462100d74047f499f9c8611cba5296 = L.popup({"maxWidth": "100%"});

        
            var html_462b09796a4843c08425cf759611ba62 = $(`<div id="html_462b09796a4843c08425cf759611ba62" style="width: 100.0%; height: 100.0%;">Buckow (Berlin) - Cluster 3</div>`)[0];
            popup_c1462100d74047f499f9c8611cba5296.setContent(html_462b09796a4843c08425cf759611ba62);
        

        circle_marker_64ba260bd7d848a18725836671d3fcaf.bindPopup(popup_c1462100d74047f499f9c8611cba5296)
        ;

        
    
    
            var circle_marker_2efb0a5bc49f4adf99df30b564cb797e = L.circleMarker(
                [52.53048000000007, 13.293710000000033],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_54323a2e56c54ea085e07a183beb1858 = L.popup({"maxWidth": "100%"});

        
            var html_cf041de9538542309b89bef0f3f89aa7 = $(`<div id="html_cf041de9538542309b89bef0f3f89aa7" style="width: 100.0%; height: 100.0%;">Charlottenburg-Nord - Cluster 3</div>`)[0];
            popup_54323a2e56c54ea085e07a183beb1858.setContent(html_cf041de9538542309b89bef0f3f89aa7);
        

        circle_marker_2efb0a5bc49f4adf99df30b564cb797e.bindPopup(popup_54323a2e56c54ea085e07a183beb1858)
        ;

        
    
    
            var circle_marker_c97e9423646b42d883f31ff63daf308e = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_1f7db5edcda1447ba2d10f01d3b747a3 = L.popup({"maxWidth": "100%"});

        
            var html_5152d1c65ce44d43a633f3b8e31512da = $(`<div id="html_5152d1c65ce44d43a633f3b8e31512da" style="width: 100.0%; height: 100.0%;">Dahlem (Berlin) - Cluster 3</div>`)[0];
            popup_1f7db5edcda1447ba2d10f01d3b747a3.setContent(html_5152d1c65ce44d43a633f3b8e31512da);
        

        circle_marker_c97e9423646b42d883f31ff63daf308e.bindPopup(popup_1f7db5edcda1447ba2d10f01d3b747a3)
        ;

        
    
    
            var circle_marker_d408d15d4d304a068952ada2019b7d2b = L.circleMarker(
                [52.58887000000004, 13.369490000000042],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_0ad9913d8b0249a7990172dae8820779 = L.popup({"maxWidth": "100%"});

        
            var html_c2958c73da3d4f2691cc7c04c5aa8cc7 = $(`<div id="html_c2958c73da3d4f2691cc7c04c5aa8cc7" style="width: 100.0%; height: 100.0%;">Wilhelmsruh - Cluster 2</div>`)[0];
            popup_0ad9913d8b0249a7990172dae8820779.setContent(html_c2958c73da3d4f2691cc7c04c5aa8cc7);
        

        circle_marker_d408d15d4d304a068952ada2019b7d2b.bindPopup(popup_0ad9913d8b0249a7990172dae8820779)
        ;

        
    
    
            var circle_marker_227c66628690429d9803cb6b186f2d3a = L.circleMarker(
                [52.56185000000005, 13.468260000000043],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_784a06b0266e4d6b90afc8692929070a = L.popup({"maxWidth": "100%"});

        
            var html_1a918bdeeb49470ebb4770dd98a16c91 = $(`<div id="html_1a918bdeeb49470ebb4770dd98a16c91" style="width: 100.0%; height: 100.0%;">Weissensee (Berlin) - Cluster 2</div>`)[0];
            popup_784a06b0266e4d6b90afc8692929070a.setContent(html_1a918bdeeb49470ebb4770dd98a16c91);
        

        circle_marker_227c66628690429d9803cb6b186f2d3a.bindPopup(popup_784a06b0266e4d6b90afc8692929070a)
        ;

        
    
    
            var circle_marker_de600df1c1c64b938b4b04358478c2b0 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_a14217039431438da037f5c4ebdcdda4 = L.popup({"maxWidth": "100%"});

        
            var html_c2618310b7de482d85b3a94f0eca2aa1 = $(`<div id="html_c2618310b7de482d85b3a94f0eca2aa1" style="width: 100.0%; height: 100.0%;">Alt-Treptow - Cluster 2</div>`)[0];
            popup_a14217039431438da037f5c4ebdcdda4.setContent(html_c2618310b7de482d85b3a94f0eca2aa1);
        

        circle_marker_de600df1c1c64b938b4b04358478c2b0.bindPopup(popup_a14217039431438da037f5c4ebdcdda4)
        ;

        
    
    
            var circle_marker_2d58f9d2e35e49b492aa03c5ddca222e = L.circleMarker(
                [52.52462000000003, 13.177070000000072],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_bfef9eb42b4c4c30b03dd56b2bf30e32 = L.popup({"maxWidth": "100%"});

        
            var html_b60d5407c23e44b588f3f2621d2b3ea8 = $(`<div id="html_b60d5407c23e44b588f3f2621d2b3ea8" style="width: 100.0%; height: 100.0%;">Wilhelmstadt - Cluster 2</div>`)[0];
            popup_bfef9eb42b4c4c30b03dd56b2bf30e32.setContent(html_b60d5407c23e44b588f3f2621d2b3ea8);
        

        circle_marker_2d58f9d2e35e49b492aa03c5ddca222e.bindPopup(popup_bfef9eb42b4c4c30b03dd56b2bf30e32)
        ;

        
    
    
            var circle_marker_9bf369b842ce4e3ab193e91066093c25 = L.circleMarker(
                [52.44873000000007, 13.436210000000074],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_f6e0295745784c23b85e65ded6ca0571 = L.popup({"maxWidth": "100%"});

        
            var html_a863657a3d0c458d9064648a0fbb9b02 = $(`<div id="html_a863657a3d0c458d9064648a0fbb9b02" style="width: 100.0%; height: 100.0%;">Britz - Cluster 2</div>`)[0];
            popup_f6e0295745784c23b85e65ded6ca0571.setContent(html_a863657a3d0c458d9064648a0fbb9b02);
        

        circle_marker_9bf369b842ce4e3ab193e91066093c25.bindPopup(popup_f6e0295745784c23b85e65ded6ca0571)
        ;

        
    
    
            var circle_marker_9eeadad231234becb60e441de91cee7d = L.circleMarker(
                [52.52570000000003, 13.340050000000076],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_7fe9538a996f4b3aa1f2bd5b955157a4 = L.popup({"maxWidth": "100%"});

        
            var html_9c317f701c42412f81556057b34f3df6 = $(`<div id="html_9c317f701c42412f81556057b34f3df6" style="width: 100.0%; height: 100.0%;">Moabit - Cluster 2</div>`)[0];
            popup_7fe9538a996f4b3aa1f2bd5b955157a4.setContent(html_9c317f701c42412f81556057b34f3df6);
        

        circle_marker_9eeadad231234becb60e441de91cee7d.bindPopup(popup_7fe9538a996f4b3aa1f2bd5b955157a4)
        ;

        
    
    
            var circle_marker_eac2019f5bfc45ceaa71f2bbc8c5b09a = L.circleMarker(
                [52.480770000000064, 13.435410000000047],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_8586d74094524d13bd801d27bd6afac8 = L.popup({"maxWidth": "100%"});

        
            var html_3dd562d4227a4ce7afd5076962477ad0 = $(`<div id="html_3dd562d4227a4ce7afd5076962477ad0" style="width: 100.0%; height: 100.0%;">Neukölln (locality) - Cluster 2</div>`)[0];
            popup_8586d74094524d13bd801d27bd6afac8.setContent(html_3dd562d4227a4ce7afd5076962477ad0);
        

        circle_marker_eac2019f5bfc45ceaa71f2bbc8c5b09a.bindPopup(popup_8586d74094524d13bd801d27bd6afac8)
        ;

        
    
    
            var circle_marker_e56ded03ad1b4e54882bdfc024e361e1 = L.circleMarker(
                [52.45550000000003, 13.51329000000004],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_211f5e5484804d0a8d7cb34eff5aa95e = L.popup({"maxWidth": "100%"});

        
            var html_993d5f00a1dd4dd6ad4c07acf9efc7ea = $(`<div id="html_993d5f00a1dd4dd6ad4c07acf9efc7ea" style="width: 100.0%; height: 100.0%;">Niederschöneweide - Cluster 2</div>`)[0];
            popup_211f5e5484804d0a8d7cb34eff5aa95e.setContent(html_993d5f00a1dd4dd6ad4c07acf9efc7ea);
        

        circle_marker_e56ded03ad1b4e54882bdfc024e361e1.bindPopup(popup_211f5e5484804d0a8d7cb34eff5aa95e)
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            var circle_marker_404e259c4ae144b4ae469d3210aaf9b1 = L.circleMarker(
                [52.58265000000006, 13.403620000000046],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_deff92f052044f1c967c74063e01474e = L.popup({"maxWidth": "100%"});

        
            var html_b3f5d96378e94dad83cdfb72899e0066 = $(`<div id="html_b3f5d96378e94dad83cdfb72899e0066" style="width: 100.0%; height: 100.0%;">Niederschönhausen - Cluster 2</div>`)[0];
            popup_deff92f052044f1c967c74063e01474e.setContent(html_b3f5d96378e94dad83cdfb72899e0066);
        

        circle_marker_404e259c4ae144b4ae469d3210aaf9b1.bindPopup(popup_deff92f052044f1c967c74063e01474e)
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            var circle_marker_3b36a74e648a4e928483d5fe7a701d6d = L.circleMarker(
                [52.56925000000007, 13.402480000000025],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_e805777f04bd41878d1a6ba91c80a7e0 = L.popup({"maxWidth": "100%"});

        
            var html_b36257d19e2d4d5ebc2d4ace0aa9ea13 = $(`<div id="html_b36257d19e2d4d5ebc2d4ace0aa9ea13" style="width: 100.0%; height: 100.0%;">Pankow (locality) - Cluster 2</div>`)[0];
            popup_e805777f04bd41878d1a6ba91c80a7e0.setContent(html_b36257d19e2d4d5ebc2d4ace0aa9ea13);
        

        circle_marker_3b36a74e648a4e928483d5fe7a701d6d.bindPopup(popup_e805777f04bd41878d1a6ba91c80a7e0)
        ;

        
    
    
            var circle_marker_fcc6189309ed489e87ee374ce5c4dec8 = L.circleMarker(
                [52.484390000000076, 13.479570000000024],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_eff075350a4f4f61a77d3a6fe27f4e59 = L.popup({"maxWidth": "100%"});

        
            var html_fb4feb34a87544b4a557e5e30a39877b = $(`<div id="html_fb4feb34a87544b4a557e5e30a39877b" style="width: 100.0%; height: 100.0%;">Plänterwald - Cluster 2</div>`)[0];
            popup_eff075350a4f4f61a77d3a6fe27f4e59.setContent(html_fb4feb34a87544b4a557e5e30a39877b);
        

        circle_marker_fcc6189309ed489e87ee374ce5c4dec8.bindPopup(popup_eff075350a4f4f61a77d3a6fe27f4e59)
        ;

        
    
    
            var circle_marker_f1175c8d0f374f90b92454c893b17b97 = L.circleMarker(
                [52.54083000000003, 13.42575000000005],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_0e88dc2c5ec34c8c9a180cc07108ff5d = L.popup({"maxWidth": "100%"});

        
            var html_36d9d35e2ea34f489ee3290fc12ec368 = $(`<div id="html_36d9d35e2ea34f489ee3290fc12ec368" style="width: 100.0%; height: 100.0%;">Prenzlauer Berg - Cluster 2</div>`)[0];
            popup_0e88dc2c5ec34c8c9a180cc07108ff5d.setContent(html_36d9d35e2ea34f489ee3290fc12ec368);
        

        circle_marker_f1175c8d0f374f90b92454c893b17b97.bindPopup(popup_0e88dc2c5ec34c8c9a180cc07108ff5d)
        ;

        
    
    
            var circle_marker_b2a20d2f2bc2490eb51dc8b5dacc64ef = L.circleMarker(
                [52.575450000000046, 13.349700000000041],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_054346da79e84500960b3fd7e232da99 = L.popup({"maxWidth": "100%"});

        
            var html_cac3d3020b3648b0b4636a21f6eb681d = $(`<div id="html_cac3d3020b3648b0b4636a21f6eb681d" style="width: 100.0%; height: 100.0%;">Reinickendorf (locality) - Cluster 2</div>`)[0];
            popup_054346da79e84500960b3fd7e232da99.setContent(html_cac3d3020b3648b0b4636a21f6eb681d);
        

        circle_marker_b2a20d2f2bc2490eb51dc8b5dacc64ef.bindPopup(popup_054346da79e84500960b3fd7e232da99)
        ;

        
    
    
            var circle_marker_bea794e38d0c4c70805c0e900209211a = L.circleMarker(
                [52.51618000000008, 13.480870000000039],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_bfd3cb3f21254eb191689bf1e59afd8a = L.popup({"maxWidth": "100%"});

        
            var html_2165bd9387f043d581818f1c5cc7fd14 = $(`<div id="html_2165bd9387f043d581818f1c5cc7fd14" style="width: 100.0%; height: 100.0%;">Lichtenberg (locality) - Cluster 2</div>`)[0];
            popup_bfd3cb3f21254eb191689bf1e59afd8a.setContent(html_2165bd9387f043d581818f1c5cc7fd14);
        

        circle_marker_bea794e38d0c4c70805c0e900209211a.bindPopup(popup_bfd3cb3f21254eb191689bf1e59afd8a)
        ;

        
    
    
            var circle_marker_8b7a55f1d7e141acaa75c4f1a0936697 = L.circleMarker(
                [52.501310000000046, 13.491270000000043],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_22c8c33203344189a1f6103edc0b19f0 = L.popup({"maxWidth": "100%"});

        
            var html_444238cbfb9246bcaabac2e4a1b6a394 = $(`<div id="html_444238cbfb9246bcaabac2e4a1b6a394" style="width: 100.0%; height: 100.0%;">Rummelsburg - Cluster 2</div>`)[0];
            popup_22c8c33203344189a1f6103edc0b19f0.setContent(html_444238cbfb9246bcaabac2e4a1b6a394);
        

        circle_marker_8b7a55f1d7e141acaa75c4f1a0936697.bindPopup(popup_22c8c33203344189a1f6103edc0b19f0)
        ;

        
    
    
            var circle_marker_b4e2cdd54c6a40ce861ca76a31263205 = L.circleMarker(
                [52.485550000000046, 13.342930000000024],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_724011599ba34471a2ea3d60d164f2cb = L.popup({"maxWidth": "100%"});

        
            var html_b561b89f5612475facab159d73557d52 = $(`<div id="html_b561b89f5612475facab159d73557d52" style="width: 100.0%; height: 100.0%;">Schöneberg - Cluster 2</div>`)[0];
            popup_724011599ba34471a2ea3d60d164f2cb.setContent(html_b561b89f5612475facab159d73557d52);
        

        circle_marker_b4e2cdd54c6a40ce861ca76a31263205.bindPopup(popup_724011599ba34471a2ea3d60d164f2cb)
        ;

        
    
    
            var circle_marker_4ce3a2500ccf491d8cda720da00a6636 = L.circleMarker(
                [52.53828000000004, 13.264060000000029],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_fad4e46eadc7425ebacb7f50a9a57087 = L.popup({"maxWidth": "100%"});

        
            var html_445b75100a8d430781b4fcf6c2bd7d2f = $(`<div id="html_445b75100a8d430781b4fcf6c2bd7d2f" style="width: 100.0%; height: 100.0%;">Siemensstadt - Cluster 2</div>`)[0];
            popup_fad4e46eadc7425ebacb7f50a9a57087.setContent(html_445b75100a8d430781b4fcf6c2bd7d2f);
        

        circle_marker_4ce3a2500ccf491d8cda720da00a6636.bindPopup(popup_fad4e46eadc7425ebacb7f50a9a57087)
        ;

        
    
    
            var circle_marker_430e5c0c3aa040e8b9ee29df4b4a8ee7 = L.circleMarker(
                [52.539600000000064, 13.142820000000029],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_77977b8a6a694de0b611fd80cd8af0f1 = L.popup({"maxWidth": "100%"});

        
            var html_808c69e9b67d473b9dc2bd2652d33bc1 = $(`<div id="html_808c69e9b67d473b9dc2bd2652d33bc1" style="width: 100.0%; height: 100.0%;">Staaken - Cluster 2</div>`)[0];
            popup_77977b8a6a694de0b611fd80cd8af0f1.setContent(html_808c69e9b67d473b9dc2bd2652d33bc1);
        

        circle_marker_430e5c0c3aa040e8b9ee29df4b4a8ee7.bindPopup(popup_77977b8a6a694de0b611fd80cd8af0f1)
        ;

        
    
    
            var circle_marker_2d3175ef469e4e53b84036030f0298bb = L.circleMarker(
                [52.50989000000004, 13.553700000000049],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_56997e2d3b414e0ba2cd9fe22f27f64c = L.popup({"maxWidth": "100%"});

        
            var html_cfbcdd7524364de691968782497bd0cd = $(`<div id="html_cfbcdd7524364de691968782497bd0cd" style="width: 100.0%; height: 100.0%;">Biesdorf (Berlin) - Cluster 2</div>`)[0];
            popup_56997e2d3b414e0ba2cd9fe22f27f64c.setContent(html_cfbcdd7524364de691968782497bd0cd);
        

        circle_marker_2d3175ef469e4e53b84036030f0298bb.bindPopup(popup_56997e2d3b414e0ba2cd9fe22f27f64c)
        ;

        
    
    
            var circle_marker_cbfd7bcd08bb47d4a4fc0b51da32d846 = L.circleMarker(
                [52.42045000000007, 13.167050000000074],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_97ac5424f6b64f948ab58633c751a60f = L.popup({"maxWidth": "100%"});

        
            var html_9f258ef9cee64cea8448dc627173409a = $(`<div id="html_9f258ef9cee64cea8448dc627173409a" style="width: 100.0%; height: 100.0%;">Wannsee - Cluster 2</div>`)[0];
            popup_97ac5424f6b64f948ab58633c751a60f.setContent(html_9f258ef9cee64cea8448dc627173409a);
        

        circle_marker_cbfd7bcd08bb47d4a4fc0b51da32d846.bindPopup(popup_97ac5424f6b64f948ab58633c751a60f)
        ;

        
    
    
            var circle_marker_a42d2db1a1b24ad491a4cfabfe2c632c = L.circleMarker(
                [52.54781000000003, 13.354730000000075],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_60191ced5bff403b8c830f52651afb4a = L.popup({"maxWidth": "100%"});

        
            var html_44528bbcbc97428e9bf4f98bf16310f3 = $(`<div id="html_44528bbcbc97428e9bf4f98bf16310f3" style="width: 100.0%; height: 100.0%;">Wedding (Berlin) - Cluster 2</div>`)[0];
            popup_60191ced5bff403b8c830f52651afb4a.setContent(html_44528bbcbc97428e9bf4f98bf16310f3);
        

        circle_marker_a42d2db1a1b24ad491a4cfabfe2c632c.bindPopup(popup_60191ced5bff403b8c830f52651afb4a)
        ;

        
    
    
            var circle_marker_8a3b192d422b4c8583595ec310afae36 = L.circleMarker(
                [52.599770000000035, 13.377760000000023],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_42d26e0f9d474f0989db3fbf3360cfc0 = L.popup({"maxWidth": "100%"});

        
            var html_bc9f0139d9c5421187b5c032d9e23e15 = $(`<div id="html_bc9f0139d9c5421187b5c032d9e23e15" style="width: 100.0%; height: 100.0%;">Rosenthal (Berlin) - Cluster 2</div>`)[0];
            popup_42d26e0f9d474f0989db3fbf3360cfc0.setContent(html_bc9f0139d9c5421187b5c032d9e23e15);
        

        circle_marker_8a3b192d422b4c8583595ec310afae36.bindPopup(popup_42d26e0f9d474f0989db3fbf3360cfc0)
        ;

        
    
    
            var circle_marker_8020645f44d7411c94a8ce714eaa0baa = L.circleMarker(
                [52.58522000000005, 13.307880000000068],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_99ae129fd6104891bd31fbe19d1e0391 = L.popup({"maxWidth": "100%"});

        
            var html_67bc78b43e4e4186aea7b6b021365200 = $(`<div id="html_67bc78b43e4e4186aea7b6b021365200" style="width: 100.0%; height: 100.0%;">Borsigwalde - Cluster 2</div>`)[0];
            popup_99ae129fd6104891bd31fbe19d1e0391.setContent(html_67bc78b43e4e4186aea7b6b021365200);
        

        circle_marker_8020645f44d7411c94a8ce714eaa0baa.bindPopup(popup_99ae129fd6104891bd31fbe19d1e0391)
        ;

        
    
    
            var circle_marker_86c1d6e94c794899b72e9933c2253315 = L.circleMarker(
                [52.437790000000064, 13.547780000000046],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_dab176e811a946e89bd904164cef38a5 = L.popup({"maxWidth": "100%"});

        
            var html_a378a83a3b404984a327235208c66a31 = $(`<div id="html_a378a83a3b404984a327235208c66a31" style="width: 100.0%; height: 100.0%;">Adlershof - Cluster 2</div>`)[0];
            popup_dab176e811a946e89bd904164cef38a5.setContent(html_a378a83a3b404984a327235208c66a31);
        

        circle_marker_86c1d6e94c794899b72e9933c2253315.bindPopup(popup_dab176e811a946e89bd904164cef38a5)
        ;

        
    
    
            var circle_marker_aca12336cf2741ae842f19b4666a0ba4 = L.circleMarker(
                [52.527730000000076, 13.466540000000066],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_e3d55ab890274b01a418e1a7572fb049 = L.popup({"maxWidth": "100%"});

        
            var html_2d0bb24a03a64567ac25cc838f9a1259 = $(`<div id="html_2d0bb24a03a64567ac25cc838f9a1259" style="width: 100.0%; height: 100.0%;">Fennpfuhl - Cluster 2</div>`)[0];
            popup_e3d55ab890274b01a418e1a7572fb049.setContent(html_2d0bb24a03a64567ac25cc838f9a1259);
        

        circle_marker_aca12336cf2741ae842f19b4666a0ba4.bindPopup(popup_e3d55ab890274b01a418e1a7572fb049)
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            var circle_marker_9f31aac7bedc450ca20b82d34384ac54 = L.circleMarker(
                [52.51402000000007, 13.454030000000046],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_e3e00bbb3c91498ca25b6eae966603f3 = L.popup({"maxWidth": "100%"});

        
            var html_e3371d501d014b848354b23c74ee15bf = $(`<div id="html_e3371d501d014b848354b23c74ee15bf" style="width: 100.0%; height: 100.0%;">Friedrichshain - Cluster 2</div>`)[0];
            popup_e3e00bbb3c91498ca25b6eae966603f3.setContent(html_e3371d501d014b848354b23c74ee15bf);
        

        circle_marker_9f31aac7bedc450ca20b82d34384ac54.bindPopup(popup_e3e00bbb3c91498ca25b6eae966603f3)
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            var circle_marker_7e1df645e7e44ee79d2d575177e03c56 = L.circleMarker(
                [52.63346000000007, 13.290380000000027],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_11ab2f46866749c9b75e40258ed0180b = L.popup({"maxWidth": "100%"});

        
            var html_bef9357982b74026868e0152615d3bd5 = $(`<div id="html_bef9357982b74026868e0152615d3bd5" style="width: 100.0%; height: 100.0%;">Frohnau - Cluster 2</div>`)[0];
            popup_11ab2f46866749c9b75e40258ed0180b.setContent(html_bef9357982b74026868e0152615d3bd5);
        

        circle_marker_7e1df645e7e44ee79d2d575177e03c56.bindPopup(popup_11ab2f46866749c9b75e40258ed0180b)
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            var circle_marker_349b1d8081374f61ac965276e37d816b = L.circleMarker(
                [52.55877000000004, 13.20831000000004],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_7ac5b1bfaf3e4d5cb41044ead719b326 = L.popup({"maxWidth": "100%"});

        
            var html_711c606c07bf4545b971c0c3102bb8bc = $(`<div id="html_711c606c07bf4545b971c0c3102bb8bc" style="width: 100.0%; height: 100.0%;">Hakenfelde - Cluster 2</div>`)[0];
            popup_7ac5b1bfaf3e4d5cb41044ead719b326.setContent(html_711c606c07bf4545b971c0c3102bb8bc);
        

        circle_marker_349b1d8081374f61ac965276e37d816b.bindPopup(popup_7ac5b1bfaf3e4d5cb41044ead719b326)
        ;

        
    
    
            var circle_marker_0f31f98b93384471ae45fe0def8277cd = L.circleMarker(
                [52.51679000000007, 13.338350000000048],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_9aadb69e4fd54e57955badafad8a630d = L.popup({"maxWidth": "100%"});

        
            var html_d690556413af431cbf01136971dc32ed = $(`<div id="html_d690556413af431cbf01136971dc32ed" style="width: 100.0%; height: 100.0%;">Hansaviertel - Cluster 2</div>`)[0];
            popup_9aadb69e4fd54e57955badafad8a630d.setContent(html_d690556413af431cbf01136971dc32ed);
        

        circle_marker_0f31f98b93384471ae45fe0def8277cd.bindPopup(popup_9aadb69e4fd54e57955badafad8a630d)
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            var circle_marker_782d37aa80fe47fea7a2a2e1c40a444b = L.circleMarker(
                [52.44330000000008, 13.583110000000033],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_83ef49ddd2dc4754862a705213ea5f32 = L.popup({"maxWidth": "100%"});

        
            var html_3f2cf6f81821431da327d7f2a8f6c490 = $(`<div id="html_3f2cf6f81821431da327d7f2a8f6c490" style="width: 100.0%; height: 100.0%;">Köpenick - Cluster 2</div>`)[0];
            popup_83ef49ddd2dc4754862a705213ea5f32.setContent(html_3f2cf6f81821431da327d7f2a8f6c490);
        

        circle_marker_782d37aa80fe47fea7a2a2e1c40a444b.bindPopup(popup_83ef49ddd2dc4754862a705213ea5f32)
        ;

        
    
    
            var circle_marker_64b130c77542401a80c8690f9f0fb6ff = L.circleMarker(
                [52.56770000000006, 13.439800000000048],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_c1a4df433d094a9c8a70f7a6a7ec6ea3 = L.popup({"maxWidth": "100%"});

        
            var html_131a6b57d6bb48eaace1084f4acf4386 = $(`<div id="html_131a6b57d6bb48eaace1084f4acf4386" style="width: 100.0%; height: 100.0%;">Heinersdorf - Cluster 2</div>`)[0];
            popup_c1a4df433d094a9c8a70f7a6a7ec6ea3.setContent(html_131a6b57d6bb48eaace1084f4acf4386);
        

        circle_marker_64b130c77542401a80c8690f9f0fb6ff.bindPopup(popup_c1a4df433d094a9c8a70f7a6a7ec6ea3)
        ;

        
    
    
            var circle_marker_b8dbdbe9951a4a22878e91d9210d2500 = L.circleMarker(
                [52.529450000000054, 13.590680000000077],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_aa1ee6726d2b42edb587bff15e826982 = L.popup({"maxWidth": "100%"});

        
            var html_04f8c0df635c45a19bfc664a4d7a20e9 = $(`<div id="html_04f8c0df635c45a19bfc664a4d7a20e9" style="width: 100.0%; height: 100.0%;">Hellersdorf - Cluster 2</div>`)[0];
            popup_aa1ee6726d2b42edb587bff15e826982.setContent(html_04f8c0df635c45a19bfc664a4d7a20e9);
        

        circle_marker_b8dbdbe9951a4a22878e91d9210d2500.bindPopup(popup_aa1ee6726d2b42edb587bff15e826982)
        ;

        
    
    
            var circle_marker_ff523466028b41a4887274d7f9ffa4d8 = L.circleMarker(
                [52.49203000000006, 13.284950000000038],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_ddb930b07f1f4275a88b374f06337182 = L.popup({"maxWidth": "100%"});

        
            var html_89ada2a9ecc446ee9fc8793c57673b03 = $(`<div id="html_89ada2a9ecc446ee9fc8793c57673b03" style="width: 100.0%; height: 100.0%;">Grunewald - Cluster 1</div>`)[0];
            popup_ddb930b07f1f4275a88b374f06337182.setContent(html_89ada2a9ecc446ee9fc8793c57673b03);
        

        circle_marker_ff523466028b41a4887274d7f9ffa4d8.bindPopup(popup_ddb930b07f1f4275a88b374f06337182)
        ;

        
    
    
            var circle_marker_81dd3f7800184873b522e618926c43c5 = L.circleMarker(
                [52.606220000000064, 13.320650000000057],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_9b666ae375f64f499ccc0960454adc59 = L.popup({"maxWidth": "100%"});

        
            var html_0e2ba26854654752b3d0bbf1f838ef38 = $(`<div id="html_0e2ba26854654752b3d0bbf1f838ef38" style="width: 100.0%; height: 100.0%;">Waidmannslust - Cluster 1</div>`)[0];
            popup_9b666ae375f64f499ccc0960454adc59.setContent(html_0e2ba26854654752b3d0bbf1f838ef38);
        

        circle_marker_81dd3f7800184873b522e618926c43c5.bindPopup(popup_9b666ae375f64f499ccc0960454adc59)
        ;

        
    
    
            var circle_marker_de90251409254d109e4c716509a497f5 = L.circleMarker(
                [52.49005000000005, 13.296020000000055],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_d75da7770c854ead9d3f4f998ced2dab = L.popup({"maxWidth": "100%"});

        
            var html_d748330495234406880bbc92d7e94fb9 = $(`<div id="html_d748330495234406880bbc92d7e94fb9" style="width: 100.0%; height: 100.0%;">Halensee - Cluster 1</div>`)[0];
            popup_d75da7770c854ead9d3f4f998ced2dab.setContent(html_d748330495234406880bbc92d7e94fb9);
        

        circle_marker_de90251409254d109e4c716509a497f5.bindPopup(popup_d75da7770c854ead9d3f4f998ced2dab)
        ;

        
    
    
            var circle_marker_8f92fcbbb61b41daa06db31e743cb779 = L.circleMarker(
                [52.37584000000004, 13.648990000000026],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_d64a15d462d1429b9e6bbe4675b9f191 = L.popup({"maxWidth": "100%"});

        
            var html_efbd299b0d334baa80b59d8770ed795e = $(`<div id="html_efbd299b0d334baa80b59d8770ed795e" style="width: 100.0%; height: 100.0%;">Schmöckwitz - Cluster 1</div>`)[0];
            popup_d64a15d462d1429b9e6bbe4675b9f191.setContent(html_efbd299b0d334baa80b59d8770ed795e);
        

        circle_marker_8f92fcbbb61b41daa06db31e743cb779.bindPopup(popup_d64a15d462d1429b9e6bbe4675b9f191)
        ;

        
    
    
            var circle_marker_a9342a565d0d4e648172bc1a6e34a7dc = L.circleMarker(
                [52.47732000000008, 13.287980000000061],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_0beffbf26d834bd980ddf7987c7120a4 = L.popup({"maxWidth": "100%"});

        
            var html_9b108356bea345e6ad61daa66f704e1d = $(`<div id="html_9b108356bea345e6ad61daa66f704e1d" style="width: 100.0%; height: 100.0%;">Schmargendorf - Cluster 1</div>`)[0];
            popup_0beffbf26d834bd980ddf7987c7120a4.setContent(html_9b108356bea345e6ad61daa66f704e1d);
        

        circle_marker_a9342a565d0d4e648172bc1a6e34a7dc.bindPopup(popup_0beffbf26d834bd980ddf7987c7120a4)
        ;

        
    
    
            var circle_marker_bd15a00e596e4b4b9ab733d3f1b3c2ff = L.circleMarker(
                [52.419170000000065, 13.493700000000047],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_d3471c04f41944c48ab6f92eb1b6cb44 = L.popup({"maxWidth": "100%"});

        
            var html_899917b142bf4a33974a5a6265add1a1 = $(`<div id="html_899917b142bf4a33974a5a6265add1a1" style="width: 100.0%; height: 100.0%;">Rudow - Cluster 1</div>`)[0];
            popup_d3471c04f41944c48ab6f92eb1b6cb44.setContent(html_899917b142bf4a33974a5a6265add1a1);
        

        circle_marker_bd15a00e596e4b4b9ab733d3f1b3c2ff.bindPopup(popup_d3471c04f41944c48ab6f92eb1b6cb44)
        ;

        
    
    
            var circle_marker_fc7c1d55cadb408fb1a21f87414d0c90 = L.circleMarker(
                [52.51915000000008, 13.306390000000022],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_ec3180f7f44944e08f51a5265352bcfe = L.popup({"maxWidth": "100%"});

        
            var html_d8beeee6c68b47e1a755d9913bf955d8 = $(`<div id="html_d8beeee6c68b47e1a755d9913bf955d8" style="width: 100.0%; height: 100.0%;">Charlottenburg - Cluster 1</div>`)[0];
            popup_ec3180f7f44944e08f51a5265352bcfe.setContent(html_d8beeee6c68b47e1a755d9913bf955d8);
        

        circle_marker_fc7c1d55cadb408fb1a21f87414d0c90.bindPopup(popup_ec3180f7f44944e08f51a5265352bcfe)
        ;

        
    
    
            var circle_marker_445004dbdbe54cea9089d2904a9e16d4 = L.circleMarker(
                [52.49382000000003, 13.383580000000052],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_6842aada6c8b45de83c638d1e035a460 = L.popup({"maxWidth": "100%"});

        
            var html_ac8957654a66441bb4eac47d2ba5b8b8 = $(`<div id="html_ac8957654a66441bb4eac47d2ba5b8b8" style="width: 100.0%; height: 100.0%;">Kreuzberg - Cluster 1</div>`)[0];
            popup_6842aada6c8b45de83c638d1e035a460.setContent(html_ac8957654a66441bb4eac47d2ba5b8b8);
        

        circle_marker_445004dbdbe54cea9089d2904a9e16d4.bindPopup(popup_6842aada6c8b45de83c638d1e035a460)
        ;

        
    
    
            var circle_marker_9fec05eb52ef4d0d931a4be4d88a4cf8 = L.circleMarker(
                [52.55191000000008, 13.16802000000007],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_651bffedc11f44c2b7b805f32b16a8ac = L.popup({"maxWidth": "100%"});

        
            var html_0e64f5ed5bbf4487a5b811ca2503b2c2 = $(`<div id="html_0e64f5ed5bbf4487a5b811ca2503b2c2" style="width: 100.0%; height: 100.0%;">Falkenhagener Feld - Cluster 0</div>`)[0];
            popup_651bffedc11f44c2b7b805f32b16a8ac.setContent(html_0e64f5ed5bbf4487a5b811ca2503b2c2);
        

        circle_marker_9fec05eb52ef4d0d931a4be4d88a4cf8.bindPopup(popup_651bffedc11f44c2b7b805f32b16a8ac)
        ;

        
    
    
            var circle_marker_fba1425bd5bc4c98a71a1a43ede1bec9 = L.circleMarker(
                [52.57038000000006, 13.463420000000042],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_53186950e8174a88874e26d781c6f7a2 = L.popup({"maxWidth": "100%"});

        
            var html_ff24533f899148d4818b13d92503925d = $(`<div id="html_ff24533f899148d4818b13d92503925d" style="width: 100.0%; height: 100.0%;">Stadtrandsiedlung Malchow - Cluster 0</div>`)[0];
            popup_53186950e8174a88874e26d781c6f7a2.setContent(html_ff24533f899148d4818b13d92503925d);
        

        circle_marker_fba1425bd5bc4c98a71a1a43ede1bec9.bindPopup(popup_53186950e8174a88874e26d781c6f7a2)
        ;

        
    
    
            var circle_marker_20c251fdc00d4cb09693a07c842f0bd1 = L.circleMarker(
                [52.62014000000005, 13.35525000000007],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_6bfe5ddc947d466e9c2c721739f8f920 = L.popup({"maxWidth": "100%"});

        
            var html_6cf4c0f7f1ff47bca5620bcf6cad50ed = $(`<div id="html_6cf4c0f7f1ff47bca5620bcf6cad50ed" style="width: 100.0%; height: 100.0%;">Lübars - Cluster 0</div>`)[0];
            popup_6bfe5ddc947d466e9c2c721739f8f920.setContent(html_6cf4c0f7f1ff47bca5620bcf6cad50ed);
        

        circle_marker_20c251fdc00d4cb09693a07c842f0bd1.bindPopup(popup_6bfe5ddc947d466e9c2c721739f8f920)
        ;

        
    
    
            var circle_marker_997f8b1a9e0a4cacbb3727e602b087df = L.circleMarker(
                [52.420060000000035, 13.539690000000064],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_ae1de4d02bc64572a958c97982b6041b = L.popup({"maxWidth": "100%"});

        
            var html_26e7b45c124e48948f78a70c76560470 = $(`<div id="html_26e7b45c124e48948f78a70c76560470" style="width: 100.0%; height: 100.0%;">Altglienicke - Cluster 0</div>`)[0];
            popup_ae1de4d02bc64572a958c97982b6041b.setContent(html_26e7b45c124e48948f78a70c76560470);
        

        circle_marker_997f8b1a9e0a4cacbb3727e602b087df.bindPopup(popup_ae1de4d02bc64572a958c97982b6041b)
        ;

        
    
    
            var circle_marker_d1f69b266a2346f398e361d50bdb64bb = L.circleMarker(
                [52.48884000000004, 13.180700000000058],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_a2acc4802e844a358a82b53d8c6d4cd5 = L.popup({"maxWidth": "100%"});

        
            var html_0507aa683033485bac48f2c1b6d90b09 = $(`<div id="html_0507aa683033485bac48f2c1b6d90b09" style="width: 100.0%; height: 100.0%;">Gatow - Cluster 0</div>`)[0];
            popup_a2acc4802e844a358a82b53d8c6d4cd5.setContent(html_0507aa683033485bac48f2c1b6d90b09);
        

        circle_marker_d1f69b266a2346f398e361d50bdb64bb.bindPopup(popup_a2acc4802e844a358a82b53d8c6d4cd5)
        ;

        
    
    
            var circle_marker_94b0524680c24924b2efe8834f960d87 = L.circleMarker(
                [52.46669000000003, 13.48840000000007],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_fc1937eb3bb646ac812ff7b0673da4bd = L.popup({"maxWidth": "100%"});

        
            var html_872c90ab382346aa906fe952646aea5c = $(`<div id="html_872c90ab382346aa906fe952646aea5c" style="width: 100.0%; height: 100.0%;">Baumschulenweg - Cluster 0</div>`)[0];
            popup_fc1937eb3bb646ac812ff7b0673da4bd.setContent(html_872c90ab382346aa906fe952646aea5c);
        

        circle_marker_94b0524680c24924b2efe8834f960d87.bindPopup(popup_fc1937eb3bb646ac812ff7b0673da4bd)
        ;

        
    
    
            var circle_marker_c4fcaf0d3746421a84aa062052cbf950 = L.circleMarker(
                [52.55535000000003, 13.377150000000029],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_779ae0ec3d674d60983384b01a9b725b = L.popup({"maxWidth": "100%"});

        
            var html_57764d5e38fc4ffb80aeb2c2d6d5bdfe = $(`<div id="html_57764d5e38fc4ffb80aeb2c2d6d5bdfe" style="width: 100.0%; height: 100.0%;">Gesundbrunnen (Berlin) - Cluster 0</div>`)[0];
            popup_779ae0ec3d674d60983384b01a9b725b.setContent(html_57764d5e38fc4ffb80aeb2c2d6d5bdfe);
        

        circle_marker_c4fcaf0d3746421a84aa062052cbf950.bindPopup(popup_779ae0ec3d674d60983384b01a9b725b)
        ;

        
    
    
            var circle_marker_02d595e8665f4480b990198d52cc5981 = L.circleMarker(
                [52.608220000000074, 13.433680000000038],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_7497a811ce354ae4ad469e3b9fedf9c1 = L.popup({"maxWidth": "100%"});

        
            var html_e9296706cf2c4e29a60c22383792d3b0 = $(`<div id="html_e9296706cf2c4e29a60c22383792d3b0" style="width: 100.0%; height: 100.0%;">Französisch Buchholz - Cluster 0</div>`)[0];
            popup_7497a811ce354ae4ad469e3b9fedf9c1.setContent(html_e9296706cf2c4e29a60c22383792d3b0);
        

        circle_marker_02d595e8665f4480b990198d52cc5981.bindPopup(popup_7497a811ce354ae4ad469e3b9fedf9c1)
        ;

        
    
    
            var circle_marker_5c507e4e8dae43d8af29368884fb47db = L.circleMarker(
                [52.57503000000003, 13.517030000000034],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_ededb98bfcfa4475b7cf14844d03c530 = L.popup({"maxWidth": "100%"});

        
            var html_6e2917a2a98d4b61a5f9f5bfb90aa01e = $(`<div id="html_6e2917a2a98d4b61a5f9f5bfb90aa01e" style="width: 100.0%; height: 100.0%;">Wartenberg (Berlin) - Cluster 0</div>`)[0];
            popup_ededb98bfcfa4475b7cf14844d03c530.setContent(html_6e2917a2a98d4b61a5f9f5bfb90aa01e);
        

        circle_marker_5c507e4e8dae43d8af29368884fb47db.bindPopup(popup_ededb98bfcfa4475b7cf14844d03c530)
        ;

        
    
    
            var circle_marker_15d6a19063a8476b8bb012972a995ec1 = L.circleMarker(
                [52.57842000000005, 13.483020000000067],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_275173e0605046e49baf3fb6781c5833 = L.popup({"maxWidth": "100%"});

        
            var html_c362948e072b4cf588f82066b4dac7a2 = $(`<div id="html_c362948e072b4cf588f82066b4dac7a2" style="width: 100.0%; height: 100.0%;">Malchow (Berlin) - Cluster 0</div>`)[0];
            popup_275173e0605046e49baf3fb6781c5833.setContent(html_c362948e072b4cf588f82066b4dac7a2);
        

        circle_marker_15d6a19063a8476b8bb012972a995ec1.bindPopup(popup_275173e0605046e49baf3fb6781c5833)
        ;

        
    
    
            var circle_marker_d3e67d9b226543efa0ad17e39a8445fd = L.circleMarker(
                [52.509930000000054, 13.363930000000039],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_2034c581a5414eeea5adfc4720c13ecd = L.popup({"maxWidth": "100%"});

        
            var html_add9d72199c24febbe7edbfd2ec5c1e1 = $(`<div id="html_add9d72199c24febbe7edbfd2ec5c1e1" style="width: 100.0%; height: 100.0%;">Tiergarten, Berlin - Cluster 0</div>`)[0];
            popup_2034c581a5414eeea5adfc4720c13ecd.setContent(html_add9d72199c24febbe7edbfd2ec5c1e1);
        

        circle_marker_d3e67d9b226543efa0ad17e39a8445fd.bindPopup(popup_2034c581a5414eeea5adfc4720c13ecd)
        ;

        
    
    
            var circle_marker_fe573fc41dc74d5daa9808e1a4a6cd5d = L.circleMarker(
                [52.41416000000004, 13.57499000000007],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_11a792f83d1a4e53bf7f10a5d17d01b1 = L.popup({"maxWidth": "100%"});

        
            var html_7fa72b6707a64e10acf322132699675d = $(`<div id="html_7fa72b6707a64e10acf322132699675d" style="width: 100.0%; height: 100.0%;">Grünau (Berlin) - Cluster 0</div>`)[0];
            popup_11a792f83d1a4e53bf7f10a5d17d01b1.setContent(html_7fa72b6707a64e10acf322132699675d);
        

        circle_marker_fe573fc41dc74d5daa9808e1a4a6cd5d.bindPopup(popup_11a792f83d1a4e53bf7f10a5d17d01b1)
        ;

        
    
    
            var circle_marker_c68c050342604212b6129d537739d1f8 = L.circleMarker(
                [52.59332000000006, 13.458050000000071],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_eb511f16599f497aa2b4cca1557a76d8 = L.popup({"maxWidth": "100%"});

        
            var html_533007e8076747a2aa2b21f94c1e7e6c = $(`<div id="html_533007e8076747a2aa2b21f94c1e7e6c" style="width: 100.0%; height: 100.0%;">Blankenburg (Berlin) - Cluster 0</div>`)[0];
            popup_eb511f16599f497aa2b4cca1557a76d8.setContent(html_533007e8076747a2aa2b21f94c1e7e6c);
        

        circle_marker_c68c050342604212b6129d537739d1f8.bindPopup(popup_eb511f16599f497aa2b4cca1557a76d8)
        ;

        
    
    
            var circle_marker_7e9800c02572427886d3e4161eff7a44 = L.circleMarker(
                [52.61855000000003, 13.390170000000069],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_f41f587dfa49473ead0bd9ca573e5f28 = L.popup({"maxWidth": "100%"});

        
            var html_014913e048a74a6c861ce4e0365a40e1 = $(`<div id="html_014913e048a74a6c861ce4e0365a40e1" style="width: 100.0%; height: 100.0%;">Blankenfelde - Cluster 0</div>`)[0];
            popup_f41f587dfa49473ead0bd9ca573e5f28.setContent(html_014913e048a74a6c861ce4e0365a40e1);
        

        circle_marker_7e9800c02572427886d3e4161eff7a44.bindPopup(popup_f41f587dfa49473ead0bd9ca573e5f28)
        ;

        
    
    
            var circle_marker_1629483c6e6f4d8f9845a9c5bae6cbe7 = L.circleMarker(
                [52.634240000000034, 13.49753000000004],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_447c34c0753f453bacf1ac600a930b1f = L.popup({"maxWidth": "100%"});

        
            var html_99a8c521b40b4d99940e25625f8a9257 = $(`<div id="html_99a8c521b40b4d99940e25625f8a9257" style="width: 100.0%; height: 100.0%;">Buch (Berlin) - Cluster 0</div>`)[0];
            popup_447c34c0753f453bacf1ac600a930b1f.setContent(html_99a8c521b40b4d99940e25625f8a9257);
        

        circle_marker_1629483c6e6f4d8f9845a9c5bae6cbe7.bindPopup(popup_447c34c0753f453bacf1ac600a930b1f)
        ;

        
    
    
            var circle_marker_2c576d77d7bf497abd061a6d05648c94 = L.circleMarker(
                [52.40091000000007, 13.559830000000034],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_d496b5de8485487ea5b69364874fb8ff = L.popup({"maxWidth": "100%"});

        
            var html_898f35062a8446c8b330a4d3428f48fa = $(`<div id="html_898f35062a8446c8b330a4d3428f48fa" style="width: 100.0%; height: 100.0%;">Bohnsdorf - Cluster 0</div>`)[0];
            popup_d496b5de8485487ea5b69364874fb8ff.setContent(html_898f35062a8446c8b330a4d3428f48fa);
        

        circle_marker_2c576d77d7bf497abd061a6d05648c94.bindPopup(popup_d496b5de8485487ea5b69364874fb8ff)
        ;

        
    
    
            var circle_marker_0dd2f936b46d438889ed4f1994cc6ce6 = L.circleMarker(
                [52.53487000000007, 13.202160000000049],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_a867449f6f874864b255391e363f744b = L.popup({"maxWidth": "100%"});

        
            var html_988843f07df74e15809554546e349def = $(`<div id="html_988843f07df74e15809554546e349def" style="width: 100.0%; height: 100.0%;">Spandau (locality) - Cluster 0</div>`)[0];
            popup_a867449f6f874864b255391e363f744b.setContent(html_988843f07df74e15809554546e349def);
        

        circle_marker_0dd2f936b46d438889ed4f1994cc6ce6.bindPopup(popup_a867449f6f874864b255391e363f744b)
        ;

        
    
    
            var circle_marker_400516fe86154186ac0d7d61d5e6e176 = L.circleMarker(
                [52.54212000000007, 13.543890000000033],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_034dd965e98e405ca17b047b806cdb18 = L.popup({"maxWidth": "100%"});

        
            var html_6753667537fe4bc1ac460d7c3a82723a = $(`<div id="html_6753667537fe4bc1ac460d7c3a82723a" style="width: 100.0%; height: 100.0%;">Marzahn - Cluster 0</div>`)[0];
            popup_034dd965e98e405ca17b047b806cdb18.setContent(html_6753667537fe4bc1ac460d7c3a82723a);
        

        circle_marker_400516fe86154186ac0d7d61d5e6e176.bindPopup(popup_034dd965e98e405ca17b047b806cdb18)
        ;

        
    
    
            var circle_marker_50ed09b2f6e040e19788ebdd264bf9c3 = L.circleMarker(
                [52.58520000000004, 13.226980000000026],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_b6f9b647eae84d92b7941d0f2e80b0ea = L.popup({"maxWidth": "100%"});

        
            var html_2831533f7171439684466c6c89f5c218 = $(`<div id="html_2831533f7171439684466c6c89f5c218" style="width: 100.0%; height: 100.0%;">Konradshöhe - Cluster 0</div>`)[0];
            popup_b6f9b647eae84d92b7941d0f2e80b0ea.setContent(html_2831533f7171439684466c6c89f5c218);
        

        circle_marker_50ed09b2f6e040e19788ebdd264bf9c3.bindPopup(popup_b6f9b647eae84d92b7941d0f2e80b0ea)
        ;

        
    
    
            var circle_marker_23c00107589b42a7b3bc7a6854c63b95 = L.circleMarker(
                [52.56958000000003, 13.538790000000063],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_0625abdb41da4133abedd7704fab1459 = L.popup({"maxWidth": "100%"});

        
            var html_7a71967a088d47e99618dee2c57cd391 = $(`<div id="html_7a71967a088d47e99618dee2c57cd391" style="width: 100.0%; height: 100.0%;">Falkenberg (Berlin) - Cluster 0</div>`)[0];
            popup_0625abdb41da4133abedd7704fab1459.setContent(html_7a71967a088d47e99618dee2c57cd391);
        

        circle_marker_23c00107589b42a7b3bc7a6854c63b95.bindPopup(popup_0625abdb41da4133abedd7704fab1459)
        ;

        
    
    
            var circle_marker_c49185c6610b46728a53c7f880ee0bb0 = L.circleMarker(
                [52.45100000000008, 13.624730000000056],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_f7e6400f0aed4b7c9f716f86fd9a8e3b = L.popup({"maxWidth": "100%"});

        
            var html_696a56bff1ce402eb29df2a9fa1d6a93 = $(`<div id="html_696a56bff1ce402eb29df2a9fa1d6a93" style="width: 100.0%; height: 100.0%;">Friedrichshagen - Cluster 0</div>`)[0];
            popup_f7e6400f0aed4b7c9f716f86fd9a8e3b.setContent(html_696a56bff1ce402eb29df2a9fa1d6a93);
        

        circle_marker_c49185c6610b46728a53c7f880ee0bb0.bindPopup(popup_f7e6400f0aed4b7c9f716f86fd9a8e3b)
        ;

        
    
    
            var circle_marker_0f222477dd914bf3a5ea68f4fdeae429 = L.circleMarker(
                [52.543360000000064, 13.241520000000037],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_1ef28db32ccd4239aedb60ad3ffe3fc7 = L.popup({"maxWidth": "100%"});

        
            var html_d9f08f9731b24288bb03b967e9bb5492 = $(`<div id="html_d9f08f9731b24288bb03b967e9bb5492" style="width: 100.0%; height: 100.0%;">Haselhorst - Cluster 0</div>`)[0];
            popup_1ef28db32ccd4239aedb60ad3ffe3fc7.setContent(html_d9f08f9731b24288bb03b967e9bb5492);
        

        circle_marker_0f222477dd914bf3a5ea68f4fdeae429.bindPopup(popup_1ef28db32ccd4239aedb60ad3ffe3fc7)
        ;

        
    
    
            var circle_marker_90016580dce145ddafdc5a4f9ba6cc13 = L.circleMarker(
                [52.41300000000007, 13.66050000000007],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_18c59efb7ab54c11a6d4c231af0e1045 = L.popup({"maxWidth": "100%"});

        
            var html_687adbaf11814212b107b99abd67902a = $(`<div id="html_687adbaf11814212b107b99abd67902a" style="width: 100.0%; height: 100.0%;">Müggelheim - Cluster 0</div>`)[0];
            popup_18c59efb7ab54c11a6d4c231af0e1045.setContent(html_687adbaf11814212b107b99abd67902a);
        

        circle_marker_90016580dce145ddafdc5a4f9ba6cc13.bindPopup(popup_18c59efb7ab54c11a6d4c231af0e1045)
        ;

        
    
    
            var circle_marker_dd125b1c52d740a9ac628b707ae1ecf1 = L.circleMarker(
                [52.606940000000066, 13.210700000000031],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_2785d0b2985849fea3298ec85bde97bd = L.popup({"maxWidth": "100%"});

        
            var html_9c1024a141884db395be828c276a3f02 = $(`<div id="html_9c1024a141884db395be828c276a3f02" style="width: 100.0%; height: 100.0%;">Heiligensee - Cluster 0</div>`)[0];
            popup_2785d0b2985849fea3298ec85bde97bd.setContent(html_9c1024a141884db395be828c276a3f02);
        

        circle_marker_dd125b1c52d740a9ac628b707ae1ecf1.bindPopup(popup_2785d0b2985849fea3298ec85bde97bd)
        ;

        
    
    
            var circle_marker_e92d246d60c14ab4821b0c40c8b95958 = L.circleMarker(
                [52.565120000000036, 13.50828000000007],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_fd12d635c8ce43858e10a81fcad4f49f = L.popup({"maxWidth": "100%"});

        
            var html_14674485bad149d78013db2568a6dc25 = $(`<div id="html_14674485bad149d78013db2568a6dc25" style="width: 100.0%; height: 100.0%;">Neu-Hohenschönhausen - Cluster 0</div>`)[0];
            popup_fd12d635c8ce43858e10a81fcad4f49f.setContent(html_14674485bad149d78013db2568a6dc25);
        

        circle_marker_e92d246d60c14ab4821b0c40c8b95958.bindPopup(popup_fd12d635c8ce43858e10a81fcad4f49f)
        ;

        
    
    
            var circle_marker_c99088f3f8bf421d9708b2065ebfd17b = L.circleMarker(
                [52.44093000000004, 13.688910000000021],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_063b2f3ac3b84d16bca23a71cee258b3 = L.popup({"maxWidth": "100%"});

        
            var html_909d3c29438c4fa6afcba56286de493d = $(`<div id="html_909d3c29438c4fa6afcba56286de493d" style="width: 100.0%; height: 100.0%;">Rahnsdorf - Cluster 0</div>`)[0];
            popup_063b2f3ac3b84d16bca23a71cee258b3.setContent(html_909d3c29438c4fa6afcba56286de493d);
        

        circle_marker_c99088f3f8bf421d9708b2065ebfd17b.bindPopup(popup_063b2f3ac3b84d16bca23a71cee258b3)
        ;

        
    
    
            var circle_marker_12da438c1a3a4764bddf2987a59b4ef7 = L.circleMarker(
                [52.50302000000005, 13.580790000000036],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_f92d4d108b4f4d09860a5fcb402ebe8a = L.popup({"maxWidth": "100%"});

        
            var html_6bde7143fcdb41ddb5ce82b2eb1d848f = $(`<div id="html_6bde7143fcdb41ddb5ce82b2eb1d848f" style="width: 100.0%; height: 100.0%;">Kaulsdorf (Berlin) - Cluster 0</div>`)[0];
            popup_f92d4d108b4f4d09860a5fcb402ebe8a.setContent(html_6bde7143fcdb41ddb5ce82b2eb1d848f);
        

        circle_marker_12da438c1a3a4764bddf2987a59b4ef7.bindPopup(popup_f92d4d108b4f4d09860a5fcb402ebe8a)
        ;

        
    
    
            var circle_marker_7a804f11e75242d3bf62cae521303471 = L.circleMarker(
                [52.61438000000004, 13.469320000000039],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_bbbfb27d3b3a427d876236fb70de5c28 = L.popup({"maxWidth": "100%"});

        
            var html_1602f15b3e564d4db9e51c70649cf051 = $(`<div id="html_1602f15b3e564d4db9e51c70649cf051" style="width: 100.0%; height: 100.0%;">Karow (Berlin) - Cluster 0</div>`)[0];
            popup_bbbfb27d3b3a427d876236fb70de5c28.setContent(html_1602f15b3e564d4db9e51c70649cf051);
        

        circle_marker_7a804f11e75242d3bf62cae521303471.bindPopup(popup_bbbfb27d3b3a427d876236fb70de5c28)
        ;

        
    
    
            var circle_marker_0d59e6e5ecd246ab9663c7418f018e23 = L.circleMarker(
                [52.44329000000005, 13.502720000000068],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_eec136f065c64a6399120a307a47efd2 = L.popup({"maxWidth": "100%"});

        
            var html_8ecccd54be9146caba2cb65836cb5227 = $(`<div id="html_8ecccd54be9146caba2cb65836cb5227" style="width: 100.0%; height: 100.0%;">Johannisthal (Berlin) - Cluster 0</div>`)[0];
            popup_eec136f065c64a6399120a307a47efd2.setContent(html_8ecccd54be9146caba2cb65836cb5227);
        

        circle_marker_0d59e6e5ecd246ab9663c7418f018e23.bindPopup(popup_eec136f065c64a6399120a307a47efd2)
        ;

        
    
    
            var circle_marker_4db708f936414185812b0733bb09fd8b = L.circleMarker(
                [52.46237000000008, 13.514380000000074],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_161824a27845489ba8cfa2762551d4c9 = L.popup({"maxWidth": "100%"});

        
            var html_59a1f07846f44540b6c8e922632bbea6 = $(`<div id="html_59a1f07846f44540b6c8e922632bbea6" style="width: 100.0%; height: 100.0%;">Oberschöneweide - Cluster 0</div>`)[0];
            popup_161824a27845489ba8cfa2762551d4c9.setContent(html_59a1f07846f44540b6c8e922632bbea6);
        

        circle_marker_4db708f936414185812b0733bb09fd8b.bindPopup(popup_161824a27845489ba8cfa2762551d4c9)
        ;

        
    
    
            var circle_marker_d575616252f74270876eb43f0faa52d1 = L.circleMarker(
                [52.547060000000045, 13.500550000000032],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_7332eadd504145fc9d8b99729d000e6e);
        
    
        var popup_faf9521f94544ce4964a0554cd10be10 = L.popup({"maxWidth": "100%"});

        
            var html_17b8181e9b484cdaaa3170be6bc29f37 = $(`<div id="html_17b8181e9b484cdaaa3170be6bc29f37" style="width: 100.0%; height: 100.0%;">Alt-Hohenschönhausen - Cluster 0</div>`)[0];
            popup_faf9521f94544ce4964a0554cd10be10.setContent(html_17b8181e9b484cdaaa3170be6bc29f37);
        

        circle_marker_d575616252f74270876eb43f0faa52d1.bindPopup(popup_faf9521f94544ce4964a0554cd10be10)
        ;

        
    
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7f5a6f2860b8>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map\nmap_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# set color scheme for the clusters\nx = np.arange(kclusters)\nys = [i+x+(i*x)**2 for i in range(kclusters)]\ncolors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\nrainbow = [colors.rgb2hex(i) for i in colors_array]\n\n# add markers to the map\nmarkers_colors = []\nfor lat, lon, poi, cluster in zip(dfmerged['Latitude'], dfmerged['Longitude'], dfmerged['Neighborhood'], dfmerged['Category']):\n    label = folium.Popup(str(poi) + ' - Cluster ' + str(cluster), parse_html=True)\n    folium.CircleMarker(\n        [lat, lon],\n        radius=5,\n        popup=label,\n        color=rainbow[cluster-1],\n        fill=True,\n        fill_color=rainbow[cluster-1],\n        fill_opacity=0.7).add_to(map_clusters)\n       \nmap_clusters"
        }, 
        {
            "source": "## Examining Clusters", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Category 1: Neighborhoods with very low number of restaurants", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 134, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 134, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Italian Restaurant</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Alt-Hohensch\u00f6nhausen</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Altglienicke</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Baumschulenweg</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Blankenburg (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Blankenfelde</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Bohnsdorf</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Buch (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Falkenberg (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Falkenhagener Feld</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Franz\u00f6sisch Buchholz</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Friedrichshagen</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Gatow</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Gesundbrunnen (Berlin)</td>\n      <td>0.010000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Gr\u00fcnau (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Haselhorst</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Heiligensee</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Johannisthal (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Karow (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Kaulsdorf (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Konradsh\u00f6he</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>L\u00fcbars</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Malchow (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Marzahn</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>M\u00fcggelheim</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Neu-Hohensch\u00f6nhausen</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>Obersch\u00f6neweide</td>\n      <td>0.013158</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>Rahnsdorf</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>Spandau (locality)</td>\n      <td>0.012195</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>Stadtrandsiedlung Malchow</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Tiergarten, Berlin</td>\n      <td>0.010000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>Wartenberg (Berlin)</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                 Neighborhood  Italian Restaurant  Category\n1        Alt-Hohensch\u00f6nhausen            0.000000         0\n3                Altglienicke            0.000000         0\n4              Baumschulenweg            0.000000         0\n6        Blankenburg (Berlin)            0.000000         0\n7                Blankenfelde            0.000000         0\n8                   Bohnsdorf            0.000000         0\n11              Buch (Berlin)            0.000000         0\n16        Falkenberg (Berlin)            0.000000         0\n17         Falkenhagener Feld            0.000000         0\n19       Franz\u00f6sisch Buchholz            0.000000         0\n22            Friedrichshagen            0.000000         0\n25                      Gatow            0.000000         0\n26     Gesundbrunnen (Berlin)            0.010000         0\n29            Gr\u00fcnau (Berlin)            0.000000         0\n33                 Haselhorst            0.000000         0\n34                Heiligensee            0.000000         0\n38      Johannisthal (Berlin)            0.000000         0\n40             Karow (Berlin)            0.000000         0\n41         Kaulsdorf (Berlin)            0.000000         0\n43                Konradsh\u00f6he            0.000000         0\n50                     L\u00fcbars            0.000000         0\n52           Malchow (Berlin)            0.000000         0\n55                    Marzahn            0.000000         0\n59                 M\u00fcggelheim            0.000000         0\n60       Neu-Hohensch\u00f6nhausen            0.000000         0\n65            Obersch\u00f6neweide            0.013158         0\n69                  Rahnsdorf            0.000000         0\n78         Spandau (locality)            0.012195         0\n80  Stadtrandsiedlung Malchow            0.000000         0\n84         Tiergarten, Berlin            0.010000         0\n87        Wartenberg (Berlin)            0.000000         0"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "merged.loc[merged['Category'] == 0]"
        }, 
        {
            "source": "### Category 2: Neighborhoods with low number of restaurants", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 135, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 135, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Italian Restaurant</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13</th>\n      <td>Charlottenburg</td>\n      <td>0.110000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Grunewald</td>\n      <td>0.130000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Halensee</td>\n      <td>0.120000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>Kreuzberg</td>\n      <td>0.090000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Rudow</td>\n      <td>0.090909</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Schmargendorf</td>\n      <td>0.145161</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>Schm\u00f6ckwitz</td>\n      <td>0.100000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>Waidmannslust</td>\n      <td>0.085714</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "      Neighborhood  Italian Restaurant  Category\n13  Charlottenburg            0.110000         1\n28       Grunewald            0.130000         1\n31        Halensee            0.120000         1\n44       Kreuzberg            0.090000         1\n72           Rudow            0.090909         1\n74   Schmargendorf            0.145161         1\n75     Schm\u00f6ckwitz            0.100000         1\n85   Waidmannslust            0.085714         1"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "merged.loc[merged['Category'] == 1]"
        }, 
        {
            "source": "### Category 3: Neighborhoods with a significant number of restaurants", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 136, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 136, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Italian Restaurant</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adlershof</td>\n      <td>0.021277</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alt-Treptow</td>\n      <td>0.020000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Biesdorf (Berlin)</td>\n      <td>0.034483</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Borsigwalde</td>\n      <td>0.036585</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Britz</td>\n      <td>0.025641</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Fennpfuhl</td>\n      <td>0.030000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Friedrichshain</td>\n      <td>0.030000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Frohnau</td>\n      <td>0.033333</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Hakenfelde</td>\n      <td>0.030303</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Hansaviertel</td>\n      <td>0.030000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Heinersdorf</td>\n      <td>0.029851</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Hellersdorf</td>\n      <td>0.017857</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>K\u00f6penick</td>\n      <td>0.016667</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>Lichtenberg (locality)</td>\n      <td>0.030000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Moabit</td>\n      <td>0.030000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Neuk\u00f6lln (locality)</td>\n      <td>0.020000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Niedersch\u00f6neweide</td>\n      <td>0.015873</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>Niedersch\u00f6nhausen</td>\n      <td>0.023256</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Pankow (locality)</td>\n      <td>0.020000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>Pl\u00e4nterwald</td>\n      <td>0.022727</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>Prenzlauer Berg</td>\n      <td>0.020000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Reinickendorf (locality)</td>\n      <td>0.039216</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Rosenthal (Berlin)</td>\n      <td>0.029412</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Rummelsburg</td>\n      <td>0.020000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>Sch\u00f6neberg</td>\n      <td>0.030000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>Siemensstadt</td>\n      <td>0.030303</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>Staaken</td>\n      <td>0.027778</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>Wannsee</td>\n      <td>0.022727</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>Wedding (Berlin)</td>\n      <td>0.020000</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>Weissensee (Berlin)</td>\n      <td>0.015625</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>Wilhelmsruh</td>\n      <td>0.024390</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>Wilhelmstadt</td>\n      <td>0.014925</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                Neighborhood  Italian Restaurant  Category\n0                  Adlershof            0.021277         2\n2                Alt-Treptow            0.020000         2\n5          Biesdorf (Berlin)            0.034483         2\n9                Borsigwalde            0.036585         2\n10                     Britz            0.025641         2\n18                 Fennpfuhl            0.030000         2\n23            Friedrichshain            0.030000         2\n24                   Frohnau            0.033333         2\n30                Hakenfelde            0.030303         2\n32              Hansaviertel            0.030000         2\n35               Heinersdorf            0.029851         2\n36               Hellersdorf            0.017857         2\n45                  K\u00f6penick            0.016667         2\n47    Lichtenberg (locality)            0.030000         2\n57                    Moabit            0.030000         2\n61       Neuk\u00f6lln (locality)            0.020000         2\n62         Niedersch\u00f6neweide            0.015873         2\n63         Niedersch\u00f6nhausen            0.023256         2\n66         Pankow (locality)            0.020000         2\n67               Pl\u00e4nterwald            0.022727         2\n68           Prenzlauer Berg            0.020000         2\n70  Reinickendorf (locality)            0.039216         2\n71        Rosenthal (Berlin)            0.029412         2\n73               Rummelsburg            0.020000         2\n76                Sch\u00f6neberg            0.030000         2\n77              Siemensstadt            0.030303         2\n79                   Staaken            0.027778         2\n86                   Wannsee            0.022727         2\n88          Wedding (Berlin)            0.020000         2\n89       Weissensee (Berlin)            0.015625         2\n91               Wilhelmsruh            0.024390         2\n92              Wilhelmstadt            0.014925         2"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "merged.loc[merged['Category'] == 2]"
        }, 
        {
            "source": "### Category 4: Neighborhoods crowded with restaurants", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 137, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 137, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Italian Restaurant</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12</th>\n      <td>Buckow (Berlin)</td>\n      <td>0.065217</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Charlottenburg-Nord</td>\n      <td>0.045977</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Dahlem (Berlin)</td>\n      <td>0.075949</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Friedenau</td>\n      <td>0.070000</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Friedrichsfelde</td>\n      <td>0.042857</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Gropiusstadt</td>\n      <td>0.063492</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Hermsdorf (Berlin)</td>\n      <td>0.058824</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Karlshorst</td>\n      <td>0.042553</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Kladow</td>\n      <td>0.071429</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>Lankwitz</td>\n      <td>0.049180</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>Lichtenrade</td>\n      <td>0.062500</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>Lichterfelde (Berlin)</td>\n      <td>0.057471</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>Mahlsdorf</td>\n      <td>0.068966</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>Mariendorf</td>\n      <td>0.054795</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>Marienfelde</td>\n      <td>0.064516</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Mitte (locality)</td>\n      <td>0.060000</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>M\u00e4rkisches Viertel</td>\n      <td>0.051282</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>Nikolassee</td>\n      <td>0.065217</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>Steglitz</td>\n      <td>0.060000</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>Tegel</td>\n      <td>0.047619</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>Tempelhof</td>\n      <td>0.050505</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>Westend (Berlin)</td>\n      <td>0.050000</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>Wilmersdorf</td>\n      <td>0.080000</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>Wittenau</td>\n      <td>0.045455</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>Zehlendorf (Berlin)</td>\n      <td>0.064516</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "             Neighborhood  Italian Restaurant  Category\n12        Buckow (Berlin)            0.065217         3\n14    Charlottenburg-Nord            0.045977         3\n15        Dahlem (Berlin)            0.075949         3\n20              Friedenau            0.070000         3\n21        Friedrichsfelde            0.042857         3\n27           Gropiusstadt            0.063492         3\n37     Hermsdorf (Berlin)            0.058824         3\n39             Karlshorst            0.042553         3\n42                 Kladow            0.071429         3\n46               Lankwitz            0.049180         3\n48            Lichtenrade            0.062500         3\n49  Lichterfelde (Berlin)            0.057471         3\n51              Mahlsdorf            0.068966         3\n53             Mariendorf            0.054795         3\n54            Marienfelde            0.064516         3\n56       Mitte (locality)            0.060000         3\n58     M\u00e4rkisches Viertel            0.051282         3\n64             Nikolassee            0.065217         3\n81               Steglitz            0.060000         3\n82                  Tegel            0.047619         3\n83              Tempelhof            0.050505         3\n90       Westend (Berlin)            0.050000         3\n93            Wilmersdorf            0.080000         3\n94               Wittenau            0.045455         3\n95    Zehlendorf (Berlin)            0.064516         3"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "merged.loc[merged['Category'] == 3]"
        }, 
        {
            "source": "### Observations", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "It is clear the Category 3 are very crowded with Italian Restaurants, and hence, Category 2 would be the best bet for opening a new restuarant because of not too much competition in these regions, but still a proven market. Client with USPs to stand out from the competition can also open new restaurants in neighborhoods in Cluster 1 with moderate competition. ", 
            "cell_type": "markdown", 
            "metadata": {}
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5", 
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        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
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            "codemirror_mode": {
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    "nbformat": 4
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