Plotting Industries and Railway Station

[Tanvi-Jain01]

@nipunbatra , @patel-zeel
I have an idea which I think can be informative and helpful to gain insights.

If we plot industries and railway stations on a map alongside air quality monitoring stations that display pollutant values, it can provide valuable insights into the surrounding areas and their impact on air quality. This information can be instrumental in understanding the environmental conditions and potential sources of pollution in the vicinity.

Let me take an example for that:

The above image is just for example
red circle indicates air station
green circle indicates industry or railway station

Now even though the marked red circle(air station) is quite far from the industries, it is still bigger in size which indicates it has high pollutant value that makes us think that the quality of air is polluted not because of vicinity but due to the wind speed and wind direction Hence, plotting Industries/Railway station might give some useful insights to us.

[Tanvi-Jain01]

@nipunbatra , @patel-zeel

output.csv
allrails.pdf

The above file includes all the railway stations of India which is 7462 with its latitude ,longitude , name of stations along with its state and we can use it with any of the state/district shape files for India.

[Tanvi-Jain01]

@nipunbatra , @patel-zeel

Idea into Implementation

I have tried to plot the railway stations along with the Air station

Code

file_path = r'C:\Users\Harshit Jain\Desktop\delhiaq\output.csv'

# Read the CSV file into a DataFrame
railway= pd.read_csv(file_path)
print(railway.columns)

railway = railway.drop(['Sno.'], axis=1)


lati = railway['Latitude']
longi = railway['Longitude']

geometry = [Point(x, y) for x, y in zip(longi, lati)]
railway=gpd.GeoDataFrame(railway,geometry=geometry)
print(railway)

hourlydata=xr.open_dataset(r'C:\Users\Harshit Jain\Desktop\delhiaq\daily_data.nc')
df=hourlydata.to_dataframe()

unique=df[['station','latitude','longitude']].drop_duplicates()

lat = unique['latitude']
lon = unique['longitude']

geometry = [Point(x, y) for x, y in zip(lon, lat)]

df = gpd.GeoDataFrame(df, geometry=geometry)
#print(df)

fig, ax = plt.subplots(figsize=(12, 12))
shapefile_extent = gdf_shape.total_bounds
plt.xlim(shapefile_extent[0], shapefile_extent[2])
plt.ylim(shapefile_extent[1], shapefile_extent[3])
 # Add the shapefile to the plot
railway.plot(ax=ax, edgecolor='black', facecolor='black',alpha=0.5,label='Railway station')

gdf_shape.plot(ax=ax, edgecolor='black', facecolor='none')
df.plot(ax=ax, edgecolor='black', facecolor='red',markersize=7*df['pm25'],label='Air station')
plt.legend()
plt.show()

In the above code I'm reading and converting Air quality dataset of Delhi and railway stations.csv file into geodataframe and plotting it.

Output:

Here, in the south west region there is no railway station but still the pollutant value is high which makes us think that in south-west region of Delhi probability of industry or wind speed is high for Air pollution

[Tanvi-Jain01]

@nipunbatra , @patel-zeel
Found latitudes and longitudes of different types of industries that effects air quality.
biomass.csv
coal.csv
diesel.csv
steam.csv

[Tanvi-Jain01]

@nipunbatra , @patel-zeel

Plotting Industries

CODE:

fig, ax = plt.subplots(figsize=(12, 12))
coal = pd.read_csv(r'C:\Users\Harshit Jain\Desktop\delhiaq\delhi wazirpur\industries\coal.csv')
coal = gpd.GeoDataFrame(coal, geometry=gpd.points_from_xy(coal.longitude, coal.latitude))

diesel = pd.read_csv(r'C:\Users\Harshit Jain\Desktop\delhiaq\delhi wazirpur\industries\diesel.csv')
diesel = gpd.GeoDataFrame(diesel, geometry=gpd.points_from_xy(diesel.longitude, diesel.latitude))

steam = pd.read_csv(r'C:\Users\Harshit Jain\Desktop\delhiaq\delhi wazirpur\industries\steam.csv')
steam = gpd.GeoDataFrame(steam, geometry=gpd.points_from_xy(steam.longitude, steam.latitude))

biomass = pd.read_csv(r'C:\Users\Harshit Jain\Desktop\delhiaq\delhi wazirpur\industries\biomass.csv')
biomass = gpd.GeoDataFrame(biomass, geometry=gpd.points_from_xy(biomass.longitude, biomass.latitude))

biomass.plot(ax=ax, edgecolor='black', facecolor='orange',alpha=0.5,label='Biomass Industries')
steam.plot(ax=ax, edgecolor='black', facecolor='pink',alpha=0.5,label='Steam Industries')
diesel.plot(ax=ax, edgecolor='black', facecolor='purple',alpha=0.5,label='Diesel Industries')
coal.plot(ax=ax, edgecolor='black', facecolor='yellow',alpha=0.5,label='Coal Industries')

plt.legend()
plt.show()

OUTPUT: