data-preview.qmd

---
title: "Data Preview"
format: html
acronyms:
  insert_loa: false
---

The various data sets (flight list, flight events, and measurements) can be used to provide insights on topics such as traffic counts, runway/taxi-way/parking position usage at airports.

::: {.callout-caution}
## Disclaimer

Please note that due to system maintenance and technical issues experienced by our data provider, OpenSky Network, some of the datasets had incomplete data for certain months when doing the below analysis. We are continuously working with OpenSky to ensure data quality and integrity and this has been resolved this in the meantime in the available data provided by the OPDI (see our Data page). This data preview will be updated soon.

Affected months include **December 2023**, **January 2024**, **February 2024**, **March 2024** and **April 2024**. Please take this into consideration when viewing the data in this data preview.

In addition, please also note that any numbers are influenced by the availability of ADS-B coverage. To view the history of ADS-B coverage by OSN, see [here](https://opensky-network.org/network/facts). 
:::

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### Traffic at top 20 European airports
Below you see the traffic at the major top 20 European airports (i.e., most observed flights during 2022 and 2024). This data is extracted from the **OPDI flight list** available for download [here](/data.html).

<div class="plotly-container1">
```{r}
library(tidyverse)
library(arrow)
library(ggplot2)
library(plotly)
library(purrr)
library(lubridate)

# Read data
combined_flight_counts <- read_parquet('data/02_analytic_data/total_flights_per_airport_pm.parquet')
combined_flight_counts <- combined_flight_counts %>% filter(!is.na(APT))

# Calculate total flights per airport and select top 20
top_20_airports <- combined_flight_counts %>%
  group_by(APT) %>%
  summarize(total_flights = sum(detected_flights, na.rm = TRUE)) %>%
  arrange(desc(total_flights)) %>%
  slice_head(n = 20) %>%
  pull(APT)

# Filter data for top 20 airports
top_20_data <- combined_flight_counts %>%
  filter(APT %in% top_20_airports)

# Assign different colors for each airport
airport_colors <- colorRampPalette(RColorBrewer::brewer.pal(9, "Set2"))(length(top_20_airports))

# Create Plotly stacked bar chart for top 20 airports
p <- plot_ly(data = top_20_data) %>%
  add_trace(
    x = ~month, 
    y = ~detected_flights, 
    type = 'bar', 
    color = ~APT, 
    colors = airport_colors, 
    text = ~APT,
    hoverinfo = 'text'
  ) %>%
  layout(
    title = 'Monthly observed flights for top 20 Airports',
    yaxis = list(title = 'Monthly detected flights'),
    xaxis = list(title = 'Month'),
    barmode = 'stack'
  )

# Show plot
p
```
</div>

### Traffic at all medium and large European airports (1121 airports)

Below you can see the detected traffic for all European medium and large airports. Use the dropdown menu to select an airport of choice (alphabetically ordered). There are 1121 airports in the dropdown menu. This data is extracted from the **OPDI flight list** available for download [here](/data.html).

<div class="plotly-container2">
```{r}
library(tidyverse)
library(arrow)
library(ggplot2)
library(plotly)
library(purrr)
library(lubridate)

combined_flight_counts <- read_parquet('data/02_analytic_data/total_flights_per_airport_pm.parquet')

combined_flight_counts <- combined_flight_counts %>% filter(!is.na(APT))

# Create Plotly stacked bar chart with dropdown for airports
p <- plot_ly(data = combined_flight_counts) %>%
  add_bars(x = ~month, y = ~detected_flights, name = 'Flights', marker = list(color = 'blue')) %>%
  layout(
    title = list(text = 'Monthly observed flights for all airports'),  # Initial title
    yaxis = list(title = 'Monthly observed flights'),
    xaxis = list(title = 'Month'),
    barmode = 'stack',
    updatemenus = list(
      list(
        type = 'dropdown',
        active = 0,
        buttons = lapply(
          sort(unique(combined_flight_counts$APT)), function(airport) {
            list(
              method = "update",
              args = list(
                list(
                  x = list(
                    combined_flight_counts$month[combined_flight_counts$APT == airport]
                  ),
                  y = list(
                    combined_flight_counts$detected_flights[combined_flight_counts$APT == airport]
                  )
                ),
                list(
                  title = paste("Monthly observed flights for", airport)  # Update title with selected airport
                )
              ),
              label = airport
            )
          }
        ),
        direction = "down",
        showactive = TRUE
      )
    )
  )

# Show plot
p
```
</div>

### Flight events for airport monitoring

Also airport event monitoring is possible using the **OPDI flight events**. An example of random set of flights is given below. Note that the availability of runway, taxiway and parking position events is dependent on the OpenSky Network ADS-B coverage and the availability of OpenStreetMap geographical data. This data is extracted from the **OPDI flight events** available for download [here](/data.html).

<div class="plotly-container3">
::: {include-file}
<iframe src="resources/plotly-airport-gantt.html" width="120%" height="800" frameborder="0"></iframe>
:::
</div>

### Flight events in function of the measurement distance flown (NM)
Analyse your flight using the predefined **OPDI flight events** and **OPDI measurements**. An example can be seen below with the included measurements. This data is extracted from the **OPDI flight events** and **OPDI measurements** available for download [here](/data.html).

```{r}
# Load required libraries
library(dplyr)
library(arrow)  # for reading parquet files
library(plotly)

tmp <- read_parquet('data/02_analytic_data/example_events.parquet')
tmp <- tmp %>% filter(type.y == 'Distance flown (NM)')

# Create scatter plot
fig <- plot_ly(tmp, x = ~value, y = ~altitude, color = ~type.x, type = 'scatter', mode = 'markers') %>%
  layout(
    xaxis = list(title = "Measurement: Distance flown (NM)"),
    yaxis = list(title = "Altitude (ft)"),
    legend = list(title = list(text = "Flight event")),
    title = "Flight events in function of the measurement distance flown (NM)"
  )

# Display plot
fig
```
<br>
```{r}
# Load required libraries
library(dplyr)
library(arrow)  # for reading parquet files
library(plotly)

tmp <- read_parquet('data/02_analytic_data/example_events.parquet')
tmp <- tmp %>% filter(type.y == 'Time Passed (s)')

# Create scatter plot
fig <- plot_ly(tmp, x = ~value, y = ~altitude, color = ~type.x, type = 'scatter', mode = 'markers') %>%
  layout(
    xaxis = list(title = "Measurement: Time Passed (s)"),
    yaxis = list(title = "Altitude (ft)"),
    legend = list(title = list(text = "Flight event")),
    title = "Flight events in function of the measurement time passed (s)"
  )

# Display plot
fig
```