car2go_map.py

car2go_map.py
¶

This script by Nicolas Kruchten was used to generate a map for a CBC.ca article showing the spatio-temporal distribution of available Car2Go vehicles in Montreal over a 30-day period during the summer of 2016. more details »

pandas and scikit-learn are used to manipulate the data, folium to make the map, and vincent to make the popup charts.

import pandas, math
from sklearn.cluster import MiniBatchKMeans
from folium import Map, CircleMarker, Vega, Popup
from vincent import Bar

¶

The dataset contains approximately 3.2 million observations, consisting of (lat, lon, hod) tuples, where hod stands for ‘hour of day’.
```
df = pandas.read_csv("lat_lon_hod.csv.gz")
```
¶

carh stands for ‘car-hours’, and since the dataset was made by querying the Car2Go API every 5 minutes, each observation means that on average, a car was present for one twelfth of an hour during the given hour of day at the given latitude and longitude.
```
samples_per_hour = 12
df["carh"] = 1.0/samples_per_hour
```
¶

Using K-means clustering, the observations are grouped into 250 spatially-contiguous zones such that each zone contains a comparable number of observations.
```
df["zone"] = MiniBatchKMeans(250).fit_predict(df[["lat", "lon"]].values)
```

A map is initialized, centered on the centroid of all the observations.

map = Map(zoom_start=12, tiles="cartodbpositron",
             location=list(df[["lat","lon"]].mean().values))

For each zone…

zones = df.groupby("zone").agg(dict(carh='sum', lon='mean', lat='mean'))
for i, zone in zones.iterrows():

¶

…a dataset is created containing the average number of car-hours/day by hour of day. (1 car-hour/day at a given hour of day in a given zone represents either 1 car available in the zone for the entire hour, or 3 cars for 20 minutes each, or 1 car for 20 minutes and 1 for 40 minutes, and so on.)
```
    num_days = 30
    carh_by_hod = df.query("zone==@i").groupby("hod").carh.sum()/num_days
```
¶

…a bar chart is created with this dataset, which shows the daily pattern of car availabilities. This is done by first generating a JSON description of a bar-chart in the Vega grammar, and then rendering it to a graphic.
```
    vega = Bar(carh_by_hod, width=450,
               height=150).axis_titles(x='Hour of Day', y='Cars Available')
    chart = Vega(vega.to_json(), width=vega.width+50, height=vega.height+50)
```
¶

… and a circle is added to the map:
```
    map.add_child( CircleMarker(
```
¶

• the circle is placed on the centroid of the zone’s observations
```
        location = [zone["lat"], zone["lon"]],
```
¶

• the circle area is proportional to the total number of car-hours observed in the zone
```
        radius = int(6*math.sqrt(zone["carh"])),
```

• the circle is coloured according to the hour of day with peak car availability: blues representing night-time, yellows representing day-time and reds representing evenings

        fill_opacity = 0.8, color=None,
        fill_color = ('#274cc9','#274cc9','#274cc9','#274cc9',
            '#274cc9','#3959bf','#647aa6','#909b8c','#bcbc73',
            '#e8dd5a','#f1e455','#f1e455','#f1e455','#f1e455',
            '#f0df56','#ecc45a','#e7a95f','#e28e63','#de7467',
            '#c46576','#9d5f8a','#76599f','#4e52b4','#274cc9'
            )[carh_by_hod.idxmax()],

• clicking on the circle pops up the daily-pattern chart

        popup = Popup(max_width=chart.width[0]).add_child(chart)
    ) )

¶

The map is then saved to disk as an HTML file with inlined data, Javascript and CSS information. This is very convenient for collaborating with colleagues without sending a large number of files or requiring them to run this script.
```
map.save('map.html')
```