How to make Choropleth Maps with Labels using Mapbox API

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Choropleth maps are interesting visualization tools that use the intensity of a color to represent aggregate data for different locations within their bounding boxes. They are a lot of fun and are fairly easy to make using the Plotly library. Choropleth maps are powerful data visual representations of data. Let’s get started with a step-by-step guide:

Set Up Your Mapbox Account

Go to https://www.mapbox.com/ and click on sign up.
Verify your e-mail address.
Now, click on “tokens” in the navbar.
Copy your Default Public Token and paste it into your notepad.

make Choropleth Maps with Labels using Mapbox API

Fig. 2 Mapbox account after logging in.

Find an Appropriate GeoJson File for Your Map

If you perform a simple google search typing “{desired map} geojson file”, chances are, that you’ll find the GeoJson file you’re looking for. In this case, we are using the GeoJson file of the Indian State-Wise Map. (you can find it here).

Picking a Suitable Dataset

For this example, we’re using the Indian map and our aim is to divide the map into its state-wise components. For the aforementioned reasons, we’ll have to pick a dataset such that it has a value corresponding to each of India’s states and union territories. Hence, we’ve created a CSV File of data pertaining to Alcohol Consumption Percentage Per State in India. (you can find the dataset on https://www.findeasy.in/alcohol-consumption-in-india/ or you can simply download the CSV file from here).

Fig. 3 CSV file of the appropriate dataset

Step 1:

Import these libraries, necessary to make our code work:

Python3

from urllib.request import urlopen 
import json 
import pandas as pd 
from shapely.geometry import shape 
import plotly.graph_objects as go 
import plotly.express as px

Step 2:

Now load your geojson file using json.load().

Python3

# url = "https://gist.githubusercontent.com/ jbrobst/ 
# 56c13bbbf9d97d187fea01ca62ea5112/raw/ 
# e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson" 
with urlopen(url) as response: 
    body = response.read() 
geo_json = json.loads(body) 
# geo_json

Output:

{‘type’: ‘FeatureCollection’,
‘features’: [{‘type’: ‘Feature’,
‘geometry’: {‘type’: ‘Polygon’,
‘coordinates’: [[[95.23391968067268, 26.68245856965871],

[95.23282005383173, 26.705791937482644],
[95.21038086477148, 26.73124215303452],…

Step 3:

Pass the data from your CSV file to a Pandas’s Dataframe.

Python3

# Paste the file path of your CSV file here 
df = pd.read_csv("D:\Documents\india_alcohol_stats.csv") 
df.head()

Output:

    state           consumption
0    Andaman & Nicobar    25.4
1    Andhra Pradesh    13.7
2    Arunachal Pradesh    28.0
3    Assam    8.8
4    Bihar    7.9

Step 4:

Place your dataset in your geojson file.

Note: In place of “ST_NM”, you can write the name of the equivalent field by looking at your geojson file.

Python3

center_pos = {} 
features = geo_json['features'] 
for feature in features: 
    k = feature['properties']['ST_NM'] 
    s = shape(feature["geometry"]) 
    p = s.centroid 
    center_pos[k] = list(p.coords)

Step 5:

Save your MAPBOX token in a variable.

Python3

# paste you token that you copied from mapbox 
mapbox_access_token = "{YOUR_TOKEN_KEY_HERE}"

Step 6:

Add this code snippet to customize what your labels will look like. Customize the “val” fields in accordance with your CSV file.

Note: In the above code, we have chosen all the labels above the 26% mark to be of white color and the rest in black because darker color labels go with light colored background and so on.

Python3

fig = go.Figure() 
  
for k,v in center_pos.items(): 
    #print(k,v) 
    val = df[df['state'] == k]['consumption'] 
      
    try: 
        if float(format(val.values[0]))>26.0: 
                colour='white'
        else: 
                colour='black'
        val = format(val.values[0])+'%'       
    except IndexError: 
        val = '{:1}'.format(1)  
      
    fig.add_trace(go.Scattermapbox( 
        lat=[center_pos[k][0][1]], 
        lon=[center_pos[k][0][0]], 
        mode='text', 
        textfont=dict( 
            color = colour, 
            size=12, 
        ), 
        text=val, 
        showlegend=False
))

Step 7:

Finally, add the following code which will help us display our data on the map. For your map, you can choose one of these colorscales.

Python3

fig.add_trace(go.Choroplethmapbox( 
    geojson=geo_json,  
    locations=df['state'], 
    featureidkey="properties.ST_NM", 
    z=df['consumption'], 
    colorscale="Blues", 
    marker_opacity=0.7, 
    marker_line_width=0
)) 
fig.update_layout( 
    mapbox_accesstoken=mapbox_access_token, 
#     mapbox_style="carto-positron", 
    mapbox_zoom=4, 
    mapbox_center = {"lat": 22.5, "lon": 81.0} 
) 
  
fig.update_layout(autosize=False, 
                  height=600, 
                  width=800, 
                  margin={"r":0,"t":0,"l":0,"b":0}, 
                 ) 
fig.show()

Step 5: Download Your Map

If you find any white-colored state (i.e. 0% in the label), consider checking your CSV file again for the correct state name and data. you can download the map by right-clicking on the camera icon on the top right of the map.