Mapping location of air quality sensing in India

In this notebook, I’ll show a quick example of how to use Folium (which internally uses LeafletJS) for visualising the location of air quality monitors in India. The purpose of this notebook is eductional in nature.

Standard Imports

Code

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline

Downloading data from OpenAQ for 2018-04-06

Code

!wget --no-check-certificate https://openaq-data.s3.amazonaws.com/2018-04-06.csv -P /Users/nipun/Downloads/

--2020-02-29 17:52:50--  https://openaq-data.s3.amazonaws.com/2018-04-06.csv
Resolving openaq-data.s3.amazonaws.com (openaq-data.s3.amazonaws.com)... 52.216.99.123
Connecting to openaq-data.s3.amazonaws.com (openaq-data.s3.amazonaws.com)|52.216.99.123|:443... connected.
WARNING: cannot verify openaq-data.s3.amazonaws.com's certificate, issued by ‘CN=DigiCert Baltimore CA-2 G2,OU=www.digicert.com,O=DigiCert Inc,C=US’:
  Unable to locally verify the issuer's authority.
HTTP request sent, awaiting response... 200 OK
Length: 133839107 (128M) [text/csv]
Saving to: ‘/Users/nipun/Downloads/2018-04-06.csv.1’

2018-04-06.csv.1     37%[======>             ]  47.37M  3.79MB/s    eta 40s    ^C

Code

import pandas as pd
df = pd.read_csv("/Users/nipun/Downloads/2018-04-06.csv")
df = df[(df.country=='IN')&(df.parameter=='pm25')].dropna().groupby("location").mean()

Code

df

	value	latitude	longitude
location
Adarsh Nagar, Jaipur - RSPCB	79.916667	26.902909	75.836853
Anand Kala Kshetram, Rajamahendravaram - APPCB	42.750000	16.987287	81.736318
Ardhali Bazar, Varanasi - UPPCB	103.666667	25.350599	82.908307
Asanol Court Area, Asanol - WBPCB	56.833333	23.685297	86.945968
Ashok Nagar, Udaipur - RSPCB	114.750000	24.588617	73.632140
...	...	...	...
Vasundhara, Ghaziabad, UP - UPPCB	223.333333	28.660335	77.357256
Vikas Sadan, Gurgaon, Haryana - HSPCB	280.250000	28.450124	77.026305
Vindhyachal STPS, Singrauli - MPPCB	144.000000	24.108970	82.645580
Ward-32 Bapupara, Siliguri - WBPCB	195.000000	26.688305	88.412668
Zoo Park, Hyderabad - TSPCB	82.500000	17.349694	78.451437

79 rows × 3 columns

Downloading World GeoJson file

Code

!wget --no-check-certificate https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/world-countries.json

--2020-02-29 17:53:17--  https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/world-countries.json
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.8.133
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.8.133|:443... connected.
WARNING: cannot verify raw.githubusercontent.com's certificate, issued by ‘CN=DigiCert SHA2 High Assurance Server CA,OU=www.digicert.com,O=DigiCert Inc,C=US’:
  Unable to locally verify the issuer's authority.
HTTP request sent, awaiting response... 200 OK
Length: 252515 (247K) [text/plain]
Saving to: ‘world-countries.json’

world-countries.jso 100%[===================>] 246.60K   376KB/s    in 0.7s    

2020-02-29 17:53:19 (376 KB/s) - ‘world-countries.json’ saved [252515/252515]

Creating india.json correspdonding to Indian data

Code

import json
e = json.load(open('world-countries.json','r'))
json.dump(e['features'][73], open('india.json','w'))

Code

import folium

folium_map = folium.Map(width = '60%',height=800,location=[20, 77],
                        zoom_start=5,
                        tiles="Stamen Terrain",min_lat=7, max_lat=35, min_lon=73, max_lon=90)
for x in df.iterrows():
    name = x[0]
    lat, lon = x[1]['latitude'], x[1]['longitude']
    folium.CircleMarker([lat, lon], radius=5, color='#000000',fill_color='#D3D3D3' , fill_opacity=1).add_to(folium_map)

folium.GeoJson('india.json').add_to(folium_map)

<folium.features.GeoJson at 0x11e497bd0>

Code

folium_map.save("map.html")

There you go!Till next time.