Visualizing Covid 19 cases in Senegal

In this quick tutorial, we will visualize the proportion of covid 19 cases in different senegalese districts around the country using folium and different other libraries . We will create a statistic map and interactive map.

In [6]:

import folium
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import geopandas
%matplotlib inline

COVID rate data

In [4]:

data = pd.read_csv('D:\Studying\PRACTICES\Geoplot and geopandas geopandas.org documentation\lesson 5 beautiful map statistic maps\Interactive maps\covid_19_confirmed_cases_senegal.csv')
data.head()

Out[4]:

	districts	cases	lat	lon
0	DAKAR-OUEST	2218	14.739241	-17.500376
1	DAKAR-SUD	2000	14.661041	-17.439310
2	TOUBA	608	14.867642	-15.917822
3	MBOUR	274	14.416536	-16.987211
4	DAKAR-CENTRE	2189	14.713699	-17.439182

In [7]:

# Convert to geodataframe
gdf = geopandas.GeoDataFrame(
    data, geometry=geopandas.points_from_xy(data.lon, data.lat))

In [8]:

gdf.head()

Out[8]:

	districts	cases	lat	lon	geometry
0	DAKAR-OUEST	2218	14.739241	-17.500376	POINT (-17.50038 14.73924)
1	DAKAR-SUD	2000	14.661041	-17.439310	POINT (-17.43931 14.66104)
2	TOUBA	608	14.867642	-15.917822	POINT (-15.91782 14.86764)
3	MBOUR	274	14.416536	-16.987211	POINT (-16.98721 14.41654)
4	DAKAR-CENTRE	2189	14.713699	-17.439182	POINT (-17.43918 14.71370)

Create a static map

Now we have a spatial layer with the geometry information. Let’s create a simple plot based on cases:

In [9]:

# Define which variable to plot
gdf.plot(column="cases")

Out[9]:

<AxesSubplot:>

In [10]:

# Adjust figure size
fig, ax = plt.subplots(1, figsize=(10, 8))

# Adjust colors and add a legend
gdf.plot(ax = ax, column="cases", scheme="quantiles", cmap="Reds", legend=True)

Out[10]:

<AxesSubplot:>

Create an interactive map

Next, we’ll plot an interactive map based on the same data, and usign the folium library, which enables us to create maps based on the JavaScript library leaflet.js.

In [11]:

# Create a Geo-id which is needed by the Folium (it needs to have a unique identifier for each row)
gdf['geoid'] = gdf.index.astype(str)

In [12]:

gdf.head()

Out[12]:

	districts	cases	lat	lon	geometry	geoid
0	DAKAR-OUEST	2218	14.739241	-17.500376	POINT (-17.50038 14.73924)	0
1	DAKAR-SUD	2000	14.661041	-17.439310	POINT (-17.43931 14.66104)	1
2	TOUBA	608	14.867642	-15.917822	POINT (-15.91782 14.86764)	2
3	MBOUR	274	14.416536	-16.987211	POINT (-16.98721 14.41654)	3
4	DAKAR-CENTRE	2189	14.713699	-17.439182	POINT (-17.43918 14.71370)	4

In [13]:

gdf.dtypes

Out[13]:

districts      object
cases           int64
lat           float64
lon           float64
geometry     geometry
geoid          object
dtype: object

In [14]:

print(gdf.crs)

None

In [15]:

# Create coordinate reference system for our data
gdf.crs = 'epsg:4326'
gdf.crs

Out[15]:

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

In [17]:

# Create a Map instance

m = folium.Map(location=[14.553810,-14.429443], tiles = 'cartodbpositron', zoom_start=8, control_scale=True)

folium.Choropleth(geo_data = gdf,
                  samadata = gdf,
                  columns=['geoid','cases'],
                  key_on='feature.id',
                  fill_color='RdYlBu',
                  line_color='white',
                  line_weight=0,
                  legend_name= 'Covid 19 rate in Senegal').add_to(m)
m