creating choropleth map using folium library

Introduction to Choropleth Maps Using Folium

A choropleth map is a type of thematic map where areas are shaded or patterned in proportion to the value of a variable being represented. Choropleth maps are useful for visualizing how a measurement varies across a geographic area. In Folium, a Python library for creating interactive maps, creating choropleth maps is straightforward and powerful.

Why Use Choropleth Maps?

Choropleth maps are particularly useful for:

1. Visualizing Spatial Distribution: Displaying data across geographic areas to reveal patterns.
2. Comparing Regions: Easily comparing data between different regions.
3. Highlighting Trends: Identifying trends and anomalies in data across locations.
4. Making Data Intuitive: Presenting complex data in an easy-to-understand visual format.

Use Cases for Choropleth Maps

1. Epidemiology: Mapping the spread of diseases across regions.
2. Demographics: Visualizing population density, age distribution, or income levels.
3. Economics: Displaying economic indicators like GDP, unemployment rates, or sales data.
4. Education: Mapping literacy rates, school performance, or educational attainment.
5. Environment: Showing pollution levels, deforestation rates, or climate data.

example code:

import folium 

import pandas as pd 

  

# Sample data: population of US states 

data = { 

  ‘State’: [‘Alabama’, ‘Alaska’, ‘Arizona’, ‘Arkansas’, ‘California’, ‘Colorado’], 

  ‘Population’: [4903185, 731545, 7278717, 3017804, 39512223, 5758736] 

} 

  

# Convert the data to a DataFrame 

df = pd.DataFrame(data) 

  

# URL for GeoJSON file for US states 

geojson_url = ‘https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us-states.json’ 

  

# Initialize the map centered around the US 

m = folium.Map(location=[37.8, -96], zoom_start=4) 

  

# Create the choropleth map 

folium.Choropleth( 

  geo_data=geojson_url, 

  name=’choropleth’, 

  data=df, 

  columns=[‘State’, ‘Population’], 

  key_on=’feature.id’, 

  fill_color=’YlGn’, 

  fill_opacity=0.7, 

  line_opacity=0.2, 

  legend_name=’Population by State’ 

).add_to(m) 

  

# Add layer control 

folium.LayerControl().add_to(m) 

  

# Save the map to an HTML file 

m.save(‘us_states_population.html’) 

  

# Display the map (only works in Jupyter notebooks) 

m 

Conclusion

Choropleth maps are a powerful tool for visualizing geospatial data, allowing for easy interpretation of complex datasets. Using Folium, you can create interactive and visually appealing choropleth maps to highlight important trends and comparisons across different geographic regions.