Creating Interactive Maps with Folium and Python

Creating engaging, interactive visualizations can greatly enhance the understanding and impact of data.

An interactive map is one of the most effective visualization methods for geospatial data. Maps provide inherent context and allow intuitive exploration of patterns, clusters, and outliers.

This article will demonstrate how to build an interactive map with Python using the Folium library.

The map will contain two layers – one displaying volcano locations and elevations and another showing country population densities. This lets readers visualize the spatial relationship between volcanoes and worldwide human populations.

The full code can be found in my GitHub repository. Now, look at the interactive map to see what insights, fork it, and get started.

We will first cover the implementation in Python using Folium and Pandas. This will involve reading the datasets, preparing the data, configuring the map parameters, adding layers and markers, and saving the final visualization.

We’ll then highlight some key insights and patterns observable in the interactive map. Folium provides a simple yet powerful way to generate eye-catching, browser-based maps. By the end, readers will understand how Python and Folium can be leveraged for various mapping and geospatial analysis applications.


To begin, we import the Folium and Pandas libraries, which provide the mapping functionality and data tools needed:

import folium
import pandas

Next, we read the raw data on volcano locations and population estimates by country:

data =pandas.read_csv('Volcanoes.txt')

This provides a Pandas dataframe with columns for latitude, longitude, and elevation of over 1,500 volcanoes worldwide.

We will use the GeoJSON file ‘world.json’ for the country population data, which contains polygon outlines and population statistics for all countries. GeoJSON is a geographic data interchange format that works nicely with mapping libraries like Folium.

We can parse and extract the key columns of data we need from the volcano dataframe:

lat = list(data["LAT"])
lon = list(data["LON"])
ele = list(data["ELEV"])

This provides lists of latitudes, longitudes, and elevations for each volcano to plot.

Next, we define a color scale function that will dynamically color the volcano markers based on their elevation value:

def colour_marker(elevation):
if elevation < 1000:
    return 'red'
elif 1000 <= elevation < 3000:
    return 'orange'
    return 'green'

Lower elevations will be red, mid-range orange, and higher green. This visually encodes volcano elevation into the map.

With the data parsed, we can now create the base Folium map centered on North America:

map = folium.Map(location=[38.58, -99.08], zoom_start=6, tiles='OpenStreetMap')

We add two FeatureGroup layers – one for volcanoes and one for population data. FeatureGroups in Folium allow easy toggling visibility on/off for different map elements.

f = folium.FeatureGroup(name="Volcanoes")
fg = folium.FeatureGroup(name="Population")

Inside these groups we can add the respective markers and overlays. For the volcanoes, we loop through the lat/lon/elev lists to add a CircleMarker for each:

for lt, ln, el in zip(lat, lon, ele):
    f.add_child(folium.CircleMarker(location=[lt, ln], radius=6, popup=str(el)+" m",
    fill_color=colour_marker(el), color = 'grey', fill=True, fill_opacity=0.7))

The CircleMarkers are colored by the elevation color function and sized based on the radius parameter. Hovering shows a popup with the height.

For the population layer, we add a choropleth overlay based on the GeoJSON data:

fg.add_child(folium.GeoJson(data=open('world.json', 'r', encoding='utf-8-sig').read(), 
    style_function=lambda x: {'fillColor':'green' if x['properties']['POP2005'] < 10000000 
     else 'orange' if 10000000 <= x['properties']['POP2005'] < 20000000 else 'red'}))

This colors each country polygon based on the population threshold ranges. The GeoJSON properties provide the population value for each feature.

Finally, we add the volcano and population layers to the map with layer controls:


The layer control adds a toggle that enables turning on/off the different data layers.

We can then export the finished map to an HTML file to view locally:"Population_Volcanoes.html")

Insights and Analysis

The resulting visualization provides an intuitive global view of volcano locations, elevations, and nearby country population densities. Here are a few key insights:

  • Volcanoes are concentrated in distinct regions related to tectonic plate boundaries, such as the Pacific Ring of Fire around the Pacific Ocean. High-elevation volcanoes visible in green occur along active continental boundaries.
  • Lower elevation volcanoes in red are found at intraplate hotspots, such as Hawaii and Yellowstone in the United States. Active volcanoes occur in isolation from the major plate boundaries in these regions.
  • Dense population centers in Asia and Europe are far from most volcanoes, while moderate population countries in Central and South America are located directly in volcanic zones. This highlights significant volcanic risk for countries like Mexico, Columbia, and Indonesia.
  • Africa and Antarctica have sparse volcanoes and population coverage, while Australia contains lower-elevation volcanoes in less inhabited areas.
  • Overall, the degree of spatial interplay between volcanoes and country populations provides critical insights for disaster planning and geologic modeling of active regions.

The ability to toggle the population layer on and off makes it easy to focus on the volcano point patterns. The interactive panning and zooming drive intuitive exploration of the data not possible with static charts or diagrams.

The color schemes also enable quick visual queries – for example, highlighting higher-risk orange countries near red high-population centers.

Overall, this map facilitates a deeper understanding of global volcano locations and their relationships to human populations. Python and Folium can produce it with just 20 lines of code.


In this article, we demonstrated how the Folium library in Python can be used to build interactive map visualizations quickly.

Combining volcano point data with country polygon population data created an insightful geospatial view with minimal code. The visualization and its online repository provide a starting point for anyone wanting to create their own custom interactive maps with Python.

The code could be extended by plotting real-time earthquake data, adding more volcano attributes like the last eruption date, sizing markers by volcano size, and incorporating vector map layers for borders and roads. The data could also be enriched by scraping additional demographic information for countries and cities near volcanoes.

Folium is one of many powerful Python geospatial libraries including GeoPandas, Descartes, and Contextily. Each provides complementary capabilities for handling vector and raster data types, performing spatial analysis, and generating static and dynamic maps. Leveraging these tools makes the possibilities for tailored mapping applications endless.

Whether visualizing volcano risks, modeling climate change patterns, or understanding urban dynamics, interactive maps provide significant value. They transform abstract datasets into tangible stories and insights. Python has cemented itself as a compelling option for crafting customizable, engaging map visualizations for any domain.

I hope you enjoyed reading this guide and feel motivated to start your Python programming journey.

Buy me a cup of coffee if you like my work and want to help me continue dropping content like this.

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