Geospatial Visualisation

Geospatial visualisation is the representation of data that has a geographic or location component — mapping data onto the real world. From election results and disease outbreaks to delivery routes and climate change, spatial context adds a powerful dimension to data analysis.

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Types of Geospatial Data

Type	Description	Examples
Point	A single location (latitude, longitude)	City locations, sensor readings, store locations
Line	A path or route	Roads, rivers, flight paths
Polygon	An area or region	Country borders, districts, building footprints
Raster	A grid of cells with values	Satellite imagery, elevation maps, temperature grids

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Common Geospatial Chart Types

Chart Type	Description	Best For
Choropleth	Regions filled with colour based on a value	Country-level statistics, election maps
Bubble map	Circles placed at locations, sized by value	City populations, earthquake magnitudes
Heatmap (spatial)	Colour intensity based on density	Crime hotspots, population density
Point map	Dots at exact locations	Store locations, event occurrences
Flow map	Lines showing movement between places	Migration, trade routes, flight paths
Cartogram	Regions resized by a data value	Population-weighted country maps

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Folium — Interactive Maps with Leaflet

Folium is a Python library that generates interactive maps using Leaflet.js. It creates HTML files that can be viewed in any browser.

Installation

pip install folium

Basic Map

import folium

# Centre on London
m = folium.Map(location=[51.5074, -0.1278], zoom_start=12)
m.save("london_map.html")
m  # Displays inline in Jupyter

Adding Markers

import folium

m = folium.Map(location=[51.5074, -0.1278], zoom_start=13)

# Add a marker
folium.Marker(
    location=[51.5014, -0.1419],
    popup="Buckingham Palace",
    tooltip="Click for info",
    icon=folium.Icon(color="red", icon="info-sign")
).add_to(m)

# Add a circle marker
folium.CircleMarker(
    location=[51.5081, -0.0759],
    radius=20,
    color="blue",
    fill=True,
    fill_opacity=0.6,
    popup="Tower of London"
).add_to(m)

m.save("london_landmarks.html")

Choropleth Map

import folium
import pandas as pd

# Sample data: country GDP
data = pd.DataFrame({
    "Country": ["GBR", "FRA", "DEU", "ITA", "ESP"],
    "GDP": [3070, 2780, 4070, 2000, 1390]
})

m = folium.Map(location=[50, 10], zoom_start=4)

folium.Choropleth(
    geo_data="https://raw.githubusercontent.com/python-visualization/folium/main/tests/data/world-countries.json",
    data=data,
    columns=["Country", "GDP"],
    key_on="feature.id",
    fill_color="YlOrRd",
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name="GDP (Billion USD)"
).add_to(m)

m.save("european_gdp.html")

Heatmap Layer

import folium
from folium.plugins import HeatMap
import numpy as np

np.random.seed(42)
lats = np.random.normal(51.5074, 0.05, 500)
lons = np.random.normal(-0.1278, 0.05, 500)
heat_data = list(zip(lats, lons))

m = folium.Map(location=[51.5074, -0.1278], zoom_start=12)
HeatMap(heat_data, radius=15).add_to(m)
m.save("london_heatmap.html")

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Plotly for Geospatial Visualisation

Plotly has built-in support for choropleth maps and scatter geo plots.