Back to posts

#30DayMapChallenge - Dimensions Map

This map was created as part of the #30DayMapChallenge, exploring the concept of 'dimensions' through population density visualization. The challenge pushes cartographers to think creatively about spatial data representation.

30 Day Map Challenge - Dimensions visualization showing population density across regions
30 Day Map Challenge - Dimensions visualization showing population density across regions

Analysis

Click markers on the map or scroll through the annotations below

1

High Density Urban Centers

The daI rkest regions represent metropolitan areas with population densities exceeding 10,000 people per square kilometer. These urban cores show the characteristic clustering pattern of modern cities.

2

Suburban Transition Zones

The gradient effect visible here illustrates the suburban sprawl phenomenon - density gradually decreasing as we move away from city centers. This transition zone often contains the highest growth rates.

3

Rural Regions

Lighter areas indicate rural regions with sparse population distribution. These areas, while covering more land, contain a small fraction of the total population.

Code

generate_dimensions_map.py
python
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

# Load population density data
gdf = gpd.read_file('population_density.geojson')

# Create custom colormap for density visualization
colors = ['#f7fbff', '#6baed6', '#2171b5', '#08306b']
cmap = LinearSegmentedColormap.from_list('density', colors)

# Set up the figure
fig, ax = plt.subplots(1, 1, figsize=(15, 10))

# Plot the choropleth
gdf.plot(
    column='density',
    ax=ax,
    cmap=cmap,
    legend=True,
    legend_kwds={'label': 'Population per km²'}
)

# Style the map
ax.set_axis_off()
ax.set_title('Population Density: A Study in Dimensions', 
             fontsize=16, fontweight='bold')

plt.tight_layout()
plt.savefig('dimensions-map.png', dpi=300, bbox_inches='tight')
plt.show()

Conclusion

This visualization demonstrates how dimensionality in cartography extends beyond simple 2D representation. By encoding population density through color gradients, we add a third dimension of information to the map, making complex spatial patterns immediately visible.