Blog: Seeing Data Differently: Spatial Data Visualization

In an Era where data drives everything from city planning to climate research, understanding the spatial location where things happen is just as significant as what is happening. That’s where spatial data visualization comes in, the practice of placing data into maps to reveal geographic patterns, relationships, and insights that raw numbers alone can’t show, and from another perspective, visuals are easier to interpret than tables and numbers when large amounts of data are involved. 

In this blog, we explore the essentials of spatial visualization, including tools, techniques, and real-world application examples. 

Why Maps Aren’t Always What They Seem: CRS and Region Size Distortion

Before we go through the visualization tools and map types, let’s address a surprising fact: not all maps are created equal.

Have you ever looked at a world map and thought, “Wow, Greenland is huge!”? It turns out that’s an illusion; Africa is about 14 times larger than Greenland. This distortion comes from the map’s Coordinate Reference System (CRS), the mathematical model used to flatten the Earth’s surface onto a 2D map. 

CRS defines how geographic coordinates (like latitude and longitude) are transformed into flat maps. Different CRSs preserve different properties. For Instance : 

• Mercator projection preserves shape and direction (great for navigation) but distorts area, making regions near the poles appear much larger. 
• Equal-area projections (like Albers or Mollweide) preserve the true size of areas but distort shapes or angles.  

What About Alaska? 

A great example of distortion within a country is Alaska. On many U.S. maps, Alaska is shrunk dramatically and moved south, often placed in a box in the lower left corner. This is done for layout convenience, but it leads to a common misconception: that Alaska is much smaller and closer to the continental U.S. than it is. 

In reality: 

• Alaska is the largest U.S. state, more than twice the size of Texas. 
• Its true position stretches far to the northwest of the contiguous U.S., close to Russia. 
• On equal-area maps with a proper CRS (like Albers Equal Area), Alaska appears massive, which is more geographically accurate but harder to fit on standard maps.  

 Why CRS Matters in Real-World Analysis 

Choosing the wrong CRS can skew your analysis: 

• Distances and areas may be inaccurate if your CRS isn’t suited for the local scale. 
• Visual comparisons can be misleading. A large-looking region might appear more significant, even if it isn’t. 
• Map layers may not align if each uses a different CRS.  

Types of Spatial Visualizations 

There are many ways to visualize spatial data. But the most common are: 

• Choropleth Maps: Color-coded regions based on a variable (e.g., population density). Great for showing comparisons across states, countries, or districts. 
• Dot Maps: Use points to show the presence or quantity of features (e.g., one dot = one reforestation site center). 
• Heatmaps / Density Maps: Use color gradients to show concentration of activity or events (e.g.,The population). 
• Flow Maps: Show movement between places (e.g., migration or shipping routes). 
• Interactive Maps: Users can zoom, click, and explore data layers in real-time dashboards. 

Tools and Technologies: From Drag-and-Drop to Code 

Coding Tool 

1. Python: 
1. GeoPandas: Handle geospatial data like regular DataFrames.  
2. Folium: Build interactive maps (based on Leaflet.js). Shapely, PyProj, Rasterio: For geometry, projections, and raster data.
2. R: Packages like sf, sp, and ggplot2 handle mapping cleanly. 

GIS Software (No-Code or Low-Code)

• QGIS (Free): A desktop app for advanced spatial analysis.
• ArcGIS (Paid): Enterprise-level platform with rich analysis tools.
• Google Earth Engine(Requires a google account): Great for satellite imagery and environmental analysis. 

Web & Visualization Platforms

• Leaflet.js / Mapbox GL JS: JavaScript libraries for beautiful, interactive maps. 
• Kepler.gl: Fast, browser-based tool by Uber, great for huge datasets. 
• Tableau / Power BI: Drag-and-drop tools that include built-in mapping features. 

Application Examples: When Spatial Data Makes a Difference

Urban Planning

City governments use spatial dashboards to manage infrastructure. For example, the City of Mesa, Arizona built an interactive map showing active development sites, helping both residents and city planners visualize zoning, utilities, and upcoming construction projects.

Public Safety 

In Redlands, California, police used ArcGIS dashboards to map theft incidents in shopping areas. By identifying “hot spots” on the map, they adjusted patrols — reducing crime and increasing store security. 

Environment & Climate 

NASA’s Earth Observatory uses satellite data to map global sea surface temperatures, tracking warming oceans and climate anomalies like El Niño. These maps inform disaster preparedness, policy, and scientific research. 

Public Health 

During the COVID-19 pandemic, dashboards like the Johns Hopkins tracker used maps to visualize infection rates and vaccination progress, helping people make informed decisions based on geography. 

Adding context to maps helps stakeholders see connections they might miss in raw tables or graphs.

References

• https://geopandas.org
• https://leafletjs.com/ 
• https://kepler.gl/ 
• https://earthobservatory.nasa.gov/ 
• https://data.gov/ 
• https://www.qgis.org/ 
• https://www.arcgis.com/ 
• https://coronavirus.jhu.edu/map.html
• https://www.mesaaz.gov/
• https://clauswilke.com/dataviz/  

- Written by Angela Nyangate John