When the world shut down in 2020, most people were panic-buying toilet paper. I, on the other hand, was busy making maps. Not just any maps—quantitative thematic maps that could tell a story more compelling than any infographic ever could. Armed with ArcGIS Online, ArcGIS Pro, and a nerdy love for cartographic detail, I created a visual narrative of California’s ICU bed distribution versus population density.
What Was I Mapping and Why?
This project wasn’t just a data dump—it was a response to a critical issue. During the peak of the pandemic, the number of ICU beds per county became a matter of life or death. So, I mapped it. My base layers included:
- Hospital Beds per County (ESRI)
- 2020 USA Population Density (ESRI)
- California County Boundaries (CALFIRE)
All were filtered to focus solely on the Golden State because, let’s be honest, California is big enough to be its own country—at least when it comes to COVID data and policy tiers.
Natural Breaks vs. Quantile: A Battle of Classifications
I started in ArcGIS Online but quickly realized its layout capabilities were… let’s say “limited.” So, I ported the project into ArcGIS Pro—because legends and scale bars aren’t just for show; they’re standard cartography flex.
For the first map, I used Natural Breaks (Jenks) classification. Why? Because it reflects real-world differences more naturally. Larger ICU bed counts got larger orange circles—simple, intuitive, and perfect for visual impact.
Then came Quantile Classification. This method forces an equal number of counties into each class, which makes the data look “even” but can mislead the eye. Suddenly, it seemed like many counties had the same number of ICU beds, which wasn’t true. It was like giving everyone in a classroom the same grade regardless of performance. Fair? Debatable. Accurate? Not always.
From Data to Meaning
By comparing ICU bed availability with county population density, the map delivered a sobering message: some of the most populated counties had dangerously few beds. This wasn’t just academic; it mirrored the real-life challenges California faced in defining COVID restriction tiers. The state needed maps like these to make informed policy decisions. I needed them to pass my GIS course. Win-win.
Why This Project Mattered
This wasn’t just a lab exercise. It was a crash course in how spatial data can shape real-world responses. It showed me how design choices—like which classification method you use—can influence how a map is interpreted. And it proved that GIS isn’t just about pushing pixels around; it’s about telling stories that matter.
