The Geography of Basketball
Matt Forrest of Wherobots is a geospatial data expert who helps GIS professionals adopt cloud-native geospatial infrastructure and hosts the Spatial Stack podcast. At the H3 Conference 2026 in New York City, he showed how H3 turned the question "where is the geography of basketball?" into a single index that can be compared across the whole country.
Four layers of basketball data
Basketball is often called the sport of New York City, and Matt wanted to know whether anyone had actually put the geography of basketball on a map. OpenStreetMap and Overture had mapped the courts, so he started pulling the pieces together:
- Outdoor courts: about 91,000 courts in Overture Maps, which pulls from OpenStreetMap and other sources.
- Indoor courts: nobody maps a court inside a building, so Matt inferred them. He looked for buildings roughly three to four times the size of a basketball court, and for places likely to have one: schools, churches, gyms, and YMCAs and YWCAs. He gave YMCAs and YWCAs extra weight, since basketball was invented at a YMCA.
- Teams: every NBA and WNBA team plus NCAA Division I men's and women's programs, each with a territory and an index of how successful it is.
- Players: placed at their hometowns.
Each layer had quirks. Overture's gym tags pulled in 24 Hour Fitness locations, which don't have basketball gyms. College teams created dense, overlapping clusters in the Northeast. And measuring an individual player's impact is hard, since a player's hometown, college and pro teams can all be in different places. Matt chose to use the hometown only.
Joining population without a spatial join
H3 cells gave Matt a consistent way to study all of this across the entire country. For population, he used the Kontur population grid, which is already published on H3 at about 400 m cells. Because both datasets share the same index, he could join population on the cell ID directly.
That is one of the things Matt likes most about H3: when data lives in a unified system, you don't need a spatial join every time. You use the index itself.
Spreading influence with neighbor rings
The hardest part was measuring the impact of things that aren't really spatial, like teams and players. Hometowns cluster unevenly. Chicago stands out because so many players come from one big city, while New York players list Brooklyn, Queens, Flushing, Staten Island or a town in New Jersey.
Before smoothing, Chicago's concentration of players put the top player cell in the middle of Lake Michigan. To fix this, Matt used H3 neighbors with weights that decrease as they move outward, a distance decay. Each player or team spreads its influence based on factors like how long they played and how many wins or championships they had. The smoothed index gives a much clearer picture than the raw version, which showed large circles where teams were drawn. A few cells crossing the antimeridian also produced odd results.
The most basketball-heavy cell
With everything combined, the top cell is in Queens. Its center court is Grover Cleveland Playground. Matt was clear that the index is subjective and can be sliced in different ways:
- Weighting heavily by population moves the top cell to Santa Clara, partly because of how many courts have been mapped there.
- Weighting by pure geography puts the center of basketball in Jacksonville, Illinois.