Spatial Statistics
Measure the pattern instead of eyeballing the map.
A map of points always looks clustered, because population is clustered. Spatial statistics is the set of tools that tell you whether a pattern is more clustered than chance would produce, and where. These guides run from the question that undermines all of them — the modifiable areal unit problem — through density surfaces, hexagonal binning, DBSCAN, spatial weights, Moran's I and Getis-Ord hotspots, with the parameter choices that decide the answer made explicit rather than left on their defaults.
Start here
The cornerstone guide — the whole workflow end to end. Everything after it goes deeper into one part of it.
Step 1 The Modifiable Areal Unit Problem Explained modifiable areal unit problem Begin the path →The full path, in order
13 guides: the concepts underneath the work, then each task, then the errors that task produces. Work down, or jump to the step you need.
- Kernel Density Explained: What a Heatmap Actually Shows kernel density explained 🧭 Concept
- How to Make a Point Density Heatmap in Python point density heatmap python ⚙️ How-To
- Your Heatmap Looks Wrong: KDE Bandwidth and Cell Size kde bandwidth wrong 🔧 Fix
- How to Bin Points into Hexagons in Python hexbin points python ⚙️ How-To
- Spatial Clustering Explained: DBSCAN, K-Means and What They Assume spatial clustering explained 🧭 Concept
- How to Cluster Points by Location with DBSCAN in Python dbscan spatial clustering python ⚙️ How-To
- DBSCAN Returns One Giant Cluster or Labels Everything Noise dbscan one cluster 🔧 Fix
- How to Build a Distance Matrix Between Two Layers in Python distance matrix geopandas ⚙️ How-To
- Spatial Autocorrelation Explained spatial autocorrelation explained 🧭 Concept
- How to Calculate Moran's I in Python morans i python ⚙️ How-To
- Spatial Weights Warn About Islands or Moran's I Returns nan spatial weights islands warning 🔧 Fix
- How to Find Hotspots with Getis-Ord Gi* in Python getis ord hotspot python ⚙️ How-To
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