Hotspot analysis: How to identify meaningful data clusters

Hotspot analysis is a spatial analysis technique that assesses whether clusters of data points in a geographic area indicate statistically significant patterns or are merely random occurrences. By evaluating spatial autocorrelation, hotspot analysis distinguishes areas of high activity (hotspots) from those with low activity (cold spots), revealing patterns that might not be evident through visual inspection alone. This method is particularly effective with dense, evenly distributed data, such as repeated incidents or measurements across a fixed area, and utilizes the Getis-Ord Gi* statistic to measure clustering strength. Common applications include public health, conservation, transportation, and urban analytics, where understanding spatial patterns can guide strategic decision-making. Tools like Felt facilitate hotspot analysis by allowing teams to visualize and share clustering results, enhancing collaborative spatial decision-making without complex GIS workflows.

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