Hotspot analysis: How to identify meaningful data clusters

Hotspot analysis is a spatial analysis technique used to evaluate clusters of data across a geographic area to determine if they represent meaningful patterns or random variations. It identifies statistically significant areas, known as hotspots, where events occur more frequently than expected, indicating a positive spatial autocorrelation, while cold spots show less frequent activity. Utilizing statistical methods like the Getis-Ord Gi* statistic, hotspot analysis discerns genuine spatial patterns from random occurrences by considering the proximity and clustering of data points rather than individual occurrences. This technique is particularly useful in fields such as public health, conservation, transportation, and urban analytics, where understanding spatial data patterns can inform strategic decision-making. Felt aids teams in visualizing statistically significant data clusters through cloud-native maps and AI-assisted tools, enabling collaborative analysis and real-time decision-making without requiring complex GIS workflows. However, hotspot analysis is most effective with dense and evenly collected data, as sparse datasets can lead to misleading results.