Hot Spot Analysis

Hot spot analysis is a spatial statistics technique that identifies statistically significant concentrations of high and low values within a geographic dataset. Using the Getis-Ord Gi* statistic, hot spot analysis evaluates each feature in the context of its neighbors to determine whether the local clustering of values is more pronounced than would be expected in a random distribution. Features with statistically significant high values surrounded by other high values are identified as hot spots, while clusters of low values are identified as cold spots.

The Getis-Ord Gi* statistic calculates a z-score and p-value for each feature in a dataset. The z-score represents the standard deviation of the local sum of values from the expected sum under spatial randomness. High positive z-scores with small p-values indicate statistically significant hot spots, where a feature and its neighbors have high values. High negative z-scores with small p-values indicate significant cold spots. Features with z-scores near zero represent areas with no significant clustering. The analysis requires defining a spatial relationship model, such as inverse distance, fixed distance band, or k-nearest neighbors, which determines how neighboring features influence each other. The choice of distance threshold significantly affects results and should be informed by the spatial scale of the phenomenon under study.

Hot spot analysis is widely applied across public safety, business, health, and environmental domains. Law enforcement agencies use hot spot analysis to identify crime concentration areas, enabling evidence-based deployment of patrol resources and prevention programs. Retailers analyze sales and customer data to identify geographic hot spots of market demand, informing store location and marketing strategies. Public health professionals map disease incidence hot spots to target vaccination campaigns, allocate healthcare resources, and investigate outbreak sources. Environmental scientists identify pollution hot spots to prioritize remediation efforts. Transportation planners analyze accident hot spots to implement safety improvements at high-risk locations. Urban planners study economic activity hot spots to guide development and infrastructure investment.

Hot spot analysis provides objective, statistically rigorous identification of spatial clusters, moving beyond subjective visual map interpretation. The z-score and p-value framework allows analysts to communicate results with confidence levels, distinguishing genuine patterns from random variation. The technique accounts for the values of neighboring features, providing a more robust assessment than simple thematic mapping of individual values. Results are intuitive to interpret and communicate to non-technical stakeholders.

Results are sensitive to the choice of spatial relationship model and distance parameters, requiring careful calibration. Small sample sizes or sparse data can produce unreliable results. The analysis assumes that the underlying process is stationary across the study area, which may not hold in all cases. Multiple testing issues arise when evaluating significance for many features simultaneously, potentially inflating the apparent number of significant hot spots.

Space-time hot spot analysis identifies emerging, intensifying, diminishing, and persistent hot spots by analyzing patterns across temporal intervals. Optimized hot spot analysis automatically selects appropriate distance parameters based on data characteristics. Integration with predictive analytics enables forecasting where future hot spots are likely to emerge. Real-time hot spot monitoring from streaming sensor data supports dynamic resource allocation.