What does a hot spot analysis tell you?
The hot spot analysis tool assesses whether high or low values (the number of crimes, accident severity, or dollars spent on sporting goods, for example) cluster spatially. If you have polygon features for your study area, you can use the Spatial Join tool to count the number of events in each polygon.
What is Gi_Bin?
The Gi_Bin field identifies statistically significant hot and cold spots regardless of whether or not the FDR correction is applied. Without FDR correction, statistical significance is based on the p-value and z-score fields.
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How does hot spot analysis ( Getis-ord Gi * ) work?
The Gi* statistic returned for each feature in the dataset is a z-score. For statistically significant positive z-scores, the larger the z-score is, the more intense the clustering of high values (hot spot). For statistically significant negative z-scores, the smaller the z-score is, the more intense the clustering of low values (cold spot).
How does the hot spot analysis tool work?
The Hot Spot Analysis tool calculates the Getis-Ord Gi* statistic (pronounced G-i-star) for each feature in a dataset. The resultant z-scores and p-values tell you where features with either high or low values cluster spatially. This tool works by looking at each feature within the context of neighboring features.
How are hot spots defined in geospatial data analysis?
This clustering is operationally defined as hot-spots and is done by comparing the sum in a particular variable within a local neighborhood network relative to the global sum of the area-of-interest extent (Getis and Ord 2010). Getis and Ord ( 2010) introduced a family of measures of spatial associated called G statistics.
How to find hot spots in a map?
This tool identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots). It creates an Output Feature Class with a z-score, p-value, and confidence level bin field ( Gi_Bin) for each feature in the Input Feature Class.