Hot Spots Analysis

refer to Spatial Autocorrelation for a general reference card about spatial autocorrelation.

Overview

Hotspot identification Hot Spot Analysis is a spatial analysis technique used to identify areas with significantly high or low values of a particular phenomenon. It's a way to visually and statistically pinpoint clusters or patterns of high (hot spots) or low (cold spots) activity levels within a given geographical area. In the context of urban health, it helps to identify specific areas within an urban environment that exhibit higher levels of health issues or disparities compared to surrounding areas. It involves analysing health data and spatial information to identify geographic areas with a concentration of health problems or vulnerabilities.

Description

Getis-Ord Gi* statistic

Use statistical methods to identify significant hotspots in the data. One commonly used method is the Getis-Ord Gi* statistic, which calculates a z-score for each location and determines if it is significantly different from its neighboring areas. The equation for the Getis-Ord Gi* statistic is as follows:

Where:
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$G_i^*$ is the standardized value of the statistic for each spatial unit \( i \).
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$w_{ij}$ is the spatial weight between units \( i \) and \( j \).
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$X_j$ is the value of the health-related indicator for each spatial unit \( j \).
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$\bar{X}$ is the mean of the health-related indicator across all spatial units.
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$S_w$ is the standard deviation of the health-related indicator across all spatial units.

Tutorial (External)

Recommend software: Geoda