Hotspot analysis

Hotspot analysis identifies clusters of unusually high or low values in spatial datasets to support Exploratory Spatial Data Analysis (ESDA) and detection of spatial autocorrelation within QGIS.

Key Features:

• Integration with QGIS: Embeds hotspot analysis functionality into QGIS by leveraging its extensible architecture.
• Hotspot detection: Implements statistical methods to identify spatial clusters (hotspots and coldspots) where values are significantly higher or lower than the average.
• Exploratory Spatial Data Analysis (ESDA): Focuses on ESDA approaches for datasets exhibiting significant spatial autocorrelation.
• Python-based implementation: Developed in Python to operate within QGIS's plugin framework.
• No external Python libraries required: Provides hotspot analysis capabilities without relying on external Python libraries.

Scientific Applications:

• Environmental monitoring: Detects localized areas of environmental change or contamination by identifying spatial clusters.
• Urban planning: Identifies concentrations of socio-environmental indicators to inform planning decisions.
• Epidemiology: Locates disease clusters and areas of elevated health risk via spatial hotspot detection.
• Resource management: Reveals spatial concentrations of resources or pressures to guide management actions.

Methodology:

Implements Exploratory Spatial Data Analysis (ESDA) and statistical hotspot detection methods for identifying clusters in spatial data, implemented in Python and integrated into QGIS to operate on datasets exhibiting spatial autocorrelation.