As wildfires in California, USA continue to cause a peril to the population residing there, a clear insight into the spatial patterns and distribution of wildfires is essential. This study aims to explore the high-risk clusters and low risk clusters for wildfire hazard utilizing the GIS tools and techniques along with Statistical modelling tool. By using the up-to-date dataset, the neighborhood effect and spatial relation among them from this study can be helpful for fire mitigation measures and land use planning and building resilience of the community. Also, the result from this study can contribute to strengthening wildfire management strategies.  

This study considers the Local Indicator of Spatial Autocorrelation and Hotspot, cold spot analysis methods to identify the neighboring effects and the clustering of the wildfire risks. An integrated approach using GIS spatial analysis and Statistical analysis tools and techniques are utilized. Data is collected from United States Department of Agriculture (Scott et. al. 2020) research data set in the form of raster data.  

The findings of the study show that regions with high-risk of burning due to wildfire tend to cluster together, whereas regions with low risk of burning exhibit a similar pattern of spatial correlation. This study can be useful for identifying localized contributing factors for wildfire as well as the hotspot for the hazard in California. Also, spatial autocorrelation is essential for prioritizing risk areas and resource allocation thus effective wildfire management together with better community resilience.