Hotspot Analysis in ArcMap | Find Spatial Clusters Using Hot Spot Analysis

Welcome to Learn GIS Easily with Abayomi! 🌍 In this first tutorial on Hot Spot Analysis in ArcMap, I'll introduce you to the workflow for preparing spatial data and identifying statistically significant spatial patterns. In this video, we'll cover: • Project Data • Copy Features • Integrate • Collect Events • Spatial Autocorrelation (Moran's I) • Incremental Spatial Autocorrelation • Hot Spot Analysis (Getis-Ord Gi*) • Understanding z-scores and p-values • Identifying clustered, random, and dispersed spatial patterns • Interpreting statistically significant hotspots and coldspots We'll also look at the basic statistical interpretation of the results. A p-value below 0.05 is commonly considered statistically significant. For a 95% confidence level, z-scores between approximately -1.96 and +1.96 are not statistically significant, while positive z-scores above +1.96 indicate significant clustering of high values and negative z-scores below -1.96 indicate significant clustering of low values. This workflow is useful for analysing spatial patterns in datasets such as crime incidents, disease cases, traffic accidents, waste disposal, environmental pollution, population data, and many other geographic datasets. This tutorial is suitable for GIS students, researchers, Surveying and Geoinformatics students, and anyone interested in spatial statistics and GIS analysis. Don't forget to like, subscribe, and share for more practical GIS tutorials.