Advanced spatial analysis refers to a set of sophisticated techniques used in geographic information systems (GIS) and remote sensing to examine spatial patterns, relationships, and processes beyond basic mapping. Unlike traditional GIS methods that primarily focus on visualization and simple overlay operations, advanced spatial analysis integrates statistical, mathematical, and computational methods to extract meaningful insights from spatial data. These analyses are crucial for understanding complex spatial phenomena, predicting future patterns, optimizing resource allocation, and supporting decision-making in fields such as urban planning, environmental management, epidemiology, transportation, and disaster risk reduction. Advanced spatial analysis includes techniques such as spatial interpolation, spatial autocorrelation, surface modeling, network analysis, hot spot analysis, and point pattern analysis, among others. Point pattern analysis, a key component of advanced spatial analysis, is concerned with the study of the spatial arrangement of discrete events or objects within a defined area. It helps determine whether points are distributed randomly, clustered, or uniformly across a region. This type of analysis is particularly useful for studying the distribution of natural resources, disease cases, crime incidents, wildlife sightings, or urban infrastructure facilities. Understanding the spatial distribution provides insights into underlying processes, potential causes, and patterns that may not be immediately apparent from raw data. The fundamental step in point pattern analysis involves mapping all point locations accurately