KDE calculates the density of point features by spreading a smooth surface over the study area, representing the density of the features more accurately than traditional methods.
Spatial autocorrelation measures the degree of spatial dependence between adjacent features and helps identify clusters or patterns of similarity.
Sorting is not typically considered a spatial analysis technique in GIS. It involves arranging data in a particular order, which may not necessarily be spatially related.
Interpolation is used to estimate values for locations where data is not available based on values from nearby known locations.
Slope analysis calculates the steepness of terrain from a DEM, helping to identify areas of high and low elevation.
Network analysis focuses on analyzing spatial relationships and connectivity between features within a network, such as roads, rivers, or utility networks.
Voronoi tessellation divides space into regions based on the proximity of each point to a set of predefined points, known as seeds or generators.
Spatial Analysis in GIS involves examining and understanding the spatial patterns, relationships, and processes within geographic data.
Spread analysis models the spread of phenomena, such as wildfires or infectious diseases, over time or space, helping to predict and mitigate their impact.
Buffering involves creating a buffer zone or boundary around a specific area of interest, typically a point, line, or polygon, within a specified distance.
Hotspot analysis identifies statistically significant clusters of high or low values within a dataset, making it useful for studying patterns of disease spread or crime incidents.
