Hot-Spot Analysis with Simple Features
Plot map of hotspot classifications
Plot map of changes in grid counts
Plot map of kernel-density values
Plot map of grid counts
Identify change in hotspots over time
Control the parameters used to classify hotspots
Classify hot-spots
Count points in cells in a two-dimensional grid
Estimate the relationship between the kernel density of two layers of ...
Identify significant spatial clusters of points
Create either a rectangular or hexagonal two-dimensional grid
Estimate two-dimensional kernel density of points
Objects exported from other packages
Identify and understand clusters of points (typically representing the locations of places or events) stored in simple-features (SF) objects. This is useful for analysing, for example, hot-spots of crime events. The package emphasises producing results from point SF data in a single step using reasonable default values for all other arguments, to aid rapid data analysis by users who are starting out. Functions available include kernel density estimation (for details, see Yip (2020) <doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot classification (Chainey (2020) ISBN:158948584X).