Stores small spatial datasets used to teach basic spatial analysis
    concepts. Datasets are based off of the 'GeoDa' software workbook and data
    site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin
    and team at the University of Chicago. Datasets are stored as 'sf' objects.

Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.

Tools for auditing how analytic variables change when data
    are transformed across administrative boundary systems. The package is
    agnostic to data source, variable type, and administrative geography,
    and is designed to quantify transformation-induced change without
    attributing blame to any specific boundary definition or allocation
    scheme.

Creates continuous density raster surfaces by joining vector spatial 
    layers with tabular data frameworks, normalizing values by ellipsoidal 
    polygon area calculations. Reconciles discrete boundary-constrained census or 
    survey counts with uniform grids. Accepts spatial objects in-memory or via 
    file paths. Methods are based on spatial rasterization workflows implemented 
    in the 'terra' package Hijmans (2025) <https://rspatial.github.io/terra/>.

Spatial stratified heterogeneity (SSH), referring to the within strata are more similar than the between strata, a model with global parameters would be confounded if input data is SSH. Note that the "spatial" here can be either geospatial or the space in mathematical meaning. Geographical detector is a novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2) test the power of determinant X of a dependent variable Y according to the consistency between their spatial distributions; and (3) investigate the interaction between two explanatory variables X1 and X2 to a dependent variable Y (Wang et al 2014 <doi:10.1080/13658810802443457>, Wang, Zhang, and Fu 2016 <doi:10.1016/j.ecolind.2016.02.052>).