Module spatial_stats 

Spatial statistics module for analyzing spatial patterns and relationships

This module provides statistical measures commonly used in spatial analysis, including measures of spatial autocorrelation, clustering, and pattern analysis.

Features

• Spatial Autocorrelation: Moran’s I, Geary’s C
• Local Indicators: Local Moran’s I (LISA)
• Distance-based Statistics: Getis-Ord statistics
• Pattern Analysis: Nearest neighbor analysis

Examples

use scirs2_core::ndarray::array;
use scirs2_spatial::spatial_stats::{morans_i, gearys_c};

// Create spatial data (values at different locations)
let values = array![1.0, 2.0, 1.5, 3.0, 2.5];

// Define spatial weights matrix (adjacency-based)
let weights = array![
    [0.0, 1.0, 0.0, 0.0, 1.0],
    [1.0, 0.0, 1.0, 0.0, 0.0],
    [0.0, 1.0, 0.0, 1.0, 0.0],
    [0.0, 0.0, 1.0, 0.0, 1.0],
    [1.0, 0.0, 0.0, 1.0, 0.0],
];

// Calculate spatial autocorrelation
let moran = morans_i(&values.view(), &weights.view()).unwrap();
let geary = gearys_c(&values.view(), &weights.view()).unwrap();

println!("Moran's I: {:.3}", moran);
println!("Geary's C: {:.3}", geary);

Functions

clark_evans_index

Calculate Clark-Evans nearest neighbor index

distance_weights_matrix

Calculate spatial weights matrix based on distance decay

gearys_c

Calculate Geary’s C statistic for spatial autocorrelation

getis_ord_gi

Calculate Getis-Ord Gi statistic for hotspot analysis

local_morans_i

Calculate Local Indicators of Spatial Association (LISA) using Local Moran’s I

morans_i

Calculate Moran’s I statistic for spatial autocorrelation