Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation

TL;DR

This paper introduces three interactive visualizations and a JavaScript library to improve the interpretation of Local Moran's I spatial autocorrelation results, making spatial analysis more accessible and holistic.

Contribution

The paper presents novel interactive visualizations and a web dashboard that enhance understanding of Local Moran's I, facilitating better spatial autocorrelation analysis.

Findings

Enhanced interpretation of Local Moran's I results

Interactive visualizations linked for holistic exploration

Open-source JavaScript library and web dashboard provided

Abstract

The Local Moran's I statistic is a valuable tool for identifying localized patterns of spatial autocorrelation. Understanding these patterns is crucial in spatial analysis, but interpreting the statistic can be difficult. To simplify this process, we introduce three novel visualizations that enhance the interpretation of Local Moran's I results. These visualizations can be interactively linked to one another, and to established visualizations, to offer a more holistic exploration of the results. We provide a JavaScript library with implementations of these new visual elements, along with a web dashboard that demonstrates their integrated use.