IEEE VIS 2024 Content: Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation

Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation

Lee Mason - NIH, Rockville, United States. Queen's University, Belfast, United Kingdom

Blánaid Hicks - Queen's University Belfast , Belfast , United Kingdom

Jonas S Almeida - National Institutes of Health, Rockville, United States

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Room: Bayshore VI

2024-10-17T16:36:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T16:36:00Z
Exemplar figure, described by caption below
A screenshot of an interactive dashboard featuring the three Local Moran's I plot designs proposed in our paper.
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Keywords

Spatial, spatial clustering, spatial autocorrelation, geospatial, GIS, interactive visualization, visual analytics, Moran's I, local indicators of spatial association

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.