Latent Spatial Heterogeneity in U.S. Cancer Mortality: A Multi-Site Clustering and Spatial Autocorrelation Analysis

This research set out to explore and delineate spatial patterns and mortality distributions for various cancer types across U.S. states between 1999 and 2021. The aim was to uncover region-specific cancer burdens and inform geographically targeted prevention efforts. We analyzed state-level cancer mortality records sourced from the CDC WONDER platform, concentrating on cancer sites consistently reported across the 48 contiguous states and Washington, D.C., excluding Hawaii, Alaska, and Puerto Rico. Multivariate clustering using Mahalanobis distance grouped states according to similarities in mortality profiles. Spatial autocorrelation was examined for each cancer type using both Global Moran's I and Local Indicators of Spatial Association (LISA). Additionally, the Getis-Ord statistic was applied to detect cancer-specific hotspots and cold spots.

Publication Details

Journal
ArXiv.org
Published
2025-08-17
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

Latent Spatial Heterogeneity in U.S. Cancer Mortality: A Multi-Site Clustering and Spatial Autocorrelation Analysis

ArXiv.org
Geochemistry and Geologic Mapping
article

Latent Spatial Heterogeneity in U.S. Cancer Mortality: A Multi-Site Clustering and Spatial Autocorrelation Analysis

article en

Abstract

This research set out to explore and delineate spatial patterns and mortality distributions for various cancer types across U.S. states between 1999 and 2021. The aim was to uncover region-specific cancer burdens and inform geographically targeted prevention efforts. We analyzed state-level cancer mortality records sourced from the CDC WONDER platform, concentrating on cancer sites consistently reported across the 48 contiguous states and Washington, D.C., excluding Hawaii, Alaska, and Puerto Rico. Multivariate clustering using Mahalanobis distance grouped states according to similarities in mortality profiles. Spatial autocorrelation was examined for each cancer type using both Global Moran's I and Local Indicators of Spatial Association (LISA). Additionally, the Getis-Ord statistic was applied to detect cancer-specific hotspots and cold spots.

ArXiv.org
Good health and well-being
Openalex Percentile: Top 71%
Geochemistry and Geologic Mapping
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Latent Spatial Heterogeneity in U.S. Cancer Mortality: A Multi-Site Clustering and Spatial Autocorrelation Analysis · ArXiv.org (2025) | TGRS Research Map | TGRS