Clusters of Jail Incarcerations in U.S. Counties: 2010–2018

Abstract

This study investigates the spatiotemporal variations in jail incarcerations in addition to associations with several risk factors and jail incarceration counts at the county level for the period 2010–2018 in the contiguous USA. The disease surveillance software SaTScanTM was used to identify and test purely spatial and spatiotemporal variations in jail incarceration. Significant spatial and space-time clusters with elevated relative risk for jail incarceration were found in analysis. Additionally, a negative binomial regression model was used to predict jail incarcerations counts based on several covariates and found significant and nonrandom spatial clusters of jail incarceration that are explained after adjusting for these covariates. The results in this study provide useful information on possible associations in geographical areas where jail incarceration rates are higher than expected and demonstrate significant correlations between jail incarceration counts and several covariates. The study and its conclusions provide an epidemiological framework for identifying and addressing geographic patterns of unusually high jail incarceration rates in the United States and provide evidence of appropriate locations to further investigate underlying causes of disproportionate incarceration. Supplementary files for this article are available online.

Keywords

• Incarceration rate
• Jail population
• Predictive modeling
• Regression analysis
• Spatial analysis
• Spatial scan statistic