Abstract
Objective: The identification of neighborhoods where registered sex offenders (RSOs) reside can facilitate evidence-based service delivery and neighborhood capacity building. This study aims to determine if there is a patterned spatial aggregation of RSOs and identify the specific neighborhood characteristics of RSO residential locations. Method: We used ArcGIS to match residential information from 938 Ohio ZIP Code areas with census data and conducted spatial autocorrelation analyses to explore and map spatial clustering of RSO residential locations. Three spatial regression models and an ordinary least squares model were built to estimate the associations between neighborhood characteristics and the rate of RSOs. Results: The percentages of non-Hispanic Black population, Hispanic population, and poverty were positively associated with the rate of RSO residences; percentages of female-headed households, the population with a bachelor’s degree, and owner-occupied households were negatively associated with the rate of RSO residences. Overall, RSOs in Ohio were spatially aggregated in socially disorganized areas. Conclusions: The findings can be used to promote evidence-informed policies to effectively manage decarcerated RSOs, distribute limited resources to assist former offenders’ rehabilitation, and empower communities in disadvantaged neighborhoods to advance smart decarceration.
Original language | English (US) |
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Pages (from-to) | 61-81 |
Number of pages | 21 |
Journal | Journal of the Society for Social Work and Research |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Accepted/In press - Jan 1 2019 |
Externally published | Yes |
Keywords
- Evidence-informed policy
- Registered sex offenders
- Smart decarceration
- Socially disorganized areas
- Spatial aggregation
ASJC Scopus subject areas
- Social Sciences (miscellaneous)
- Sociology and Political Science