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Block-Level Analysis of the Attractors of Robbery in a Downtown Area

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  • Kingsley U. Ejiogu

Abstract

This article examines the predictions of crime pattern theory in a unique neighborhood type. It tested potential crime attracting facilities against street robbery data from 2009 to 2013 in the Police Districts I & II in Downtown Houston. The analysis modeled the four daily human routine periods described in the American Time Use Survey (ATUS). Generalized linear simultaneous negative binomial regression model was used to determine the size of the influence of the variables (beta coefficients) and their significance for each model outcome. The findings show some distinct patterns of street robbery due to the immediate and lagged effects of the variables relatable to the study environment’s unique setting. Two variables, geographic mobility, and barbershops were particularly significant across three of the outcome models. The results suggest that the physical and social structure of neighborhoods determined by land-use regulations would enhance understanding of the time-based influence on robbery patterns due to crime-attracting facilities.

Suggested Citation

  • Kingsley U. Ejiogu, 2020. "Block-Level Analysis of the Attractors of Robbery in a Downtown Area," SAGE Open, , vol. 10(4), pages 21582440209, October.
    • Handle: RePEc:sae:sagope:v:10:y:2020:i:4:p:2158244020963671
    DOI: 10.1177/2158244020963671
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    Cited by:

    1. Daniela Cueva & Pablo Cabrera-Barona, 2024. "Spatial, Temporal, and Explanatory Analyses of Urban Crime," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 174(2), pages 611-629, September.
    2. Daqian Liu & Wei Song & Chunliang Xiu & Jun Xu, 2021. "Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach," Sustainability, MDPI, vol. 13(19), pages 1-15, September.
    3. Kingsley U. Ejiogu, 2023. "Risk Terrain and Multilevel Modeling of Street Robbery Distribution in Baltimore City," SAGE Open, , vol. 13(4), pages 21582440231, December.

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