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Testing Indicators of Risk Populations for Theft from the Person across Space and Time: The Significance of Mobility and Outdoor Activity

Author

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  • Guangwen Song
  • Lin Liu
  • Wim Bernasco
  • Luzi Xiao
  • Suhong Zhou
  • Weiwei Liao

Abstract

In recent years, it has increasingly been recognized that due to the uncertain geographic context problem caused by daily human mobility, the residential population is too static to serve as a valid measure of the population at risk for criminal victimization. Various alternative measures have been suggested instead. Guided by the routine activity approach, this study furthers the concept of crime risk population and its measurement across space and time. Using exceptionally comprehensive data sets on population mobility and on theft from the person in a large city in China, we select the best indicator of the risk population from the following four candidates: residential population, subway ridership, taxi ridership, and mobile phone users. Controlling for the potentially confounding effects of offender and guardian presence, we show that on both weekdays and weekends, the best indicators of risk population vary over the course of the day. In the morning, residential population outperforms other measures. In the afternoon and evening, taxi ridership and phone users are better indicators. Although the mobile phone user base forms an arguably more representative measure of ambient population, during some periods taxi ridership is superior because it provides a better indicator of outdoor (as opposed to indoor) activities. In terms of practical applications to security policy and law enforcement, these findings can help identify crime hot spots by calculating accurate crime risks.

Suggested Citation

  • Guangwen Song & Lin Liu & Wim Bernasco & Luzi Xiao & Suhong Zhou & Weiwei Liao, 2018. "Testing Indicators of Risk Populations for Theft from the Person across Space and Time: The Significance of Mobility and Outdoor Activity," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(5), pages 1370-1388, September.
  • Handle: RePEc:taf:raagxx:v:108:y:2018:i:5:p:1370-1388
    DOI: 10.1080/24694452.2017.1414580
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    Citations

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    Cited by:

    1. Minxuan Lan & Lin Liu & Andres Hernandez & Weiyi Liu & Hanlin Zhou & Zengli Wang, 2019. "The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    2. Fangye Du & Lin Liu & Chao Jiang & Dongping Long & Minxuan Lan, 2019. "Discerning the Effects of Rural to Urban Migrants on Burglaries in ZG City with Structural Equation Modeling," Sustainability, MDPI, vol. 11(3), pages 1-13, January.
    3. Felson, Marcus & Xu, Yanqing & Jiang, Shanhe, 2022. "Property crime specialization in Detroit, Michigan," Journal of Criminal Justice, Elsevier, vol. 82(C).
    4. 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.
    5. Huafang Xie & Lin Liu & Han Yue, 2022. "Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique," IJERPH, MDPI, vol. 19(21), pages 1-22, October.
    6. Naseer Ahmad & Ali Raza Elahi, 2023. "The Effectiveness of Promotion through Brochure Advertising on Merchandise Sales: A Case Study of Multiple Retail Stores of Pakistan," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(2), pages 732-740.

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