Investigating the diurnal effects of on-street population and streetscape physical environment on street theft crime: A machine learning and negative binomial regression approach using street view images-Reference-Cited by-同舟云学术

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Investigating the diurnal effects of on-street population and streetscape physical environment on street theft crime: A machine learning and negative binomial regression approach using street view images

Published:2024-02 Issue: Volume:163 Page:103194
ISSN:0143-6228
Container-title:Applied Geography
language:en
Short-container-title:Applied Geography

Author:

Yue Han^ORCID,Liu Lin^ORCID,Xu Chong^ORCID,Song Guangwen,Chen Jianguo,He Li^ORCID,Duan Lian^ORCID

Publisher

Elsevier BV

Reference92 articles.

1. Crime and time: The temporal patterning of causal variables;Agnew;Theoretical Criminology,2011

2. Crime measures and the spatial analysis of criminal activity;Andresen;British Journal of Criminology,2006

3. Diurnal movements and the ambient population: An application to municipal-level crime rate calculations;Andresen;Canadian Journal of Criminology and Criminal Justice,2010

4. The crime kaleidoscope: A cross-jurisdictional analysis of place features and crime in three urban environments;Barnum;Applied Geography,2017

5. Robberies in Chicago: A block-level analysis of the influence of crime generators, crime attractors, and offender anchor points;Bernasco;Journal of Research in Crime and Delinquency,2011

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