Unraveling the Nexus: how street network morphology influences crime in Detroit.
Urban streets are primary settings for criminal activities. Although prior studies have examined various environmental factors influencing criminal behavior, insufficient attention has been paid to street configurational types within street network morphology. To address this gap, this study employs...
| Publicado en: | Humanities & Social Sciences Communications Vol. 12; no. 1; pp. 1 - 17 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
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Springer Nature
7/2/2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186339624&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186339624 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: MVR0 jtl: Humanities & Social Sciences Communications maglogo: N pubinfo: dt: 7/2/2025 vid: 12 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 186339624 10.1057/s41599-025-05362-1 ppf: 1 ppct: 16 formats: tig: atl: Unraveling the Nexus: how street network morphology influences crime in Detroit. aug: au: Mao, Yuanyuan Huang, Shuqi Ning, Yueqiao Wang, Can Li, Wenchao Huang, Ziheng Jiang, Shanhe Xu, Yanqing affil: https://ror.org/05kvm7n82 Department of Urban and Rural Planning, Soochow University, Suzhou, Jiangsu, China https://ror.org/00q4vv597 Society Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China https://ror.org/033vjfk17 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China https://ror.org/01070mq45 Department of Criminal Justice, Wayne State University, Detroit, MI, USA su: Criminal behavior Crime prevention Offenses against property Probability density function Criminal methods sug: subj: Criminal behavior Crime prevention Offenses against property Probability density function Criminal methods ab: Urban streets are primary settings for criminal activities. Although prior studies have examined various environmental factors influencing criminal behavior, insufficient attention has been paid to street configurational types within street network morphology. To address this gap, this study employs kernel density estimation and spatial autocorrelation to analyze the spatial distribution patterns of assault, robbery, larceny, and motor vehicle theft in Detroit in 2019. Based on these spatial patterns, a comprehensive street environmental indicator system was constructed, incorporating three dimensions: street network morphology, nodal characteristics, and socioeconomic attributes. Negative binomial regression models were subsequently employed to analyze the effects of these factors on the spatial distribution of the four crime types. The findings reveal significant spatial clustering of all four types of crimes in Detroit. Neighborhoods with a higher proportion of community roads, increased street permeability for pedestrians, a higher density of dining establishments, and elevated rental rates were more likely to experience criminal activities. Conversely, neighborhoods with a greater number of intersections exhibited lower crime frequencies. Areas with a higher percentage of residents holding bachelor's degrees or above were more prone to property crimes. In terms of street network morphology, the proportion of ring roads (cell-ratio) had a positive impact on assault, robbery, and larceny. This study contributes to the literature by expanding the indicator system for analyzing the relationship between street environments and crimes, enriching existing crime pattern theories, and providing new perspectives and practical guidance for street design aimed at crime prevention. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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