Spatial Modeling in Neighborhood and Crime Studies: An Empirical Examination.
We theoretically discuss why addressing spatial dependence is important and empirically demonstrate its methodological advantages in the context of neighborhood and crime studies. We found that as the uncertainty in measuring neighbors increases, the bias in the coefficient estimates increases. Howe...
| Publicado en: | Crime & Delinquency Vol. 72; no. 8; pp. 2185 - 2210 |
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| Autores principales: | , |
| Formato: | Artículo |
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Sage Publications Inc.
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193982827&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193982827 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00111287 CRD jtl: Crime & Delinquency issn: 00111287 maglogo: Y pubinfo: dt: Jul2026 vid: 72 iid: 8 pid: 344 pub: Sage Publications Inc. artinfo: ui: 193982827 10.1177/00111287261416406 ppf: 2185 ppct: 25 formats: tig: atl: Spatial Modeling in Neighborhood and Crime Studies: An Empirical Examination. aug: au: Kim, Young-An Jeong, Jongwoo affil: Florida State University, Tallahassee, USA Georgia State University, Atlanta, USA su: Crime analysis Spatial behavior Neighborhoods Geographic spatial analysis Estimation bias Measurement uncertainty (Statistics) Spatial data structures sug: subj: Crime analysis Spatial behavior Neighborhoods Geographic spatial analysis Estimation bias Measurement uncertainty (Statistics) Spatial data structures keyword: crime neighborhoods spatial analysis crime neighborhoods spatial analysis ab: We theoretically discuss why addressing spatial dependence is important and empirically demonstrate its methodological advantages in the context of neighborhood and crime studies. We found that as the uncertainty in measuring neighbors increases, the bias in the coefficient estimates increases. However, importantly, we also observed that even with a high rate of uncertainty in the spatial matrix, the bias is smaller than in the non-spatial models. Likewise, as the uncertainty in defining neighbors increases, models tend to underestimate the standard errors. However, even with higher uncertainty in the spatial weight matrix, underestimation seems smaller than that of the non-spatial model. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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