Space–Time Clustering of Crime Events and Neighborhood Characteristics in Houston.
Spatial–temporal interaction analysis is employed to identify repeat and near-repeat patterns of crime in time and space. Most research to date addresses burglary and shooting incidents. Using the Knox method for space–time interaction, this study analyzes crime data in 12 “super neighborhoods” loca...
| Publicado en: | Criminal Justice Review (Sage Publications) Vol. 40; no. 3; pp. 340 - 361 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Sage Publications Inc.
Sep2015
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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=108924623&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 108924623 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07340168 1GV4 jtl: Criminal Justice Review (Sage Publications) issn: 07340168 maglogo: Y pubinfo: dt: Sep2015 vid: 40 iid: 3 pid: 344 pub: Sage Publications Inc. artinfo: ui: 108924623 10.1177/0734016815573309 ppf: 340 ppct: 21 formats: tig: atl: Space–Time Clustering of Crime Events and Neighborhood Characteristics in Houston. aug: au: Zhang, Yan Zhao, Jihong Ren, Ling Hoover, Larry affil: College of Criminal Justice, Sam Houston State University Huntsville, TX, USA su: Houston (Tex.) Crime Social problems Spacetime Document clustering sug: subj: Crime Social problems Houston (Tex.) Spacetime Document clustering keyword: crime Knox test space–time clustering crime Knox test space–time clustering ab: Spatial–temporal interaction analysis is employed to identify repeat and near-repeat patterns of crime in time and space. Most research to date addresses burglary and shooting incidents. Using the Knox method for space–time interaction, this study analyzes crime data in 12 “super neighborhoods” located in Houston’s crime-heavy southwest quadrant to explore spatial–temporal clustering of three types of crime, namely, residential burglary, street robbery, and aggravated assault. The findings suggest that each type of crime event has a unique clustering signature. Residential burglaries show significant space–time clustering in a relatively longer time range (up to 90 days) and distance interval (up to 1.55 miles). In contrast, street robberies present significant clustering only up to 6 days and a quarter of a mile. For aggravated assault, the clusters of pairs occur within the interval of 7 days and within a little more than 1 mile of an initial assault. Examination of the socioeconomic characteristics of the neighborhoods indicates that crime events cluster more often in low income and racially/ethnically diverse neighborhoods. Significant spatial correlations of crime clusters are detected. The findings offer insight into potential suppression of crime events that are time and space correlated. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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