Shooting on the street: measuring the spatial influence of physical features on gun violence in a bounded street network
Objectives Accurately estimate the strength and extent (distance) of the spatial influence of physical features on gun violence using a street network measurement strategy. Methods Treating disaggregated point locations as the unit-of-analysis, the spatial influence of various physical features of p...
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| Tipo de documento: | Electrónico Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| En: |
Journal of quantitative criminology
Año: 2017, Volumen: 33, Número: 2, Páginas: 237-253 |
| Acceso en línea: |
Volltext (Resolving-System) |
| Journals Online & Print: | Gargar...
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| Verificar disponibilidad: | HBZ Gateway |
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MARC
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| 245 | 1 | 0 | |a Shooting on the street |b measuring the spatial influence of physical features on gun violence in a bounded street network |c Jie Xu, Elizabeth Griffiths |
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| 520 | |a Objectives Accurately estimate the strength and extent (distance) of the spatial influence of physical features on gun violence using a street network measurement strategy. Methods Treating disaggregated point locations as the unit-of-analysis, the spatial influence of various physical features of place on all 2012 incidents of gun violence in Newark, NJ is estimated along a street network plane rather than a planar plane, and using a continuous operationalization of street network distances as opposed to Euclidean or Grid distances. Network-based computation methods clarify the path distances over which physical features of place, or shooting attractors, exert a significant spatial influence on gun violence. Segmented regression models estimate feature-specific distance decay patterns by demarcating the exact network distances at which the strength of attraction weakens or dissipates entirely. Results Findings show that liquor stores, grocery stores, bus stops, and residential foreclosures are shooting attractors in Newark, NJ. The magnitude of spatial influence is strongest in the immediate vicinity of each physical feature, and declines precipitously thereafter; yet the nature and strength of the decay varies by feature. A comparison of results analyzed on a street network plane to those based on an unbounded plane illustrates the potential biases in traditional approaches. Conclusions Determining whether and how strongly physical features operate as crime attractors requires constraining the analyses to the street network plane and accurately measuring continuous distances along the street network. The methodology articulated in this study can be used to more precisely estimate the spatial influence and distance decay of various physical features of place on crime density. | ||
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