Shooting on the Street: Measuring the Spatial Influence of Physical Features on Gun Violence in a Bounded Street Network
- 672 Downloads
Accurately estimate the strength and extent (distance) of the spatial influence of physical features on gun violence using a street network measurement strategy.
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.
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.
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.
KeywordsSpatial influence Physical features of place Street network Network Cross K Function Gun violence
- FBI (2010) Crime in the United States by Metropolitan Statistical Area, 2010. Crime 2010Google Scholar
- Fried C (2008) America’s safest city: Amherst, NY; The most dangerous: Newark, NJ. Money MagazineGoogle Scholar
- Krause EF (1975) Taxicab geometry. Addison-Wesley, CaliforniaGoogle Scholar
- Levine N (2005) CrimeStat III: A Spatial Statistics Program for the Analysis of Crime Incident Locations (version 3.0). Ned Levine & Associates, Houston, TX.; National Institute of Justice, Washington, DCGoogle Scholar
- Levine N, Wachs M, Shirazi E (1986) Crime at bus stops: a study of environmental factors. J Archit Plan Res 3:339–361Google Scholar
- Murr A, Noonoo J (2007) A return to the bad old days? Newsweek, August 17, 2007Google Scholar
- New Jersey State Police (n.d.) Operation Cease Fire. Retrieved from: http://www.njsp.org/divorg/invest/operation-cease-fire.html
- SANET. A Spatial Analysis along Networks (Ver. 4.1). Atsu Okabe, Kei-ichi Okunuki and SANET Team, Tokyo, JapanGoogle Scholar
- United States Census Bureau (2012) The Counties and Most Populous Cities and Townships in 2010 in New Jersey: 2000 and 2010Google Scholar