Advertisement

Journal of Quantitative Criminology

, Volume 33, Issue 2, pp 237–253 | Cite as

Shooting on the Street: Measuring the Spatial Influence of Physical Features on Gun Violence in a Bounded Street Network

  • Jie XuEmail author
  • Elizabeth Griffiths
Original Paper

Abstract

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.

Keywords

Spatial influence Physical features of place Street network Network Cross K Function Gun violence 

References

  1. Baddeley A, Turner R (2005) Spatstat: an R package for analyzing spatial point patterns. J Stat Softw 12:1–42CrossRefGoogle Scholar
  2. Bernasco W, Block R (2011) Robberies in Chicago: a block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. J Res Crime Delinq 48:33–57CrossRefGoogle Scholar
  3. Braga AA, Papachristos AV, Hureau DM (2014) The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q 31:633–663CrossRefGoogle Scholar
  4. Brantingham PJ, Brantingham PL (1995) Criminality of place: crime generators and crime attractors. Eur J Crim Policy Res 3:1–26CrossRefGoogle Scholar
  5. Caplan JM, Kennedy LW, Miller J (2011) Risk terrain modeling: brokering criminological theory and GIS methods for crime forecasting. Justice Q 28:361–381CrossRefGoogle Scholar
  6. Cohen L, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44:588–608CrossRefGoogle Scholar
  7. Davies T, Johnson SD (2015) Examining the relationship between road structure and burglary risk via quantitative network analysis. J Quant Criminol 31:481–507CrossRefGoogle Scholar
  8. Fagan J, Davies G (2000) Crime in public housing: two-way diffusion effects in surrounding neighborhoods. In: Goldsmith V, McGuire PG, Mollenkopf JH, Ross TA (eds) Analyzing crime patterns: frontiers of practice. Sage Publications, Thousand Oaks, pp 121–135CrossRefGoogle Scholar
  9. FBI (2010) Crime in the United States by Metropolitan Statistical Area, 2010. Crime 2010Google Scholar
  10. Fried C (2008) America’s safest city: Amherst, NY; The most dangerous: Newark, NJ. Money MagazineGoogle Scholar
  11. Gorman D, Speer PW, Gruenewald PJ, Labouvie EW (2001) Spatial dynamics of alcohol availability, neighborhood structure and violent crime. J Stud Alcohol 62:628–636CrossRefGoogle Scholar
  12. Grannis R (1998) The importance of trivial streets: residential streets and residential segregation. Am J Sociol 103:1530–1564CrossRefGoogle Scholar
  13. Griffiths E, Tita G (2009) Homicide in and around public housing: is public housing a hotbed, a magnet, or a generator of violence for the surrounding community? Soc Probl 56:474–493CrossRefGoogle Scholar
  14. Groff E (2014) Quantifying the exposure of street segments to drinking places nearby. J Quant Criminol 30:527–548CrossRefGoogle Scholar
  15. Groff E, Lockwood B (2014) Criminogenic facilities and crime across street segments in Philadelphia: uncovering evidence about the spatial extent of facility influence. J Res Crime Delinq 51:277–314CrossRefGoogle Scholar
  16. Kennedy LW, Caplan JM, Piza EL, Buccine-Schraeder H (2015) Vulnerability and exposure to crime: applying risk terrain modeling to the study of assault in Chicago. Appl Spat Anal. doi: 10.1007/s12061-015-9165-z Google Scholar
  17. Kim H, Fay MP, Feuer EJ, Midthune DN (2000) Permutation tests for join point regression with applications to cancer rates. Stat Med 19:335–351CrossRefGoogle Scholar
  18. Krause EF (1975) Taxicab geometry. Addison-Wesley, CaliforniaGoogle Scholar
  19. LaGrange T (1999) The impact of neighborhoods, schools, and malls on the spatial distribution of property damage. J Res Crime Delinq 36:393–422CrossRefGoogle Scholar
  20. 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
  21. Levine N, Wachs M, Shirazi E (1986) Crime at bus stops: a study of environmental factors. J Archit Plan Res 3:339–361Google Scholar
  22. Lipton R, Gruenewald PJ (2002) The spatial dynamics of violence and alcohol outlets. J Stud Alcohol 63:187–195CrossRefGoogle Scholar
  23. Lu Y, Chen X (2007) On the false alarm of planar K-function when analyzing urban crime distributed along streets. Soc Sci Res 36:611–632CrossRefGoogle Scholar
  24. McNulty TL, Holloway SR (2000) Race, crime, and public housing in Atlanta: testing a conditional effect hypothesis. Soc Forces 79:707–729CrossRefGoogle Scholar
  25. Murr A, Noonoo J (2007) A return to the bad old days? Newsweek, August 17, 2007Google Scholar
  26. Okabe A, Yamada I (2001) The K-Function method on a network and its computational implementation. Geogr Anal 33:271–290CrossRefGoogle Scholar
  27. New Jersey State Police (n.d.) Operation Cease Fire. Retrieved from: http://www.njsp.org/divorg/invest/operation-cease-fire.html
  28. Okabe A, Yomono H, Kitamura M (1995) Statistical analysis of the distribution of points on a network. Geogr Anal 27:152–175CrossRefGoogle Scholar
  29. Ratcliffe JH (2004) Geocoding crime and a first estimate of a minimum acceptable hit rate. Int J Geogr Inf Sci 18:61–72CrossRefGoogle Scholar
  30. Ratcliffe JH (2012) The spatial extent of criminogenic places on the surrounding environment: a change-point regression of violence around bars. Geogr Anal 44:302–320CrossRefGoogle Scholar
  31. Ratcliffe JH, Rengert GF (2008) Near repeat patterns in Philadelphia shootings. Secur J 21:58–76CrossRefGoogle Scholar
  32. Roncek DW, Faggiani D (1985) High schools and crime. Sociol Q 26:491–505CrossRefGoogle Scholar
  33. Roncek DW, Francik JMA (1981) Housing projects and crime. Soc Probl 29:151–166CrossRefGoogle Scholar
  34. Roncek DW, Maier PA (1991) Bars, blocks, and crimes revisited: linking the theory of routine activities to the empiricism of “hot spots”. Criminology 29:725–753CrossRefGoogle Scholar
  35. SANET. A Spatial Analysis along Networks (Ver. 4.1). Atsu Okabe, Kei-ichi Okunuki and SANET Team, Tokyo, JapanGoogle Scholar
  36. Scribner RA, MacKinnon DP, Dwyer JH (1995) The risk of assaultive violence and alcohol availability in Los Angeles County. Am J Public Health 85:335–340CrossRefGoogle Scholar
  37. Smith W, Frazee SG, Davison E (2000) Furthering the integration of routine activity and social disorganization theories: small units of analysis and the study of street robbery as a diffusion process. Criminology 38:489–523CrossRefGoogle Scholar
  38. Stucky TD, Ottensmann JR (2009) Land use and violent crime. Criminology 47:1223–1264CrossRefGoogle Scholar
  39. Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240CrossRefGoogle Scholar
  40. Townsley M, Sidebottom A (2010) All offenders are equal, but some are more equal than others: variation in journeys to crime between offenders. Criminology 48:897–917CrossRefGoogle Scholar
  41. United States Census Bureau (2012) The Counties and Most Populous Cities and Townships in 2010 in New Jersey: 2000 and 2010Google Scholar
  42. Weisburd D, Groff ER, Yang S (2012) The criminology of place: street segments and our understanding of the crime problem. Oxford University Press, OxfordCrossRefGoogle Scholar
  43. Yamada I, Thill JC (2004) Comparison of planar and network K-functions in traffic accident analysis. J Transp Geogr 12:149–158CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Criminal JusticeRutgers UniversityNewarkUSA

Personalised recommendations