Previous research has consistently identified a crime spillover effect from public housing communities. However, variation in spillover between communities has also been observed. Drawing upon environmental criminology and recent advancements in crime and place research, we examine whether street block betweenness conditions the relationship between street block distance to public housing communities and robbery levels. Negative binomial regression results indicate that street block distance is inversely related to robberies and betweenness is positively associated with robbery levels. The interaction between distance to the nearest public housing community and betweenness suggests robberies decrease more slowly farther from public housing communities on street blocks with higher betweenness. Findings demonstrate the importance of considering the context in which potentially criminogenic facilities are situated.
AndersonE. (1999). Code of the street: Decency, violence, and the moral life of the inner city. W. W. Norton.
2.
AnselinL. (2003). Spatial externalities, spatial multipliers, and spatial econometrics. International Regional Science Review, 26, 153–166.
3.
BarthélemyM. (2004). Betweenness centrality in large complex networks. The European Physical Journal B: Condensed Matter and Complex Systems, 38, 163–168.
BeavonD. J. K.BrantinghamP. L.BrantinghamP. J. (1994). The influence of street networks on the patterning of property offences. Crime Prevention Studies, 2, 115–148.
6.
BernascoW.BlockR. (2011). Robberies in Chicago: A block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. Journal of Research in Crime and Delinquency, 48, 33–57.
7.
BevisC.NutterJ. B. (1977, November16–20). Changing street layouts to reduce residential burglary [Paper presentation]. Annual Meeting of the American Society of Criminology, Atlanta, GA, United States.
8.
BollobasB. (2002). Modern graph theory. Springer.
9.
BrantinghamP. J.DyresonD. A.BrantinghamP. L. (1976). Crime seen through a cone of resolution. American Behavioral Scientist, 20, 261–273.
10.
BrantinghamP. L.BrantinghamP. J. (1991). Notes on the geometry of crime. In BrantinghamP. J.BrantinghamP. L. (Eds.), Environmental criminology (pp. 27–54). Waveland.
11.
BrantinghamP. L.BrantinghamP. J. (1993). Environment, routine, and situation: Toward a pattern theory of crime. In ClarkeR. V.FelsonM. (Eds.), Routine activity and rational choice (pp. 259–294). Transaction.
12.
BrantinghamP. L.BrantinghamP. J. (1995). Criminality of place: Crime generators and crime attractors. European Journal of Criminal Policy and Research, 3, 1–26.
13.
BrantinghamP. L.BrantinghamP. J. (1999). A theoretical model of crime hot spot generation. Studies on Crime & Crime Prevention, 8(1), 7–26.
14.
BursikR. J.GrasmickH. (1993). Neighborhoods and crime: The dimensions of effective community control. Lexington.
15.
CameronA. C.TrivediP. K. (2013). Regression analysis of count data. Cambridge University Press.
16.
ChaineyS.RatcliffeJ. H. (2005). GIS and crime mapping. Wiley.
ClarkeR. V.CornishD. B. (1985). Modeling offenders’ decisions: A framework for research and policy. Crime and Justice, 6, 147–185.
20.
CohenL. E.FelsonM. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.
21.
DaviesT.BowersK. J. (2018). Street networks and crime. In BruinsmaG. J. N.JohnsonS. D. (Eds.), The Oxford handbook of environmental criminology. Oxford University Press, pp. 545-578.
22.
DaviesT.JohnsonS. D. (2015). Examining the relationship between road structure and burglary risk via quantitative network analysis. Journal of Quantitative Criminology, 31, 481–507.
23.
DearM. (1992). Understanding and overcoming the NIMBY syndrome. Journal of the American Planning Association, 58, 288–302.
24.
EckJ. E.ClarkeR. V.GueretteR. T. (2007). Risky facilities: Crime concentration in homogeneous sets of establishments and facilities. Crime Prevention Studies, 21, 225–264.
25.
FaganJ.DaviesG. (2000). Crime in public housing: Two-way diffusion effects in surrounding neighborhoods. In GoldsmithV.McGuireP. G.MollenkopfJ. (Eds.), Analyzing crime patterns: Frontiers of practice (pp. 121–135). SAGE.
26.
ForsythA. J.OakesM.LeeB.SchmitzK. H. (2009). The built environment, walking, and physical activity: Is the environment more important to some people than others?Transportation Research Part D: Transport and Environment, 14, 42–49.
27.
FrithM. J.JohnsonS. D.FryH. M. (2017). Role of the street network in burglars’ spatial decision-making. Criminology, 55, 344–376.
28.
GilJ. (2016). Street network analysis “edge effects”: Examining the sensitivity of centrality measures to boundary conditions. Environment and Planning B: Urban Analytics and City Science, 44, 819–836.
29.
GriffithsE.TitaG. (2009). Homicide in and around public housing: Is public housing a hotbed, a magnet, or a generator of violence for the surrounding community?Social Problems, 56, 474–493.
30.
GroffE. R.LockwoodB. (2014). Criminogenic facilities and crime across street segments in Philadelphia: Uncovering evidence about the spatial extent of facility influence. Journal of Research in Crime and Delinquency, 51, 277–314.
31.
GroffE. R.WeisburdD.YangS. (2010). Is it important to examine crime trends at the “micro” level? A longitudinal analysis of street to street variability in crime trajectories. Journal of Quantitative Criminology, 26, 7–32.
32.
HabermanC. P.GroffE. R.TaylorR. B. (2013). The variable impacts of public housing community proximity on nearby street robberies. Journal of Research in Crime and Delinquency, 50, 163–188.
33.
HabermanC. P.RatcliffeJ. H. (2015). Testing for temporally differentiated relationships among potentially criminogenic places and census block street robbery counts. Criminology, 53, 457–483.
34.
HillierB. (1996). Space is the machine: A configurational theory of architecture. Cambridge University Press.
35.
HollowayS. R.McNultyT. L. (2003). Contingent urban geographies of violent crime: Racial segregation and the impact of public housing in Atlanta. Urban Geography, 24, 187–211.
36.
HolzmanH. R.HyattR. A.KudrickT. R. (2005). Measuring crime in and around public housing using GIS. In WangF. (Ed.), Geographic information systems and crime analysis (pp. 311–329). IDEA Group.
37.
HortonF. E.ReynoldsD. R. (1971). Action space differentials in cities. In McConnellH.YaseenD. (Eds.), Perspectives in geography: Models of spatial interaction (pp. 83–102). Northern Illinois University Press.
38.
JacobsJ. (1961). The death and life of great American cities. Vintage.
39.
JohnsonS. D.BowersK. J. (2010). Permeability and burglary risk: Are cul-de-sacs safer?Journal of Quantitative Criminology, 26, 89–111.
40.
LaGrangeT. C. (1999). The impact of neighborhoods, schools, and malls on the spatial distribution of property damage. Journal of Research in Crime and Delinquency, 36, 393–422.
41.
LongJ. S.FreeseJ. (2014). Regression models for categorical dependent variables using Stata (3rd ed.). Stata Press.
42.
McCordE. S.RatcliffeJ. H. (2007). A micro-spatial analysis of the demographic and criminogenic environment of drug markets in Philadelphia. Australian & New Zealand Journal of Criminology, 40, 43–63.
43.
McCordE. S.RatcliffeJ. H.GarciaM. R.TaylorR. B. (2007). Nonresidential crime attractors and generators elevate perceived neighborhood crime and incivilities. Journal of Research in Crime and Delinquency, 44, 295–320.
44.
McCutcheonJ. C.WeaverG. S.Huff-CorzineL.CorzineJ.BurrastonB. (2016). Highway robbery: Testing the impact of interstate highways on robbery. Justice Quarterly, 33, 1292–1310.
45.
McNultyT. L.HollowayS. R. (2000). Race, crime, and public housing in Atlanta: Testing a conditional effect hypothesis. Social Forces, 79, 707–729.
46.
MonkK. M.HeinonenJ. A.EckJ. E. (2010). Street robbery. Office of Community Oriented Policing Services, U.S. Department of Justice.
47.
NewmanO. (1972). Defensible space. Macmillan.
48.
NewmanO. (1996). Creating defensible space. Office of Policy Development and Research, U.S. Department of Housing and Urban Development.
49.
O’BrienD. T.WinshipC. (2017). The gains of greater granularity: The presence and persistence of problem properties in urban neighborhoods. Journal of Quantitative Criminology, 33, 649–674.
50.
O’SullivanD. (2014). Spatial network analysis. In FischerM. M.NijkampP. (Eds.), Handbook of regional science (pp. 1253–1273). Springer.
51.
PendallR. (1999). Opposition to housing: NIMBY and beyond. Urban Affairs Review, 35, 112–136.
52.
PetersonR. D.KrivoL. J.HarrisM. A. (2000). Disadvantage and neighborhood violent crime: Do local institutions matter?Journal of Research in Crime and Delinquency, 37, 31–63.
53.
PortaS.CrucittiP.LatoraV. (2006). The network analysis of urban streets: A primal approach. Environment and Planning B: Urban Analytics and City Science, 33, 705–725.
54.
RatcliffeJ. H. (2004). Geocoding crime and a first estimate of a minimum acceptable hit rate. International Journal of Geographical Information Science, 18, 61–72.
55.
RengertG. F.RatcliffeJ. H.ChakravortyS. (2005). Policing illegal drug markets: Geographic approaches to crime reduction. Criminal Justice Press.
56.
RengertG. F.WasilchickJ. (1985). Suburban burglary: A time and a place for everything. C.C. Thomas.
57.
ReynaldD. M. (2010). Guardians on guardianship: Factors affecting the willingness to supervise, the ability to detect potential offenders, and the willingness to intervene. Journal of Research in Crime and Delinquency, 47(3), 358–390.
58.
RomanC. G. (2005). Routine activities of youth and neighborhood violence: Spatial modeling of place, time and crime. In WangF. (Ed.), Geographic information systems and crime analysis (pp. 293–310). IDEA Group.
59.
RoncekD. W.BellR. (1981). Bars, blocks, and crimes. Journal of Environmental Systems, 11, 35–47.
60.
RoncekD. W.BellR.FrancikJ. M. A. (1981). Housing developments and crime: Testing a proximity hypothesis. Social Problems, 29, 151–166.
61.
RoncekD. W.FaggianiD. (1985). High schools and crime: A replication. The Sociological Quarterly, 26, 491–505.
62.
RoncekD. W.LoBoscoA. (1983). The effect of high schools on crime in their neighborhoods. Social Science Quarterly, 64, 598–613.
63.
RoncekD. W.MaierP. A. (1991). Bars, blocks, and crimes revisited: Linking the theory of routine activities to the empiricism of hot spots. Criminology, 29, 725–753.
64.
SampsonR. J.RaudenbushS. W.EarlsF. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924.
65.
SchnellC.BragaA. A.PizaE. L. (2017). The influence of community areas, neighborhood clusters, and street segments on the spatial variability of violent crime in Chicago. Journal of Quantitative Criminology, 33, 469–496.
66.
SchweitzerJ. H.KimJ. W.MackinJ. R. (1999). The impact of the built environment on crime and fear of crime in urban neighborhoods. Journal of Urban Technology, 6, 59–73.
67.
SevtsukA.MekonnenM. (2012). Urban network analysis. A new toolbox for ArcGIS. Revue Internationale de Géomatique, 22, 287–305.
68.
SteenbeekW.WeisburdD. (2016). Where the action is in crime? An examination of variability of crime across different spatial units in The Hague, 2001–2009. Journal of Quantitative Criminology, 32, 449–469.
69.
St. JeanP. K. (2008). Pockets of crime: Broken windows, collective efficacy, and the criminal point of view. University of Chicago Press.
70.
SummersL.JohnsonS. D. (2017). Does the configuration of the street network influence where outdoor serious violence takes place? Using space syntax to test crime pattern theory. Journal of Quantitative Criminology, 33, 397–420.
71.
TaylorR. B. (1997). Social order and disorder of street blocks and neighborhoods: Ecology, microecology, and the systemic model of social disorganization. Journal of Research in Crime and Delinquency, 34, 113–155.
72.
U.S. Census Bureau. (2015). United States census quick facts.
73.
WeisburdD. (2015). The law of crime concentration and the criminology of place. Criminology, 53, 133–157.
74.
WeisburdD.AmramS. (2014). The law of concentrations of crime at place: The case of Tel Aviv-Jaffa. Police Practice and Research: An International Journal, 15, 101–114.
75.
WeisburdD.BushwayS.LumC.YangS. (2004). Trajectories of crime at places: A longitudinal study of street segments in the city of Seattle. Criminology, 42, 283–322.
76.
WeisburdD.GreenL. (1995). Policing drug hot spots: The Jersey City drug market analysis experiment. Justice Quarterly, 12, 711–735.
77.
WeisburdD.GroffE. R.YangS. (2012). The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press.
78.
WheelerA. P. (2018). Quantifying the local and spatial effects of alcohol outlets on crime. Crime & Delinquency, 65(6), 845–871. https://doi.org/10.1177/0011128718806692
79.
WhiteG. F. (1990). Neighborhood permeability and burglary rates. Justice Quarterly, 7, 57–67.
80.
WilcoxP.EckJ. E. (2011). Criminology of the unpopular: Implications for policy aimed at payday lending facilities. Criminology & Public Policy, 10, 473–482.
81.
WortleyR.MazerolleL. (2008). Environmental criminology and crime analysis: Situating the theory, analytic approach and application. In WortleyR.MazerolleL. (Eds.), Environmental criminology and crime analysis (pp. 1–5). Willan.
82.
WrightR. T.DeckerS. H. (1997). Armed robbers in action: Stickups and street culture. Northeastern University Press.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
