Skip to main content
SpringerLink
Log in

Pathways: Examining Street Network Configurations, Structural Characteristics and Spatial Crime Patterns in Street Segments

  • Revisions
  • Published:
Journal of Quantitative Criminology Aims and scope Submit manuscript

Abstract

Objectives

Although theories suggest that street network configurations (pathways) are important factors for understanding the spatial patterns of crime, relatively less attention has been paid to the association between the physical configuration of the street network and the level of crime in place. Consequently, we employed the concept of betweenness centrality in the context of the street network to empirically measure the potential foot traffic passing through a given street segment.

Methods

We introduce a methodological refinement by accounting for the characteristics of origin and destination of each potential trip (where travelers are from and tend to go) using residential population in origins and destinations and the number of various types of business employees in destinations. Moreover, we posit that the effect of potential foot traffic into a given street segment will be moderated by certain social environmental characteristics such as socioeconomic status of place. By using data on a sample of 300,000 street segments in the Southern California region across 130 cities, we estimate a set of negative binomial regression models including the betweenness measures.

Results

Our results show that betweenness centrality has a curvilinear relationship with violent and property crime: At lower levels, increases in betweenness results in increased crime, yet the pattern becomes crime-reducing at higher values of the betweenness measure. We also found that the pattern is moderated by the socioeconomic status of the street segment.

Conclusions

The current study highlights that there is an important relationship of the physical environment in terms of the street network configuration and crime in street segments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Online Appendix Figure A26 shows how the street network is represented to an abstract graph. As shown, we are interested in calculating betweenness of street segment \(X_{12}\) (green-shaded street segment) which captures how frequently \(X_{12}\) is traversed along the given street network from any origins to destinations through the shortest routes.

  2. Another advantage of our weighting approach is that it implicitly adjusts for the varying lengths of street segments. That is, the unweighted approach simply counts the number of segments which send paths through a particular street segment, and therefore in neighborhoods with many short streets this can result in high betweenness centrality values simply because there are many (short) streets in the surrounding area. This can be seen in Fig. 2, below, where there is a cluster of red street segments in Venice, CA (located on the west wide of the map next to the coast), simply because there are many short street segments in this area. However, our weighted approach accounts for the number of persons traveling through such segments, and therefore shorter street segments that have fewer residents will be adjusted appropriately in our approach.

  3. Whereas the percent single-parent households variable is available for blocks, for the other measures we use synthetic estimation for ecological inference as described by Boessen and Hipp (2015) to impute them from block groups to blocks (Cohen and Di Zhang 1988; Steinberg 1979). Variables used in the imputation model were: percent owners, racial composition, percent divorced households, percent households with children, percent vacant units, population density, and age structure (percent aged: 0–4, 5–14, 15–19, 20–24, 25–29, 30–44, 45–64, 65 and up, with age 15–19 as the reference category).

  4. Refer to Kim (2018) for a detailed explanation on why racial/ethnic heterogeneity in street segments may function in a different way compared to other larger neighborhood units.

References

  • Ackerman JM, Kim Rossmo D (2015) How far to travel? A multilevel analysis of the residence-to-crime distance. J Quant Criminol 31:237–262

    Google Scholar 

  • Armitage R (2007) Sustainability versus safety; confusion, conflict and contradiction in designing out crime. In: Jhonson SD, Monsey TM (eds) Imagination for crime prevention: essays in honour of Ken Pease (Crime Prevention Studies 21). Criminal Justice Press, Oxford

    Google Scholar 

  • Atash F (1994) Redesigning suburbia for walking and transit: emerging concepts. J Urban Plan Dev 120(1):48–57

    Google Scholar 

  • Baran PK, Smith WR, Toker U (2006) Conflict between space and crime: exploring the relationship between spatial configuration and crime location. In: Paper presented at the 37th annual conference of the Environmental Design Research Association (EDRA), Atlanta

  • Baran PK, Smith WR, Toker U (2007) The space syntax and crime: evidence from a suburban community. In: Kubat AS, Ertekin O, Guney YI, Eyboglu E (eds) Proceedings of the sixth international space syntax symposium. Istanbul Technical University, Instanbul

  • Beavon DJ, Brantingham PL, Brantingham PJ (1994) The influence of street networks on the patterning of property offenses. In: Clarke RB (ed) Crime prevention studies. Criminal Justice Press, Monsey

    Google Scholar 

  • Bernasco W, Block RL (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(1):33–57

    Google Scholar 

  • Bevis C, Nutter J (1977) Changing street layouts to reduce residential burglary. Minnesota Crime Prevention Center, Minnesota

    Google Scholar 

  • Boessen A, Hipp JR (2015) Close-ups and the scale of ecology: land uses and the geography of social context and crime. Criminology 53(3):399–426

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1984) Patterns in crime. Macmillan, New York

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1993a) Environment, routine, and situation: toward a pattern theory of crime. In: Clarke RVG, Felson M (eds) Routine activity and rational choice. Transaction Publishers, Piscataway

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1993b) Nodes, paths and edges: considerations on the complexity of crime and the physical environment. J Environ Psychol 13:3–28

    Google Scholar 

  • Brantingham P, Brantingham P (1995) Criminality of place: crime generators and crime attractors. Eur J Crim Policy Res 3(3):1–26

    Google Scholar 

  • Browning CR, Jackson AL (2013) The social ecology of public space: active streets and violent crime in urban neighborhoods. Criminology 51(4):1009–1043

    Google Scholar 

  • Browning CR, Byron RA, Calder CA, Krivo LJ, Kwan M-P, Lee J-Y, Peterson RD (2010) Commercial density, residential concentration, and crime: land use patterns and violence in neighborhood context. J Res Crime Delinq 47(3):329–357

    Google Scholar 

  • Budd T (1999) Burglary of domestic dwellings: findings from the British crime survey. Home Office, London

    Google Scholar 

  • Bursik R (1988) Social disorganization and theories of crime and delinquency: problems and prospects. Criminology 26:519–552

    Google Scholar 

  • Bursik R, Grasmick H (1993) Neighborhoods and crime: the dimensions of effective community cotrol. Lexington Books, San Francisco

    Google Scholar 

  • Carpenter A, Peponis J (2010) Poverty and connectivity. J Space Syntax 1(1):108–120

    Google Scholar 

  • Cohen ML, Di Zhang X (1988) The difficulty of improving statistical synthetic estimation. In: Statistical research division report series. Bureau of the Census, Washington, DC

  • Davies T, Johnson SD (2015) Examining the relationship between road structure and Burglary risk via quantitative network analysis. J Quant Criminol 31(3):481–507

    Google Scholar 

  • Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Google Scholar 

  • Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Google Scholar 

  • Frith MJ, Johnson SD, Fry HM (2017) Role of the street network in burglars’ spatial decision-making. Criminology 55(2):344–376

    Google Scholar 

  • Groff E, Weisburd D, Yang SM (2010) Is it important to examine crime trends at a local “micro” level? A longitudinal analysis of street to street variability in crime trajectories. J Quant Criminol 26(1):7–32

    Google Scholar 

  • Hillier B (1996) Space is the machine. Cambridge University Press, Cambridge

    Google Scholar 

  • Hillier B (2004) Can streets be made safe? Urban Des Int 9(1):31–45

    Google Scholar 

  • Hipp JR (2007) Block, tract, and levels of aggregation: neighborhood structure and crime and disorder as a case in point. Am Soc Rev 72(5):659–680

    Google Scholar 

  • Hipp JR, Kim Y-A (2019) Temporal and spatial dimensions of robbery: differences across measures of the physical and social environment. J Crim Justice 60(1):1–12

    Google Scholar 

  • Iwanski N, Frank R, Reid A, Dabbaghian V (2012) A computational model for predicting the location of crime attractors on a road network. In: 2012 European on intelligence and security informatics conference (EISIC). IEEE

  • Jacobs J (1961) The death and life of great American cities. Vintage, New York

    Google Scholar 

  • Johnson SD, Bowers KJ (2010) Permeability and burglary risk: Are cul-de-sacs safer? J Quant Criminol 26(1):89–111

    Google Scholar 

  • Kim Y-A (2018) Examining the relationship between the structural characteristics of place and crime by imputing census block data in street segments: Is the pain worth the gain? J Quant Criminol 34(1):67–110. https://doi.org/10.1007/s10940-016-9323-8

    Article  Google Scholar 

  • Kim Y-A, Hipp JR (2017) Physical boundaries and city boundaries: Consequences for crime patterns on street segments? Crime Delinquency 64(2):227–254. https://doi.org/10.1177/0011128716687756

    Google Scholar 

  • Lin J, Ban Y (2013) Complex network topology of transportation systems. Transp Rev 33(6):658–685

    Google Scholar 

  • Morenoff JD, Sampson RJ, Raudenbush SW (2001) Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology 39(3):517–559

    Google Scholar 

  • Newman O (1972) Defensible space. Macmillan, New York

    Google Scholar 

  • Newman O, Franck KA (1980) Factors influencing crime and instability in urban housing developments: executive summary. US Department of Justice, National Institute of Justice, New York

    Google Scholar 

  • Sampson R, Groves B (1989) Community structure and crime: testing social-disorganization theory. Am J Soc 94(4):774–802

    Google Scholar 

  • Sampson RJ, Raudenbush SW, Earls F (1997) Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277(5328):918–924

    Google Scholar 

  • Smith WR, Frazee SG, Davison EL (2000) Furthering the integration of routine activity and social disorganization theories: small units of analysis and the study of street roberry as a diffusion process. Criminology 38(2):489–524

    Google Scholar 

  • Steinberg J (ed) (1979) Synthetic estimates for small areas: statistical workshop papers and discussion, vol 24. National Institute on Drug Abuse Research Monograph Series. National Institute on Drug Abuse, Washington, DC

  • Summers L, Johnson SD (2017) Does the configuration of the street network influence where outdoor serious violence takes place? Using space syntax to test crime pattern theory. J Quant Criminol 33(2):397–420. https://doi.org/10.1007/s10940-016-9306-9

    Article  Google Scholar 

  • Taylor R (1997) Social order and disorder of street blocks and neighborhoods: ecology, microecology, and the systemic model of social disorganization. J Res Crime Delinq 34(1):113–155

    Google Scholar 

  • Taylor RB (2015) Community criminology: fundamentals of spatial and temporal scaling, ecological indicators, and selectivity bias. New York University Press, New York

    Google Scholar 

  • Taylor R, Gottfredson S (1986) Enivronmental design, crime, and prevention: an examination of community dynamics. In: Chicago TM (ed) Communities and crime. University of Chicago Press, Chicago

    Google Scholar 

  • Taylor R, Koons BA, Kurtz EM, Greene JR, Perkins DD (1995) Street blocks with more nonresidential land use have more physical deterioration: evidence from Baltimore and Philadelphia. Urban Affairs Rev 31:120–136

    Google Scholar 

  • Vaughan L (2005) The relationship between physical segregation and social marginalisation in the urban environment. World Archit 185:88–96

    Google Scholar 

  • Vaughan L (2007) The spatial syntax of urban segregation. Progr Plan 67(3):205–294

    Google Scholar 

  • Vaughan L, Chatford Clark DL, Sahbaz O, Haklay M (2005) Space and exclusion: Does urban morphology play a part in social deprivation? Area 37(4):402–412

    Google Scholar 

  • Weisburd D, Groff ER, Yang S-M (2012) The criminology of place: street segments and our understanding of the crime problem. Oxford University Press, New York

    Google Scholar 

  • Weisburd D, Groff ER, Yang S-M (2014) Understanding and controlling hot spots of crime: the importance of formal and informal social controls. Prev Sci 15(1):31–43

    Google Scholar 

  • White GF (1990) Neighborhood permeability and burglary rates. Justice Q 7(1):57–67

    Google Scholar 

  • Wicker AW (1987) Behavior settings reconsidered: Temporal stages, resources, internal dynamics, context. In: Stokels D, Altman I (eds) Handbook of environmental psychology. Wiley, New York

    Google Scholar 

  • Yang Y, Diez-Roux AV (2012) Walking distance by trip purpose and population subgroups. Am J Prev Med 43(1):11–19

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Criminology and Criminal Justice, Florida State University, 308 Eppes Hall, 112 S. Copeland Street, Tallahassee, FL, 32306-1273, USA

    Young-An Kim

  2. Department of Criminology, Law & Society, University of California-Irvine, Irvine, CA, USA

    John R. Hipp

Authors
  1. Young-An Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John R. Hipp
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Young-An Kim.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 357 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, YA., Hipp, J.R. Pathways: Examining Street Network Configurations, Structural Characteristics and Spatial Crime Patterns in Street Segments. J Quant Criminol 36, 725–752 (2020). https://doi.org/10.1007/s10940-019-09428-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-019-09428-7

Keywords

Search

Navigation