Skip to main content

The Topography of Robbery: Does Slope Matter?

Journal of Quantitative Criminology volume 37, pages 625–645 (2021)Cite this article

Abstract

Objectives

To examine the influence of street block slope on robbery in Cincinnati, Ohio.

Methods

Data visualizations were used to examine how street block slope varies across the city. Negative binomial regression models were used to estimate the influence of street block slope on robbery net of betweenness, facility composition, and socio-demographics.

Results

A 1% increase in street block slope was associated with roughly 4.5% fewer street block robberies per foot of street block length. Street blocks with a higher expected usage potential, measured via betweenness, were also observed to have higher expected robbery levels. In addition, numerous facilities and neighborhood socio-demographic characteristics linked to higher robbery levels.

Conclusions

Steeper street blocks may have fewer robberies because they make the physical costs for committing robberies too high, are too difficult to escape from, and/or provide fewer robbery opportunities due to relatively lower usage. Moreover, more robberies appear to occur on street blocks with higher betweenness due to more potential opportunities there. Finally, the influence of facilities and community characteristics were largely consistent with theoretical expectations and past studies. Future studies should continue to examine how topography influences aggregate crime levels and offender decision making in other settings to bolster the external validity of the present findings.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3

Notes

  1. As one reviewer noted, a reasonable hypothesis is that busy places will have less crime by increasing guardianship. This hypothesis is often framed using Jacobs’ (1961) notion of “eyes on the street”. The environmental criminology literature reviewed here generally does not support that hypothesis. This may be because guardianship requires more than just the presence of people (Reynald 2009) and many people fail to act when present during crimes and other events (Fischer et al. 2011). Nonetheless, the guardianship hypothesis remains viable for future research, and suggests two-tailed hypothesis tests should be used when estimating facilities-crime relationships.

  2. The least effort principle suggests people will exert the least effort possible to achieve their goals, thus it is hypothesized that offenders also seek to exert the least effort possible during offending (Zipf 1950).

  3. We thank the manuscript’s editor, Arjan Blokland, and the anonymous reviewers for helping us explicate our ideas more clearly here.

  4. Of course, as the editor Arjan Blokland noted during review, the preceding two mechanisms would not apply to robberies where the offender uses an automobile. Given the operationalization of robbery described in the “Data and Method” section, this critique is minimized for the present study.

  5. Due to Cincinnati’s hilly nature, 108 pedestrian-only segments remained in the cleaned dataset. These segments were predominantly staircases connecting street blocks (usually with the same street name). On average, these segments were quite steep (Slope Mean = 13.17). These segments, however, had valid address ranges and two robberies occurred on these segments, thus they were included in the analyses. Effectively identical results were observed when those 108 segments were excluded from the analyses.

  6. Re-estimating the models after calculating the betweenness measure using the edited street network dataset did not impact the results. However, because Cincinnatians might use the edited out street features to travel, we present the results with the betweenness measure calculated for the unedited street network.

  7. We note two nuances to the facilities data. First, the facilities data precede the crime data by 1 year. While this establishes temporal ordering, it is possible some facilities closed and others opened in that time. Weisburd et al. (2012) suggest facilities are relatively stable over time. The legal process for zoning also makes it unlikely locations changed facility types entirely in that time. But readers should keep that point in mind. Second, hypothetically all facilities could be captured with count variables. In reality, multiple facilities of a homogenous type will be located on a street block for only some facilities. Whether a facility was represented by a count or indicator variable is denoted for each facility when it is introduced in the manuscript.

  8. Some readers may be concerned that spatially lagged predictors increase the number of significance tests conducted and the likelihood of making a Type-I error (Bernasco et al. 2017), thus prefering more parsimonious spatially lagged outcome models. A robustness check using a spatially lagged outcome model was also conducted. The results were not substantively different from the more theoretically defensible results shown herein, but are shown in the Online Appendix.

  9. Zero-inflated count models were not considered because we lacked a theoretical reason for why some street blocks could never experience a robbery (also see Groff and Lockwood 2014; Haberman et al. 2018).

  10. Moran’s I tests were performed in GeoDa 1.10.0.8. GeoDa only accepts point and polygon vector files, so the street block data were converted to mid-points prior estimating the Moran’s I results. In order to ensure the results were robust to different specifications, spatial relationships were specified with different spatial weights matrices: (1) queen contiguity and (2) k-nearest neighbors of orders 4, 5, and 6. The k-orders were chosen because the average number of neighbors per street block were 4.22 and 99% of street blocks had six or fewer total neighbors.

  11. We thank an anonymous reviewers for this important idea.

References

  • Anderson E (1999) Code of the street: decency, violence, and the moral life of the inner city. W.W. Norton and Company, New York

    Google Scholar 

  • Andresen MA, Curman AS, Linning SJ (2017) The trajectories of crime at places: understanding the patterns of disaggregated crime types. J Quant Criminol 33(3):427–449

    Article  Google Scholar 

  • Anselin L (1988) Spatial econometrics: methods and models. Kluwer, Dordrecht

    Book  Google Scholar 

  • Barthelemy M (2004) Betweenness centrality in large complex networks. Eur Phys J B 38(2):163–168

    Article  Google Scholar 

  • Beavon DJK, Brantingham PJ, Brantingham PL (1994) The influence of street networks on the patterning of property offenses. In: Clarke RV (ed) Crime prevention studies. Criminal Justice Press, Monsey, pp 115–148

    Google Scholar 

  • Bernasco W (2010) A sentimental journey to crime: effects of residential history on crime location choice. Criminology 48(2):389–416

    Article  Google Scholar 

  • 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(1):33–57

    Article  Google Scholar 

  • Bernasco W, Kooistra T (2010) Effects of residential history on commercial robbers’ crime location choices. Eur J Criminol 7(4):251–265

    Article  Google Scholar 

  • Bernasco W, Nieuwbeerta P (2005) How do residential burglars select target areas? A new approach to the analysis of criminal location choice. Br J Criminol 45(3):296–315

    Article  Google Scholar 

  • Bernasco W, Block R, Ruiter S (2013) Go where the money is: modeling street robbers’ location choices. J Econ Geogr 13(1):119–143

    Article  Google Scholar 

  • Bernasco W, Ruiter S, Block R (2017) Do street robbery location choices vary over time of day or day of week? A test in Chicago. J Res Crime Delinq 54(2):244–275

    Article  Google Scholar 

  • Block RL, Block CB (2000) The Bronx and Chicago: street robbery in the environs of rapid transit stations. In: Goldsmith V, McGuire P, Mellkopf J, Ross T (eds) Analyzing crime patterns: frontiers of practice. Sage Publications Inc, Thousand Oaks, pp 121–135

    Google Scholar 

  • Block RL, Davis S (1996) The environs of rapid transit stations: a focus for street crime or just another risky place? In: Clarke RV (ed) Crime and place. Criminal Justice Press, Monsey, pp 145–184

    Google Scholar 

  • Brantingham PJ, Brantingham PL (1991) Notes on the geometry of crime. In: Brantingham P, Brantingham P (eds) Environmental criminology. Waveland Press Inc., Prospect Heights, pp 27–54

    Google Scholar 

  • Brantingham PJ, Brantingham PL (1993) Environment, routine, and situation: toward a pattern theory of crime. In: Clarke RV, Felson M (eds) Routine activity and rational choice. Transaction, New Brunswick, pp 27–54

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1999) A theoretical model of crime hot spot generation. Stud Crime Crime Prev 8(1):7–26

    Google Scholar 

  • Breetzke GD (2012) The effect of altitude and slope on the spatial patterning of burglary. Appl Geogr 34:66–75

    Article  Google Scholar 

  • Bursik RJ, Grasmick H (1993) Neighborhoods and crime: the dimensions of effective community control. Lexington, New York

    Google Scholar 

  • Byun G, Ha M (2016) Factors of a surveillance environment that affect burglaries in commercial districts. J Asian Archit Build Eng 15(1):73–80

    Article  Google Scholar 

  • Cameron AC, Trivedi PK (2013) Regression analysis of count data, 2nd edn. Cambridge University Press, New York

    Book  Google Scholar 

  • Census Bureau US (2015) United States census quick facts. United States Census Bureau, Washington, DC

    Google Scholar 

  • Cervero R, Duncan M (2003) Walking, bicycling, and urban landscapes: evidence from the San Francisco bay area. Am J Public Health 93(9):1478–1483

    Article  Google Scholar 

  • Cincinnati Area Geographic Information System (2017) Statistical neighborhood approximation boundaries. Retrieved from http://cagisonline.hamilton-co.org/cagisonline/index.html. Accessed 5 Feb 2017

  • City of Cincinnati (2018) City planning. Retrieved from https://www.cincinnati-oh.gov/planning/. Accessed 5 Feb 2017

  • Clarke RV, Cornish DB (1985) Modeling offender’s decisions: a framework for research and policy. Crime Justice 6:147–185

    Article  Google Scholar 

  • Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44(4):588-608.

    Article  Google Scholar 

  • Cornish DB, Clarke RV (1986) Introduction. In: Cornish DB, Clarke RV (eds) The reasoning criminal: rational choice perspectives on offending. Springer, New York, pp 1–16

    Chapter  Google Scholar 

  • Cullen FT (2011) Beyond adolescent-limited criminology: choosing our future—the American society of criminology 2013 Sutherland address. Criminology 49(2):287–330

    Article  Google Scholar 

  • Davies T, Bowers KJ (2018) Street Networks and Crime. In: Bruinsma GJN, Johnson SD (eds) The Oxford handbook of environmental criminology. Oxford University Press, New York

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Felson M (1987) Routine activities and crime prevention in the developing metropolis. Criminology 25(4):911–932

    Article  Google Scholar 

  • Felson M (2002) The topography of crime. Crime Prev Community Saf 4(1):47–51

    Article  Google Scholar 

  • Fischer P, Krueger JI, Greitemeyer T, Vogrincic C, Kastenmüller A, Frey D, Heene M, Wicher M, Kainbacher M (2011) The bystander-effect: a meta-analytic review on bystander intervention in dangerous and non-dangerous emergencies. Psychol Bull 137(4):517

    Article  Google Scholar 

  • Forsyth A, Oakes JM, Lee B, Schmitz KH (2009) The built environment, walking, and physical activity: is the environment more important to some people than others? Transp Res Part D 14:42–49

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gibbs JP, Martin WT (1962) Urbanization, technology, and the division of labor: international patterns. Am Sociol Rev 27(5):667–677

    Article  Google Scholar 

  • Gil J (2017) Street network analysis “edge effects”: examining the sensitivity of centrality measures to boundary conditions. Environ Plan B Urban Anal City Sci 44(5):819–836

    Article  Google Scholar 

  • Groff ER (2011) Exploring ‘near’: characterizing the spatial extent of drinking place influence on crime. Aust N Z J Criminol 44(2):156–179

    Article  Google Scholar 

  • Groff ER, 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(3):277–314

    Article  Google Scholar 

  • Groff ER, McCord ES (2011) The role of neighborhood parks as crime generators. Secur J 25(1):1–24

    Article  Google Scholar 

  • Groff ER, Weisburd D, Morris NA (2009) Where the action is at places: examining spatio-temporal patterns of juvenile crime at places using trajectory analysis in GIS. In: Weisburd D, Bernasco W, Bruinsma GJN (eds) Putting crime in its place. Springer, New York, pp 61–86

    Chapter  Google Scholar 

  • Groff ER, Weisburd D, Yang S (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

    Article  Google Scholar 

  • Groff ER, Taylor RB, Elesh DB, McGovern J, Johnson L (2014) Permeability across a metropolitan area: conceptualizing and operationalizing a macrolevel crime pattern theory. Environ Plan A 46(1):129–152

    Article  Google Scholar 

  • Haberman CP, Ratcliffe JH (2015) Testing for temporally differentiated relationships among potentially criminogenic places and census block street robbery counts. Criminology 53(3):457–483

    Article  Google Scholar 

  • Haberman CP, Groff ER, Taylor RB (2013) The variable impacts of public housing community proximity on nearby street robberies. J Res Crime Delinq 50(2):163–188

    Article  Google Scholar 

  • Haberman CP, Sorg ET, Ratcliffe JH (2018) The seasons they are a changin’ testing for seasonal effects of potentially criminogenic places on street robbery. J Res Crime Delinq 55(3):425–459

    Article  Google Scholar 

  • Horton FE, Reynolds DR (1971) Effects of urban spatial structure on individual behavior. Econ Geogr 47(1):36–48

    Article  Google Scholar 

  • Jacobs J (1961) The death and life of great American cities. Random House, 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

    Article  Google Scholar 

  • Kennedy DM, Braga AA, Piehl AM (1997) The (un) known universe: mapping gangs and gang violence in Boston. In: Weisburd D, McEwen T (eds) Crime prevention studies. Criminal Justice Press, Mosney, pp 219–262

    Google Scholar 

  • Lee C, Moudon AV (2006) Correlates of walking for transportation or recreation purposes. J Phys Act Health 3(s1):S98

    Article  Google Scholar 

  • Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Prog Phys Geogr 32(1):31–49

    Article  Google Scholar 

  • Long JS, Freese J (2014) Regression models for categorical dependent variables using stata, 3rd edn. Stata Press, College Station

    Google Scholar 

  • McCord ES, Houser KA (2017) Neighborhood parks, evidence of guardianship, and crime in two diverse US cities. Secur J 30(3):807–824

    Article  Google Scholar 

  • McCord ES, Ratcliffe JH (2007) A micro-spatial analysis of the demographic and criminogenic environment of drug markets in Philadelphia. Aust N Z J Criminol 40(1):43–63

    Article  Google Scholar 

  • Megler V, Banis D, Chang H (2014) Spatial analysis of graffiti in San Francisco. Appl Geogr 54:63–73

    Article  Google Scholar 

  • Monk KM, Heinonen JA, Eck JE (2010) Street robbery. U.S. Department of Justice Office of Community Oriented Policing Services, Washington, DC

    Google Scholar 

  • Mt. Adams Neighborhood Association (2017) Mt. Adams today. Retrieved from https://www.mtadamstoday.com/. Accessed 5 Feb 2017

  • Peacefull L (1996) A geography of Ohio. Kent State University Press, Kent

    Google Scholar 

  • Peterson RD, Krivo LJ (2010) Divergent social worlds: neighborhood crime and the racial-spatial divide. Russell Sage Foundation, New York

    Google Scholar 

  • Phillips SW, Wheeler A, Kim D (2016) The effect of police paramilitary unit raids on crime at micro-places in Buffalo, New York. Int J Police Sci Manag 18(3):206–219

    Article  Google Scholar 

  • Pierce J, Kolden CA (2015) The hilliness of US cities. Geogr Rev 105(4):581–600

    Article  Google Scholar 

  • Porta S, Crucitti P, Latora V (2006) The network analysis of urban streets: a primal approach. Environ Plan 33(5):705–725

    Article  Google Scholar 

  • Ratcliffe JH (2004) Geocoding crime and a first estimate of a minimum acceptable hit rate. Int J Geogr Inf Sci 18(1):61–72

    Article  Google Scholar 

  • Ratcliffe JH (2012) The spatial extent of criminogenic places: a changepoint regression of violence around bars. Geog Anal 44(4):302–320

    Article  Google Scholar 

  • Ratcliffe JH (2014) What is the future… of predictive policing? Transl Criminol 6:151–166

    Google Scholar 

  • Reynald DM (2009) Guardianship in action: developing a new tool for measurement. Crime Prev Community Saf 11(1):1–20

    Article  Google Scholar 

  • Rodrı́guez DA, Joo J (2004) The relationship between non-motorized mode choice and the local physical environment. Transp Res Part D Transport Environ 9(2):151–173

    Article  Google Scholar 

  • Roman CG (2005) Routine activities of youth and neighborhood violence: spatial modelling of place, time, and crime. In: Wang F (ed) Geographic information systems and crime analysis. Idea Group, Hershey, pp 293–310

    Chapter  Google Scholar 

  • Roncek DW, Bell R (1981) Bars, blocks, and crimes. J Environ Syst 11(1):35–47

    Article  Google Scholar 

  • Roncek DW, Faggiani D (1985) High schools and crime: a replication. Sociol Q 26(4):491–505

    Article  Google Scholar 

  • Roncek DW, LoBosco A (1983) The effect of high schools on crime in their neighborhoods. Soc Sci Q 64(3):588–613

    Google Scholar 

  • Roncek DW, Maier PA (1991) Bars, blocks, and crime revisited: linking the theory of routine activities to the empiricism of “hot spots”. Criminology 29(4):725–753

    Article  Google Scholar 

  • Sampson RJ (2012) Great American city: Chicago and enduring neighborhood effect. The University Chicago Press, Chicago

    Book  Google Scholar 

  • Schnell C, Braga AA, Piza EL (2017) The influence of community areas, neighborhood clusters, and street segments on the spatial variability of violent crime in Chicago. J Quant Criminol 33(3):469–496

    Article  Google Scholar 

  • Sevtsuk A, Mekonnen M (2012) Urban network analysis toolbox. Int J Geomat Spat Anal 22(2):287–305

    Google Scholar 

  • Sevtsuk A, Mekonnen M, Kalvo R (2013) Urban network analysis toolbox for ArcGIS 10/10.1/10.2, Singapore. Retrieved from http://cityform.mit.edu/projects/urban-network-analysis.html. Accessed 5 Feb 2017

  • Shan J, Aparajithan S (2005) Urban DEM generation from raw LiDAR data. Photogramm Eng Remote Sens 71(2):217–226

    Article  Google Scholar 

  • Shaw CR, McKay HD (1942) Juvenile delinquency and urban areas: a study of delinquency in relation to differential characteristics of local communities in American cities. The University of Chicago Press, Chicago

    Google Scholar 

  • Steenbeek W, Weisburd D (2016) Where the action is in crime? An examination of variability of crime across different spatial units in the Hague, 2001–2009. J Quant Criminol 32(3):449–469

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Taylor RB (1997) Order and disorder of streetblocks and neighborhoods: ecology, microecology and the systemic model of social organization. J Res Crime Delinq 34(1):113–115

    Article  Google Scholar 

  • Tompson L, Bowers K (2013) A stab in the dark? A research note on temporal patterns of street robbery. J Res Crime Delinq 50(4):616–631

    Article  Google Scholar 

  • Tufte E (1983) The visual display of quantitative information. Graphics Press, Cheshire

    Google Scholar 

  • U.S. Geological Survey (2017) USGS NED 1/3 arc-second n40w085 1 × 1 degree ArcGrid 2017. U.S. Geological Survey, Washington, DC

    Google Scholar 

  • Vandeviver C, Van Daele S, Vander Beken T (2014) What makes long crime trips worth undertaking? Balancing costs and benefits in burglars’ journey to crime. Br J Criminol 55(2):399–420

    Article  Google Scholar 

  • Weisburd D (2015) The law of crime concentration and the criminology of place. Criminology 53(2):133–157

    Article  Google Scholar 

  • Weisburd D, Bushway S, Lum C, Yang S (2004) Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. Criminology 42(2):283–321

    Article  Google Scholar 

  • Weisburd D, Morris NA, Groff ER (2009) Hot spots of juvenile crime: a longitudinal study of arrests incidents at street segments in Seattle, Washington. J Quant Criminol 25:443–467

    Article  Google Scholar 

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

    Book  Google Scholar 

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

    Article  Google Scholar 

  • Wheeler AP (2016) Quantifying the local and spatial effects of alcohol outlets on crime. Crime Delinq. https://doi.org/10.2139/ssrn.2869198

    Article  Google Scholar 

  • Wheeler AP, Worden RE, McLean SJ (2016) Replicating group-based trajectory models of crime at micro-places in Albany, NY. J Quant Criminol 32(4):589–612

    Article  Google Scholar 

  • Wilcox P, Eck JE (2011) Criminology of the unpopular: implications for policy aimed at payday lending facilities. Criminol Public Policy 10(2):473–482

    Article  Google Scholar 

  • Wilke CO (2018) Package ‘ggridges’, Austin. Retrieved from https://cran.r-project.org/package=ggridges. Accessed 5 Feb 2017

  • Wolfgang ME (1963) Uniform crime reports: a critical appraisal. Univ Pa Law Rev 111:708–738

    Article  Google Scholar 

  • Wright RT, Decker SH (1997) Armed robbers in action: stickups and street culture. Northeastern University Press, Boston

    Google Scholar 

  • Ye VY, Becker CM (2017a) The Z-axis: elevation gradient effects in urban America. Reg Sci Urban Econ 70:312–329

    Article  Google Scholar 

  • Ye VY, Becker CM (2017b) The (literally) steepest slope: spatial, temporal, and elevation variance gradients in urban spatial modelling. J Econ Geogr 18(2):421–460

    Google Scholar 

  • Zipf GK (1950) Human behavior and the principle of least effort. Addison-Wesley Press, Reading

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Criminal Justice, University of Cincinnati, PO Box 210389, 660H Teachers-Dyer, Cincinnati, OH, 45221-0389, USA

    Cory P. Haberman

  2. Department of Criminology and Criminal Justice, University of Texas at Arlington, PO Box 19595, 362 University Hall, Arlington, TX, 76019, USA

    James D. Kelsay

Authors
  1. Cory P. Haberman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James D. Kelsay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cory P. Haberman.

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 890 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Haberman, C.P., Kelsay, J.D. The Topography of Robbery: Does Slope Matter?. J Quant Criminol 37, 625–645 (2021). https://doi.org/10.1007/s10940-020-09451-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-020-09451-z

Keywords

  • Topography
  • Slope
  • Crime pattern theory
  • Geography of crime
  • Robbery