A quasi-experimental evaluation of the impact of bike-sharing stations on micro-level robbery occurrence

  • Cory P. Haberman
  • Jeffrey E. Clutter
  • Samantha Henderson
Article

Abstract

Objectives

To examine if the implementation of bike-sharing stations is linked to robbery occurrence in micro-level street corner units in Cincinnati, OH, USA.

Methods

Propensity score matching was used to select comparison street corner units. The effect of bike-sharing station implementation on robbery occurrence across weekly, biweekly, and monthly observations was estimated using repeated measures multi-level logistic regression models.

Results

Bike-sharing stations did not statistically significantly link to robbery occurrence in immediate or nearby street corner units after implementation.

Conclusions

Numerous explanations consistent with Crime Pattern Theory may explain the null effect of bike-sharing stations on robbery occurrence. Future research should continue to examine how changes in the urban backcloth, such as bike-sharing stations, impact geographic crime patterns.

Keywords

Bike-sharing stations Crime Pattern Theory Geography of crime Criminology of place Robbery 

Notes

Acknowledgements

The authors thank J.C. Barnes and the anonymous reviewers for their insightful comments on drafts of the manuscript.

Supplementary material

11292_2017_9312_MOESM1_ESM.docx (192 kb)
ESM 1 (DOCX 191 kb)

References

  1. Alta Planning + Design. (2012). Cincinnati bike sharing feasibility study. Cincinnati, OH: City of Cincinnati.Google Scholar
  2. Anderson, E. (1978). A place on the corner. Chicago, IL: University of Chicago Press.Google Scholar
  3. Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of the inner city. New York, NY: W.W. Norton & Company.Google Scholar
  4. Andresen, M. A., & Malleson, N. (2013). Crime seasonality and its variations across space. Applied Geography, 43, 25–35.CrossRefGoogle Scholar
  5. Austin, P. C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083–3107.CrossRefGoogle Scholar
  6. Bernasco, W., & Block, R. (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(1), 33–57.CrossRefGoogle Scholar
  7. Bernasco, W., & Steenbeek, W. (2017). More places than crimes: implications for evaluating the law of crime concentration at place. Journal of Quantitative Criminology, 33(3), 451–467.CrossRefGoogle Scholar
  8. Billings, S. B., Leland, S., & Swindell, D. (2011). The effects of the announcement and opening of light rail transit stations on neighborhood crime. Journal of Urban Affairs, 33(5), 549–566.CrossRefGoogle Scholar
  9. Block, R. L., & Davis, S. (1996). The environs of rapid transit stations: A focus for street crime or just another risky place? In R. V. Clarke (Ed.), Crime and place (pp. 145–184). Monsey, NY: Criminal Justice Press.Google Scholar
  10. Brantingham, P. J., & Brantingham, P. L. (1993). Environment, routine, and situation: Toward a pattern theory of crime. In R. V. Clarke & M. Felson (Eds.), Routine activity and rational choice (pp. 259–294). New Brunswick, NJ: Transaction.Google Scholar
  11. Brantingham, P. J., & Brantingham, P. L. (1995). Criminality of place: Crime generators and crime attractors. European Journal on Criminal Policy and Research, 3(3), 5–26.CrossRefGoogle Scholar
  12. Brantingham, P. L., & Brantingham, P. J. (1999). A theoretical model of crime hot spot generation. Studies on Crime & Crime Prevention, 8(1), 7–26.Google Scholar
  13. Ceccato, V., & Uittenbogaard, A. C. (2014). Space–time dynamics of crime in transport nodes. Annals of the Association of American Geographers, 104(1), 131–150.CrossRefGoogle Scholar
  14. Chainey, S. P., & Ratcliffe, J. H. (2005). GIS and crime mapping. London: Wiley.CrossRefGoogle Scholar
  15. Clarke, R. V., Belanger, M., & Eastman, J. (1996). Where angels fear to tread: A test in the new York City subway of the robbery/density hypothesis. Crime Prevention Studies, 6, 217–236.Google Scholar
  16. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: a routine activity approach. American Sociological Review, 44(4), 588–608.CrossRefGoogle Scholar
  17. Cohn, E. G., & Rotton, J. (1997). Assault as a function of time and temperature: a moderator-variable time-series analysis. Journal of Personality and Social Psychology, 72(6), 1322–1344.CrossRefGoogle Scholar
  18. DeMaio, P., & Meddin, R. (2016). The bike-sharing world map. Retrieved from http://www.bikesharingmap.com
  19. Firestine, T. (2016). Technical brief: Bike-share stations in the U.S.. Washington, DC: Bureau of Transportation Statistics, U.S. Department of Transportation.Google Scholar
  20. Fishman, E., Washington, S., & Haworth, N. (2013). Bike share: A synthesis of the literature. Transport Reviews, 33(2), 148–165.CrossRefGoogle Scholar
  21. Goodell, R. (2012). Official playing rules and casebook of the National Football League. New York, NY: National Football League.Google Scholar
  22. Groff, E. R., & Lockwood, B. (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(3), 277–314.CrossRefGoogle Scholar
  23. Groff, E. R., Weisburd, D., & Morris, N. A. (2009). Where the action is at places: Examining spatio-temporal patterns of juvenile crime at places using trajectory analysis and GIS. In D. Weisburd, W. Bernasco, & G. J. N. Bruinsma (Eds.), Putting crime in its place (pp. 61–86). New York, NY: Springer.CrossRefGoogle Scholar
  24. Groff, E. R., Weisburd, D., & Yang, S. M. (2010). Is it important to examine crime trends at a local “micro” level?: A longitudinal analysis of street to street variability in crime trajectories. Journal of Quantitative Criminology, 26(1), 7–32.CrossRefGoogle Scholar
  25. Guerette, R. T., & Clarke, R. V. (2003). Product life cycles and crime: Automated teller machines and robbery. Security Journal, 16(1), 7–18.CrossRefGoogle Scholar
  26. Guo, S., & Fraser, M. W. (2015). Propensity score analysis: Statistical methods and applications. Thousand Oaks, CA: Sage Publications.Google Scholar
  27. Haberman, C. P., & Ratcliffe, J. H. (2015). Testing for temporally differentiated relationships among potentially criminogenic places and census block street robbery counts. Criminology, 53(3), 457–483.CrossRefGoogle Scholar
  28. Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 67(9), 2047–2059.CrossRefGoogle Scholar
  29. Harries, K. D., & Stadler, S. J. (1983). Determinism revisited: Assault and heat stress in Dallas, 1980. Environment and Behavior, 15(2), 235–256.CrossRefGoogle Scholar
  30. Harries, K. D., Stadler, S. J., & Zdorkowski, R. T. (1984). Seasonality and assault: Explorations in inter-neighborhood variation, Dallas 1980. Annals of the Association of American Geographers, 74, 590–604.CrossRefGoogle Scholar
  31. Hart, T. C., & Miethe, T. D. (2014). Street robbery and public bus stops: A case study of activity nodes and situational risk. Security Journal, 27(2), 180–193.CrossRefGoogle Scholar
  32. Ihlanfeldt, K. R. (2003). Rail transit and neighborhood crime: the case of Atlanta, Georgia. Southern Economic Journal, 70(2), 273–294.CrossRefGoogle Scholar
  33. Irvin-Erickson, Y., & La Vigne, N. (2015). A spatio-temporal analysis of crime at Washington, DC metro rail: Stations’ crime-generating and crime-attracting characteristics as transportation nodes and places. Crime Science, 4(1), 1–13.CrossRefGoogle Scholar
  34. Jacobs, J. (1961). The death and life of great American cities. New York, NY: Random House.Google Scholar
  35. Kooi, B. R. (2007). Policing public transportation: An environment and procedural evaluation of bus stops. El Paso, TX: LFB Scholarly Publishing LLC.Google Scholar
  36. Kooi, B. R. (2013). Assessing the correlation between bus stop densities and residential crime typologies. Crime Prevention & Community Safety, 15(2), 81–105.CrossRefGoogle Scholar
  37. Levine, N., Wachs, M., & Shirazi, E. (1986). Crime at bus stops: A study of environmental factors. Journal of Architectural and Planning Research, 3(4), 339–361.Google Scholar
  38. Loukaitou-Sideris, A., Liggett, R., & Iseki, H. (2002). The geography of transit crime: Documentation and evaluation of crime incidence on and around the green line stations in Los Angeles. Journal of Planning Education and Research, 22(2), 135–151.CrossRefGoogle Scholar
  39. McCord, E. S., & Ratcliffe, J. H. (2007). A micro-spatial analysis of the demographic and criminogenic environment of drug markets in Philadelphia. Australian & New Zealand Journal of Criminology, 40(1), 43–63.CrossRefGoogle Scholar
  40. McCord, E. S., & Ratcliffe, J. H. (2009). Intensity value analysis and the criminogenic effects of land use features on local crime patterns. Crime Patterns and Analysis, 2(1), 17–30.Google Scholar
  41. McDowall, D., Loftin, C., & Pate, M. (2012). Seasonal cycles in crime, and their variability. Journal of Quantitative Criminology, 28(3), 389–410.CrossRefGoogle Scholar
  42. Pfrommer, J., Warrington, J., Schildbach, G., & Morari, M. (2014). Dynamic vehicle redistribution and online price incentives in shared mobility systems. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1567–1578.CrossRefGoogle Scholar
  43. Phillips, S. W., Wheeler, A., & Kim, D. Y. (2016). The effect of police paramilitary unit raids on crime at micro-places in Buffalo, New York. International Journal of Police Science & Management, 18(3), 206–219.CrossRefGoogle Scholar
  44. Poister, T. H. (1996). Transit-related crime in suburban areas. Journal of Urban Affairs, 18(1), 63–75.CrossRefGoogle Scholar
  45. Rabe-Hesketh, S., & Skrondal, A. (2012a). Multilevel and longitudinal modeling using Stata, Volume I: Continuous responses (3rd ed.). College Station, TX: Stata Press.Google Scholar
  46. Rabe-Hesketh, S., & Skrondal, A. (2012b). Multilevel and longitudinal modeling using Stata, Volume II: Categorical responses, counts, and survival (3rd ed.). College Station, TX: Stata Press.Google Scholar
  47. Ratcliffe, J. H. (2004). Geocoding crime and a first estimate of a minimum acceptable hit rate. International Journal of Geographical Information Science, 18(1), 61–72.CrossRefGoogle Scholar
  48. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.Google Scholar
  49. Red Bike (2016). Red Bike: Launch through 2015 annual report. Cincinnati, OH: Cincinnati Red Bike.Google Scholar
  50. Rotton, J., & Cohn, E. G. (1999). Errors of commission and omission: Comment on Anderson and Anderson’s (1998) “temperature and aggression”. Psychological Reports, 85(2), 611–620.CrossRefGoogle Scholar
  51. Simon, D., & Burns, E. (1997). The corner: A year in the life of an inner-city neighborhood. New York, NY: Broadway Books.Google Scholar
  52. Sorg, E. T. (2015). An ex post facto evaluation of the Philadelphia GunStat model. Doctoral dissertation. Retrieved from Temple University.Google Scholar
  53. St. Jean, P. K. B. (2007). Pockets of crime: Broken windows, collective efficacy, and the criminal point of view. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
  54. StataCorp. (2013). Stata statistical software: Release 13. College Station, TX: StataCorp LP.Google Scholar
  55. Stucky, T. D., & Smith, S. L. (2017). Exploring the conditional effects of bus stops on crime. Security Journal, 30(1), 290–309.CrossRefGoogle Scholar
  56. Truman, J. L., & Morgan, R. E. (2016). Criminal victimization, 2015. (No. NCJ 250180). Washington, DC: Bureau of Justice Statistics.Google Scholar
  57. Weisburd, D., Bushway, S., Lum, C., & Yang, S. M. (2004). Trajectories of crime at places: A longitudinal study of street segments in the city of Seattle. Criminology, 42(2), 283–322.CrossRefGoogle Scholar
  58. Weisburd, D., Groff, E. R., & Yang, S.-M. (2012). The criminology of place: Street segments and our understanding of the crime problem. New York, NY: Oxford University Press.Google Scholar
  59. Wicker, A. W. (1987). Behavior settings reconsidered: Temporal stages, resources, internal dynamics, context. In D. Stokols & I. Altman (Eds.), Handbook of environmental psychology (pp. 157–192). New York: John Wiley.Google Scholar
  60. Wright, R. T., & Decker, S. H. (1997). Armed robbers in action: Stickups and street culture. Boston, MA: Northeastern University Press.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Cory P. Haberman
    • 1
  • Jeffrey E. Clutter
    • 2
  • Samantha Henderson
    • 1
  1. 1.School of Criminal JusticeUniversity of CincinnatiCincinnatiUSA
  2. 2.Marywood University, Department of Social Sciences, Criminal Justice ProgramScrantonUSA

Personalised recommendations