Skip to main content
Log in

Early Warning System for Temporary Crime Hot Spots

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

Abstract

Objectives

We investigate the potential for preventing crimes at temporary hot spots in addition to chronic hot spots. Using data on serious violent crimes from Pittsburgh, Pennsylvania, we investigate an early warning system (EWS) for starting/stopping police deployments at temporary hot spots in coordination with constant prevention work at chronic hot spots.

Methods

We estimate chronic hot spots using kernel density smoothing. We use simple rules for detecting flare-ups of temporary hot spots, predicting their persistence, deploying police, and stopping deployments. We also consider a combination program including the hottest chronic hot spots, with EWS applied to remaining areas. Using 2000–2010 data, we run computational experiments varying the size of chronic hot spots and varying rule thresholds to tune the EWS. Tradeoff curves with percentage of crimes exposed to prevention versus percentage area of the city with crime prevention workload provide tools for coordinating chronic and temporary hot spot programs.

Results

The combination program is the most efficient, equitable, and responsive program. After first allocating police prevention resources to the hottest chronic hot spots, the marginal benefits of adding more chronic hot spot area is not as high as adding temporary hot spots. Chronic hot spots are limited to large commercial and adjoining residential areas. Temporary hot spots are widely scattered throughout Pittsburgh.

Conclusions

Temporary hot spots exist outside of chronic hot spots and are targets for prevention as supplements to chronic hot spots. A combination program targeting both chronic and temporary hot spots is recommended.

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

Access this article

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

Similar content being viewed by others

Notes

  1. The status of hot spot policing in Pittsburgh over 2000–2010 was confirmed by reading annual reports of the Pittsburgh Police Bureau and from interviews of commanders of the two zones with the highest violent crime rates. Authority to use hot spot programs rests with the six zone commanders, and they used saturation patrols in only two locations for short periods. A 2012 master’s student project in the Heinz College of Carnegie Mellon University designed chronic hot spots using the methods provided in this paper for one of the commanders, who has since implemented the design. In the early 1990s, one Pittsburgh neighborhood, Homewood, famously had intensive hot spot policing for an entire summer, supported by a DMAP crime mapping project (Olligschlaeger 1998) but that kind of effort was not repeated in the study period.

  2. This estimate was provided by Commander Brackney of the Pittsburgh Police Bureau through personal communication. In contrast, one estimate in the literature (Famega et al. 2005) is that up to 75 % of patrol officers’ time is not spent answering calls, so other cities may have higher average times available for hot spot patrol than Pittsburgh.

References

  • Block CR (1995) STAC hot-spot areas: a statistical tool for law enforcement decisions. In: Crime analysis through computer mapping. Police Executive Research Forum, Washington, pp 15–32

  • Blumstein A, Cohen J, Das S, Moitra SD (1988) Specialization and seriousness during adult criminal careers. J Quant Criminol 4:303–345

    Google Scholar 

  • Bowers KJ, Johnson SD, Pease K (2004) Prospective hot-spotting the future of crime mapping? Br J Criminol 44:641–658

    Article  Google Scholar 

  • Box S, Hale C, Andrews G (1988) Explaining fear of crime. Br J Criminol 28:340–356

    Google Scholar 

  • Brackney, Commander R (2/14/2013) Pittsburgh Bureau of Police, private communication

  • Braga AA, Weisburd DL (2010) Policing problem places: crime hot spots and effective prevention. Oxford University Press, USA

    Book  Google Scholar 

  • Braga AA, Weisburd DL, Waring EJ, Mazerolle LG, Spelman W, Gajewski F (1999) Problem-oriented policing in violent crime places: a randomized controlled experiment. Criminology 37:541–580

    Article  Google Scholar 

  • Braga AA, Papachristos AV, Hureau DM (2010) The concentration and stability of gun violence at micro places in Boston, 1980–2008. J Quant Criminol 26:33–53

    Article  Google Scholar 

  • Braga AA, Hureau DM, Papachristos AV (2011) The relevance of micro places to citywide robbery trends: a longitudinal analysis of robbery incidents at street corners and block faces in Boston. J Res Crime Delinquency 48:7–32

    Article  Google Scholar 

  • Braga AA, Papachristos AV, Hureau DM (2012) The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q 29:1–31

    Google Scholar 

  • Brown RG (1959) Statistical forecasting for inventory control. McGraw-Hill, New York

  • Brown RG (1963) Smoothing, forecasting and prediction of discrete time series. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Chainey S, Ratcliffe J (2005) GIS and crime mapping. Wiley, New York

  • Chainey S, Reid S, Stuart N (2002) When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime. Taylor and Francis, London, pp 21–36

    Google Scholar 

  • Chainey S, Tompson L, Uhlig S (2008) The utility of hotspot mapping for predicting spatial patterns of crime. Secur J 21:4–28

    Article  Google Scholar 

  • Clarke RV, Weisburd DL (1994) Diffusion of crime control benefits: observations on the reverse of displacement. In: Clarke RV (ed) Crime prevention studies. Criminal Justice Press, New York

    Google Scholar 

  • Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44:588–605

    Article  Google Scholar 

  • Cohen J, Tita G (1999) Spatial diffusion in homicide: exploring a general method of detecting spatial diffusion processes. J Quant Criminol 15:451–493

    Article  Google Scholar 

  • Cohen J, Gorr W, Singh P (2003) Estimating intervention effects in varying risk settings: do police raids reduce illegal drug dealing at nuisance bars. Criminology 41:257–292

    Article  Google Scholar 

  • Cohen J, Gorr WL, Olligschlaeger AM (2007) Leading indicators and spatial interactions: a crime-forecasting model for proactive police deployment. Geogr Anal 39:105–127

    Article  Google Scholar 

  • Cohen J, Garman S, Gorr W (2009) Empirical calibration of time series monitoring methods using receiver operating characteristic curves. Int J Forecast 25:484–497

    Article  Google Scholar 

  • Corcoran JJ, Wilson ID, Ware JA (2003) Predicting the geo-temporal variations of crime and disorder. Int J Forecast 19:623–634

    Article  Google Scholar 

  • Craglia M, Haining R, Wiles P (2000) A comparative evaluation of approaches to urban crime pattern analysis. Urban Stud 37:711–729

    Article  Google Scholar 

  • Croston JD (1972) Forecasting and stock control for intermittent demands. J Oper Res Soc 23:289–303

    Article  Google Scholar 

  • DeLisi M (2001) Extreme career criminals. Am J Crim Justice 25:239–252

    Article  Google Scholar 

  • Doran BJ, Burgess MB (2012) Putting fear of crime on the map. Springer, New York

    Book  Google Scholar 

  • Eck JE (1994) Drug markets and drug places: a case-control study of the spatial structure of illicit drug dealing. Doctoral dissertation, University of Maryland, Faculty of the Graduate School

  • Eck JE (1997) Preventing crime at places. In: Sherman LW, Gottfredson D, MacKenzie D, Eck J, Reuter P, Bushway S (eds) Preventing crime: what works, what doesn’t, what’s promising—a report to the attorney general of the United States, Chap. 7. United States Department of Justice, Office of Justice Programs, Washington

  • Famega CN, Frank J, Mazerolle L (2005) Managing police patrol time: the role of supervisor directives. Justice Q 22:540–559

    Article  Google Scholar 

  • Felson M (1986) Linking criminal choices, routine activities, informal control, and criminal outcomes. The reasoning criminal. Springer New York, pp 119–128

  • Felson M (1987) Routine activities and crime prevention in the developing metropolis. Criminology 25:911–931

    Article  Google Scholar 

  • Garland R (9/14/2012) Pittsburgh initiative to reduce crime. University of Pittsburgh, private communication

  • Gorr WL (2009) Forecast accuracy measures for exception reporting using receiver operating characteristic curves. Int J Forecast 25:48–61

    Article  Google Scholar 

  • Gorr WL, Lee YJ (2012) Longitudinal study of crime hot spots: dynamics and impact on part 1 violent crime. In: Proceedings of the 32nd international symposium on forecasting, June 2012

  • Greene JA (1999) Zero tolerance: a case study of police policies and practices in New York City. Crime Delinquency 45:171–187

    Article  Google Scholar 

  • Grohe BR (2007) Perceptions of crime, fear of crime, and defensible space in Fort Worth neighborhoods. Doctoral dissertation, University of Texas at Arlington

  • Grubesic TH (2006) On the application of fuzzy clustering for crime hot spot detection. J Quant Criminol 22:77–105

    Article  Google Scholar 

  • Haining R (2012) Ecological analysis of urban offence and offender data. In: The urban fabric of crime and fear. Springer, Netherlands, pp 141–163

  • Harries K (1999) Mapping crime: principle and practice. No. NCJ 178919

  • Hesseling RBP (1995) Displacement: a review of the empirical literature. Crime prevention studies. Criminal Justice Press, New York

    Google Scholar 

  • Hinkle JC, Weisburd DL (2008) The irony of broken windows policing: a micro-place study of the relationship between disorder, focused police crackdowns and fear of crime. J Crim Justice 36:503–512

    Article  Google Scholar 

  • Jacobs BA, Topalli V, Wright R (2000) Managing retaliation: drug robbery and informal sanction threats. Criminology 38:171–198

    Article  Google Scholar 

  • Jefferis E (1999) A multi-method exploration of crime hot spots: a summary of findings. US Department of Justice, National Institute of Justice, Crime Mapping Research Center, Washington

    Google Scholar 

  • Johnson SD, Bowers KJ (2004a) The burglary as clue to the future the beginnings of prospective hot-spotting. Eur J Criminol 1:237–255

    Article  Google Scholar 

  • Johnson SD, Bowers KJ (2004b) The stability of space-time clusters of burglary. Br J Criminol 44:55–65

    Article  Google Scholar 

  • Keane C (1998) Evaluating the influence of fear of crime as an environmental mobility restrictor on women’s routine activities. Environ Behav 30:60–74

    Article  Google Scholar 

  • Kling JR, Liebman JB, Katz LF (2007) Experimental analysis of neighborhood effects. Econometrica 75:83–119

    Article  Google Scholar 

  • Levine N (2013) CrimeStat IV: a spatial statistics program for the analysis of crime incident locations. Ned Levine and Associates/National Institute of Justice, Houston/Washington

  • Makridakis S, Hibon M (2000) The M3 competition: results, conclusions and implications. Int J Forecast 16:451–476

    Article  Google Scholar 

  • Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen E, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecast 1:111–153

    Article  Google Scholar 

  • McClain JO (1988) Dominant time series monitoring methods. Int J Forecast 18:563–572

    Article  Google Scholar 

  • McGuire PG, Williamson D (1999) Mapping tools for management and accountability. In: Third international crime mapping research center conference, Orlando, pp 11–14

  • Miethe T (1991) Citizen-based crime control activity and victimization risks: an examination of displacement and free rider effects. Criminology 29:419–441

    Article  Google Scholar 

  • Mohler GO, Short MB, Brantingham PJ, Schoenberg FP, Tita GE (2011) Self-exciting point process modeling of crime. J Am Stat Assoc 106:100–108

    Article  Google Scholar 

  • Moore MH, Trojanowicz RC (1988) Policing and the fear of crime. National Institute of Justice, Washington, pp 1–7

    Google Scholar 

  • Morgan F (2001) Repeat burglary in a Perth suburb: indicator of short-term or long-term risk? Crime Prevent Stud 12:83–118

    Google Scholar 

  • Nagin DS (1999) Analyzing developmental trajectories: a semiparametric group-based approach. Psychol Methods 4:139–157

    Article  Google Scholar 

  • Nasar JL, Fisher B, Grannis M (1993) Proximate physical cues to fear of crime. Landsc Urban Plann 26:161–178

    Article  Google Scholar 

  • Neill DB (2009) Expectation-based scan statistics for monitoring spatial time series data. Int J Forecast 25:498–517

    Article  Google Scholar 

  • Neill DB, Gorr WL (2007) Detecting and preventing emerging epidemics of crime. Adv Dis Surveill 4:13

    Google Scholar 

  • Olligschlaeger AM in McEwen, T, Weisburd, D (ed) (1998) Crime mapping & crime prevention. Criminal Justice Press, Monsey

  • Pease K (1998) Repeat victimisation: taking stock (Crime Detection and Prevention Series Paper 90). Home Office Police Research Group, London

    Google Scholar 

  • Piquero A (2000) Frequency, specialization, and violence in offending careers. J Res Crime Delinquency 37:392–418

    Article  Google Scholar 

  • Popkin SJ, Levy DK, Harris LE, Comey J, Cunningham MK, Buron L, Woodley W (2002) HOPE VI panel study: Baseline report (http://www.urban.org/publications/410590.html)

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ratcliffe JH, Rengert GF (2008) Near-repeat patterns in Philadelphia shootings. Security J 21:58–76

    Article  Google Scholar 

  • Ratcliffe J, Taniguchi T, Groff E, Wood J (2011) The Philadelphia foot patrol experiment: a randomized controlled trial of police patrol effectiveness in violent crime hotspots. Criminology 49:795–831

    Article  Google Scholar 

  • Regattieri A, Gamberi M, Gamberini R, Manzini R (2005) Managing lumpy demand for aircraft spare parts. J Air Transp Manag 11:426–431

    Article  Google Scholar 

  • Rengert GF, Piquero AR, Jones P (1999) Distance decay reexamined. Criminology 37:427–446

    Article  Google Scholar 

  • Reppetto T (1976) Crime prevention and the displacement phenomenon. Crime Delinquency 22:166–177

    Article  Google Scholar 

  • Rosenbaum DP (2006) The limits of hot spots policing. In: Weisburd D, Braga AA (eds) Police innovation: contrasting perspectives. Cambridge University Press, New York, pp 245–263

  • Sanbonmatsu L, Ludwig J, Katz LF, Gennetian LA, Duncan GJ, Kessler RC, Adam E, McDade TW, Lindau ST (2011) Moving to opportunity for fair housing demonstration program—final impacts evaluation

  • Scherdin MJ (1986) The halo effect: psychological deterrence of electronic security systems. Inf Technol Libr 5:232–235

    Google Scholar 

  • Sherman LW (1995) Hot spots of crime and criminal careers of places. Crime Place 4:35–52

    Google Scholar 

  • Sherman L, Buerger M, Gartin P (1989a) Repeat call address policing: the Minneapolis RECAP experiment. Crime Control Institute, Washington

    Google Scholar 

  • Sherman LW, Gartin PR, Buerger ME (1989b) Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27:27–55

    Article  Google Scholar 

  • Skogan W (1986) Fear of crime and neighborhood change. Crime Justice 8:203–229

    Google Scholar 

  • Skogan WG, Maxfield MG (1981) Coping with crime: individual and neighborhood reactions. Sage Publications, Beverly Hills, p 272

    Google Scholar 

  • Spelman W (1995) Criminal careers of public places. In: Eck JE, Weisburd D (eds) Crime and places: crime prevention studies 4. Willow Tree Press, New York

    Google Scholar 

  • Taylor RB, Hale M (1986) Testing alternative models of fear of crime. J Crim Law Criminol 77:151–189

    Article  Google Scholar 

  • Tita G, Ridgeway G (2007) The impact of gang formation on local patterns of crime. J Res Crime Delinquency 44:208–237

    Article  Google Scholar 

  • Tonry M (2011) Less imprisonment is no doubt a good thing. Criminol Public Policy 10:137–152

    Article  Google Scholar 

  • Townsley M, Homel R, Chaseling J (2003) Infectious burglaries: a test of the near repeat hypothesis. Br J Criminol 43:615–633

    Article  Google Scholar 

  • Trigg DW (1964) Monitoring a forecasting system. Oper Res Q 15:271–274

    Article  Google Scholar 

  • Weisburd D, Braga AA (eds) (2006) Hot spots policing as a model for police innovation. Police innovation: contrasting perspectives. Cambridge University Press, New York, pp 225–244

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

    Article  Google Scholar 

  • Weisburd DL, Wyckoff LA, Ready J, Eck JE, Hinkle JC, Gajewski F (2006) Does crime just move around the corner? A controlled study of spatial displacement and diffusion of crime control benefits. Criminology 44:549–592

    Article  Google Scholar 

  • Weisburd DL, Hinkle JC, Famega C, Ready J (2011) The possible “backfire” effects of hot spots policing: an experimental assessment of impacts on legitimacy, fear and collective efficacy. J Exp Criminol 7:297–320

    Article  Google Scholar 

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

  • Wilcox P, Eck JE (2011) Criminology of the unpopular. Criminol Public Policy 10:473–482

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to Professors Al Blumstein, John Eck, Daniel Neill, Jerry Ratcliffe, and David Weisburd for their suggestions on our earlier research leading to the current paper. We also wish to thank Acting Chief Regina McDonald, Commander RaShall Brackney, and Detective Deborah Gilkey of the Pittsburgh Bureau of Police for assistance with the crime data used in this paper and their insights into crime patterns in Pittsburgh. Finally, we owe our gratitude to the referees and editors for their many insightful suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wilpen L. Gorr.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gorr, W.L., Lee, Y. Early Warning System for Temporary Crime Hot Spots. J Quant Criminol 31, 25–47 (2015). https://doi.org/10.1007/s10940-014-9223-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-014-9223-8

Keywords

Navigation