SpringerLink
Log in

Using Space–Time Analysis to Evaluate Criminal Justice Programs: An Application to Stop-Question-Frisk Practices

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

Abstract

Objectives

Effects of place-based criminal justice interventions extend across both space and time, yet methodological approaches for evaluating these programs often do not accommodate the spatiotemporal dimension of the data. This paper presents an example of a bivariate spatiotemporal Ripley’s K-function, which is increasingly employed in the field of epidemiology to analyze spatiotemporal event data. Advantages of this technique over the adapted Knox test are discussed.

Methods

The study relies on x–y coordinates of the exact locations of stop-question-frisk (SQF) and crime incident events in New York City to assess the deterrent effect of SQFs on crime across space at a daily level.

Results

The findings suggest that SQFs produce a modest reduction in crime, which extends over a three-day period. Diffusion of benefits is observed within 300 feet from the location of the SQF, but these effects decay as distance from the SQF increases.

Conclusions

A bivariate spatiotemporal Ripley’s K-function is a promising approach to evaluating place-based crime prevention interventions, and may serve as a useful tool to guide program development and implementation in criminology.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. For a more general examination of SQFs in New York City and discussion of broader policy implications, see Weisburd et al. (Under review).

  2. This correction is employed when computing the space–time K-function in R using the splancs or spatstat packages (Pélissier and Goreaud 2015; Ye et al. 2015), which require the user to specify a sampling window boundary.

  3. All events were geocoded by the NYPD, and a hit rate of 96.8 and 97.9 % was obtained for crime incidents and SQFs, respectfully. The geocoding hit rate is well above the 85 % suggested threshold for a minimal reliable geocoding rate (Ratcliffe 2004). Crime incidents involving rape and other sex crimes were not included in the analyses because the x–y coordinates were redacted by the NYPD (1 % of incidents).

  4. \(\hat{D}_{0} (s,t)\) may be estimated exclusively in R software, but K1D was selected to calculate the unbiased estimate of K(t) due to computational efficiency (see also Bigler et al. 2007; Hu et al. 2006; Long et al. 2007; Schoennagel et al. 2007). K1D software is available from the University of Oregon, Department of Geography’s website: http://geog.uoregon.edu/envchange/pbl/software.html/.

  5. A subset of events were analyzed from the Bronx to ensure that K-function estimates were robust against spatial heterogeneity within the study area (such as that caused by rivers, etc.).

  6. The confidence intervals for \(\hat{D}_{FS} (s,t)\) and \(\hat{D}_{CC} (s,t)\) are very narrow and would not be visible if included in Figs. 5 and 6, so they have been included the in-text description of the results below.

  7. A total of 37.2 % of crime incidents in the sample occurred at the same location as an SQF during the study period. The majority of instances when crime and SQF locations match exactly is likely due to the events co-occurring on street intersections. This inference is based on research demonstrating that the majority of SQFs and nearly a quarter of crime incidents in NYC occur at intersections (Weisburd et al. 2014). A comparison of the type of crime incidents occurring on intersections versus street segments for years 2006–2011 are as follows: personal (35.6 vs 33.9 %); property (32.9 vs 45.9 %); drugs/alcohol and prostitution (22.1 vs 13.8 %); and other (9.4 vs 6.3 %).

References

  • Arbia G, Espa G, Quah D (2008) A class of spatial econometric methods in the empirical analysis of clusters of firms in the space. Empir Econs 34(1):81–103

    Article  Google Scholar 

  • Arbia G, Espa G, Guiliani D (2015) Analysis of spatial concentration and dispersion. In: Karlsson C, Andersson M, Normal T (eds) Handbook of research methods and applications in economic geography. Edward Elgar Publishing, UK, pp 135–157

    Google Scholar 

  • Baddeley AJ (1998) Spatial sampling and censoring. In: Kendall WS, van Lieshout MNM (eds) Stochastic geometry: likelihood and computation. Chapman & Hall/CRC, Boca Raton, pp 37–70

    Google Scholar 

  • Bhopal RS, Diggle PJ, Rowlingson BS (1992) Pinpointing clusters of apparently sporadic Legionnaires’ disease. Brit Med J 304:1022–1027

    Article  Google Scholar 

  • Bigler C, Gavin DG, Gunning C, Veblen TT (2007) Drought induces lagged tree mortality in a subalpine forest in the Rocky Mountains. Oikos 116:1983–1994

    Article  Google Scholar 

  • Bonner MR, Han D, Nie J, Rogerson P, Vena JE, Freudenheim JL (2003) Positional accuracy of geocoded addresses in epidemiologic research. Epid 14(4):408–412

    Google Scholar 

  • Bowers KJ, Johnson SD, Guerette RT, Summers L, Poynton S (2011) Spatial displacement and diffusion of benefits among geographically focused policing initiatives: a meta-analytical review. J Exper Criminol 7:347–374

    Article  Google Scholar 

  • Braga AA (2007) The effects of hot spots policing on crime. Campbell Syst Rev 1:1–27

  • Braga AA, Papachristos AV, Hureau DM (2014) The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Just Q 31:633–663

    Article  Google Scholar 

  • Cayo MR, Talbot TO (2003) Positional error in automated geocoding of residential addresses. Inter J Health Geog 2(1):10

    Article  Google Scholar 

  • Clarke RV, Weisburd D (1994) Diffusion of crime control benefits: observations on the reverse of displacement. Crim Prev Stud 2:165–184

    Google Scholar 

  • Cressie N (1993) Statisticals for spatial data. Wiley, New York

    Google Scholar 

  • Dario LM, Morrow WJ, Wooditch A, Vickovic SG (2015) The point break effect: an examination of surf, crime, and transitory opportunities. Crim Just Stud. doi:10.1080/1478601X.2015.1032409

    Google Scholar 

  • David FN, Barton DE (1966) Two space-time interaction tests for epidemicity. Brit J Prev Soc Med 20(1):44–48

    Google Scholar 

  • De La Cruz M, Romao RL, Escudero A, Maestre FT (2008) Where do seedlings go? A spatio-temporal analysis of seedling mortality in a semi-arid gypsophyte. Ecography 31(6):720–730

    Article  Google Scholar 

  • Diggle PJ (1983) Statistical analysis of spatial point patterns. Academic Press, London

    Google Scholar 

  • Diggle PJ, Chetwynd AG, Häggkvist R, Morris SE (1995) Second-order analysis of space-time clustering. Stat Meth Med Res 4(2):124–136

    Article  Google Scholar 

  • Dixon P (2002) Ripley’s K-function. In: El-Shaarawi AH, Piergorsch WW (eds) The encyclopedia of environmetrics. Wiley, New York, pp 1796–1803

    Google Scholar 

  • Fagan J, Geller A, Davies G, West V (2010) Street stops and broken windows revisited. In: Rice SK, White MD (eds) Race, ethnicity, and policing: new and essential readings. New York University Press, New York, pp 309–348

    Chapter  Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography: perspectives on spatial data analysis. Sage, London

    Google Scholar 

  • French NP, McCarthy HE, Diggle PJ, Proudman CJ (2005) Clustering of equine grass sickness cases in the United Kingdom: a study considering the effect of position-dependent reporting on the space–time K-function. Epidem Infect 133(02):343–348

    Article  Google Scholar 

  • French NP, Webster S, Zheng P, Fenton S, Clough H, Diggle P (2006) K-function analysis: recent developments and novel applications. In: International symposia on veterinary epidemiology and economics proceedings, ISVEE 11: Proceedings of the 11th symposium of the international society for veterinary epidemiology and economics, Cairns, Australia, Theme 4—Tools & training for epidemiologists: Spatial epidemiology session. Obtained from http://www.sciquest.org.nz/elibrary/download/64373/T4-5.1.1_-_K-function_analysis_%3A_recent_developmen.pdf?%22

  • Gatrell AC, Baile TC, Diggle PJ, Rowlingson BS (1996) Spatial point pattern analysis and its application in geographical epidemiology. Trans Instit Brit Geog 21:256–274

    Article  Google Scholar 

  • Gavin DG, Hu FS, Lertzman KP, Corbett P (2006) Weak climatic control of forest fire history during the late Holocene. Ecol 87:1722–1732

    Article  Google Scholar 

  • Gavin DG, Beckage B, Osborn B (2008) Forest dynamics and the growth decline of red spruce and sugar maple on Bolton Mountain, Vermont: a comparison of modeling methods. Can J For Res 38:2635–2649

  • Gehlke CE, Biehl K (1934) Certain effects of grou** upon the size of the correlation coefficient in census tract material. J Am Stat Assoc 29(185A):169–170

    Article  Google Scholar 

  • Gelman A, Fagan J, Kiss A (2007) An analysis of the New York City police department’s “stop-and-frisk” policy in the context of claims of racial bias. J Am Stat Assoc 102:813–823

    Article  Google Scholar 

  • Goreaud F, Pélissier R (2003) Avoiding misinterpretation of biotic interactions with the intertype K12‐function: population independence vs. random labelling hypotheses. J Veg Sci 14(5):681–692

  • Green L (1995) Cleaning up drug hot spots in Oakland, California: the displacement and diffusion effects. Just Q 12(4):737–754

    Article  Google Scholar 

  • Grubesic TH, Mack EA (2008) Spatio-temporal interaction of urban crime. J Quant Crim 24:285–306

    Article  Google Scholar 

  • Guerette RT, Bowers KJ (2009) Assessing the extent of crime displacement and diffusion of benefits: a review of situational crime prevention evaluations. Criminology 47:1331–1368

    Article  Google Scholar 

  • Hu FS, Brubaker LB, Gavin DG, Higuera PE, Lynch JA, Rupp TS, Tinner W (2006) How climate and vegetation influence the fire regime of the Alaskan Boreal biome: the Holocene perspective. Mitig Adapt Strat Glob Chang 11:829–846

    Article  Google Scholar 

  • Johnson SD, Bowers KJ (2004a) The stability of space-time clusters of burglary. Brit J Crim 44(1):55–65

    Article  Google Scholar 

  • Johnson SD, Bowers KJ (2004b) The burglary as clue to the future the beginnings of prospective hot-spotting. Euro J Crim 1(2):237–255

    Article  Google Scholar 

  • Johnson SD, Bernasco W, Bowers KJ, Elffers H, Ratcliffe J, Rengert G, Townsley M (2007a) Space–time patterns of risk: a cross national assessment of residential burglary victimization. J Quant Crim 23(3):201–219

    Article  Google Scholar 

  • Johnson, SD, Birks DJ, McLaughlin L, Bowers KJ, Pease K (2007b) Prospective crime map** in operational context: final report. UCL, Jill Dando Institute of Crime Science

  • Johnson SD, Summers L, Pease K (2009) Offender as forager? A direct test of the boost account of victimization. J Quant Crim 25:181–200

    Article  Google Scholar 

  • Jones-Brown DD, Gill J, Trone J (2010) Stop, question & frisk policing practices in New York City: a primer. Center on Race, Crime and Justice, John Jay College of Criminal Justice

  • Knox G (1963) Detection of low intensity epidemicity. Brit J Prev Soc Medic 17:121–127

    Google Scholar 

  • Knox G (1964) The detection of space-time interactions. Appl Stat 13:25–29

    Article  Google Scholar 

  • Koper CS (1995) Just enough police presence: reducing crime and disorderly behavior by optimizing patrol time in crime hot spots. Just Q 12(4):649–672

    Article  Google Scholar 

  • Lee JSW, Kulperger RJ, Yu H (2013) An R package for large-scale spatial analysis with parallel computing. The University of Western Ontario, London, ON, Canada. http://www.statistics.gov.hk/wsc/IPS031-P2-S.pdf

  • Levine N (2004) CrimeStat III: a spatial statistics program for the analysis of crime incident locations (version 3.0). Houston, TX: Ned Levine & Associates/National Institute of Justice, Washington, DC

  • Lewis P, Shedler G (1979) Simulation of nonhomogeneous poisson processes by thinning. Navel Res Logis Quart 26:403–413

    Article  Google Scholar 

  • Long CJ, Whitlock C, Bartlein PJ (2007) Holocene vegetation and fire history of the Coast Range, western Oregon, USA. The Holocene 17:917–926

    Article  Google Scholar 

  • Loosmore NB, Ford ED (2006) Statistical inference using the G or K point pattern spatial statistics. Ecol 87(8):1925–1931

    Article  Google Scholar 

  • Lotwick HW, Silverman BW (1982) Methods for analysing spatial processes of several types of points. J R Stat Soc Ser B(44):406–413

  • Lynch H (2006) Spatiotemporal dynamics of insect-fire interactions. Dissertation. Harvard University, Cambridge

  • Lynch HJ, Moorcroft PR (2008) A spatiotemporal Ripley’s K-function to analyze interactions between spruce budworm and fire in British Columbia, Canada. Can J For Res 38(12):3112–3119

    Article  Google Scholar 

  • Malizia N (2012) The effect of data inaccuracy on tests of space-time interaction. Geoda Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University. http://geodacenter.asu.edu/drupal_files/2012-02.pdf

  • Mantel, N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27(2 Part 1):209–220

  • McNally RJ, Ducker S, James OF (2009) Are transient environmental agents involved in the cause of primary biliary cirrhosis? Evidence from space–time clustering analysis. Hepatology 50(4):1169–1174

    Article  Google Scholar 

  • McNally RJ, James PW, Picton SV, McKinney PA, van Laar M, Feltbower RG (2012) Space-time clustering of childhood central nervous system tumours in Yorkshire, UK. BMC Cancer 12(1):13

    Article  Google Scholar 

  • Nagin DS, Weisburd D (2013) Evidence and public policy. Criminol Pub Pol 12:651–679

    Article  Google Scholar 

  • New York Bar Association (2007) Report on the NYPDs stop-question-frisk policy. http://www2.nycbar.org/pdf/report/uploads/20072495-StopFriskReport.pdf

  • New York City Police Department (2014) NYC Crime Map for January 1, 2014 to May 31st, 2014. http://maps.nyc.gov/crime/ on July 20th, 2014

  • Ogata Y (1981) On lewis’ simulation method for point processes. IEEE Trans Infor Theor 17:23–31

  • Openshaw S, Taylor PJ (1979) A million or so correlation coefficients: three experiments on the modifiable areal unit problem. Stat App Spat Sci 21:127–144

    Google Scholar 

  • Pease K, Laycock G (1999) Revictimisation: reducing the heat on hot victims. Australian Institute of Criminology, Canberra, pp 1–6

    Google Scholar 

  • Pélissier R, Goreaud F (2015) A fast unbiased implementation of the K-function family for studying spatial point patterns in irregular-shaped sampling windows. J Stat Soft 63:1–18

    Article  Google Scholar 

  • Picado A, Guitian FJ, Pfeiffer DU (2007) Space–time interaction as an indicator of local spread during the 2001 FMD outbreak in the UK. Prev Veter Med 79(1):3–19

    Article  Google Scholar 

  • Poljak Z, Dewey CE, Rosendal T, Friendship RM, Young B, Berke O (2010) Spread of porcine circovirus associated disease (PCVAD) in Ontario (Canada) swine herds: part I. Exploratory spatial analysis. BMC Veter Res 6(1):59

    Article  Google Scholar 

  • R Development Core Team (2005) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna http://www.R-project.org

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

    Article  Google Scholar 

  • Ratcliffe JH (2010) Crime map**: Spatial and temporal challenges. In: Weisburd D, Piquero A (eds) Handbook of quantitative criminology. Springer, New York, pp 5–24

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  • Ridgeway G (2007) Analysis of racial disparities in the New York Police Department’s stop, question, and frisk practices. Rand Corporation

  • Ripley BD (1976) The second-order analysis of stationary point processes. J App Prob 13:255–266

    Article  Google Scholar 

  • Ripley BD (1982) Edge effects in spatial stochastic processes. In: Ranneby B (ed) Statistics in theory and practice. Swedish University of Agricultural Sciences, Umea, pp 247–262

    Google Scholar 

  • Rosenfeld R, Fornango R (2014) The impact of police stops on precinct robbery and burglary rates in New York City, 2003–2010. Just Q 31(1):96–122

    Article  Google Scholar 

  • Rosenfeld R, Chauhan P, Weisburd D (2012) The impact of NYPD’s SQF strategy on crime rates. Report prepared for the Open Society Foundations

  • Rudovsky D, Rosenthal L (2013) The constitutionality of stop-and-frisk in New York City. U Penn Law Rev Online 162:5

    Google Scholar 

  • Sanchez J, Stryhn H, Flensburg M, Ersbøll AK, Dohoo I (2005) Temporal and spatial analysis of the 1999 outbreak of acute clinical infectious bursal disease in broiler flocks in Denmark. Prev Veter Med 71:209–223

    Article  Google Scholar 

  • Schabenberger O, Pierce FJ (2001) Contemporary statistical models for the plant and soil sciences. CRC Press, Boca Raton

    Book  Google Scholar 

  • Schneiderman ET (2013) A report on arrests arising from the New York City Police Department’s stop-and-frisk practices. New York State Office of the Attorney General, New York

    Google Scholar 

  • Schoenberg FP (2003) Multidimensional residual analysis of point process models for earthquake occurrences. J Am Stat Assoc 98:789–798

    Article  Google Scholar 

  • Schoennagel T, Veblen TT, Kulakowski D, Holz A (2007) Multidecadal climate variability and interactions among Pacific and Atlantic sea surface temperature anomalies affect subalpine fire occurrence, western Colorado (USA). Ecol 88(11):2891–2902

    Article  Google Scholar 

  • Schootman M, Sterling DA, Struthers J, Yan Y, Laboube T, Emo B, Higgs G (2007) Positional accuracy and geographic bias of four methods of geocoding in epidemiologic research. Ann Epid 17(6):464–470

    Article  Google Scholar 

  • Sherman LW (1990) Police crackdowns: initial and residual deterrence. Crim Just 12:1–48

    Article  Google Scholar 

  • Sherman LW, Weisburd D (1995) General deterrent effects of police patrol in crime “hot spots”: a randomized, controlled trial. Just Q 12(4):625–648

    Article  Google Scholar 

  • Sherman LW, Gartin PR, Buerger ME (1989) Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27(1):27–56

    Article  Google Scholar 

  • Stoud BG, Fine M, Fox M (2011) Growing up policed in the age of aggressive policing policies. John Jay College of Criminal Justice, New York

    Google Scholar 

  • Tarling R, Morris K (2010) Reporting crime to the police. Brit J Crim 50(3):474–490

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Taylor RB (1998) Crime and small-scale places: what we know, what we can prevent, and what else we need to know. In: Taylor RB et al. (eds) Crime and place: plenary papers of the 1997 conference on criminal justice research and evaluation. National Institute of Justice, Washington, pp 1–22

  • Townsley M, Homel R, Chaseling J (2003) Infectious burglaries: a test of the near repeat hypothesis. Brit J Crim 43(3):615–633

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Weisburd D, Green L (1995) Measuring immediate spatial displacement: methodological issues and problems. In: Eck J, Weisburd D (eds) Crime and place. Crime Prevention Studies, vol 4. Criminal Justice Press, Monsey, pp 349–361

  • Weisburd D, Mazerolle LG (1995) Measuring immediate spatial displacement: methodological issues and problems. Unpublished manuscript

  • Weisburd D, Wyckoff LA, Ready J, Eck JE, Hinkle JC, Gajewski F (2006a) 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 D, Groff ER, Yang SM (2012) The criminology of place: street segments and our understanding of the crime problem. Oxford University Press, New York

    Book  Google Scholar 

  • Weisburd D, Telep CW, Lawton BA (2014) Could innovations in policing have contributed to the New York City crime drop even in a period of declining police strength?: the case of stop, question and frisk as a hot spots policing strategy. Just Q 31(1):129–153

    Article  Google Scholar 

  • Weisburd D, Wooditch A, Weisburd S, Yang SM (under review) Do stop-question-frisk practices deter crime? Evidence at micro units of space and time. Revise and resubmit at Criminology and Public Policy

  • Weisburd D, Wyckoff LA, Ready J, Eck JE, Hinkle JC, Gajewski F (2006b) 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 

  • Wells W, Wu L, Ye X (2012) Patterns of near-repeat gun assaults in Houston. J Res Crim Delinq 49:86–212

    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-Interscience, New York, pp 613–653

    Google Scholar 

  • Wilesmith JW, Stevenson MA, King CB, Morris RS (2003) Spatio-temporal epidemiology of foot-and-mouth disease in two counties of Great Britain in 2001. Prevent Veter Med 61(3):157–170

    Article  Google Scholar 

  • Wooditch A, Lawton B, Taxman FS (2013) The geography of drug abuse epidemiology among probationers in Baltimore. J Drug Iss 43(2):231–249

    Article  Google Scholar 

  • Wyant BR, Taylor RB, Ratcliffe JH, Wood J (2012) Deterrence, firearm arrests, and subsequent shootings: a micro-level spatio-temporal analysis. Just Q 29(4):524–545

    Article  Google Scholar 

  • Ye X, Xu X, Lee J, Zhu X, Wu L (2015) Space–time interaction of residential burglaries in Wuhan, China. Appl Geog 60:210–216

    Article  Google Scholar 

  • Zhan FB, Brender JD, De Lima I, Suarez L, Langlois PH (2006) Match rate and positional accuracy of two geocoding methods for epidemiologic research. Ann Epid 16(11):842–849

    Article  Google Scholar 

  • Zhang Y, Zhao J, Ren L, Hoover L (2015) Space–time clustering of crime events and neighborhood characteristics in Houston. Crim Just Rev 1–21. doi:10.1177/0734016815573309

  • Zhao HX, Moyeed RA, Stenhouse EA, Demaine AG, Millward BA (2002) Space–time clustering of childhood Type 1 diabetes in Devon and Cornwall, England. Diab Med 19(8):667–672

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Criminology, Law and Society, Center for Evidence-Based Criminology, George Mason University, 4400 University Drive MS6D12, Fairfax, VA, 22030, USA

    Alese Wooditch & David Weisburd

  2. The Hebrew University, My. Scopus, 91905, Jerusalem, Israel

    David Weisburd

Authors
  1. Alese Wooditch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Weisburd
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alese Wooditch.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wooditch, A., Weisburd, D. Using Space–Time Analysis to Evaluate Criminal Justice Programs: An Application to Stop-Question-Frisk Practices. J Quant Criminol 32, 191–213 (2016). https://doi.org/10.1007/s10940-015-9259-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-015-9259-4

Keywords

Search

Navigation