Journal of Quantitative Criminology

, Volume 33, Issue 3, pp 595–632 | Cite as

Measuring Crime Concentration Across Cities of Varying Sizes: Complications Based on the Spatial and Temporal Scale Employed

  • John R. HippEmail author
  • Young-An Kim
Original Paper



We argue that assessing the level of crime concentration across cities has four challenges: (1) how much variability should we expect to observe; (2) whether concentration should be measured across different types of macro units of different sizes; (3) a statistical challenge for measuring crime concentration; (4) the temporal assumption employed when measuring high crime locations.


We use data for 42 cities in southern California with at least 40,000 population to assess the level of crime concentration in them for five different Part 1 crimes and total Part 1 crimes over 2005–2012. We demonstrate that the traditional measure of crime concentration is confounded by crimes that may simply spatially locate due to random chance. We also use two measures employing different temporal assumptions: a historically adjusted crime concentration measure, and a temporally adjusted crime concentration measure (a novel approximate solution that is simple for researchers to implement).


There is much variability in crime concentration over cities in the top 5 % of street segments. The standard deviation across cities over years for the temporally adjusted crime concentration measure is between 10 and 20 % across crime types (with the average range typically being about 15–90 %). The historically adjusted concentration has similar variability and typically ranges from about 35 to 100 %.


The study provides evidence of variability in the level of crime concentration across cities, but also raises important questions about the temporal scale when measuring this concentration. The results open an exciting new area of research exploring why levels of crime concentration may vary over cities? Either micro- or macro- theories may help researchers in exploring this new direction.


Neighborhoods Crime Aggregation Imputation 



This research is supported in part by NIJ Grant 2012-R2-CX-0010.


  1. Andresen MA, Curman ASN, Linning SJ (2016) The trajectories of crime at places: understanding the patterns of disaggregated crime types. J Quant Criminol. doi: 10.1007/s10940-016-9301-1
  2. Braga AA, Papachristos AV, Hureau DM (2010) The concentration and stability of gun violence at micro places in Boston, 1980–2008. J Quant Criminol 26:33–53CrossRefGoogle Scholar
  3. Curman ASN, Andresen MA, Brantingham PJ (2014) Crime and place: a longitudinal examination of street segment patterns in Vancouver, BC. J Quant Criminol 31:127–147CrossRefGoogle Scholar
  4. Eck J, Gersh J, Taylor C (2000) Finding crime hot spots through repeat address mapping. In: Mollenkopf J, Ross T (eds) Analyzing crime patterns: frontiers of practice. Sage Publications, Thousand OaksGoogle Scholar
  5. Gorr WL, Lee YJ (2014) Early warning system for temporary crime hot spots. J Quant Criminol 31:25–47CrossRefGoogle Scholar
  6. Groff ER, 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 26:7–32CrossRefGoogle Scholar
  7. Grubesic TH, Mack EA (2008) Spatio-temporal interaction of urban crime. J Quant Criminol 24:285–306CrossRefGoogle Scholar
  8. Hipp JR, Roussell A (2013) Micro- and Macro-environment population and the consequences for crime rates. Soc Forces 92:563–595CrossRefGoogle Scholar
  9. Hipp JR, Tita GE, Boggess LN (2011) A new twist on an old approach: a random-interaction approach for estimating rates of inter-group interaction. J Quant Criminol 27:27–51CrossRefGoogle Scholar
  10. Kemp AW, Kemp CD (1990) A composition-search algorithm for low-parameter Poisson generation. J Stat Comput Simul 35:239–244CrossRefGoogle Scholar
  11. Kemp CD, Kemp AW (1991) Poisson random variate generation. Appl Stat 40:143–158CrossRefGoogle Scholar
  12. Levin A, Rosenfeld R, Deckard MJ (2015) Using quantitative methods to explore measures of crime concentration in a large city. American Society of Criminology, Washington, D.C.Google Scholar
  13. Muthén B, Muthén LK (2000) Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res 24:882–891CrossRefGoogle Scholar
  14. Nagin DS (1999) Analyzing developmental trajectories: a semiparametric, group-based approach. Psychol Methods 4:139–157CrossRefGoogle Scholar
  15. Quetelet LAJ 1969 (1842). A treatise on man: and the development of his faculties. Scholars’ Facsimiles & Reprints, GainesvilleGoogle Scholar
  16. Sherman LW (1995) Hot spots of crime and criminal careers of places. Crime Place 4:35–52Google Scholar
  17. Sherman L, Gartin P, Buerger M (1989) Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27:27–56CrossRefGoogle Scholar
  18. Spelman W (1995) Criminal careers of public places. In: Eck JE, Weisburd D (eds) Crime and place. Criminal Justice Press, MonseyGoogle Scholar
  19. 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):449–469CrossRefGoogle Scholar
  20. Weisburd D (2015) The law of crime concentration and the criminology of place. Criminology 53:133–157CrossRefGoogle Scholar
  21. Weisburd D, Amram S (2014) The law of concentrations of crime at place: the case of Tel Aviv-Jaffa. Police Pract Res 15:101–114CrossRefGoogle Scholar
  22. Weisburd D, Mazerolle LG (2000) Crime and disorder in drug hot spots: implications for theory and practice in policing. Police Q 3:331–349CrossRefGoogle Scholar
  23. Weisburd D, Bushway S, Lum C, Yang SM (2004) Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. Criminology 42:283–321CrossRefGoogle Scholar
  24. Weisburd D, Groff E, Yang SM (2012) The criminology of place. Oxford University Press, New YorkCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Criminology, Law and Society and Department of SociologyUniversity of CaliforniaIrvineUSA
  2. 2.Department of Criminology, Law and SocietyUniversity of California IrvineIrvineUSA

Personalised recommendations