Skip to main content


Log in

Risky Facilities: Crime Radiators or Crime Absorbers? A Comparison of Internal and External Levels of Theft

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



To undertake the first exploration of the nature of the relationship between internal crime (those that happen within facilities) and external crime (those occurring outside but in the nearby locale of facilities). The following questions are addressed. Do those localities that suffer high volumes of crime internally within their facilities also suffer high levels of crime in their immediate external environment? How is this influenced by the distribution of internal theft across facilities? What are the likely mechanisms for any relationship found?


Spatial regression is used to explore these relationships using data for 30,144 incidents of theft from a Metropolitan area of the UK arranged into small 50 × 50 m grid squares. Variables used in the analysis include counts of external and internal theft, counts of victimized and ‘risky’ facilities, indicators of land-use and a proxy for the on-street population.


There is found to be a strong positive relationship between internal and external theft that appears to be strengthened by the existence of facilities suffering particularly high crime volumes. Results suggest that internal theft problems precede external ones and that the physical concentration of chronically risky facilities is a particularly strong predictor of external theft problems.


An argument is made that risky facilities act as crime ‘radiators’, causing crime in the immediate environment as well as internally. This has implications for crime prevention policy in terms of facility placement and management.

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.

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

Similar content being viewed by others


  1. In this research, land-use is used in a broad sense and it is taken to mean the defined function of a piece of land or a place.

  2. There are also crime ‘enablers’ that occur when there is a lack of regulation or enforcement of rules (Clarke and Eck 2005).

  3. Note for example that this sample is 40 % of the size of the robbery data set used by Bernasco and Block (2011) in their large scale analysis of 25,000 census blocks in Chicago.

  4. It is possible that a number of uncategorised ‘facilities’ were in fact private residential properties. However, for a large number of these, this seems unlikely for two reasons. First, the theft types involved: snatches, pick pocketing and other theft are by definition supposed to occur outside the home, so any of these crimes would be a misclassification. Second, the study area was located in a central business district, and hence did not contain many residential properties.

  5. Here, for convenience ‘victimized facilities’ are those properties that had one or more recorded thefts in the data set time scale.

  6. As an additional precaution, the test regression was also re-run for two transformed versions of the dependent variable (the square root of the count of external crime and the logarithm of 0.1+ the external crime count). This was to check that the use of raw crime count data didn’t affect the results of analysis. Similar results, with the same independent variables demonstrating significance in the same direction, were found using these alternatives.

  7. Note there is fairly large attrition in the number of facilities that can be included in the analyses as risker facilities are chosen.

  8. These were used rather than the cumulative categories in Table 4 so that the variables would be independent of each other.

  9. The cut-off point for identifying the most risky grid squares as those in the top 20th percentile for external theft was chosen because it sits well with the 80-20 rule which is often applied when discussing risky facilities

  10. The ability of a risky facility to radiate risk is presumably unaffected by whether it is a crime attractor or a crime generator.

  11. Ekblom’s commentary on this can be found in ‘Crime Risk Assessment/Crime Impact Assessment’ available at: Last accessed on 19 oct 2011.


  • Andresen MA, Jenion G (2010) Ambient populations and the calculation of crime rates and risk. Secur J 23:114–133

    Article  Google Scholar 

  • Anselin L, Syabri I, Kho Y (2006) GeoDa: an introduction to spatial data analysis. Geogr Anal 38(1):5–22

    Article  Google Scholar 

  • Barr R, Pease K (1990) Crime placement, displacement and deflection. In: Tonry MH, Morris N (eds) Crime and justice: a review of research, vol 12. University of Chicago Press, Chicago, IL

    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 

  • Block R, Block CR (1999) The Bronx and Chicago: street robbery in the environs of rapid transit stations. In: Goldsmith V, McGuire PG, Mollenkopf JB, Ross TA (eds) Analyzing crime patterns: frontiers of practice. Sage, Thousand Oaks, CA

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1981) Notes on the geometry of crime. In: Brantingham PJ, Brantingham PL (eds) Environmental criminology. Sage, Beverly Hills, CA

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1984) Burglar mobility and crime prevention planning. In: Clarke RV, Hope T (eds) Coping with burglary. Kluwer–Nijhoff, Boston, MA, pp 77–95

    Chapter  Google Scholar 

  • Brantingham PL, Brantingham PJ (1993) Nodes, paths, and edges: considerations on the complexity of crime and the physical environment. J Environ Psychol 13:3–28

    Article  Google Scholar 

  • Brantingham PJ, Brantingham PL (1995) Criminality of place: crime generators and crime attractors. Eur J Crim Policy Res 3:5–26

    Article  Google Scholar 

  • Brooks Leah (2008) Volunteering to be taxed: business improvement districts and extra-governmental provision of public safety. J Public Econ 92:388–406

    Article  Google Scholar 

  • Clarke Ronald V, Cornish Derek B (1985) Modeling offenders’ decisions: a framework for research and policy. In: Tonry M, Morris N (eds) Crime and justice: an annual review of research, vol 6. University of Chicago Press, Chicago

    Google Scholar 

  • Clarke RV, Eck JE (2005) Crime analysis for problem solvers in 60 small steps. Office of Community Oriented Policing Services, US Department of Justice, Washington, DC

    Google Scholar 

  • Clarke RV, Weisburd D (1994) Diffusion of crime control benefits: observations on the reverse of displacement. In: Clarke RV (ed) Crime prevention studies, vol 2. Criminal Justice Press, Monsey, NY

    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 

  • Cook TD, Campbell DT (1979) Quasi-experimentation: design and analysis for field settings. Rand McNally, Chicago, Illinois

    Google Scholar 

  • Cook PJ, MacDonald J (2011) Public safety through private action: an economic assessment of BIDS. Econ J 121:445–462

    Article  Google Scholar 

  • Cornish D, Clarke RV (1987) Understanding crime displacement: an application of rational choice theory. Criminology 25(4):933–947

    Article  Google Scholar 

  • Eck JE (1993) The threat of crime displacement. Crim Justice Abstr 253:527–546

    Google Scholar 

  • Eck JE (1995) A general model of the geography of illicit retail marketplaces. In: Weisburd D, Eck JE (eds) Crime and place. Crime prevention studies, 4th edn. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Eck JE, Clarke RV, Guerette RT (2007) Risky facilities: crime concentration in homogeneous sets of establishments and facilities. In: Farrell G, Bowers K, Johnson SD, Townsley M (eds) Crime prevention studies, 21st edn. Willan, Devon

    Google Scholar 

  • Ekblom P (1997) Gearing up against crime: a dynamic framework to help designers keep up with the adaptive criminal in a changing world. Intl J Risk Secur Crime Prev 2(4):249–265

    Google Scholar 

  • Ekblom P (2002) Future imperfect: preparing for the crimes to come. Criminal Justice Matters 46 Winter 2001/02:38–40. London: Centre for Crime and Justice Studies, Kings College

  • Fagan J, Garth D (2000) Crime in public housing: Two-way diffusion effects in surrounding neighbourhoods. In: Goldsmith V, McGuire PG, Mollenkopf JB, Ross TA (eds) Analyzing crime patterns: frontiers of practice. Sage, Thousand Oaks, CA

    Google Scholar 

  • Farrell G, Roman J (2006) Crime as pollution: Proposal for market-based incentives to reduce crime externalities. In: Moss K, Stephens M (eds) Crime reduction and the law. Routledge, London

    Google Scholar 

  • Felson M, Clarke RV (1998) Opportunity makes the thief: practical theory for crime prevention. Police research series paper 98. Home Office, London

  • Gladwell M (2002) The tipping point: how little things can make a big difference. Little Brown, Boston

    Google Scholar 

  • Guerette RT, Bowers KJ (2009) Assessing the extent of crime displacement and diffusion of benefit: a systematic review of situational crime prevention evaluations. Criminology 47(4):1331–1368

    Article  Google Scholar 

  • Hesseling R (1994) Displacement: a review of the empirical literature. In: Clarke RV (ed) Crime prevention studies, vol 3. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Hillier B (2004) Can streets be made safer? Urban Des Int 9:31–45

    Article  Google Scholar 

  • Hillier B, Penn A, Hanson J, Grajewski T, Xu J (1993) Natural movement: or, configuration and attraction in urban pedestrian movement. Environ Plan B Plan Des 20:29–66

    Article  Google Scholar 

  • Holloway SR, McNulty TL (2003) Contingent urban geographies of violent crime: racial segregation and the impact of public housing in Atlanta. Urban Geogr 24:187–211

    Article  Google Scholar 

  • LaGrange TC (1999) The impact of neighbourhoods, schools, and malls on the spatial distribution of property damage. J Res Crime Delinq 36:393–422

    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 40:43–63

    Article  Google Scholar 

  • McCord ES, Ratcliffe JH (2009) Intensity value analysis and the criminogenic effects of land use features on local crime patterns. Crime Patterns Anal 2:17–30

    Google Scholar 

  • Newton A, Bowers KJ (2007) The geography of bus shelter damage: the influence of crime, neighbourhood characteristics and land-use. Internet J Criminol. Accessed Sept 2013

  • Openshaw S (1984) The modifiable areal unit problem. Geo Books, Norwich

    Google Scholar 

  • Pease K (1997) Predicting the future: the roles of routine activity and rational choice theory. In: Newman G, Clarke RV, Shoban S (eds) Rational choice and situational crime prevention: theoretical. Dartmouth, Ashgate

    Google Scholar 

  • Rengert GF, Ratcliffe JH, Chakravorty S (2005) Policing illegal drug markets: geographic approaches to crime reduction. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Roman CG (2005) Routine activities of youth and neighbourhood violence: spatial modeling of place, time, and crime. In: Wang F (ed) Geographic information systems and crime analysis. Idea Group, Hershey

    Google Scholar 

  • Roman J, Farrell G (2002) Cost-benefit analysis for crime prevention: opportunity costs, routine savings, and crime externalities. In: Tilley N (ed) Crime prevention studies, 14th edn. Willan, Devon

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Shaw CR, McKay HD (1942) Juvenile delinquency in urban areas. University of Chicago Press, Chicago

    Google Scholar 

  • Sidebottom A, Bowers KJ (2010) Bag theft in bars: an analysis of relative risk, perceived risk and modus operandi. Secur J 23(3):206–224

    Article  Google Scholar 

  • Weisburd D, Bruinsma GJN, Bernasco W (2009) Units of analysis in geographic criminology: historic development, critical issues, and open questions. In: Weisburd D, Bernasco W, Brujinsma G (eds) Putting crime in its place. Springer, New York, pp 3–31

    Chapter  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 

  • 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 

Download references


This work was supported by AHRC Research Grant, Ref No: 120340. Thanks goes to the UK Police Force that provided data for the research. Thanks to all those that have made suggestions or given advice including John Eck, Shane Johnson, Ken Pease, Aiden Sidebottom and Lusine Tarhanyan.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Kate Bowers.


Appendix 1

See Table 11.

Table 11 Relationship between external theft, internal theft and risky facilities: 100 × 100 m grid squares (n = 49)

Appendix 2

See Table 12.

Table 12 Relationship between external theft, internal theft and risky facilities: negative binomial regression (n = 210)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bowers, K. Risky Facilities: Crime Radiators or Crime Absorbers? A Comparison of Internal and External Levels of Theft. J Quant Criminol 30, 389–414 (2014).

Download citation

  • Published:

  • Issue Date:

  • DOI: