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1 Where is the Full Fat Fear of Crime: Has 25 Years of Homogenised Data Misled Criminologists and Policy Makers? Is it time to Get Real? Mike Sutton and.

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Presentation on theme: "1 Where is the Full Fat Fear of Crime: Has 25 Years of Homogenised Data Misled Criminologists and Policy Makers? Is it time to Get Real? Mike Sutton and."— Presentation transcript:

1 1 Where is the Full Fat Fear of Crime: Has 25 Years of Homogenised Data Misled Criminologists and Policy Makers? Is it time to Get Real? Mike Sutton and Andromachi Tseloni Nottingham Trent University

2 2 Pseudo Neighbourhoods (Hope 2007) –Using BCS sampling structure to cluster individuals together –Using common geo-referencing systems to attach info from other sources such as the Census to seek to characterise the environments of respondents (Kennedy & Forde 1990, Osborn at al. 1992, 1996, Trickett et al. 1995, Rountree & Land 1996, Ellingworth et al. 1997, Osborn & Tseloni 1998, Lauritsen 2001, Hope et al. 2001, Kershaw & Tseloni 2005, Tseloni 2006, 2007)

3 3 What Does Pseudo Neighbourhood Analysis Tell Us? (Kershaw & Tseloni 2005). 20% of pseudo neighbourhoods alone contribute to one third of household crime in England and Wales. 20% of pseudo neighbourhoods contribute to nearly half of personal crime

4 4 How are Fear and Crime Distributed across Pseudo Neighbourhoods? (Kershaw & Tseloni 2005) Household crime incidence rates in the 10% highest such crime pseudo neighbourhoods (p- ns) are just over three times higher than in the 10% safest p-ns (0.66 vs 0.18). 2005) Personal crime rates differ by a multiplier of roughly 6 (1.15 vs 0.02) between the highest and lowest such crime p-ns. Fear of crime in the 10% most fearful pseudo neighbourhoods is almost three times higher than in the perceived safest ones (5.5 vs 2).

5 5 Is Fear of Crime related to Crime within Pseudo Neighbourhoods? (Tseloni 2007) Fear of crime and household victimisation are correlated (0.35) between pseudo neighbourhoods.

6 6 How Pseudo Neighbourhoods profile affects Fear & Household Crime? (Tseloni 2007) Fear of crime and household crime rates rise in pseudo neighbourhoods with: –High proportion of young (16-24) people –High population density and –High deprivation But only fear of crime increases in pseudo-neighbourhoods with high ethnic minority population

7 7 What fear? (Ditton and Farrall 2007) Fear of crime has been inadequately measured in national crime surveys Fear of crime taps crime experiences closer when the construct best reflects the theoretical concept (Gray et al 2008; Tseloni & Zarafonitou 2008). Current survey measures show raised fear for the crime type mostly covered in the media at the time of fieldwork and they are conceptually non-comparable over time or cross-nationally due to changes in survey questions, media reports & political agendas on crime & crime prevention (Ditton et al. 2003; 2005; Pleysier et al. 2005)

8 8 How Might Pseudo Neighbourhood Analysis Inform Policy? Tricket, Osborn and Ellingworth (1995) Pseudo Neighbourhood (area) contribute 60% of the explanation of the likelihood of household victimisation. Tseloni (2006) Pseudo Neighbourhood effects are weaker than household characteristics for the explanation of the number of crimes per household (also perhaps due to the 8 years gap between crime and area information from the Census).

9 9 How Might Pseudo Neighbourhood Analysis Inform Policy? (Tseloni and Pease, forthcoming) Crimes against two households with similar characteristics who reside in the same pseudo-neighbourhood are correlated (0.33) Burglaries and thefts are highly correlated (0.54) Event communicability within an area is higher when the crime category is narrowed down (p.142) as well as when the household profile is that of a more vulnerable type.

10 10 How Might Pseudo Neighbourhood Analysis Inform Policy? Measurement of fear of crime which may accurately reflect the theoretical concept (Ditton and Farrall 2007). Fear of crime distribution over crime- related pseudo neighbourhood deciles by specific crime type as well as accounting for sampling variation Employment of concurrent pseudo neighbourhood and crime information

11 11 How Might Pseudo Neighbourhood Analysis Inform Policy? If between-individual differences are deemed more important for repeat victimisation (Pease & Laycock 1996) and between-neighbourhood differences are more important for victimisation risk (Tricket et al. 1995) then social policy may be directed to targeting vulnerable households within problem areas to reduce crime in a cost-effective manner.

12 12 Is Pseudo Neighbourhood Analysis Pseudo-Science? A Telling Question Have pseudo-neighbourhoods EVER been tested to compare results from them with what happens when BCS pseudo neighbourhoods are compared with REAL named, geographically distinct notorious problem neighbourhoods and REAL desirable neighbourhoods? NO!

13 13 The Need To Analyse REAL neighbourhoods with secondary data analysis BCS postcode data should be examined to sample postcodes of respondents within: 1.Known notorious real neighbourhoods at the city level 2.Known prestigious low-property crime neighbourhoods at the city level

14 14 IT MAY BE TIME TO GET REAL! If secondary research finds that existing samples are too small in REAL neighbourhoods, then there may be a strong case to be made to sample REAL neighbourhoods in future national crime surveys (Sutton 2007) Expensive to conduct, yet valuable in their own right, city-level surveys of known notorious neighbourhoods and known prestigious neighbourhoods would help to assess the accuracy of national and regional generalisations that arise from BCS findings. The real CONCENTRATION of crime would be found in REAL neighbourhoods – not pseudo neighbourhoods. This would allow us to compare REAL risks with fear of crime in known, named, notorious high crime neighbourhoods and make more accurate comparisons.

15 15 References Ditton, J. and Farrall, S. (2007) The British Crime Survey and the fear of crime. In (Hough, M. and Maxfield, M. (eds) Surveying Crime in the 21 st Century. Crime Prevention Studies. Vol. 22. N.Y. Criminal Justice Press. Ditton, J., Chadee, D. and Khan, F. (2003) The stability of global and specific measures of fear of crime: Results from a two wave Trinidad longitudinal study. International Review of Victimology, 9, Ditton, J., Khan, F. and Chadee, D.(2005) Fear of crime quantitative measurement instability revisited and qualitative consistency added: Results from a three wave Trinidad longitudinal study. International Review of Victimology, 12, Ellingworth, D., Hope T., Osborn, D.R., Trickett, A. and Pease, K. (1997), Prior Victimisation and Crime Risk, International Journal of Risk, Security and Crime Prevention, 2, Hope, T., Bryan, J., Trickett, A. and Osborn, D.R. (2001) The phenomena of multiple victimisation. British Journal of Criminology, 41, Hope, T. (2007) The Distribution of Household property Crime Victimisation: Insights from the British Crime Survey. In (Hough, M. and Maxfield, M. (eds) Surveying Crime in the 21 st Century. Crime Prevention Studies. Vol. 22. N.Y. Criminal Justice Press. Kennedy, L. W. and Forde, D. R. (1990) Routine activities and crime: an analysis of victimisation in Canada.Criminology, 28, 137–152. Kershaw, C. and Tseloni, M. (2005) Predicting crime rates, fear and disorder based on area information: Evidence from the 2000 British Crime Survey. International Review of Victimology, 12, Lauritsen, J. (2001) The social ecology of violent victimisation: Individual and contextual effects in the NCVS. Journal of Quantitative Criminology, 17, Osborn, D.R., Trickett, A. and Elder, R. (1992), Area Characteristics and Regional Variates as Determinants of Area Property Crime Levels, Journal of Quantitative Criminology, 8,

16 16 References Osborn, D.R., Ellingworth, D., Hope, T. and Trickett, A. (1996), Are Repeatedly Victimised Households Different?, Journal of Quantitative Criminology, 12, Osborn, D.R. and Tseloni, A. (1998). The distribution of household property crimes. Journal of Quantitative Criminology, 14, Pleysier, S., Pauwels, L., Vervaeke, G. and Goethals, J. (2005) Temporal invariance in repeated cross-sectional fear of crime research. International Review of Victimology, 12. Rountree, P. W. and Land, K. C. (1996) Burglary victimisation, perceptions of crime risk, and routine activities:a multilevel analysis across Seattle neighborhoods and census tracts. J. Res. Crime Delinquency, 33, 147–180. Sutton, M. (2007) Improving National Crime Surveys: With a Focus Upon Strangely Neglected Offenders and their Offences, Including Fraud, High-Tech Crimes and Handling Stolen Goods. In (Hough, M. and Maxfield, M. (eds.) Surveying Crime in the 21 st Century. Crime Prevention Studies. Vol. 22. N.Y. Criminal Justice Press. Trickett, A. Osborn, D.R. and Ellingworth, D. (1995) Property crime victimisation: The roles of individual and area influences. International Review of Victimology, 3, Tseloni, M. (2006) Multi-level modelling of the number of property crimes: Household and area effects. Journal of the Royal Statistical Society, Series, A, 169, Tseloni, A. (2007) Fear of Crime, Perceived Disorders and Property Crime: A Multivariate Analysis at the Area Level. In Farrell, G., K. Bowers, S.D. Johnson and M. Townsley (Eds.) Imagination for Crime Prevention: Essays in Honor of Ken Pease. Crime Prevention Studies, vol. 21. Monsey, NY: Criminal Justice Press, Tseloni, A. and Pease, K. (forthcoming) Property crimes and repeat victimisation: A fresh look. In International Handbook of Victimology, (eds) Shoham, S., Knepper, P. and Kett, M. Taylor and Francis USA, pages


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