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Journey-to-Crime by Gender and Age Group in Manchester, England

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Crime Modeling and Mapping Using Geospatial Technologies

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 8))

Abstract

This study examines journey-to-crime trips by gender and by age group for offenders who committed crimes in Manchester, England. The data are 97,429 crimes committed in 2006 by 56,368 offenders in which both the residence location of the offender and the crime location were known. Approximately one in six crimes was committed by women and by juveniles. The analysis showed gender differences in crime travel with interactions by age group, location of the crime, the presence of co-offenders, and ethnicity. Juvenile males had the shortest average trip lengths while adult males had the longest. Female offenders, both juveniles and adults, had crime trips of intermediate length but with a higher percentage being committed in major commercial centres. Around one-quarter of the trips were committed in conjunction with co-offenders, who generally lived quite close to the offender.

A negative binomial regression model showed that multiple factors contribute to the journey-to-crime distance traveled including type of crime, age and ethnicity of the offender, crime prolificacy of the offender, presence of co-offenders, and location and land use where crimes occurred. Controlling for these factors, with the exception of shoplifting, female offenders traveled shorter distances in committing their crimes, on average, than male offenders. For shoplifting, female offenders traveled longer distances because a higher proportion of those crimes were committed in the central retail core or in town centres.

The results indicate that simple generalisations about criminal travel are suspect. Instead, crime travel must be understood as reflecting the interaction of the type of crime, the characteristics of the metropolitan structure, the presence of accomplices, and offender characteristics, particularly gender and age.

The authors wish to thank the National Institute of Justice for funding (grant 2005-IJ-CX-K037) and the Greater Manchester Police for access to crime and offender data, compliant with the 1998 U.K. Data Protection Act. The authors also wish to thank Dr. Richard Block of Loyola University, Chicago, for suggestions on improving the paper. An earlier version of this paper was presented at the 4th International Conference on Women’s Issues in Transportation sponsored Transportation Research Board, Newport Beach, CA., 2009.

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Notes

  1. 1.

    These records provided information on the offenders and their crimes. The address where the offender was living at the time of his or her arrest was taken as the residence address. We recognise, of course, that some offenders may have moved since the time they committed their crime, a well known phenomenon with police records (Bernasco 2010). Change of address details are not indicated on crime reports, but as intelligence on an individual tracking system. Upon investigation, any change of address is dealt with according to what is happening to the offender. For instance the offender may have been bailed from one offence, and have moved since. The individual could then be arrested whilst on bail, in which case the new address would be recorded on the second crime record. Bail would then be opposed, citing the fact that the offender moved whilst on bail for the previous offence. Analysts on the other hand would refer to the intelligence about offenders’ addresses to assess the geography of their criminal activities; attribute other crimes to them (comparative case analysis) or suggest locations for finding them if they had gone missing.

  2. 2.

    For example, in the previous year (2004/05) there were 85,816 persons arrested in the Greater Manchester region and in the subsequent year (2006/07) there were 89,510 arrested compared to 87,858 for 2005/06 (Ministry of Justice 2008). The trend over the three years represents an average annual increase of 2%.

  3. 3.

    We did not examine the remaining crimes as a minor category (17%) because they represented many different types.

  4. 4.

    Unfortunately, as is well known in police departments, crimes committed ‘on the street’ is a miscellaneous category that is non-specific as to the land use relationship. In an earlier study (Levine and Wachs 1986), it was shown that a sizeable proportion of crimes that occurred in central Los Angeles were categorised as being committed ‘on the street’, many of which were related to transit use by the victims.

  5. 5.

    Note that this organisation is different than in most U.S. cities where large suburban shopping centres compete with those in the central city. Many cities in the U.S. have reduced commercial activities in the central city.

  6. 6.

    The Arndale Centre was redeveloped after the bombing of the earlier centre by the Provisional Irish Republican Army in 1996 (Arndale 2012; Manchester 2011; Wikipedia 2010). The bombing became a rallying point for redeveloping the entire central city.

  7. 7.

    This was estimated by the difference between the total number of crimes per district and the number of crimes in our database per district. We recognise that some of the crimes attributed to non-arrested offenders may actually have been committed by the arrested offenders. However, this seemed a better test than comparing the distribution of crimes by arrested offenders to the total distribution of crimes.

  8. 8.

    The function was estimated with the CrimeStat journey-to-crime calibration routine that interpolates travel distances to a distance scale using a smoothing function, called a kernel, which is then re-scaled as a proportion (Levine 2005, Ch. 10).

  9. 9.

    Even though the Mann–Whitney ‘U’ test is used as a non-parametric version of the t-test, it actually is a distributional test and not just of the means relative to their standard deviations.

  10. 10.

    This analysis is based on a crime location that has the same coordinates as the residence location. We cannot determine whether the crime occurred within the residence of the offender (domestic crime) or at another unit within the same building.

  11. 11.

    The maps were created with CrimeStat using the trip distribution module tools (Levine 2004, Ch. 14). Essentially, a fine grid was overlaid on to a map of Manchester and the routine assigned the crime events to both an origin cell and a destination cell. The number of links between the origin and the destinations are calculated and output as a line.

  12. 12.

    Over-dispersion occurs when the expected (predicted) variance is greater than the expected mean and is most likely the result of combining different underlying Poisson distributions. The Poisson distribution assumes that the expected variance will equal the expected mean. However, most count distributions show over-dispersion and are the primary rationale for using models like the negative binomial (Cameron and Trivedi 1998).

  13. 13.

    The model was tested with negative binomial regression model in version 3.3 of CrimeStat (Levine 2010).

  14. 14.

    This is true for conventional shopping, not online shopping where men exceed women in purchases; Ditmar et al. 2004.

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  1. Ned Levine and Associates, Houston, 8422 Bluegate St, 77025-3212, TX, USA

    Ned Levine

  2. Greater Manchester Police, Manchester, UK

    Patsy Lee

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Levine, N., Lee, P. (2013). Journey-to-Crime by Gender and Age Group in Manchester, England. In: Leitner, M. (eds) Crime Modeling and Mapping Using Geospatial Technologies. Geotechnologies and the Environment, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4997-9_7

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