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Targeting Factors that Predict Clearance of Non-domestic Assaults

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Crime Solvability Factors

Abstract

The research described in this chapter identifies solvability factors for non-domestic violent offences and then develops this research by building an algorithmic prediction model for the solvability of violent crime before testing it against an existing experiential model. It is based on a complete population of 29,105 violent offences reported to the UK’s West Midlands Police between 1 March 2012 and 31 December 2013. The data set was split in half, with one half being used to build the model and the other to test its accuracy. Twenty-five solvability factors were identified, along with thirteen case-limiting factors, which allowed a logit model to be built to predict the solvability of cases. Despite the cut-off point for inclusion being adjusted to minimise the impact of incorrectly filed reports, and additional opt-in factors being included to reduce damage to public confidence, the new algorithmic model was 22.16% more accurate than the existing experiential crime-screening model used by West Midlands Police.

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References

  • Ayres, I. (2007). Supercrunchers: How anything can be predicted. London: Murray.

    Google Scholar 

  • Baskin, D., & Sommers, I. (2012). The influence of forensic evidence on the case outcomes of assault and robbery incidents. Criminal Justice Policy Review, 23(2), 186–210.

    Article  Google Scholar 

  • Bieck, W., & Kessler, D. A. (1977). Response time analysis. Kansas City: Missouri Board of Police Commissioners.

    Google Scholar 

  • Blake, L., & Coupe, R. T. (2001). The impact of single and two-officer patrols on catching burglars in the act. The British Journal of Criminology, 41(2), 381–396.

    Article  Google Scholar 

  • Bond, J. (2007). Value of DNA evidence in detecting crime. Journal of Forensic Sciences, 52(1), 128–136.

    Article  Google Scholar 

  • Bond, J. (2009). The value of fingerprint evidence in detecting crime. International Journal of Police Science & Management, 11(1), 77–84.

    Article  Google Scholar 

  • Brand, S., & Price, R. (2000). The economic and social costs of crime (Home Office Research Study 217). London: Home Office.

    Google Scholar 

  • Burrows, J., Hopkins, M., Hubbard, R., Robinson, A., Speed, M., & Tilley, N. (2005). Understanding the attrition process in volume crime investigations (Home Office Research Study 295). London: Home Office.

    Google Scholar 

  • Clawson, C., & Chang, S. K. (1977). Relationship of response delays and arrest rates. Journal of Police Science and Administration, 5(1), 53–68.

    Google Scholar 

  • Coupe, R. T., & Blake, L. (2006). Daylight and darkness strategies and the risks of offenders being seen at residential burglaries. Criminology, 44(2), 431–463.

    Article  Google Scholar 

  • Coupe, R. T., & Kaur, S. (2005). The role of alarms and CCTV in detecting non-residential burglary. Security Journal, 18(2), 53–72.

    Article  Google Scholar 

  • Coupe, T., & Griffiths, M. (1996). Solving residential burglary (Police Research Group Crime Detection and Prevention Services, Paper 77). London: Home Office.

    Google Scholar 

  • D’Alessio, S. J., & Stolzenberg, I. (2003). Race and the probability of arrest. Social Forces, 81(4), 1381–1397.

    Article  Google Scholar 

  • Dhiri, S., & Brand, S. (1999). Analysis of costs and benefits: guidance for evaluators. London: Home Office.

    Google Scholar 

  • Eck, J. E. (1979). Managing case assignments: The burglary investigation decision model replication. Washington, DC: Police Executive Research Forum.

    Google Scholar 

  • Eitle, D., Stolzenberg, I., & D’Alessio, S. J. (2005). Police organizational factors, the racial composition of the police, and the probability of arrest. Justice Quarterly, 22(1), 30–57.

    Article  Google Scholar 

  • Gill, M., Hart, J., Livingstone, K., & Stevens, J. (1996). The crime allocation system: Police investigations into burglary and auto crime (Police Research Series, Paper 16). London: Home Office.

    Google Scholar 

  • Greenberg, B., Elliot, C. V., Kraft, L. P., & Procter, S. H. (1977). Felony investigation decision model—An analysis of investigation elements of information. Washington, DC: US Government Printing Office.

    Google Scholar 

  • Greenwood, P. W., Chaiken, J. M., Petersilia, J., & Prusoff, L. (1975). The criminal investigation process: Volume III: Observations and analysis. Santa Monica, CA: The RAND Corporation.

    Google Scholar 

  • Her Majesty’s Inspectorate of Constabulary and Fire & Rescue Services. (2012). Review of police crime and incident reports: West Midlands Police. London: HMIC.

    Google Scholar 

  • Jansson, K. (2005). Volume crime investigations—A review of the research literature (Home Office Online Report OLR 44/05). London: Home Office.

    Google Scholar 

  • Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA: SAGE Publications.

    Google Scholar 

  • Litwin, K. J. (2004). A multilevel multivariate analysis of factors affecting homicide clearances. Journal of Research in Crime and Delinquency, 41(4), 327–351.

    Article  Google Scholar 

  • Lösel, F. (2007). Doing evaluation research in criminology: Balancing scientific and practical demands. In R. D. King & E. Wincup (Eds.), Doing research on crime and justice (pp. 141–170). Oxford: Oxford University Press.

    Google Scholar 

  • MedCalc Software. (2014a). MedCalc manual: Logistic regression, version 14.12.0—last modified December 3, 2014. http://www.medcalc.org/manual/logistic_regression.php. Accessed December 31, 2014.

  • MedCalc Software. (2014b). MedCalc manual: Logit transformation, version 14.12.0—last modified December 3, 2014. http://www.medcalc.org/manual/logit_transformation_table.php. Accessed December 31, 2014.

  • Meehl, P. (1954). Clinical vs. statistical prediction: A theoretical analysis and a review of the evidence. Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Newiss, G. (2002). Responding to and investigating street robbery. In K. Jansson (Ed.), Volume crime investigations—A review of the research literature (Home Office Online Report OLR 44/05). London: Home Office.

    Google Scholar 

  • Paine, C. (2012). Solvability factors in dwelling burglaries in Thames Valley (unpublished MSt thesis). University of Cambridge.

    Google Scholar 

  • Peterson, J., Sommers, I., Baskin, D., & Johnson, D. (2010). The role and impact of forensic evidence in the criminal justice process. Washington, D.C.: National Institute of Justice.

    Google Scholar 

  • Peterson, J. L., Hickman, M. J., Strom, K. J., & Johnson, D. J. (2013). Effect of forensic evidence on criminal justice case processing. Journal of Forensic Sciences, 58(S1), S78–S90.

    Article  Google Scholar 

  • Puckett, J. L., & Lundman, R. J. (2003). Factors affecting homicide clearances: Multivariate analysis of a more complete conceptual framework. Journal of Research in Crime and Delinquency, 40(2), 171–193.

    Article  Google Scholar 

  • Random.org. (n.d.). Random sequence generator. https://www.random.org/sequences/. Accessed September 12, 2014.

  • Regoeczi, W. C., Jarvis, J., & Riedel, M. (2008). Clearing murders: Is it about time? Journal of Research in Crime and Delinquency, 45(2), 142–162.

    Article  Google Scholar 

  • Riedel, M., & Boulahanis, J. G. (2007). Homicides exceptionally cleared and cleared by arrest: An exploratory study of police/prosecutor outcomes. Homicide Studies, 11(2), 151–164.

    Article  Google Scholar 

  • Robb, P., Coupe, R. T., & Ariel, B. (2015). ‘Solvability’ and detection of metal theft on railway property. European Journal on Criminal Policy and Research, 21(4), 463–484.

    Article  Google Scholar 

  • Roberts, A. (2008). The influences of incident and contextual characteristics on crime clearance of nonlethal violence: A multilevel event history analysis. Journal of Criminal Justice, 36(1), 61–71.

    Article  Google Scholar 

  • Sherman, L. W. (2013). The rise of evidence-based policing: Targeting, testing, and tracking. Crime and Justice, 42(1), 377–451.

    Article  Google Scholar 

  • Smith, K., Taylor, P., & Elkin, M. (2013). Crimes detected in England and Wales 2012/13. London: Home Office.

    Google Scholar 

  • Spelman, W., & Brown, D. K. (1981). Calling the police: Citizen reporting of serious crime. Washington, DC: Police Research Executive Forum.

    Google Scholar 

  • Stevens, J. M., & Stipak, B. (1982). Factors associated with police apprehension productivity. Police Science and Administration, 10(1), 52–57.

    Google Scholar 

  • Tilley, N., Robinson, A., & Burrows, J. (2007). The investigation of high volume crime. In T. Newburn, T. Williamson, & A. Wright (Eds.), Handbook of criminal investigation (pp. 226–254). London: Willan Publishing.

    Google Scholar 

  • United States Naval Observatory. (2014). Sun or moon rise/set table for one year. http://aa.usno.navy.mil/data/docs/RS_OneYear.php. Accessed December 13, 2014.

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Author information

Authors and Affiliations

  1. Institute of Criminology, University of Cambridge, Cambridge, UK

    Tom Olphin & Katrin Mueller-Johnson

Authors
  1. Tom Olphin
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  2. Katrin Mueller-Johnson
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Correspondence to Tom Olphin .

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Editors and Affiliations

  1. Institute of Criminology, University of Cambridge, Cambridge, UK

    Richard Timothy Coupe

  2. Institute of Criminology, University of Cambridge, Cambridge, UK

    Barak Ariel

  3. Institute of Criminology, University of Cambridge, Cambridge, UK

    Katrin Mueller-Johnson

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Olphin, T., Mueller-Johnson, K. (2019). Targeting Factors that Predict Clearance of Non-domestic Assaults. In: Coupe, R., Ariel, B., Mueller-Johnson, K. (eds) Crime Solvability Factors. Springer, Cham. https://doi.org/10.1007/978-3-030-17160-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-17160-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17159-9

  • Online ISBN: 978-3-030-17160-5

  • eBook Packages: Law and CriminologyLaw and Criminology (R0)

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