Built environment attributes and crime: an automated machine learning approach

Journal article


Dakin, Kyle, Parkinson, Simon, Saad, Kahn, Monchuck, Leanne and Pease, Ken 2020. Built environment attributes and crime: an automated machine learning approach. Crime Science. https://doi.org/10.1186/s40163-020-00122-9
AuthorsDakin, Kyle, Parkinson, Simon, Saad, Kahn, Monchuck, Leanne and Pease, Ken
Abstract

This paper presents the development of an automated machine learning approach to gain an understanding of the built environment and its relationship to crime. This involves the automatic capture of street-level photographs using Google Street View (GSV), followed by the use of supervised machine learning techniques (specifically image feature recognition) to recognise features of the built environment. In this exploratory proof-of-concept work, 8 key features (building, door, fence, streetlight, tree, window, hedge, and garage) are considered and a worked case-study is demonstrated for a small geographical area (8300 square kilometres) in Northern England. A total of 60,100 images were automatically collected and analysed across the area where 5288 crime incidents were reported over a twelve- month period. Dependency between features and crime incidents are measured; however, no strong correlation has been identified. This is unsurprisingly considering the high number of crime incidents in a small geographic region (8300 square kilometres), resulting in an overlap between specific features and multiple crime incidents. Further- more, due to the unknown precise location of crime instances, an approximation technique is developed to survey a crime’s local proximity. Despite the absence of a strong correlation, this paper presents a first-of-a-kind cross-disci- pline approach to attempt and use computation techniques to produce new empirical knowledge. There are many avenues of future research in this fertile and important area.

Keywords: Crime prevention, Supervised machine learning, Feature recognition, Crime analytics
Year2020
JournalCrime Science
PublisherBMC
ISSN2193-7680
Digital Object Identifier (DOI)https://doi.org/10.1186/s40163-020-00122-9
Web address (URL)http://hdl.handle.net/10545/624980
http://creativecommons.org/licenses/by/4.0/
hdl:10545/624980
Publication dates08 Jul 2020
Publication process dates
Deposited10 Jul 2020, 15:55
Accepted02 Jun 2020
Rights

Attribution 4.0 International

ContributorsUniversity of Huddersfield and University of Derby
File
File Access Level
Open
File
File Access Level
Open
Permalink -

https://repository.derby.ac.uk/item/950y1/built-environment-attributes-and-crime-an-automated-machine-learning-approach

Download files

  • 22
    total views
  • 0
    total downloads
  • 2
    views this month
  • 0
    downloads this month

Export as

Related outputs

Wise Policing: Soft Skills and Strong Principles
Moss, K. and Pease, K. 2022. Wise Policing: Soft Skills and Strong Principles . in: Kilgallon, M. and Wright, M. (ed.) Behavioural Skills for Policing: The Service Speaks St Albans Critical Publishing.
Police misconduct, protraction and the mental health of accused police officers
McDaniel, John L.M., Moss, Kate, Pease, Ken and Singh, Paramjit 2020. Police misconduct, protraction and the mental health of accused police officers. in: Routledge.
First-generation immigrant judgements of offence seriousness: evidence from the crime survey for England and Wales
Los, Greg, Ignatans, Dainis and Pease, Ken 2017. First-generation immigrant judgements of offence seriousness: evidence from the crime survey for England and Wales. Crime Prevention and Community Safety. https://doi.org/10.1057/s41300-017-0019-2
Evolutionary Psychology and Terrorism
Taylor, Max, Roach, Jason and Pease, Ken 2016. Evolutionary Psychology and Terrorism. Routledge.
Terrorism's footprint of fear
Roach, Jason, Pease, Ken and Charlotte, Sanson 2016. Terrorism's footprint of fear. in: Routledge.
Taking crime seriously: Playing the weighting game
Ignatans, Dainis and Pease, Ken 2015. Taking crime seriously: Playing the weighting game. Policing. https://doi.org/10.1093/police/pav029.
Police overestimation of criminal career homogeneity
Pease, Ken and Roach, Jason 2013. Police overestimation of criminal career homogeneity. Journal of Investigative Psychology and Offender Profiling. https://doi.org/10.1002/jip.1405
Area and individual differences in personal crime victimisation incidence: The role of individual, lifestyle/routine activities and contextual predictors
Pease, Ken and Andromachi, Tseloni 2014. Area and individual differences in personal crime victimisation incidence: The role of individual, lifestyle/routine activities and contextual predictors. International Review of Victimology. https://doi.org/10.1177/0269758014547991
Voles don't take taxis
Pease, Ken 2014. Voles don't take taxis. Legal and Criminological Psychology. https://doi.org/10.1111/lcrp.12056
Are victims of crime mostly angry or mostly afraid?
Ignatans, Dainis and Pease, Ken 2019. Are victims of crime mostly angry or mostly afraid? Crime Prevention and Community Safety.
Whatever happened to repeat victimisation?
Pease, Ken, Ignatans, Dainis and Batty, Lauren 2018. Whatever happened to repeat victimisation? Crime Prevention and Community Safety.
Is it just a guessing game? The application of crime prevention through environmental design (CPTED) to predict burglary.
Monchuk, Leanne, Pease, Ken and Armitage, Rachel 2018. Is it just a guessing game? The application of crime prevention through environmental design (CPTED) to predict burglary. Planning Practice & Research. https://doi.org/10.1080/02697459.2018.1510276
Crime concentrations: Hot dots, hot spots and hot flushes.
Ignatans, Dainis and Pease, Ken 2018. Crime concentrations: Hot dots, hot spots and hot flushes. in: Oxford University Press.
Preventing repeat and near repeat crime concentrations.
Farrell, Graham and Pease, Ken 2017. Preventing repeat and near repeat crime concentrations. in: Routledge.
Repeat victimisation.
Farrell, Graham and Pease, Ken 2016. Repeat victimisation. in: Routledge.
What have criminologists done for us lately?
Farrell, Graham and Pease, Ken 2014. What have criminologists done for us lately? in: Palgrave Macmillan.
A situational approach to heritage crime prevention.
Grove, Louise and Pease, Ken 2014. A situational approach to heritage crime prevention. in: Palgrave Macmillan.
Preventing crime and evoking altruism.
Pease, Ken 2014. Preventing crime and evoking altruism. in: Springer.
Distributive justice and the crime drop.
Ignatans, Dainis and Pease, Ken 2015. Distributive justice and the crime drop. in: Palgrave Macmillan.
How to morph experience into evidence.
Roach, Jason and Pease, Ken 2017. How to morph experience into evidence. in: Routledge.
Self-selection policing: Theory, research and practice.
Roach, Jason and Pease, Ken 2016. Self-selection policing: Theory, research and practice. Palgrave Macmillan.
Using modeling to predict and prevent victimization.
Pease, Ken and Tseloni, Andromachi 2014. Using modeling to predict and prevent victimization. Springer.
Preventing repeat and near repeat crime concentrations.
Farrell, Graham and Pease, Ken 2017. Preventing repeat and near repeat crime concentrations. in: Routledge.
On whom does the burden of crime fall now? Changes over time in counts and concentration.
Ignatans, Dainis and Pease, Ken 2015. On whom does the burden of crime fall now? Changes over time in counts and concentration. International Review of Victimology. https://doi.org/10.1177/0269758015610854
Area and individual differences in personal crime victimization incidence.
Tseloni, Andromachi and Pease, Ken 2014. Area and individual differences in personal crime victimization incidence. International Review of Victimology. https://doi.org/10.1177/0269758014547991