|Presenters' slides and posters - International Crime and Intelligence Analysis Conference, 25-26 February, Manchester (UK)|
WHAT WORKS CLASSES
27 September 2016
7-10th November 2016
15th November 2016
Date to be confirmed
Date to be confirmed
Date to be confirmed
Date to be confirmed
ICIAC 2012 Seminar stream 1B
Abstracts and slides
Spatial analysis: from research to practice (G)
The Application of Journey to Crime Data to the deployment of ANPR Technology
Matthew Stiff, West Mercia Police
Key words: Journey to Crime, ANPR , Strategic planning, Burglary, Travelling criminality
Slides: Yet to be supplied by presenter
Fixed site ANPR technology has been proven to produce countless benefits across the spectrum of Police business. The challenge for Police forces is to ensure that this expensive tactic is deployed in an intelligence-led manner to maximise operational benefits (i.e. reduce crime and maximise detections).
Journey to crime research has had a place in modern criminology for decades now. Researchers have worked tirelessly to make better sense of the sometimes chaotic behaviour that offenders display in their travel to and from crime. Studies over the years have increased our understanding of how far, on average, different groups of offenders are willing to travel and how this varies by offence type. In particular there is a wealth of discussion on the merits of different travel demand models. The challenge for intelligence analysts is to translate this wealth of knowledge and theory into day to day operational decisions, such as which roads should be patrolled today and where should new ANPR cameras be deployed?
West Mercia Police have developed a novel analytical method to guide the deployment of ANPR technology, utilising journey-to-crime data and route-planning software. This technique enables the force to objectively review and prioritise sites in the face of challenging budgetary conditions. Based on data for key offence types from the last two years, the technique uses route-planning software to examine which roads offenders were most likely to have used to travel to offences. Each potential ANPR deployment can then be scored on a simple matrix based on how many distinct routes pass through the site and the geographic extent of those routes. An assessment of the strategic impact of the site can then be made as well as the potential impact on travelling criminality. The technique is still evolving but has so far been used to support an ongoing force bid for over a million pounds of further fixed site ANPR cameras.
This presentation will explore some of the more tactical options this method facilitates, such as the identification of patrol routes for Roads Policing assets in force and also guiding the redeployment of West Mercia’s temporary ANPR cameras. In particular it will show how by focusing the data on key groups of offenders or offences this technique can be used to generate intelligence-led products for force tasking. As the dataset is expanded to encompass more geo-coded data held by the Police it will further increase our understanding of the movements and behaviour of key nominals. This will lead to a constantly evolving map of “hot routes” for the force. To test the hypotheses behind these deployments the ANPR data captured will be continually analysed. Compliance with tasking will be assessed using Police vehicle tracking data (AVLS).
Above and Below: examining risk of theft on the transport network
Henry Partridge and Andy Gill, Transport for London, and Andrew Newton, University of Huddersfield
Keywords: Theft, Transport, Analysis, Risk, Transmission
All on-train incidents are recorded by the British Transport Police at the location where they were reported, not necessarily where they were committed. This ‘end of line reporting’ presents a challenge to the police because both the location and timing of such on-train offences are necessarily skewed. The British Transport Police and Transport for London have recently sought to address this problem by taking a probabilistic modelling approach (Gill 2007), which considers the likelihood that an on-train incident could have occurred at any stage of a journey not just at the end location it was reported at. This research builds on this recent work by bringing together three disparate data sources for the first time to better understand on-train incidents including: theft from person/shops in the environments surrounding stations above ground (based on Metropolitan police data); theft from person at over ground stations themselves (based on British Transport Police data); and theft from person on the underground (based on modelling route sections of the Transport for London data).
Research Aims and Methodology
This research aims to improve the understanding of this risk on the underground rail network, and to examine whether offenders are likely to differentiate between potential above ground and underground targets. It uses a probabilistic modelling approach (ibid) to identify offence risk by route sections (based on route and journey frequencies and other variables). It then compares the locations of these high risk route sections with other characteristics of these route sections. The research will attempt to answer the following questions.
- Do the locations and times of underground route sections with high probabilities of theft risk coincide with (i) above ground stations with high risk of theft and (ii) above ground risk of theft in general?
- Do other environmental conditions around stations influence the risk of theft underground (socio-economic/demographic variables, land use, and crime rates in general)?
- Is it likely that offenders differentiate between above and below ground targets?
The key provisional findings evident (based on analysis of one underground line) are:
- As expected, the theft probability risk appears concentrated at particular stations (the 80/20 rule applies)
- There is strong correlation between theft property risk on the underground, theft within stations, and theft above ground
- There is less correlation between theft property risk and other crimes above ground and local Socio-Economic Characteristics
Implications for Policy and Practice/Future Research
The findings suggest that offenders do not distinguish between below ground and above ground targets, and potentially may even use the impact of different policing jurisdictions to their advantage. This has clear policy implications for joint operational policing. Future research will aim to further improve and refine this model by segmenting by route frequency, route length, passenger volumes, and extend the analysis across the underground entire network.
Gill, A., (2007), Developing aoristic network analysis upon London’s transport system. Presentation delivered at the National Crime Mapping Conference 2007.
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