Crime, Policing and Citizenship (CPC) - Space-Time Interactions of Dynamic Networks

Crime continues to cast a shadow over citizen well-being in big cities today, while also imposing huge economic and social costs. Prevention, early detection and strategic mitigation are all critical to effective policy intervention, especially in domains where coordinated responses are required. Every day, about 10,000 incidents are reported by citizens, recorded and geo-referenced in the London Metropolitan Police Service Computer Aided Dispatch (CAD) database. Today, impending funding cuts bring new pressures for central accountability and improved efficiency, while community empowerment initiatives bring new opportunities and challenges to policing. Timely understanding of how criminality emerges and how crime patterns evolve is crucial to anticipating crime, dealing with it when it occurs and developing public confidence in the police service. It is widely understood that policing, crime and public trust all have strong spatial and temporal dimensions. An integrated approach to space-time analysis is needed in order to analyse crime patterns, police activity patterns and community characteristics, so as to understand and predict the when, where and what of how criminal activities emerge and are sustained.

This research will consolidate achievements in integrated spatio-temporal data mining and emergent network complexity to uncover patterning in crime, policing and citizen perceptions at a range of spatial and temporal scales. Each dataset of police movement, crime (and disorder) reported in the CAD, and citizens making '999' calls constitutes a spatio-temporal network (STN), which has its own characteristic patterning and behaviour in space-time, and which interacts with the other STNs. The (geotagged) deployment of police manpower in space and time, the spatio-temporal patterning of crime and disorder, and the perceptions of members of the public are likely to be interlinked to differing extents. The first of these purportedly both anticipates and responds to the second, while the third is a lagged response to the first two, giving reason to anticipate that all three networks should be tightly coupled. The project will first analyse spatio-temporal patterns of individual STNs, then associate the patterns among these STNs via integrated spatio-temporal data mining developed using innovative statistical regression and machine learning.

This research will utilise a range of disciplines (crime, geography, geoinformatics, and computer science) to help engineer effective practical solutions to crime problems. It proposes a new method for exploring crime patterns and integrating information on crime and police activity. It systematically addresses a structured programme of analytical issues in spatio-temporal data mining, which are becoming core to Geographical Information Sciences. It will advance the theory, methodology and application of research into network complexity by evaluating the forms and interactions of the networks that characterise crime and other socio-economic phenomena. This will make it possible to not only understand activity networks but also to use them for prediction and decision making.

This addresses the aims of RCUK's Global Uncertainties Programme in crime, terrorism, and ideologies and beliefs. It will extend our appreciation of the subtle interplay of different forms of complex systems, in ways that will contextualise tactical and strategic responses to terrorism and organised crime. It will enable intelligent policing of London Met Police by granting unforeseen levels of prediction. The best practice of individual Metropolitan boroughs can be extended to others in the UK. The methodology developed here will be transferrable to other international cities using similar incident report systems. This will directly benefit people who live, work and visit London and those cities to make them feel safe.

1. Government: London Metropolitan Police Service and policing policy makers
The industrial partner, London Metropolitan Police Service (MPS, with about 32,000 officers and 4,500 Support Officers), will directly benefit from the research as it addresses their operational and policy issues - providing 'the right service at the right price'. The intelligent allocation of police resource will make best use of their resources under the current budgetary requirement of achieving savings of 3% per annum (or £7.5 million).

It will help the individual Borough police forces to consider their activities as active or reactive, and to evaluate this balance in relation to neighbouring London Boroughs. It will help Borough police forces to think in terms of coordinated responses to outbreaks of different types of crime, based upon new understanding of spatio-temporal dynamics. It will contribute to best policing practice by providing detailed geographic analysis of crime patterns and public perceptions of crime. This in turn will stimulate wider debate by refocusing discussion of performance metrics away from clear-up rates alone to assessing how proactive policing can reduce citizen incident report numbers and encourage a more broadly based confidence in the police. In future this approach will allow links to be made on re-offending so that crime prevention can be targeted more sustainably and economically.

The research findings will be transferable to other potential end users elsewhere in the UK, particularly metropolitan areas such as Manchester and Liverpool. The findings will also be transferable to other international cities (such as Beijing, New York, Paris) that use similar incident reporting systems to improve their policing. The UCL Jill Dando Institute of Crime Science (the first university department in the world devoted specifically to reducing crime) will be well equipped to use these findings to encourage best practice through its continuing professional development activities, and the National Crime Mapping Conference and International Crime Science Conference.

2. People: Citizens and local communities
Benefits for the police go hand in hand with improved citizen well-being for those who live and work within London (over 7.4 million), who wish to feel safe and secure. Improvements in timeliness, visibility and accountability of policing will improve confidence and engagement with the police service. It will also contribute towards making London's visitors (3.2 million in 2010, and the many more international visitors are expected for the 2012 Olympics) feel safer. The experience acquired in London will also be transferrable to other international cities. There will be direct benefits to the communities who join the participatory mapping exercises. Borough police will be able to adjust their resource according to the results of the local mapping to make neighbourhoods safer.

3. Social enterprises: Mapping for Change
Mapping for Change (MfC) is a social enterprise, jointly owned by UCL and the charity London 21 Sustainability Network. It specialises in community mapping, citizen science and participatory GIS. The organisation has so far worked in the area of environmental and social issues with communities across the UK. The proposed project will allow the organisation to develop its skills in engaging communities in mapping perceptions of crime and policing: a human subject that has not yet been addressed by MfC. This will also allow MfC to develop methods that can be used with police forces across the country to improve public understanding and engaging of police activities.

4. Software Industry
The innovative machine learning algorithms to be developed within the project, such as space-time ANN (STANN),support vector machines (STSVM) and space-time weighted regression (STWR) may be integrated with industry software for GIS (ESRI) and Data Mining (Matlab), or open source software (R).