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Through our academic excellence and wide-ranging research we aim to help tackle the complex crime, health and disorder issues facing society, locally and regionally, as well as nationally and internationally.
“Through our research, we aim to interpret and help transform the complex crime and disorder problems confronting society and the police. In this, we recognise the value of collaboration and co-production. In other words, we appreciate that better and more impactful research and training rests upon direct engagement and partnership with those confronting a particular challenge, upon an understanding of their priorities, resources and constraints”.
Professor Jon Bannister, BDC Director
The Crime & Well Being Big Data Centre aims to deliver world-class theoretically driven, methodologically innovative and internationally relevant research to help tackle the complex crime, health and disorder issues facing society. Moreover, it seeks to deliver impactful research, supporting the strategic and operational decision-making of partners, within the region, nationally and internationally.
Our research projects engage with a range of issues in the areas of crime, policing, justice and health and well-being and are underpinned by methodological innovation and the development of secure and intelligent data systems.
This project was commissioned by Greater Manchester Police (GMP) to support the development of a ‘Data Science’ analytics capability to inform better strategic and operational decision-making across the force. This project commenced in November 2016.
The aims of the project are:
GMP senior officers identified three areas of policing demand for this project to focus on, namely; Missing Persons, Domestic Abuse and Mental Ill Health.
This project involves six work strands:
The outputs of this project include a series of research reports, expected to be released in 2020.
The Crime and Well-Being Big Data Centre is a key partner in the delivery of a three-year research project Understanding Inequalities funded by the Economic and Social Research Council (ESRC Grant Reference ES/P009301/1), which aims to explore the causes and consequences of multidimensional and multiscale social inequalities in Scottish society and beyond.
The Understanding Inequalities project is a multidisciplinary research programme examining multiple types of inequalities in employment, education, housing, crime, justice, well-being, gender, age and ethnicity. The Crime &Well-Being Big Data Centre is advancing the Understanding Inequalities research theme concerned with the causal drivers of inequalities in the exposure to crime. Our research seeks to address four research questions:
This research will explore the patterns and trends of inequality in the exposure to crime and its causal drivers, and generate insights to inform efficient and effective policing interventions. Working in partnership with Police Scotland, Greater Manchester Police and West Midlands our ambition is to develop crime projections and support the design of place sensitive crime prevention strategies targeted at the redress of inequality in the exposure to crime.
Another main strand of this work is the development of new methodological approaches to deal with the challenges of understanding inequalities in the exposure to crime and working to establish and support a network of international partners prepared to advance a comparable research agenda across multiple cities including Amsterdam, Ghent, Brisbane, Guangzhou, Los Angeles and Chicago.
Further information about the project and current research papers can be found here.
In 2019 the UK government made available £100 million to tackle serious violence. Around a third of this funding was committed for the establishment of Violence Reduction Units in eighteen Police Forces areas in England and Wales. The Violence Reduction Units bring together multiple agencies, including the police, local government, health, community organisations and other key partners to tackle violent crime by understanding its underlying causes.
The Greater Manchester Combined Authority (GMCA) has commissioned the Crime & Well-Being Big Data Centre (BDC) to make an assessment of the nature and extent of violence, its causes and consequences in the Greater Manchester region.
The Crime & Well Being Big Data Centre will support the VRU through a range of activities:
Further information about the Violence Reduction Unit can be found here.
In 2018, the Scottish Government implemented a Minimum Unit Pricing (MUP) policy in recognition that the alcohol problem in Scotland was so significant that ground breaking measures were required.
The Crime & Well-Being Big Data Centre is contributing to the evaluation of MUP led by NHS Scotland through a study of the impact of MUP on crime and disorder, public safety and public nuisance in Scotland.
The evaluation seeks to answer the following research questions:
This project will run until April 2021.
More information about the evaluation of the Minimum Unit Pricing can be found here.
Knife crime has increased dramatically in recent years across the UK. Effective interventions to reduce knife crime require a better understanding of the drivers of knife crime and improved analytical tools capable of identifying those (groups and areas) most at-risk of knife crime.
In 2019 the Crime & Well-Being was commissioned by Greater Manchester Police to generate new evidence on the extent and nature of knife crime in Greater Manchester. This project seeks to answer the following research questions:
The project utilises data mining approaches to improve understanding about the recording of knife-related offences and the measurement of knife crime. It will also generate new analysis of the spatial and temporal patterns of knife crime to support effective policing interventions towards tackling knife crime.
Recent advancements in computing power and the availability of high-quality data have meant that spatial dimensions of crime can now be studied over increasingly lengthy periods of time and at evermore fine-grained spatial scales. This has prompted a surge of contemporary research which has found remarkable stability in crime concentrations over time, even amidst citywide volatility. Such stability has been demonstrated across a number of different cities and at numerous spatial scales, and in doing so, has gathered a robust evidence-base. That said, comparable research into offender residential concentrations remains limited, despite there being a known relationship between where crimes occur and where offenders live. Key research questions remain unposed, and unanswered, namely:
1. What is the most appropriate spatial scale to study offender residential concentrations?
2. To what extent do offender residential concentrations demonstrate stability over time?
3. How can we explain longitudinal trajectories of offender residential concentrations?
This project seeks to answer these questions using individual-level geocoded residence data on known offenders in Birmingham between 2006 to 2016. Data has been provided through a collaborative agreement with West Midlands Police Force.
Descriptive and multilevel analysis are used to explore which small area census unit is most suitable for studying the spatial distribution of offender residences. Once a suitable spatial scale is chosen, local and global measures of concentration can help examine the extent of longitudinal stability in residence locations. Particular attention is paid to trajectory clustering methods which disentangle non-uniformity in small area trajectories. Creating a categorisation scheme serves to simplify the data, reducing issues arising from overplotting, and allows one to visualise the spatial pattern of trajectory groups. Finally, geographically sensitive inferential statistics can be used to explain the longitudinal trajectories observed. Independent variables are drawn from open data sources, constructed with consideration to the relevant criminological theories. Experimental steps will also be made into modelling offender residential population flows to demonstrate how research using aggregated data, however well-conceived, still might conceal more complex underlying patterns.
In answering these questions, this project seeks to highlight the empirical and theoretical (dis)similarities between spatial dimensions of crime and offenders. Generating an evidence-base along the three key dimensions (spatial scale, stability, explanation) is expected to hold significant value in assisting police forces understand the crime-based demand for their services.
The daily rhythms of the city, the ebb and flow of populations as they undertake routine activities, impact on the cause and spatio-temporal manifestation of urban problems. At the BDC, we are engaged in evaluating the impact of population flows on the spatial and temporal patterning of crime, i.e., crime hot spots.
This requires that we calculate spatially and temporally sensitive population denominators as well as explore the relation between population trip motivation and the characteristics of the urban environment. Our findings indicate the importance of calculating the exposed population, the population present in a spatial unit at a given time that holds the capacity to play an active role as an offender, victim or guardian, when identifying crime hot spots.
Big data affords exciting new research opportunities, opening prospect of insight in to multiple policy issues that have hitherto remained impenetrable with traditional data sources. However, the sheer scale and high-dimensionality of big data pose significant methodological challenges, such as scalability, noise and spurious correlation. To address these challenges, there is a need for new statistical thinking and methodological development. At the Crime and Well-Being Big Data Centre (BDC), we focus on advancing novel methodological approaches in geostatistics, machine learning and Agent-based simulation (ABM) to enable us to unlock the potential of big data to shed light on crime and policing problems.
(1) Spatiotemporal (ST) clustering:
Spatiotemporal clustering techniques can be used to extract meaningful patterns and relationships from datasets with spatial and temporal (ST) markers. At the BDC, we have advanced two ST clustering methods, the Extended ST Co-location technique and a novel longitudinal clustering technique (named ‘ak-means’). Further information and open-source codes for these tools can be found here.
(2) Text Mining:
Over 80% of police databases are unstructured (i.e., they are in text form). To date, limited endeavour has been made to explore these data, to assess their potential to usefully inform strategic and operational decision-making. Usually, police forces rely on the manual assessment of these databases, which is both time consuming and error prone. At the BDC, we have developed a data mining technique to explore the content of incident logs (the ‘Incident Log Text Miner (ILTM)’).We have applied the ILTM to identify the nature and extent of mental ill health demand upon the police. The performance analysis of ILTM evidences a very high level of information discovery with minimal errors. Our goal is to make this tool available to law enforcement agencies
Please contact us if you would like to know more about our research or if you are looking for a research partner.
