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The drivers of inequality in the exposure to crime
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The research underpinning this area of the Understanding Inequalities project is founded on the evidence of persistent spatial inequalities in the exposure to crime and the bedrock conclusion of existing research that these inequalities are tied to the morphology of deprivation (including poverty). Set against these observations, police recorded community-based crime has exhibited long term decline, whilst the spatial distribution of deprivation is being reshaped by global and urban processes. It is in this context that we aim to explore the patterns and trends of inequality in the exposure to crime and its drivers. Further, given recent austerity measures and the rising (non-crime) demands being placed upon agencies seeking to redress inequality in the exposure to crime, we aim to generate insights capable of informing efficient and effective interventions.
This research is built around three inter-related questions:
• Has exposure to crime and disorder changed across space and through time?
• What are the drivers of inequality in exposure to crime?
• How do models of risk assessment and deployment impact crime inequalities?
These questions have been shaped through dialogue with Understanding Inequalities research partners (including data providers) to address key policy challenges in Scotland and internationally.
Key Findings
Changing patterns of exposure to crime and their drivers and the influence of models of risk assessment and deployment on crime inequalities
1) There is significant inequality in exposure to crime, by crime type, within and between UK urban centres. Neighbourhood deprivation holds a close association with both absolute and relative inequality in exposure to crime. Under the crime drop relative inequality in exposure to crime, controlling for neighbourhood deprivation, has increased in some UK urban centres whilst decreasing in others.
2) The decentralisation of poverty in UK cities is associated with a spatial reordering of exposure to both property and violent crime. This being said and through time, area-based indicators of deprivation appear to hold a weakening capacity to account for exposure to property crime, though they retain a strong capacity to account for exposure to violent crime.
Lymperopoulou, K., and Bannister, J. (in development) The spatial reordering of poverty and its association with property and violent crime in Glasgow and Birmingham 2001-2016, (contact Kitty Lymperopoulou)
3) Violent crime in public space is shaped by the activities undertaken in, and the qualities of, particular settings. Over the course of a typical evening, the volume of the population in public space declines, whilst the volume of violent crime increases. This implies that understanding the time variant mix of activities as well as the propensities of a population to perform an active role as an offender, victim or guardian in settings is vital in the endeavour to develop effective crime prevention strategies.
4) The demand for policing is changing and increasingly driven by non-crime (public safety and welfare) events. The spatial and temporal patterning of public safety and welfare demand reflects, to an extent, the patterning of crime and disorder. The demand for policing, therefore, can be explained with reference to place-based characteristics as well as to the recurrent mobility of the citizenry. The deployment of frontline policing resource, however, cannot be fully explained by this same set of factors. Rather, it is shaped by the both people and place-based characteristics.
5) Reducing inequalities in exposure to crime requires more efficient and targeted police interventions, including more effective measurement of relative inequality in exposure to crime. Policing alone cannot reduce inequality in exposure to crime - this will require a whole (relational) systems approach and a reduction in wider forms of inequality and the concentration of poverty. At least 20% of policing resources are directed at managing incidents involving aspects of mental-ill health. This highlights the necessity of partnership working.
Langton, S., Bannister, J., Ellison, M., Haleem, S. and Krzemieniewska-Nandwani, K. (in development) Policing and mental ill-health: Using big data to assess the scale and severity of, and the frontline resources committed to, mental ill-health related calls-for-service (contact Jon Bannister)
6) We have advanced a theoretical model capable of accounting for, and framing the empirical investigation of, the urban conditionality of exposure across cities in diverse polities. This has not only informed our research in Scotland and England, but also the commencement of an international collaboration with research centres in the United States, Europe, Australia, and China that is seeking to both delineate and explain shifting inequalities (concentration and duration) in the exposure to crime at the neighbourhood level.
Bannister, J. and O’Sullivan, A. (2020) Planetary urban criminology, Criminological Encounters, 3(1), 10-31.
Data, methodological and software insights to support research on inequality in the exposure to crime
1) We advanced two novel methodological contributions to support the investigation of the exposure to crime. Firstly, a theoretically driven longitudinal clustering technique, termed anchored k-medoids. We advocate the deployment of anchored k-medoids and kmeans in concert as an approach capable of unpicking the long and shorter-term drivers of the exposure to crime across neighbourhoods. Secondly, a measure to assess (shifting) relative exposure to crime.
2) We identified the value of using “calls-for-service” data (in relation to, and when deployed in concert with, recorded crime data) as a reliable measure of the lived experience of crime and as a mechanism for identifying the “hidden” exposure to crime across local areas. Accurately quantifying and qualifying demand to inform service capacity and capability is a key imperative for police forces across the UK. We applied novel spatio-temporal Bayesian modelling techniques, using Integrated Laplace Approximation (INLA), to explore spatio-temporal variations in exposure to crime.
Krzemieniewska-Nandwani, K., Wallace, S., Lymperopoulou, K. and Bannister, J. (in development) Hidden inequalities in the exposure to crime: the gap between “calls-for-service” and recorded crime data, (contact Jon Bannister)
3) We developed and piloted a novel ‘exposed’ population-at-risk denominator to help quantify spatial and temporal variations in inequality in the exposure to crime.
5) We developed and demonstrated the efficacy of novel automated text mining methodologies to both quantify and qualify mental ill health-related demand being placed upon the police. We also deployed a similar methodology to provide an accurate count of inequality in the exposure to knife crime.
Langton, S., Bannister, J., Ellison, M., Haleem, S., & Krzemieniewska-Nandwani, K. (2021). Policing and mental ill-health: Using big data to assess the scale and severity of, and the frontline resources committed to, mental ill-health related calls-for-service.
The research underpinning this area of the Understanding Inequalities project is founded on the evidence of persistent spatial inequalities in the exposure to crime and the bedrock conclusion of existing research that these inequalities are tied to the morphology of deprivation (including poverty). Set against these observations, police recorded community-based crime has exhibited long term decline, whilst the spatial distribution of deprivation is being reshaped by global and urban processes. It is in this context that we aim to explore the patterns and trends of inequality in the exposure to crime and its drivers. Further, given recent austerity measures and the rising (non-crime) demands being placed upon agencies seeking to redress inequality in the exposure to crime, we aim to generate insights capable of informing efficient and effective interventions.
This research is built around three inter-related questions:
• Has exposure to crime and disorder changed across space and through time?
• What are the drivers of inequality in exposure to crime?
• How do models of risk assessment and deployment impact crime inequalities?
These questions have been shaped through dialogue with Understanding Inequalities research partners (including data providers) to address key policy challenges in Scotland and internationally.
Key Findings
Changing patterns of exposure to crime and their drivers and the influence of models of risk assessment and deployment on crime inequalities
1) There is significant inequality in exposure to crime, by crime type, within and between UK urban centres. Neighbourhood deprivation holds a close association with both absolute and relative inequality in exposure to crime. Under the crime drop relative inequality in exposure to crime, controlling for neighbourhood deprivation, has increased in some UK urban centres whilst decreasing in others.
Adepeju, M., Langton, S. and Lymperopoulou, K. (2020) Have poor and affluent neighbourhoods benefited equally from the crime drop? Shifting inequality in exposure to crime in Glasgow and Birmingham
2) The decentralisation of poverty in UK cities is associated with a spatial reordering of exposure to both property and violent crime. This being said and through time, area-based indicators of deprivation appear to hold a weakening capacity to account for exposure to property crime, though they retain a strong capacity to account for exposure to violent crime.
Lymperopoulou, K. and Bannister, J. (2020). Is crime in Glasgow following poverty and dispersing to the suburbs?
Lymperopoulou, K., and Bannister, J. (in development) The spatial reordering of poverty and its association with property and violent crime in Glasgow and Birmingham 2001-2016, (contact Kitty Lymperopoulou)
3) Violent crime in public space is shaped by the activities undertaken in, and the qualities of, particular settings. Over the course of a typical evening, the volume of the population in public space declines, whilst the volume of violent crime increases. This implies that understanding the time variant mix of activities as well as the propensities of a population to perform an active role as an offender, victim or guardian in settings is vital in the endeavour to develop effective crime prevention strategies.
Lee, W-D., Haleem, M., Ellison, M. and Bannister, J. (2020) The influence of intra-daily activities and settings upon weekday violent crime in public space in Manchester, UK, European Journal on Criminal Policy and Research, European Journal on Criminal Policy and Research.
Haleem, M., Lee, W-D., Ellison, M. and Bannister, J. (2020) The ‘exposed’ population, violent crime in public space and the night-time economy in Manchester, UK, European Journal on Criminal Policy and Research European Journal on Criminal Policy and Research.
4) The demand for policing is changing and increasingly driven by non-crime (public safety and welfare) events. The spatial and temporal patterning of public safety and welfare demand reflects, to an extent, the patterning of crime and disorder. The demand for policing, therefore, can be explained with reference to place-based characteristics as well as to the recurrent mobility of the citizenry. The deployment of frontline policing resource, however, cannot be fully explained by this same set of factors. Rather, it is shaped by the both people and place-based characteristics.
Ellison, M., Bannister, J., Haleem, M, S, and Lee, W-D. (2021) Understanding policing demand and deployment through the lens of the city and with the application of big data, Urban Studies.
5) Reducing inequalities in exposure to crime requires more efficient and targeted police interventions, including more effective measurement of relative inequality in exposure to crime. Policing alone cannot reduce inequality in exposure to crime - this will require a whole (relational) systems approach and a reduction in wider forms of inequality and the concentration of poverty. At least 20% of policing resources are directed at managing incidents involving aspects of mental-ill health. This highlights the necessity of partnership working.
Langton, S., Bannister, J., Ellison, M., Haleem, S. and Krzemieniewska-Nandwani, K. (in development) Policing and mental ill-health: Using big data to assess the scale and severity of, and the frontline resources committed to, mental ill-health related calls-for-service (contact Jon Bannister)
6) We have advanced a theoretical model capable of accounting for, and framing the empirical investigation of, the urban conditionality of exposure across cities in diverse polities. This has not only informed our research in Scotland and England, but also the commencement of an international collaboration with research centres in the United States, Europe, Australia, and China that is seeking to both delineate and explain shifting inequalities (concentration and duration) in the exposure to crime at the neighbourhood level.
Bannister, J. and O’Sullivan, A. (2020) Planetary urban criminology, Criminological Encounters, 3(1), 10-31.
Data, methodological and software insights to support research on inequality in the exposure to crime
1) We advanced two novel methodological contributions to support the investigation of the exposure to crime. Firstly, a theoretically driven longitudinal clustering technique, termed anchored k-medoids. We advocate the deployment of anchored k-medoids and kmeans in concert as an approach capable of unpicking the long and shorter-term drivers of the exposure to crime across neighbourhoods. Secondly, a measure to assess (shifting) relative exposure to crime.
Adepeju, M., Langton, S., & Bannister, J. (2019). Akmedoids tool for longitudinal data clustering. R-statistical package version 0.1.2.
Adepeju, M., Langton, S. and Lymperopoulou, K. (2020) Have poor and affluent neighbourhoods benefited equally from the crime drop? Shifting inequality in exposure to crime in Glasgow and Birmingham.
Adepeju, M., Langton, S. and Bannister, J. (2021) Anchored k-medoids: a novel adaptation of k-medoids further refined to measure instability in the exposure to crime, Journal of Computational Social Science.
Adepeju, M., Langton, S. and Bannister, J. (2020) Akmedoids R package for generating directionally-homogeneous clusters of longitudinal data sets, Journal of Open Source Software, 5(56), 2379.
2) We identified the value of using “calls-for-service” data (in relation to, and when deployed in concert with, recorded crime data) as a reliable measure of the lived experience of crime and as a mechanism for identifying the “hidden” exposure to crime across local areas. Accurately quantifying and qualifying demand to inform service capacity and capability is a key imperative for police forces across the UK. We applied novel spatio-temporal Bayesian modelling techniques, using Integrated Laplace Approximation (INLA), to explore spatio-temporal variations in exposure to crime.
Krzemieniewska-Nandwani, K., Wallace, S., Lymperopoulou, K. and Bannister, J. (in development) Hidden inequalities in the exposure to crime: the gap between “calls-for-service” and recorded crime data, (contact Jon Bannister)
3) We developed and piloted a novel ‘exposed’ population-at-risk denominator to help quantify spatial and temporal variations in inequality in the exposure to crime.
Haleem, M., Lee, W-D., Ellison, M. and Bannister, J. (2020) The ‘exposed’ population, violent crime in public space and the night-time economy in Manchester, UK, European Journal on Criminal Policy and Research European Journal on Criminal Policy and Research.
4) We developed an analytical framework to extract public opinion, from twitter data, of the policing response to COVID-19.
Adepeju, M. (2021) ‘Opitools’: A Tool for Analyzing Opinions in a Text Document. R-statistical package version 0.1.2.
Adepeju, M. and Jimoh, F. (2021) An Analytical Framework for Measuring Inequality in the Public Opinion on Policing—Assessing the Impacts of COVID-19 Pandemic Using Twitter Data. Journal of Geographic Information System, 13, 122-147.
5) We developed and demonstrated the efficacy of novel automated text mining methodologies to both quantify and qualify mental ill health-related demand being placed upon the police. We also deployed a similar methodology to provide an accurate count of inequality in the exposure to knife crime.
Langton, S., Bannister, J., Ellison, M., Haleem, S., & Krzemieniewska-Nandwani, K. (2021). Policing and mental ill-health: Using big data to assess the scale and severity of, and the frontline resources committed to, mental ill-health related calls-for-service.