CPC: Crime, Policing and Citizenship - Intelligent Policing and Big Data

Crime, Policing and Citizenship (CPC) – Space-Time Interactions of Dynamic Networks has been a major UK EPSRC-funded research project. It has been a multidisciplinary collaboration of geoinformatics, crime science, computer science and geography within University College London (UCL), in partnership with the Metropolitan Police Service (MPS). The aim of the project has been to develop new methods and applications in space-time analytics and emergent network complexity, in order to uncover patterning and interactions in crime, policing and citizen perceptions. The work carried out throughout the project will help inform policing at a range of scales, from the local to the city-wide, with the goal of reducing both crime and the fear of crime. The CPC project is timely given the tremendous challenging facing policing in big cities nationally and globally, as consequences of changes in society, population structure and economic well-being. It addresses these issues through an intelligent approach to data-driven policing, using daily reported crime statistics, GPS traces of foot and vehicular patrols, surveys of public attitudes and geo-temporal demographic data of changing community structure. The analytic focus takes a spatio-temporal perspective, reflecting the strong spatial and temporal integration of criminal, policing and citizen activities. Street networks are used throughout as a basis for analysis, reflecting their role as a key determinant of urban structure and the substrate on which crime and policing take place. The project has presented a manifesto for ‘intelligent policing’ which embodies the key issues arising in the transition from Big Data into actionable insights. Police intelligence should go beyond current practice, incorporating not only the prediction of events, but also how to respond to them, and how to evaluate the actions taken. Cutting-edge network-based crime prediction methods have been developed to accurately predict crime risks at street segment level, helping police forces to focus resources in the right places at the right times. Methods and tools have been implemented to support senior offices in strategic planning, and to provide guidance to frontline officers in daily patrolling. To evaluate police performance, models and tools have been developed to aid identification of areas requiring greater attention, and to analyse the patrolling behaviours of officers. Methods to understand and model confidence in policing have also been explored, suggesting strategies by which confidence in the police can be improved in different population segments and neighbourhood areas. A number of tools have been developed during the course of the project include data-driven methods for crime prediction and for performance evaluation. We anticipate that these will ultimately be adopted in daily policing practice and will play an important role in the modernisation of policing. Furthermore, we believe that the approaches to the building of public trust and confidence that we suggest will contribute to the transformation and improvement of the relationship between the public and police

Spatiotemporal crime patterns and the urban environment: Evidence for planning and place-based policy

Crime and disorder influence individual quality of life, community social cohesion, and processes of neighbourhood and urban change. Existing studies that analyze local crime and disorder patterns generally focus only on where crime and disorder events occur. However, understanding the spatiotemporal patterning of crime and disorder, or both where and when events occur, is central to the design, implementation, and evaluation of crime prevention policies and programs. This dissertation explores the connections between local spatiotemporal patterns of crime and disorder, the urban environment, and urban planning through three research articles. Each article makes theoretical contributions that improve understanding of how characteristics of the urban environment influence crime and disorder, methodological contributions that advance spatiotemporal modeling of small-area crime data, and policy-oriented contributions that inform place-based crime prevention initiatives in urban planning, local government, and law enforcement. The first research article examines if, and how, physical disorder, social disorder, property crime, and violent crime share a common spatial pattern and/or a common time trend. Three multivariate models are compared and the results of the best-fitting model show that all crime and disorder types share a common spatial pattern and a common time trend. The shared spatial pattern is found to explain the largest proportion of variability for all types of crime and disorder, and type-specific spatiotemporal hotspots of crime and disorder are identified and investigated to contextualize broken windows theory. This study supports collective efficacy theory, which contends that multiple crime and disorder types are associated with same underlying processes, and highlights specific areas where crime prevention interventions should be designed to address all, or only one, type(s) of crime and disorder. The second article quantifies the time-varying relationships between land use and property crime for twelve seasons at the small-area scale. A set of spatiotemporal regression models with time-constant and time-varying regression coefficients are compared and the results of the best-fitting model show that parks and eating and drinking establishments exhibit recurring seasonal relationships, where parks are more positively associated with property crime during spring/summer and eating and drinking establishments are more positively associated with property crime during autumn/winter. Local land use composition is shown to have a more substantial impact on the spatial, rather than the spatiotemporal, patterning of crime. Applied to policy, the results of this article inform the design and coordination of time-constant and time-varying crime prevention initiatives as implemented by urban planning and law enforcement agencies, respectively. The third article investigates the spatiotemporal patterning of violent crime across multiple spatial scales. Violent crime data are measured at the small-area scale (lower-level units) and small-areas are nested in neighbourhoods, electoral wards, and patrol zones (higher-level units). A cross-classified multilevel model is applied to accommodate the three higher-level units that are non-hierarchical and have overlapping boundaries. Accounting for sociodemographic, built environment, and civic engagement characteristics, planning neighborhoods, electoral wards, and patrol zones are found to explain approximately fourteen percent of the total spatiotemporal variation of violent crime. Planning neighborhoods are the most important source of variation amongst the higher-level units. This article advances understanding of the multiscale processes that influence where and when violent crime events occur and provides area-specific crime risk information within the geographical frameworks used by policymakers in urban planning (neighbourhoods), local government (wards), and law enforcement (patrol zones). Broadly, this dissertation advances research focused on the connections between crime and disorder and the urban environment by (1) quantifying the degree to which spatiotemporal crime and disorder patterns are stable and/or dynamic, (2) examining the relationships between crime and disorder and local sociodemographic and built environment characteristics, (3) illustrating a set of statistical models that make sense of spatiotemporal crime and disorder patterns at the small-area scale, and (4) providing local spatiotemporal information that can be used to design and implement place-based crime prevention initiatives in urban planning, local government, and law enforcement

Spatio-temporal mixed membership models for criminal activity

Abstract We suggest a probabilistic approach to study crime data in London and highlight the benefits of defining a statistical joint crime distribution model which provides insights into urban criminal activity. This is achieved by developing a hierarchical mixture model for observations, crime occurrences over a geographical study area, that are grouped according to multiple time stamps and crime categories. The mixture components correspond to spatial crime distributions over the study area and the goal is to infer, based on the observations, how and to what degree the latent distributions are shared across the groups.</jats:p

Development of Hotzone Identification Models for Simultaneous Crime and Collision Reduction

This research contributes to developing macro-level crime and collision prediction models using a new method designed to handle the problem of spatial dependency and over-dispersion in zonal data. A geographically weighted Poisson regression (GWPR) model and geographically weighted negative binomial regression (GWNBR) model were used for crime and collision prediction. Five years (2009-2013) of crime, collision, traffic, socio-demographic, road inventory, and land use data for Regina, Saskatchewan, Canada were used. The need for geographically weighted models became clear when Moran's I local indicator test showed statistically significant levels of spatial dependency. A bandwidth is a required input for geographically weighted regression models. This research tested two bandwidths: 1) fixed Gaussian and 2) adaptive bi-square bandwidth and investigated which was better suited to the study's database. Three crime models were developed: violent, non-violent and total crimes. Three collision models were developed: fatal-injury, property damage only and total collisions. The models were evaluated using seven goodness of fit (GOF) tests: 1) Akaike Information Criterion, 2) Bayesian Information Criteria, 3) Mean Square Error, 4) Mean Square Prediction Error, 5) Mean Prediction Bias, and 6) Mean Absolute Deviation. As the seven GOF tests did not produce consistent results, the cumulative residual (CURE) plot was explored. The CURE plots showed that the GWPR and GWNBR model using fixed Gaussian bandwidth was the better approach for predicting zonal level crimes and collisions in Regina. The GWNBR model has the important advantage that can be used with the empirical Bayes technique to further enhance prediction accuracy. The GWNBR crime and collision prediction models were used to identify crime and collision hotzones for simultaneous crime and collision reduction in Regina. The research used total collision and total crimes to demonstrate the determination of priority zones for focused law enforcement in Regina. Four enforcement priority zones were identified. These zones cover only 1.4% of the Citys area but account for 10.9% of total crimes and 5.8% of total collisions. The research advances knowledge by examining hotzones at a macro-level and suggesting zones where enforcement and planning for enforcement are likely to be most effective and efficient

Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions - a crime case study

Many physical and sociological processes are represented as discrete events in time and space. These spatio-temporal point processes are often sparse, meaning that they cannot be aggregated and treated with conventional regression models. Models based on the point process framework may be employed instead for prediction purposes. Evaluating the predictive performance of these models poses a unique challenge, as the same sparseness prevents the use of popular measures such as the root mean squared error. Statistical likelihood is a valid alternative, but this does not measure absolute performance and is therefore difficult for practitioners and researchers to interpret. Motivated by this limitation, we develop a practical toolkit of evaluation metrics for spatio-temporal point process predictions. The metrics are based around the concept of hotspots, which represent areas of high point density. In addition to measuring predictive accuracy, our evaluation toolkit considers broader aspects of predictive performance, including a characterisation of the spatial and temporal distributions of predicted hotspots and a comparison of the complementarity of different prediction methods. We demonstrate the application of our evaluation metrics using a case study of crime prediction, comparing four varied prediction methods using crime data from two different locations and multiple crime types. The results highlight a previously unseen interplay between predictive accuracy and spatio-temporal dispersion of predicted hotspots. The new evaluation framework may be applied to compare multiple prediction methods in a variety of scenarios, yielding valuable new insight into the predictive performance of point process-based prediction

GIS and urban data science

With the emergence of new forms of geospatial/urban big data and advanced spatial analytics and machine learning methods, new patterns and phenomena can be explored and discovered in our cities and societies. In this special issue, we presented an overview of nine studies to understand how to use urban data science and GIS in healthcare services, hospitality and safety, transportation and mobility, economy, urban planning, higher education, and natural disasters, spreading across developed countries in North America and Europe, as well as Global South areas in Asia and the Middle East. The embrace of diverse geo-computational methods in this special issue brings forward an outlook to future GIS and Urban Data Science towards more advanced computational capability, global vision and urban-focused research

Crime Generators and Crime Attractors: Updates to Research

Crime Attractor and Crime Generator research is advancing rapidly. Research explores the context of criminal events by looking at the awareness space of offenders and victims and location of targets and explores how mobility in an urban environment and the mosaic of the urban landscape influences safety, perceived safety, human agency and decision making. Research finds a heavy concentration of crime at major attractor nodes, primary pathways to these nodes and along sharp edges separating neighborhood

Macro-Level Collision and Crime Analysis: Case Study for the City of Regina

Traffic collisions and crimes are issues of concern in most neighbourhoods and cities and they are certainly a concern for the City of Regina. The traditional approach to either preventing or reducing the severity of collisions and crimes has been a reactive one: identifying locations as problematic based on historical data before taking action. An advanced and recently introduced approach for dealing with the issues of collision and crime is the Data-Driven Approaches to Crime and Traffic Safety (DDACTS). DDACTS is a proactive, place-based approach that identifies problematic locations that require interventions. Results from a macro-level analysis are used for planning purposes. Traffic Analysis Zones for the City of Regina were considered in this research. Traffic Analysis Zones are a spatial aggregation of census blocks and are, in part, a function of population, used by city planners, for planning new neighbourhoods and resource allocation, as well as by transportation officials for tabulating traffic-related data. Traffic Analysis Zones level collision and crime prediction models have been developed to estimate safety and security effects of neighbourhood level land use, socio-economic factors, road network characteristics, and demographic variables on collisions and crimes. Furthermore, the Empirical Bayes technique are adopted to estimate expected frequencies of collisions and crimes. The expected frequencies are used in determining hotspots that require enforcement and countermeasures. The Negative Binomial modeling technique was adopted in this study to predict numbers of collisions and crimes. Models were calibrated and validated using multiple goodness-of-fit tests. Results from the goodness-of-fit tests were used as basis to determine the best model for predicting each type of collision and crime. Maps were then created to display both spatial patterns and spatio-temporal trends of collisions and crimes. Traffic Analysis Zones with significant frequencies of collisions and crimes, both separately and in unison, were then identified. Some of the conclusions drawn from the collision prediction models include: both intersection density and intersection road density had positive associations with collisions; and when comparing 3-leg and 4-leg intersections, 3-leg intersections had fewer safety concerns. Also, low density residential areas have collision reduction effects. Results from collision prediction models developed in this study can help transportation engineering officials, and city planners in traffic safety decision. At the planning stage of new neighbourhoods, the safety effects of individual predictors or sets of predictors can be determined by creating multiple scenarios that involve interested sets of variables. The developed crime models provided information about how land use type, socio-demographics, and residential land use type influence different crime types. Some conclusion drawn include the following: commercial areas and retail spaces were target areas for high numbers of violent crimes; high population density neighbourhoods attracted high numbers of crimes; higher numbers of residents within the age groups of 18 to 24 and 25 to 44 were positively associated with both violent and non-violent crimes; residents within the age groups of 44 to 65 as well as 65 years and over had a crime reduction effect, regardless of the crime occurrence type. Also, low density residential areas attracted many non-violent crimes; industry and office areas also attracted many non-violent crimes; and multiple or mixed land use areas also attracted a high volume of auto-involving theft crimes. The results of this research is intended to improve the lives of the residents of the City of Regina by providing tools that can be used to reduce traffic collisions and crimes