Crime statistics are ubiquitous in modern society – but how accurate are they? This book investigates the science of crime measurement focussing on four main questions: how do we count crime? How do we calculate crime rates? Are there other measurements of crime? What are the issues surrounding crime statistics? All too often we take the measurement of crime at face value when there is, in fact, a science behind it. This book specifically deals with issues related to spatially-referenced crime data that are used to analyse crime patterns across the urban environment. The first section of the book considers alternative crime rate calculations. The second section of the book contains a thorough discussion of a measure of crime specialisation. Finally, the third section of the book addresses a number of aggregation issues that are present with such data: crime type aggregations, temporal aggregations of crime data, the stability of crime patterns over time, and the importance of spatial scale. This book builds on a growing body of literature on the science of crime measurement and offers a comprehensive account of this growing subfield of criminology. The book speaks to wider debates in the fields of crime analysis, environmental criminology and crime prevention and will be perfect reading for advanced level undergraduate and graduate students looking to find out more about the measurement of crime.

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.

Combining insights from two distinct research traditions—the communities and crime tradition that focuses on why some neighborhoods have more crime than others, and the burgeoning crime and place literature that focuses on crime in micro-geographic units—this book explores the spatial scale of crime. Criminologist John Hipp articulates a new theoretical perspective that provides an individual- and household-level theory to underpin existing ecological models of neighborhoods and crime. A focus is maintained on the agents of change within neighborhoods and communities, and how households nested in neighborhoods might come to perceive problems in the neighborhood and then have a choice of exit, voice, loyalty, or neglect (EVLN). A characteristic of many crime incidents is that they happen at a particular spatial location and a point in time. These two simple insights suggest the need for both a spatial and a longitudinal perspective in studying crime events. The spatial question focuses on why crime seems to occur more frequently in some locations than others, and the consequences of this for certain areas of cities, or neighborhoods. The longitudinal component focuses on how crime impacts, and is impacted by, characteristics of the environment. This book looks at where offenders, targets, and guardians might live, and where they might spatially travel throughout the environment, exploring how vibrant neighborhoods are generated, how neighborhoods change, and what determines why some neighborhoods decline over time while others avoid this fate. Hipp’s theoretical model provides a cohesive response to the general question of the spatial scale of crime and articulates necessary future directions for the field. This book is essential for students and scholars interested in spatial-temporal criminology.

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.

Putting Crime in its Place: Units of Analysis in Geographic Criminology focuses on the units of analysis used in geographic criminology. While crime and place studies have been a part of criminology from the early 19th century, growing interest in crime places over the last two decades demands critical reflection on the units of analysis that should form the focus of geographic analysis of crime. Should the focus be on very small units such as street addresses or street segments, or on larger aggregates such as census tracts or communities? Academic researchers, as well as practical crime analysts, are confronted routinely with the dilemma of deciding what the unit of analysis should be when reporting on trends in crime, when identifying crime hot spots or when mapping crime in cities. In place-based crime prevention, the choice of the level of aggregation plays a particularly critical role. This peer reviewed collection of essays aims to contribute to crime and place studies by making explicit the problems involved in choosing units of analysis in geographic criminology. Written by renowned experts in the field, the chapters in this book address basic academic questions, and also provide real-life examples and applications of how they are resolved in cutting-edge research. Crime analysts in police and law enforcement agencies as well as academic researchers studying the spatial distributions of crime and victimization will learn from the discussions and tools presented.

Crime control continues to be a growth industry, despite the drop in crime indicators throughout the nation. This volume shows how state-of-the-art geographic information systems (GIS) are revolutionizing urban law enforcement, with an award-winning program in New York City leading the way. Electronic "pin mapping" is used to display the incidence of crime, to stimulate effective strategies and decision making, and to evaluate the impact of recent activity applied to hotspots. The expert information presented by 12 contributors will guide departments without such tools to understand the latest technologies and successfully employ them. Besides describing and assessing cutting-edge techniques of crime mapping, this book emphasizes: * the organizational and intellectual contexts in which spatial analysis of crime takes place, * the technical problems of defining, measuring, interpreting, and predicting spatial concentrations of crime, * the common use of New York City crime data, and * practical applications of what is known (e.g., a review of mapping and analysis software packages using the same data set). Students, academics, researchers, practitioners, and policymakers in the areas of criminal justice, corrections, geography, social problems, law and government, public administration, and public policy analysis will need to look at the interdisciplinary nature of both GIS and spatial dimensions of crime in order to comprehend the variety of different approaches address important analytic problems, reassess public facilities and resources, and prepare to respond more quickly to emerging hotspots.