Editorial

In recent decades, the analysis of different geographic scales for studying the spatial patterning of crime has profoundly deepened our theoretical grasp of crime dynamics. However, a similar investigation is lacking when it comes to the patterning of offender residences, despite there being clear theoretical and empirical reasons for doing so, among them, the close relationship between where offenders live and where their corresponding crimes are committed. This paper delves into the concentration and variance of offender residences across different levels of spatial aggregation. The data used contains the locations of residence for known offenders in Birmingham between the years 2006 and 2016. Resident locations are aggregated to Output Areas (OA), nested within Lower Super Output Areas (LSOA), further nested within Middle Super Output Areas (MSOA). Descriptive and model-based statistics are deployed to quantify concentration and variation at each spatial scale. Results suggest that most variance (~48%) in offender residence concentrations is attributable to the largest spatial scale (MSOA level). Output Areas capture approximately 38% of the variance. Findings open up discussions on the role of urban development in determining the appropriateness of spatial scale

Offender Residential Concentrations: A Longitudinal Study in Birmingham, England

The overarching aim of this thesis is to advance understanding into the geographic distribution of offender residences, that is, where known offenders live. Although this strand of research emerged amidst the earliest studies in spatial criminology, contemporary research has since favoured the examination of offences, much at the expense of offender residences. This shift has occurred despite there being strong theoretical and empirical reasons for studying both. To revive interest into offender residences, and achieve the aim of this thesis, three key themes are identified through a comprehensive review of existing literature, relating to spatial scale, longitudinal stability and explanation. From these, three research questions are posed, the answers to which constitute the original contribution of this thesis. Firstly, what is the most appropriate spatial scale to study offender residential concentrations? Secondly, to what extent do offender residential concentrations demonstrate stability over time? Thirdly, how can we explain the longitudinal (in)stability of offender residential concentrations? To answer these research questions, analysis is conducted on longitudinal police recorded data of known offender residences in Birmingham between 2007 and 2016, supplied by West Midlands Police Force, and census data under Open Government Licence. The methods deployed are largely inspired by the (considerably more advanced) offence strand of research, and include descriptive statistics, extensive (spatial) visualisations, multilevel variance partitions, novel longitudinal clustering techniques and spatially lagged multivariable regression models. Findings suggest that small (‘micro’) spatial scales are most suitable for studying the geography of offender residences. The degree to which concentrations demonstrate longitudinal (in)stability varies by the methods deployed, but findings suggest a reasonable degree of volatility over time, some of which is due to the individuallevel residential mobility of offenders. Longitudinal trends can be explained by a number of demographic characteristics, including deprivation, ethnic diversity and housing tenure. Discussions emerge from these findings which have implications for methodology, theory and policy, opening prospect to generate avenues for future research

Open Data for Crime and Place Research: A Practical Guide in R

Access to data in crime and place research has traditionally been reserved for those who have the means to collect fresh data themselves, pay for access, or obtain data through formal data sharing agreements. Even when access is granted, the usage of these data often comes with conditions that circumscribe how the data can be used through licensing or policy (Kitchin, 2014). Even the public dissemination of findings which emerge from analysis might be subject to restrictions. This can lead to unequal access, controlled usage and curb the diffusion of findings, severely limiting the insight that can be obtained from data

Describing the scale and composition of calls for police service: a replication and extension using open data

This paper describes the scale and composition of emergency demand for police services in Detroit, United States. The contribution is made in replication and extension of analyses reported elsewhere in the United States. Findings indicate that police spend a considerable proportion of time performing a social service function. Just 51% of the total deployed time responding to 911 calls is consumed by crime incidents. The remainder is spent on quality of life (16%), traffic (15%), health (7%), community (5%), and proactive (4%) duties. A small number of incidents consume a disproportionately large amount of police officer time. Emergency demand is concentrated in time and space, and can differ between types of demand. The findings further highlight the potential implications of radically reforming police forces in the United States. The data and code used here are openly available for reproduction, reuse, and scrutiny

Anchored k-medoids : a novel adaptation of k-medoids further refined to measure long-term instability in the exposure to crime

Longitudinal clustering techniques are widely deployed in computational social science to delineate groupings of subjects characterized by meaningful developmental trends. In criminology, such methods have been utilized to examine the extent to which micro places (such as streets) experience macro-level police-recorded crime trends in unison. This has largely been driven by a theoretical interest in the longitudinal stability of crime concentrations, a topic that has become particularly pertinent amidst a widespread decline in recorded crime. Recent studies have tended to rely on a generic implementation k-means to unpick this stability, with little consideration for its theoretical suitability. This study makes two methodological contributions. First, it demonstrates the application of k-medoids to study longitudinal crime concentrations, and second, it develops a novel ‘anchored k-medoids’ (ak-medoids), a bespoke clustering method specifically designed to meet the theoretical requirements of micro-place investigations into long-term stability. Using both simulated data and 15-years of police-recorded crime data from Birmingham, England, we compare the performances of k-medoids against ak-medoids. We find that both methods highlight instability in the exposure to crime over time, but the consistency and contribution of cluster solutions determined by ak-medoids provide insight overlooked by k-medoids, which is sensitive to short-term fluctuations and subject starting points. This has important implications for the theories said to explain longitudinal crime concentrations, and the law enforcement agencies seeking to offer an effective and equitable service to the public

The spatial patterning of emergency demand for police services: a scoping review

This preregistered scoping review provides an account of studies which have examined the spatial patterning of emergency reactive police demand (ERPD) as measured by calls for service data. To date, the field has generated a wealth of information about the geographic concentration of calls for service, but the information remains unsynthesised and inaccessible to researchers and practitioners. We code our literature sample (N = 79) according to the types of demand studied, the spatial scales used, the theories adopted, the methods deployed and the findings reported. We find that most studies focus on crime-related call types using meso-level (e.g., neighborhood) spatial scales. Descriptive methods demonstrate the non-random distribution of calls, irrespective of their type, while correlational findings are mixed, providing minimal support for theories such as social disorganization theory. We conclude with suggestions for future research, focusing on how the field can better exploit open data sources to ‘scale-up’ analyses

The accuracy of crime statistics: Assessing the impact of police data bias on geographic crime analysis

Objectives: Police-recorded crimes are used by police forces to document community differences in crime and design spatially-targeted strategies. Nevertheless, crimes known to police are affected by selection biases driven by underreporting. This paper presents a simulation study to analyze if crime statistics aggregated at small spatial scales are affected by larger bias than maps produced for larger geographies. Methods: Based on parameters obtained from the UK Census, we simulate a synthetic population consistent with the characteristics of Manchester. Then, based on parameters derived from the Crime Survey for England and Wales, we simulate crimes suffered by individuals, and their likelihood to be known to police. This allows comparing the difference between all crimes and police-recorded incidents at different scales. Results: Measures of dispersion of the relative difference between all crimes and police-recorded crimes are larger when incidents are aggregated to small geographies. The percentage of crimes unknown to police varies widely across small areas, underestimating crime in certain places while overestimating it in others. Conclusions: Micro-level crime analysis is affected by a larger risk of bias than crimes aggregated at larger scales. These results raise awareness about an important shortcoming of micro-level mapping, and further efforts are needed to improve crime estimates

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie