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Group-Based Modeling of Development

Predicting when and where crimes are likely to occur is crucial for prioritizing police resources. Prior victimization is an excellent predictor of risk. Repeat victimization, when it occurs, tends to occur swiftly after an initial incident. The predictive power of prior victimization is greater than that of other analysed variables (see Budd 1999). Self-evidently, prior victimization yields no prediction about properties as yet unvictimized. This article, using data about the crime of domestic burglary, contends that research should seek to realize the predictive potential to be gained from both pre-and post-victimization factors. One of the advantages of crime reduction through the prevention of repeats is that it offers a constant (and, it is hoped, declining) rate of events that trigger preventive action, and hence a natural pace for preventive work. In that spirit, postvictimization prevention should, as well as targeting the victimized home, also protect other properties that are similar with respect to the dimensions used by burglars in target selection. The central purpose of the research here reported is to identify the ways in which it is prudent to allocate crime reduction resources in the wake of an offence and across time and location relative to the burgled home. We analysed police-recorded crime burglary data for the county of Merseyside. Using statistical techniques developed to study the transmission of disease, we first confirmed that burglaries do cluster in space and time. The operational payoff of this result is that a residential burglary flags the elevated risk of further residential burglaries in the near future (1-2 months) and in close proximity (up to 300-400 metres) to the victimized home. Put simply, the burglary event should trigger preventive action that is not restricted to the burgled home. The data enable prospective burglary hot-spotting.

The Burglary as Clue to the Future: The Beginnings of Prospective Hot-Spotting

Online mapping technologies such as Google Maps and Street View have become increasingly accessible. These technologies have many convenient uses in everyday life, but law enforcement agencies have expressed concern that they could be exploited by offenders and might alter existing offending patterns and habits. For environmental criminologists, they have the potential to open up new approaches to conducting research. This paper draws on the results of earlier studies in related fields and a handful of criminological studies to discuss how these online mapping applications can trigger new research questions, and how they could be considered a valuable methodological addition to criminological research.

https://biblio.ugent.be/publication/4344882/file/5763027.pdf

Should criminologists be feeling lucky!? Applying Google Maps and Google Street View in criminological research

How COVID-19 reshaped quality of life in cities: A synthesis and implications for urban planning.

බළන්ගොඩ ප්රදේශය ආශ්රිත ශාන්තිකර්ම පිළිබඳ තුලනාත්මක අධ්යයනයක් (Unpublished doctoral dissertation)

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.

Offender residential concentrations : a longitudinal study in Birmingham, England

The visualisation of spatial information is a powerful tool for researchers in urban analytics seeking to convey their findings to the wider research community and the public in an accessible way. ...

Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations:

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

/pdf/anchored-k-medoids-a-novel-adaptation-of-k-medoids-further-4u097qjzdy.pdf

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

In social and behavioural sciences, longitudinal clustering is widely used for identifying groups of individual trends that correspond to certain developmental processes over time. Whilst popular clustering techniques, such as k-means, are suited for identifying spherical clusters (Curman et al., 2015; Genolini & Falissard, 2011), there has been little attempt to modify such methods to identify alternative forms of cluster, such as those that represent linear growth over time (i.e. directionally-homogeneous clusters). To address this shortcoming, we introduce Anchored k-medoids, a package referred to as Ak-medoids, which implements a medoid-based expectation maximisation (MEM) procedure within a classical k-means clustering framework. The package includes functions to assist in the manipulation of longitudinal data sets prior to the clustering procedure, and the visualisation of solutions post-procedure. The potential application areas of Ak-medoids include criminology, transport, epidemiology and brain imaging.

/pdf/akmedoids-r-package-for-generating-directionally-homogeneous-1nvdw5pah6.pdf

Akmedoids R package for generating directionally-homogeneous clusters of longitudinal data sets

Addressing public safety and welfare, inclusive of responding to incidents involving persons with mental ill-health (PMIH) has become an integral dimension of, and a significant challenge to, contemporary policing. Yet, little is known of the scale and severity of such PMIH-related policing demand, nor of the extent of frontline resource consumed in resolving such incidents. To address this shortfall, we deploy a bespoke text mining algorithm on police incident logs to estimate the proportion and severity of calls-for-service involving PMIH in a study of Greater Manchester, UK. Furthermore, and using Global Positioning System data, we then assess the amount of time spent by frontline officers responding to these calls. Findings suggest that existing police recording practices serve to significantly underestimate the scale and severity of PMIH-related demand. The amount of time spent dealing with PMIH-related incidents is both substantial and disproportionate relative to other forms of police demand.

/pdf/policing-and-mental-ill-health-using-big-data-to-assess-the-3br8hmd9hd.pdf

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

Samuel Langton

Papers

Offender residential concentrations : a longitudinal study in Birmingham, England

Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations:

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

Akmedoids R package for generating directionally-homogeneous clusters of longitudinal data sets

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