89 research outputs found
Burglary in London: insights from statistical heterogeneous spatial point processes
To obtain operational insights regarding the crime of burglary in London, we consider the estimation of the effects of covariates on the intensity of spatial point patterns. Inspired by localized properties of criminal behaviour, we propose a spatial extension to mixtures of generalized linear models from the mixture modelling literature. The Bayesian model proposed is a finite mixture of Poisson generalized linear models such that each location is probabilistically assigned to one of the groups. Each group is characterized by the regression coefficients, which we subsequently use to interpret the localized effects of the covariates. By using a blocks structure of the study region, our approach enables specifying spatial dependence between nearby locations. We estimate the proposed model by using Markov chain Monte Carlo methods and we provide a Python implementation
New urbanism, crime and the suburbs: a review of the evidence
Sustainability now influences government policy in the UK, Australia and USA and planning policy currently advocates high density, mixed-use residential developments in 'walkable', permeable neighbourhoods, close to public transport, employment and amenities. This clearly demonstrates the growing popularity, influence and application of New Urbanist ideas.This paper reviews the criminological research relating to New Urbanism associated with the three key issues of permeability, rear laneway car parking and mixed-use development. These key issues are discussed from an environmental criminology perspective and challenge New Urbanist assumptions concerning crime. The paper proposes that crime prevention through environmental design (CPTED) and its crime risk assessment model represents a valuable tool for New Urbanists to utilise to reduce opportunities for crime and tackle fear of crime in the community. Recommendations for future research and collaboration are discussed
It Looks Good, but What is it Like to Live There? Exploring the Impact of Innovative Housing Design on Crime
This paper reports on the findings of a collaborative project (funded by the Home Office and managed by the Commission for Architecture and the Built Environment - CABE) which was conducted in late 2009 and early 2010. The project set out to strengthen and update the evidence base on the impact of design on a range of crime types – with a specific focus upon housing developments acclaimed for their innovative design and award winning architecture. This paper presents the findings of an in-depth assessment of the impact of housing design features on crime. Utilising a comprehensive data collection exercise, the specific design features of thousands of homes were collated and assessed against police recorded crime data. The design features were based upon the key elements of Crime Prevention Through Environmental Design (CPTED) including road layout, house design, surveillance, territoriality, car parking, communal space, management and maintenance and physical security. The unique and painstaking methodology not only provided an excellent dataset for analysis, but also highlighted the need both for greater conceptual clarity within CPTED and for crime-risk assessments to be based on the careful operationalisation and measurement of CPTED factors. As well as assessing the impact of specific (and combined) design features upon crime, the research also resulted in the production of a new data collection tool designed to address the weaknesses of existing checklists in assessing innovative contemporary developments, which are often unconventional in nature. The paper explores the degree of conflict and/or synergy between the traditional principles of CPTED and contemporary directions in architecture and design. Finally the paper considers the extent to which traditional CPTED principles remain relevant within contemporary residential developments and explores whether areas of revision are required
(Looking) Back to the Future: Using Space—Time Patterns to Better Predict the Location of Street Crime
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