Spatial variations in road collision propensities in London

Propensity to be involved in a road traffic collision in Greater London is likely to depend on many factors, including personal mobility, lifestyle, behaviour, neighbourhood characteristics and environment. This paper seeks to identify in terms of geodemographic type the propensity of individuals to be involved in collisions and to examine geographic variations in such propensities with distance from Central London. Results for Central London suggest only a small number of Mosaic types portray a higher than average index score (over 100), translating into a higher risk for a smaller proportion of London’s geodemographic types. This contrasts with results which show a larger number of Mosaic classifications having higher than average index scores further from Central London. The results highlight a need, through enhanced spatial analysis, for better understanding of the spatially incidence of collisions which are putting at risk the lives of London residents

Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end,this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.tgis_1197 283..29

Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end,this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.tgis_1197 283..29

Modifying a Geodemographic Classification of the e-Society using public feedback

The e-Society geodemographic classification (Longley et al., 2008) categories neighbourhoods based on their engagement with new information communication technologies. This classification was launched online in 2006, and allowed users to both view and comment on the accuracy of their assigned neighbourhood Type. This paper utilises the user generated feedback on the accuracy of the e-Society classification and through external validation calculates their accuracy. The pilot methodology developed in this paper is scalable and could be repeated for any classification. We believe that this methodology gives the recipients of these classification procedures a voice that their concerns of classification accuracy can be heard

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

Using casualty assessment and weighted hit rates to calibrate spatial patterns of Boko Haram insurgency for emergency response preparedness

Since the beginning of the current millennium, Boko Haram has terrorised the residents of Northern Nigeria with devastating and high profile campaigns resuming in 2010. First responders struggle to cope with planning for and responding to the aftermath of these attacks. This paper describes analysis that can help emergency services pre-empt the geography and magnitude of susceptibility to attacks and the potential of the terrorists to generate severe attacks. The data used for the study were five years of terrorist activities. Results suggest that the efficiency of Boko Haram is not necessarily random and that attacks are generally well calculated to hit communities with disproportionate concentrations of vulnerable residents. The analysis is the first attempt to examine how a spatial segmentation framework might offer insight and intelligence towards understanding the configuration of terrorism for operational response

Local and Application-Specific Geodemographics for Data-Led Urban Decision Making

This work seeks to introduce improvements to the traditional variable selection procedures employed in the development of geodemographic classifications. It presents a proposal for shifting from a traditional approach for generating general-purpose one-size-fits-all geodemographic classifications to application-specific classifications. This proposal addresses the recent scepticism towards the utility of general-purpose applications by employing supervised machine learning techniques in order to identify contextually relevant input variables from which to develop geodemographic classifications with increased discriminatory power. A framework introducing such techniques in the variable selection phase of geodemographic classification development is presented via a practical use-case that is focused on generating a geodemographic classification with an increased capacity for discriminating the propensity for Library use in the UK city of Leeds. Two local classifications are generated for the city, one a general-purpose classification, and the other, an application-specific classification incorporating supervised Feature Selection methods in the selection of input variables. The discriminatory power of each classification is evaluated and compared, with the result successfully demonstrating the capacity for the application-specific approach to generate a more contextually relevant result, and thus underpins increasingly targeted public policy decision making, particularly in the context of urban planning

The stability of geodemographic cluster assignments over an intercensal period

A geodemographic classification provides a set of categorical summaries of the built and socio-economic characteristics of small geographic areas. Many classifications, including that developed in this paper, are created entirely from data extracted from a single decennial census of population. Such classifications are often criticised as becoming less useful over time because of the changing composition of small geographic areas. This paper presents a methodology for exploring the veracity of this assertion, by examining changes in UK census-based geodemographic indicators over time, as well as a substantive interpretation of the overall results. We present an innovative methodology that classifies both 2001 and 2011 census data inputs utilising a unified geography and set of attributes to create a classification that spans both census periods. Through this classification, we examine the temporal stability of the clusters and whether other secondary data sources and internal measures might usefully indicate local uncertainties in such a classification during an intercensal period

Creating Open Source Geodemographic Classifications for Higher Education Applications

This paper explores the use of geodemographic classifications to investigate the social, economic and spatial dimensions of participation in higher education. Education is a public service that confers very significant and tangible benefits upon receiving individuals: as such, we argue that understanding the geodemography of educational opportunity requires an application-specific classification, that exploits under-used educational data sources. We develop a classification for the UK higher education sector, and apply it to the Gospel Oak area of London. We discuss the wider merits of sector specific applications of geodemographics, with particular reference to issues of public service provision

Open source GIS based strategies for firms: a spatial analysis application to the inland terminal of Livorno

The paper explores the use of open source geographic information system (GIS) applied to firms. Most data available in a company have a spatial dimension and even decisions in marketing and management often have a spatial dimension. The paper is focus on illustrating the variegated opportunities for an open source GIS based strategy for firms. We argue that open source GIS are today as good as its proprietary competitors, and under certain circumstances, they are a superior alternative to their proprietary counterparts. A GIS based strategy for firms, as any other new application of geographical knowledge, it is a prospect of a new area for geography studies. This paper can be considered an initial essay on the role that geographers can play in spatial analysis applied to business strategy. The application is an example of applied geography supporting firm strategies and it has the purpose to identify spatial customer potentials for a specific infrastructure, the inland terminal of Guasticce (Italy).spatial analysis, open source, Geographic Information System (GIS), geography, inland port