The increased availability of digital data and the increased scrutiny of public expenditure are opening new opportunities for detailed spatial analysis of social behaviour and policy initiatives to target resources where they are most needed. Two such policy areas in which the use of GIS combined with spatial analysis tools has made significant progress are health and police services, which are at the top of the political agenda due to increasing 'demand' and spiralling costs. Against this background, this paper presents the results of a collaborative research project carried out in Sheffield on the use of GIS for crime pattern analysis. The research described is significant in a number of respects: it is based on high-quality detailed crime data and geographical data for the whole of Sheffield; it compares two different methodologies for crime pattern analysis, one developed specifically for crime, the other for health research; and it demonstrates the policy value of this transfer of methodologies across disciplines.
Cities are central to the economic and social development of European society, not only because over 80% of European citizens live in urban areas, but also because cities are at the same time centres of production, innovation, employment, and culture, and loci of segregation, deprivation, and ethnic conflict. The emergence of a European-wide urban policy, has given new impetus to the need for comparable indicators of the quality of life to monitor development and policy implementation. This paper reviews the literature on quality of life indicators, and argues that traditional measures of the quality of life need to be supplemented with two new dimensions that reflect more recent postmodernist thinking about the composition of urban landscapes, and the contribution to the quality of life of the emerging information society. We argue that the challenges of building appropriate indicators reflecting these new dimensions are considerable, even in urban environments so rich in information systems and data sources, if they are to qualify as ‘digital cities’. There are difficulties in finding common workable definitions of the indicators themselves, as well as definitions of the relevant populations, including city residents, and users. By raising these issues and suggesting possible avenues for addressing these challenges we contribute to a much-needed debate on how to define such indicators, which is the prerequisite for their development and use.
The widespread diffusion of sensors, mobile devices, social media and open data are reconfiguring the way data underpinning policy and science are being produced and consumed. This in turn is creating both opportunities and challenges for policy-making and science. There can be major benefits from the deployment of the IoT in smart cities and environmental monitoring, but to realize such benefits, and reduce potential risks, there is an urgent need to address current limitations, including the interoperability of sensors, data quality, security of access and new methods for spatio-temporal analysis. Within this context, the manuscript provides an overview of the AirSensEUR project, which establishes an affordable open software/hardware multi-sensor platform, which is nonetheless able to monitor air pollution at low concentration levels. AirSensEUR is described from the perspective of interoperable data management with emphasis on possible use case scenarios, where reliable and timely air quality data would be essential.
Police forces responsible for large metropolitan areas in England and Wales have claimed that within certain parts of their urban areas there exist high-intensity crime areas (HIAs). These are areas that raise special policing problems because of the particularly violent forms of crime sometimes found within them and because of the unwillingness or inability of the resident population to co-operate fully with the police in part because of fears for their own safety. A sample of metropolitan police forces was asked to identify the location of their HIAs and this paper reports the results of a GIS-based spatial analysis to try and model the location of these areas using census data. Three police force areas were used to develop the model. This was subsequently validated against a further set of HIA data from different police forces. The model suggests that HIAs are characterised by populations that are deprived and live at high density, and by higher levels of population turnover.
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