The Smart City is defined, among other things, by its ability “to measure and recognize potential problems just as they start to arise […] and act to make the necessary corrections.” [1] This concept is borrowed from feedback control theory and reflects the centrality of timely, accurate and explicative information in the process of urban governance. Postulating that information is accurate and explicative, though, requires a rather exceptional assumption: that available data and methodologies justify inferences, thus making policy decisions “evidence-based”. In this work, we define a three-step process to provide decision-makers with information that is sufficient to justify evidence-based local policy decisions on urban crime. Of the three steps, the first one contains a number of methodological innovations and therefore is discussed in greater detail, whereas the other two, while depending on the outcome of the former, are straightforward applications of existing methods. This first step consists of an analytical methodology to explore and model the supposed influence of socio-economic, demographic and spatial factors on crime. The main contribution on the actual literature is the integration of data originating from different databases and with different territorial levels. In particular, we propose a variation of the most widely used techniques which analyze urban structures in terms of networks and graphs, an innovation which gives space a more relevant position in statistical models for evidence based decision making rather than a mere spatial distance matrix. When implemented as an ongoing and evolving process, such methodology produces an extensive knowledge of the covariates of crime, which is the input of the second step, a set of quasi-experimental designs [2] of small scale pilot policy actions on plausible crime determinants. Finally, in the third step, the evaluation of the pilot policy actions and their outcomes provides the necessary evidence to infer on the plausible determinants of crime, therefore supporting (or not) large scale policy decisions. Smart Security as a process implies three main elements of innovation: the introduction of spatial configuration as a component of urban crime models because of its influence on pedestrian and vehicular movement patterns [3]; the collection and analysis of data from different sources and at different scales; the identification of a finite number of urban environment types with different performances in terms of security. To illustrate these points, we make use of data from the 2011 UK Census in London and from the UK Police records during 18 months in the period 2013 – 2014.

Smart Security: data analysis and inference for evidence-based security policies in the “Smart City”

DI BELLA, ENRICO;CORSI, MATTEO;LAGAZIO, CORRADO;PERSICO, LUCA
2014

Abstract

The Smart City is defined, among other things, by its ability “to measure and recognize potential problems just as they start to arise […] and act to make the necessary corrections.” [1] This concept is borrowed from feedback control theory and reflects the centrality of timely, accurate and explicative information in the process of urban governance. Postulating that information is accurate and explicative, though, requires a rather exceptional assumption: that available data and methodologies justify inferences, thus making policy decisions “evidence-based”. In this work, we define a three-step process to provide decision-makers with information that is sufficient to justify evidence-based local policy decisions on urban crime. Of the three steps, the first one contains a number of methodological innovations and therefore is discussed in greater detail, whereas the other two, while depending on the outcome of the former, are straightforward applications of existing methods. This first step consists of an analytical methodology to explore and model the supposed influence of socio-economic, demographic and spatial factors on crime. The main contribution on the actual literature is the integration of data originating from different databases and with different territorial levels. In particular, we propose a variation of the most widely used techniques which analyze urban structures in terms of networks and graphs, an innovation which gives space a more relevant position in statistical models for evidence based decision making rather than a mere spatial distance matrix. When implemented as an ongoing and evolving process, such methodology produces an extensive knowledge of the covariates of crime, which is the input of the second step, a set of quasi-experimental designs [2] of small scale pilot policy actions on plausible crime determinants. Finally, in the third step, the evaluation of the pilot policy actions and their outcomes provides the necessary evidence to infer on the plausible determinants of crime, therefore supporting (or not) large scale policy decisions. Smart Security as a process implies three main elements of innovation: the introduction of spatial configuration as a component of urban crime models because of its influence on pedestrian and vehicular movement patterns [3]; the collection and analysis of data from different sources and at different scales; the identification of a finite number of urban environment types with different performances in terms of security. To illustrate these points, we make use of data from the 2011 UK Census in London and from the UK Police records during 18 months in the period 2013 – 2014.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/730385
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