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A Multi-indicator Approach for Smart Security Policy Making

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Abstract

Measuring urban crime is a well-established practice for many police forces, local governments and public administrations all over the world. There seems to be, however, a large gap between the rigorous academic definition of how crime indicators should be calculated and interpreted and the actual use that is made of them. Crime counts and rates are unadvisedly put out with little effort to clarify their different meaning; population-based crime rates remain a standard measure despite quite compelling arguments against the use of population as an offset for cross-sectional comparisons; most importantly, little attention seems to have been paid to the consequences of formulating preventive policies based on poorly defined and understood indicators. This issue looks even more evident with high-definition indicators that detail the levels of crime for very small statistical units (streets, street segments and blocks) with situational interventions in mind. After a review of the literature, we illustrate, through a case study, the different landscapes of urban safety and risk of crime when five different families of indicators are alternatively used: crime counts, population-based crime rates, risk-based crime rates, crime density and location quotients. We propose a multi-indicator approach to the ranking and prioritization of urban security issues based on partial order scalogram analysis by coordinates that presents substantial advantages as an operational tool for Public Administrations in a Smart Cities framework.

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Notes

  1. The concept of “Smart City” is notoriously liquid, scarcely formalized and, in some degree, subject to different ideological interpretations (e.g. Hollands 2008; Deakin and Al Waer 2011; Caragliu et al. 2011). However, elements like data (Celino and Contessa 2012), information and communication technologies and urban governance are almost ubiquitous in discussions about Smart Cities.

  2. Routine activity theory suggests that “… any successfully completed violation requires at a minimum an offender with both criminal inclinations and the ability to carry out those inclinations, a person or object providing a suitable target for the offender, and the absence of capable guardians capable of preventing the violation” (Felson and Cohen 1980, p. 392). From the very influential point of view of Felson and Cohen, crime is determined by three conditions: suitable targets, motivated offenders and the absence of capable guardians. Appropriately calculated crime rates, incorporating the actual population at risk are, therefore, very consistent with the first condition and may be consistent with the other two as well, giving researchers and practitioners much better indices to work with than population-based crime rates.

  3. In Italy, complaints are collected by multiple police forces, five of which are federal agencies (Polizia di Stato, Carabinieri, Guardia di Finanza, Polizia Penitenziaria and Corpo Forestale dello Stato). As a consequence of the functional and territorial organization of police forces, a percentage of all the complaints collected by a local station usually consists of complaints for events that took place in different districts of the city than the one for which the station is responsible. Conversely, while a single station usually collects a large majority of the complaints referred to a certain area, no station actually collects all of them because a percentage is dispersed through other stations across the city. The database used in this research was obviously filtered of all complaints not referred to the district of Marassi, but a single database containing all the local complaints in the study period was not available at the time of the research.

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Authors and Affiliations

  1. Department of Economics and Business Studies, University of Genoa, Via Vivaldi 5, 16126, Genoa, Italy

    Enrico di Bella & Matteo Corsi

  2. Department of Political Sciences, University of Genoa, Genoa, Italy

    Lucia Leporatti

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  1. Enrico di Bella
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  2. Matteo Corsi
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Correspondence to Enrico di Bella.

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di Bella, E., Corsi, M. & Leporatti, L. A Multi-indicator Approach for Smart Security Policy Making. Soc Indic Res 122, 653–675 (2015). https://doi.org/10.1007/s11205-014-0714-7

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