Abstract
In the age of Big Data, the lack of relevant data, information and knowledge and the limits of the instruments and legislation for Risk Prevention and Management (RPM) do not allow decision makers to act efficiently and in a participatory way in territorial management. The task of this paper is to find a way to foster a proactive coordination between RPM instruments, practices and stakeholders, in order to identify consistent policy choices. A territorial organization model is defined, the Observatory for Territorial Participation (OTP), based on the following tasks: involvement of local actors; use of interactive computerized techniques and tools for decision support; access and sharing of big data and information in real time. The use of a “strategic planning” software, tested within a real setting, not only helps to focus the discussion and the process of definition of RPM policies, but it also leads at possible strategic organizational paths in the short, medium and long run.
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Notes
- 1.
ASA Software (2007), Laboratory of Analysis and Models for planning (LAMP), University of Sassari (web site: <http://www.lampnet.org>).
- 2.
The options have been nominated with the same identification number of the goals which they relates to. First capital letter refer to the three sets of policy options, numbers refer to policy options.
- 3.
Financial costs of technical implementation of options are ordinal variables expressing a measure, in this case using a scale ranging from 0 to 100.
- 4.
Direct Relevance (RD) is an ordinal variable expressing a measure, in this case using a scale ranging from 0 to 10.
- 5.
The value indicating the synergistic effects of each ordered pair of options is: low synergy (average value 0%); medium synergy (average value 25%); strong synergy (average value 50%); negative synergy.
- 6.
The results of the desk analysis on current RPM EU/national/regional framework (1) were better specified through interviews with relevant local authorities, emergency services as well as local policy makers.
- 7.
For example, the actor with influence level ‘b’ is less important for the development of an option than the actor with influence level ‘a’, but in the same way, a couple of actors with influence level ‘b’, putting equal efforts, have the same importance for the general development of the option than an actor with influence level ‘a’.
- 8.
ASA user graphical interface represents each sequence of options as a “bubble”. Optimal sequences are in the second quadrant (upper right).
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Plaisant, A., Mastinu, M., Sini, D. (2017). Risk Prevention and Management. A Multi-actor and Knowledge-Based Approach in Low Density Territories. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10406. Springer, Cham. https://doi.org/10.1007/978-3-319-62398-6_18
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