Enhancing the spatial dimensions of open data: Geocoding open PA information using geo platform fusion to support planning process

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Abstract

The complexity of planning process exponentially increased during last decades matching together a wide range of instances deriving from the evolution of national and local regulations and laws, an heterogeneous methodological framework, the contribution of technologies and especially the affirmation of web and social communities as relevant dimensions for citizen' participation. The general increase of data availability strongly forced planning process and today the planner has mainly the task to select, to organize and to share data in order to support decisions at different scales. The technological wide spread, open data, 2.0 approach and social-network interactions generates data continuously. We can affirm that data are everywhere, but how to get good information? It is the case of several open data services by P.A.s distributing numbers of file not fully exploitable by final users. The paper investigates some relevant examples from the Italian case in order to demonstrate the benefits of data territorialisation and the opportunity to use some specific tools developed within an open source framework: Geo Platform by GeoSDI. In particular we refer to the geocoding process translating a physical property address such as for a house, business or landmark into spatial coordinates. Geocoding intelligence implies the overcoming of semantics barriers in data code and the 'ex-ante' definition of the specific purpose of spatial application in order to accept variables accuracy levels in the final output. Conclusions regard potential application and methodological recommendation for data coding optimization. © 2013 Springer-Verlag Berlin Heidelberg.

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APA

Izzi, F., La Scaleia, G., Dello Buono, D., Scorza, F., & Las Casas, G. (2013). Enhancing the spatial dimensions of open data: Geocoding open PA information using geo platform fusion to support planning process. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7973 LNCS, pp. 622–629). https://doi.org/10.1007/978-3-642-39646-5_45

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