Ontology and Spatial Planning

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1 Ontology and Spatial Planning Beniamino Murgante and Francesco Scorza Laboratory of Urban and Territorial Systems, University of Basilicata, 10, Viale dell Ateneo Lucano, Potenza, Italy Abstract. In last decades a key problem in adopting technologies in planning process was a chronic lack of data. But in recent times, such problem was inverted due to the overabundance of data produced in different periods, with various purposes, at multiple scales and with different cognitive models. This situation generated three types of barriers to data interoperability: bureaucratic, technological, semantic. While the first two issues have been solved taking various initiatives, the last one could be solved using ontologies. Concepts are the cornerstone of the ontology, but it is not easy to define a concept without any ambiguity, discordance or vagueness. A concept can be clear or not; ambiguity occurs when a concept is not much clear; while discordance arises when an agreement is missing. If the concept definition can present some incoherence, the broad boundaries model can be useful in Ontology representation. This model is an extension of the 9-intersection model used for the topological relationship among geographical objects. The model with broad boundaries deals with uncertainty in spatial data taking into account ill defined aspects. This model is based on the definitions of inner and broad boundaries. Using this model in Ontology field, the inner boundary is the edge of the part of a concept without doubts and the broad boundary is the grey zone, with a certain level of uncertainty, useful to represent ambiguity, discordance and vagueness. Topology rules represent the relationship among concepts. If two concepts are identical, the equal rule can be used; if they share some parts, the overlap rule is suitable. If two concepts are completely different, the disjoint rule can be applied. If a concept is a subset of another, there are several rules which can help us ( covers, covered by, contains and inside ). In case all concepts are clear, these relationships can be modelled using the 9-intersection model. The way to define the part of concept included inside the inner boundary and the other one included in the broad boundary can be achieved using rough set theory. All the aspects of a concept classified in the same way represent the indiscernible part of the concept and are included inside lower approximation (inner boundary). The remaining part represents an uncertainty zone and it falls within the upper approximation (outer boundary). The measure of the degree of uncertainty inside the upper approximation can be modelled using fuzzy set theory. This approach has been tested with several concepts particularly suitable to verify the hypothesis. Keywords: Ontology, Spatial Planning, Semantic Interoperability, Planning Documents, Rough Set. B. Murgante et al. (Eds.): ICCSA 2011, Part II, LNCS 6783, pp , Springer-Verlag Berlin Heidelberg 2011

2 256 B. Murgante and F. Scorza 1 Spatial Planning and Cognitive Shared Frameworks In last decades the great problem which limited planners in using technologies to support planning processes was a chronic lack of data. Subsequently, great part of public and private organizations, at any level, made their spatial information systems operating in a very isolated way, sometimes slapdash and without an overall coordination. Generally, in this period data without general information and detailed documentation were produced and, consequently, all the information was an exclusive property of people who provided to make them. This approach, based on insulation, could be used as long as the funding model of local authorities was mainly based on direct money transfer from Ministries. The dramatic and continued reduction of funding to local authorities produced a competition between organizations in pursuing national and European funds through projects which, in most cases, encouraged aggregations of local authorities at different levels. The need of presenting proposals by a consortium highlighted the existence of various types of barriers in spatial data bases interoperability. In order to be competitive respect to all new funding channels, it is important that all spatial databases of local authorities can talk to each other [1]. The main barriers towards a full interoperability are determined by three factors: bureaucratic, due to a poor practice to share data which, in most cases, leads to a sort of personal property right by the employer, who provides to its management; technological, mainly produced by differences between systems, structures and format of data; semantic, due to the lack of correspondence in meanings. Bureaucratic interoperability can be pursued by imposing clear and transparent procedures and adopting an open approach in data production. Executive Order (1994), Coordinating Geographic Data Acquisition and Access: The National Spatial Data Infrastructure (NSDI), is an important example of a clear procedure, imposed to American agencies, organizations and local authorities, which led to a huge resource optimization. INSPIRE Directive lacks in adoption effectiveness, because it has to be implemented in different countries with different laws, procedures, languages, etc., because bureaucracy finds great interference in Europe and because European directives, even if adopted by individual member states, often are not applied,. In parallel with procedural aspects, the complete elimination of bureaucratic barriers could be achieved with a wiki approach to data production. For this type of activity several terms have been coined: Volunteered Geographic Information [2], a mass collaboration has been adopted to create, manage and disseminate spatial data; "Crowdsourcing" [3], when high-quality data, once produced only by specialized agencies, are realized by distributed masses (e.g. OpenStreetMap); "Neogeography" [4], to describe a bottom-up approach to data production integrating maps with geotagged photos, videos, blogs, Wikipedia, etc..

3 Ontology and Spatial Planning 257 In order to improve technological interoperability a consortium called the Open Geospatial Consortium (OGC)was created, consisting of organizations, universities, enterprises, etc., whose activities are fully devoted to the problems of technical interoperability. Open Geospatial Consortium has defined several standards, some of which are now commonly used (e.g. WMS Web Map Service, WFS Web Feature Service, WCS Web Coverage Service), which significantly improve technological interoperability. Semantic barriers are undoubtedly the most difficult to overcome. but they are not sufficient to transfer the information meaning. Unfortunately, it rarely happens that before modelling data structure is considered the most complex question of what it represents. The main question in using Geographical Information Systems is where an object is located, without thinking in a deep way about what you are looking for. Very often a concept has different definitions depending on whether it is analyzed in a general or in a very specific way. In the former case it is easy to get shared definitions, in the latter case, if the concept is analyzed by specialists, each of them will highlight aspects of a detailed cognitive representation, which tends to deeply shape the world that each of them has in his mind, rather than the perceived world [5]. It is therefore necessary that, at the same time, side by side to databases integration, the search of semantic correspondences is pursued. The most adopted approach in trying to achieve this kind of interoperability is represented by ontologies, which originates from philosophical disciplines, reaching a large spread in the field of artificial intelligence. A definition, shared by philosophers and computer scientists, considers ontology as "the theory of objects and their relationships" [6]. An ontology defines, therefore, the basic conditions, relationships, domain vocabulary, rules for combining terms and relations to define any vocabulary extension [7]. Ontologies, with the aim of ensuring systems interoperability, allow concepts clarification and deepening, often considered known with a certain superficiality [8]. They can help a community to define and adopt a common language, even more today, when globalization processes and the advent of new information and telecommunication technologies lead to a sudden increase of terms, which represent an additional semantic barrier. Sectoral plans in a lot of cases consider territory for parts and are written by consultants primarily specialized in one sector. Spatial planning, basing its knowledge framework on very detailed analysis, developed by domain specialists with very distant cultural backgrounds, reuses ontologies developed in other communities, trying to provide a common and shared vision by different groups [9]. Spatial planning pursues a kind of generalization of sectoral analysis domain, producing a semantic agreement, sometimes losing part of the original meaning or a certain level of detail [10]. 2 Ontology and Spatial Planning The field of spatial planning, managing the enormous territory complexity, is particularly suitable for testing semantic correspondences. Planning instruments

4 258 B. Murgante and F. Scorza regulate different areas and consequently adopt articulated semantics, which depend on the coexistence of variegated physical characteristics, heterogeneous problems and different cultural and socio-economic contexts. Their cognitive framework is also based on analyses which adopt semantics related to specific domains, often very distant from typical semantics adopted by planners [11]. If during the analytical phase, concept definitions are shared by small communities of specialists, sectoral plans can be considered as a first degree of generalization, in which, while remaining at a high level of detail, a broader agreement is reached within different communities. There is also the problem of integration and connection of the plan with other planning instruments and documents produced in different periods. It is highly probable that two plans drew up in the same area at different dates analyze a greatly changed space, have different missions and objectives and are characterized by concepts that have different definitions using different semantics. A plan reasoning is often based on different levels of synthesis [12]. In each step from one level to another a certain degree of specialization is lost in advantage of a greater sharing (fig. 1). Indovina [13] defines planning activity as a political decision technically aided. But it is important to compare technical dimension with political and social dimension. Fig. 1. Relationship between the level of detail and the level of sharing in concepts adopted in spatial planning For instance, considering the concept of street, the domain of civil engineering studies aspects concerning geometry, pavements, geotechnics, etc., the domain of transport planning analyzes nodes, arcs, traffic flow, etc., the domain of urban and regional planning considers the street as an element of the relational system, while in

5 Ontology and Spatial Planning 259 common language the street is an entity that connects two places or a space for walking. There is a transition from levels where some concepts are seen and treated with a highly specialized approach to a level where aspects more related to planning domain are extracted. The last step is the exit from technical sphere reaching very general, but more shared levels, such as political and common (social) knowledge. A clear and shared concept within a technical sphere probably does not match with a concept with the same name in political and social sphere. Very frequently it happens that a plane is distorted in the process of adoption or during its implementation. In participatory process to plan it frequently happens that stakeholders suppose to reach an agreement that satisfies everyone, but in a subsequent assembly the same participants, even in good faith, do not identify themselves in the syntheses developed by experts, precisely because of semantic differences [14]. Semantics adopted in the political sphere vary between populism, where the concepts are oversimplified or trivialized, and political contorted and involuted jargon language, often incomprehensible to great part of population. So, while all types of interaction with citizens in planning process are always desirable, it is important to consider the obvious problem of speaking the same language and having the same concept in mind. If planning through debate [15] represents a collaborative approach that brings an enrichment of points of view, in analyzing problems it is important to consider the rational ignorance [16]. Citizens often trivialize the concepts or manifest inertia in understanding technical issues. In recent years it has been a transition from participation forms with the sole purpose of creating a consensus on previously defined actions, to experiences of Open Government based on forms of collaborative democracy where decision-makers desire to listen and implement proposals emerged from discussions with citizens [17]. The use of the Internet has enabled a significant expansion of participatory basis, beyond the constraints of space and time [18], but it is not enough if no one provides to implement such instances. At the same time, the collaborative approach generates a different type of relationship between concepts: in addition to modelling based on domain experts which adopts several kinds of relationships, such as taxonomic (IS_A), meronomic (PART_OF), Telic (PURPOSE_OF) etc.., there is another type of relationship, called Folksonomy [19] where users, by means of free-tagging of available information, generate a bottom-up structure using their vocabulary with personal meanings. Kitsuregawa et al. [20] define the contemporary era, characterised by a large amount of information produced both by human activities and by automated systems, as the Information-Explosion Era. The activities of acquisition, processing and management of this information is called "ubiquitous computing" and it is a sort of connection between computers and real world, accounting for the social dimension of human environments [21]. This information, generated for other purposes, may be useful for planning activities, despite the use of terms very different form the language normally adopted by planners. Information-Explosion Era also includes all Volunteered Information and it represents a formidable information in supporting planning activities.

6 260 B. Murgante and F. Scorza 3 Broad Ontology A concept may have a precise and detailed definition inside a community, which has not been fully recognized by other communities. In most cases renouncing to a part of the specific definition it is possible to find an agreement which is recognized by both communities. In ontology building the construction of ontologies starts from very general definitions that become less and less shared with the increase of domain peculiarities. It is possible to schematize this approach using an analogy with rough set theory [22]. This theory is primarily based on indiscernibility relation and Lower and upper approximation (Fig. 2a). a) b) c) Fig. 2. a) Main elements rough set theory. b) Concepts overlap by means of rough set theory. c) Membership functions in Fuzzy Set Theory All definitions fully shared by one or more communities contribute to define the lower approximation; all aspects of a concept, classified in the same way, represent the indiscernible part of the concept and are included within the lower approximation [23]. Partly shared definitions will be included in the boundary between upper and lower approximation (Fig. 2b). Within this boundary, it is possible to define the degree of membership of a concept to a community through fuzzy set theory (Fig.2c) [24]. In case all concepts are included in lower approximation the relationships between concepts can be schematized through 9-intersection model [25], used in defining topological rules (Fig. 3).

7 Ontology and Spatial Planning 261 Fig Intersection Model (Egenhofer and Hering, 1991) If two concepts are identical, the "equal" rule is adopted; if they share some parts, the "overlap" rule is more appropriate. If two concepts are completely different, the "disjoint" rule can be applied. If a concept is a subset of another, there are several rules that can help: "cover", "covered by ", "contains " and "inside". For concepts included in the boundary between upper and lower approximation, the relations between concepts can be described using broad boundaries model [26]. This model (Fig. 4) is an extension of 9-intersection model and it is based on the definitions of inner and outer boundary. The region included between the inner and outer boundary is the broad boundary. There are several possible types of relationships: between inner boundaries, between outer boundaries or between inner boundary and outer boundary. In the first case the 9-intersection model is used, in other cases the broad boundaries model is adopted.

8 262 B. Murgante and F. Scorza Fig. 4. Broad Boundaries Model (Clementini e Di Felice, 2001) 4 Conclusions Planning activity is closely related to other aspects, such as management of natural resources and natural disasters prevention. In most cases it is important to study these phenomena in cross-border situations. Very complex issues, such as environmental preservation and natural hazards protection, considerably increase the level of complexity in cross-border regions. Where an aspect is very difficult to understand, a plan indication on one side of the border can impact the other side. Spatial planning and related information have therefore a strategic importance not only at local and national levels, but also at the international level, where the crossing of various European nations is characterized by a continuum of settlements [27]. In many European projects the need of data harmonization in the field of planning with the aim of ensuring an easy readability of this information emerges. This need for interoperability, sometimes leads to dangerous proposals such as unified legends, which could denature the role of planning and standardize all territories. There is a strong analogy of the current situation in the field of geographical information with conditions occurred in 90s with the development of the Internet, with the development of first search engines. Nowadays, we have a huge amount of data, shared in SDI in the better situation (Spatial Data Infrastructure), and few geo-portals which drive in information searching. There are a lot of researches in the field of spatial semantic web, which could lead to an integration of spatial aspects in search engines. In this scenario, it is important to consider the problem of semantic correspondences also in planning field, where there is a strong interaction with other disciplines with their cognitive models and with political and social dimensions.

9 Ontology and Spatial Planning 263 Unfortunately, while there is a proliferation of initiatives in ontology building in many fields related to physical aspects (geology, soil classification, fauna, flora, et..) and economic ones (tourism, economic programming, industrial districts, etc.), in planning sector there are few and more disconnected initiatives. References 1. Laurini, R., Murgante, B.: Interoperabilità semantica e geometrica nelle basi di dati geografiche nella pianificazione urbana. In: Murgante, B. (ed.) L informazione geografica a supporto della pianificazione territoriale, FrancoAngeli, Milano (2008) 2. Goodchild, M.F.: Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0. International Journal of Spatial Data Infrastructures Research 2, (2007) 3. Goodchild, M.F.: NeoGeography and the nature of geographic expertise. Journal of Location Based Services 3, (2009) 4. Turner, A.: Introduction to Neogeography. O Reilly Media, Sebastopol (2006) 5. Couclelis, H.: Ontologies of geographic information. International Journal of Geographical Information Science 24(12), (2010) 6. Ferraris, M.: Dove sei? Ontologia del telefonino, Bompiani editore, Milano (2005) 7. Neches, R., Fikes, R.E., Finin, T., Gruber, T.R., Senator, T., Swartout, W.R.: Enabling technology for knowledge sharing. AI Magazine 12 (1991) 8. Murgante, B.: L informatica, i Sistemi Informativi Geografici e la Pianificazione del Territorio. In: Murgante, B. (a cura di) L informazione geografica a supporto della pianificazione territoriale, FrancoAngeli, Milano (2008) 9. Chandrasekaran, B., Johnson, T.R., Benjamins, V.R.: Ontologies: what are they? why do we need them? IEEE Intelligent Systems and Their Applications 14(1) (1999) 10. Fonseca, F., Egenhofer, M., Davis, C., Câmara, G.: Semantic Granularity in Ontology- Driven Geographic Information Systems. Annals of Mathematics and Artificial Intelligence 36 (2002) 11. Murgante, B., Scardaccione, G., Las Casas, G.: Building ontologies for disaster management: seismic risk domain. In: Krek, A., Rumor, M., Zlatanova, S., Fendel, E.M. (eds.) Urban and Regional Data Management, pp CRC Press, Taylor & Francis, London (2009) 12. Las Casas, G., Murgante, B.: Il Documento preliminare al Piano strutturale della Provincia di Potenza: i termini di un approccio strategico Archivio di studi urbani e regionali, A. XXXVII, N , pp , edizioni FrancoAngeli, Milano (2006) 13. Indovina, F., Fregolent, L.: Un futuro amico. Sostenibilità ed equità, FrancoAngeli, Milano (2002) 14. Murgante, B., Tilio, L., Lanza, V., Scorza, F.: Using participative GIS and e-tools for involving citizens of Marmo Platano Melandro area in European programming activities special issue on E-Participation in Southern Europe and the Balkans. Journal of Balkans and Near Eastern Studies 13(1), (2011), doi: / Healey, P.: Planning through debate: the communicative turn in planning theory. Town Planning Review 63, (1992) 16. Krek, A.: Rational Ignorance of the Citizens in Public Participatory Planning. In: Proceedings of the CORP 2005 & Geomultimedia Conference, Vienna (April 2005)

10 264 B. Murgante and F. Scorza 17. Noveck, B.S.: Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful. Brookings Institution Press, Washington (2009) 18. Salvini, A.: L Analisi delle Reti Sociali. Risorse e Meccanismi. Edizioni PLUS Pisa University Press (2005) 19. Vander Wal T.: Folksonomy Coinage and Definition (2006), (last access 12/12/09) 20. Kitsuregawa, M., Matsuoka, S., Matsuyama, T., Sudoh, O., Adachi, J.: Cyber Infrastructure for the Information-Explosion Era. Journal of Japanese Society for Artificial Intelligence 22(2), (2007) 21. Greenfeld, A., Shepard, M.: Urban Computing and Its Discontents, The Architectural League of New York (2007) 22. Couclelis, H.: Ontologies of geographic information. International Journal of Geographical Information Science 24(12), (2010) 23. Pawlak, Z.: Rough Sets. International Journal of Information & Computer Sciences 11, (1982) 24. Zadeh, L.: Fuzzy sets. Information Control 8, (1965) 25. Egenhofer, M.J., Herring, J.: Categorizing binary topological relationships between regions, lines, and points in geographic databases. Technical Report, Department of Surveying Engineering, University of Maine, Orono (1991) 26. Clementini, E., Di Felice, P.: A spatial model for complex objects with a broad boundary supporting queries on uncertain data. Data & Knowledge Engineering 37(3), (2001) 27. Di Donato, P., Berardi, L., Salvemini, M., Vico, F., Murgante, B.: Plan4all: European Network of Best Practices for Interoperability of Spatial Planning Information. In: Las Casas, G., Pontrandolfi, P., Murgante, B. (eds.) Informatica e Pianificazione Urbana e Territoriale, Libria Melfi (2009)

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