Jacques Teller. Ontologies for Interconnecting Urban Models. The use of urban models, particularly 3D city models, is increasing in urban planning. The consequence of an integrated approach in urban planning is the use of different models, most of the time in an interconnected way able to simulate the urban issues together with their inter-relations.
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In the first part of the chapter, we will present our needs and expectations in terms of urban information: modelling and interconnection of the information. An important issue related to the representation of urban information is then discussed: the comparison of the role of conceptual schemas and ontologies, since strong links do exist between the two approaches. The chapter then analyses three ontology- based approaches in relation with urban modelling.
The interconnection of urban models through ontologies is described in the last part of the chapter and examples are given on the basis of real case studies. Metral unige. Falquet et al. Cutting-Decelle 7. Since this period, the term urban model has usually been related to simplifications and abstractions of real cities, in contrast to its earlier usage referring to ideal cities Foot In the first case they usually have no functionality beyond display while, in the latter case, they can be associated with spatial queries.
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In fact 3D models are named mock-ups while the term urban models usually refer to dynamic models. According to Foot , urban models: —— are used to evaluate the effects of changes in relation to certain land-use activities such as residential or industrial development , transport network, etc. Air quality models, for example, are associated with complex processes taking into account many parameters related to pollutant sources, prevailing wind, or the configuration of the streets and buildings.
According to the point of view and the purpose, the same reality can give rise to different models: for example a physical or a numerical mock-up, an informa- tion model associated with geo-data or a mathematical model of in-play pro- cesses represented through differential equations, as shown on the Fig. If urban models can be seen as decision-making tools, they most of the time relate to one domain at the same time, such as transportation, air quality or building energy consumption, or to the physical aspects of the city as in 3D city models.
Urban models could benefit from data being directly available within 3D city models while providing results which could, in return, be used and visualised through city models. As urban issues are interrelated in the real world, the interconnection of urban models can be considered as reflecting the reality more precisely. They also allow urban actors to explore the city and to plan it prior to acting on it in a more global way.
On the basis of case studies related to the urban field, this chapter will explain how domain ontologies can provide a robust and reusable method to interconnect urban models. In doing so, the modeler follows an established paradigm, such as object-orientation or entity-relational, that is chosen in terms of the available programming environ- ment.
Such an approach forces the modeler to mentally map concepts acquired from the real world to instances of abstractions available in his paradigm of choice. The basic reason for these conflicts is the lack of an initial agreement between user and modeler on the concepts of the real world. Such an agreement could be established by means of an ontology, which is a shared conceptualisation of an application domain. Cutting-Decelle On the other hand, the consolidation of concepts and knowledge represented by a conceptual schema can be useful in the initial steps of ontology construction.
Studies have been performed in the geographic domain which is closely related to the urban domain. To adequately represent the geographic world, we must have computer representations capable of not only capturing descriptive attri- butes about its concepts, but also capable of describing the geometrical and posi- tional components of these concepts.
These representations also need to capture the spatial and temporal relationships between instances of these concepts. For example, in order to represent a public transportation system, the application ontology must contain concepts such as street, neighborhood, bus stop, and timetable. Unlike the case of conventional information systems, most of these spatial and temporal relationships are not explicitly represented in a GIS, and can often be deduced using geographic functions. Guarino advises against using ontology as just a fancy name denoting the result of activities like conceptual analysis and domain modelling.
Fikes and Farquhar consider that ontologies can be used as building block components of conceptual schemas. While an ontology describes a spe- cific domain, a conceptual schema is created to describe the contents of a database. Bishr and Kuhn consider that an ontology is external to information systems and is a specification of possible worlds, while a conceptual schema is internal to information systems and is chosen as the specification of one possible world. Conceptual schemas are built to organize what is going to be stored in a database, and then are used to document it. An ontology represents concepts in the real world.
This point of view is expressed in the ontology that this community has specified. For instance, a reservoir is a reservoir, regardless of whether it is represented, for the purposes of an information system, by an aerial photograph, a polygon, or a digital terrain model. A conceptual schema that intends to capture all the peculiarities of geographic data should specify differently each of the three representations. In fact, the theory on the use of ontologies is being developed with the broader intention of providing a basis for knowledge consolidation and exchange, a goal that is far beyond the capabilities of current data modelling tools and tech- niques.
Generally speaking, conceptual schemas correspond to a certain level of knowledge formalisation, even though they discard a number of concepts and ideas about which the data modeler and the user have agreed upon. On the other way, ontologies facilitate the integration, in the model, of background knowledge about the entire information systems development process. In this chapter, and in order to keep a track of this background knowledge, we will work on ontology-based approaches and on an interconnection of models based on ontologies.
The general methodology can be summarized in two main steps: —— represent as ontologies i. The following sections present the approach, on the basis of real case studies. A first part explains the way of creating the ontologies while the second part focuses on the articulation between the resulting ontologies. Cutting-Decelle Fig.
As we can see, a TransportationComplex is a particular kind of TransportationObject which is itself a particular kind of CityObject and is subdivided thematically into TrafficArea representing the areas used for the traffic of cars, trains, public transport, airplanes, bicycles or pedestrians and AuxiliaryTrafficArea associated with grass for example. Here are some examples to illustrate the way according which class attributes have been translated: —— function as a relation between TransportationComplex and Transportation-ComplexFunctionType itself defined as a concept; —— surfaceMaterial also as a relation between the concepts TrafficArea and TrafficSurfaceMaterialType but with the following restriction: a TrafficArea has at most one TrafficSurfaceMaterialType.
In this paper we describe the part of OUPP related to soft mobility aspects. This method is composed of four phases: 1 identify the purpose of the ontology, 2 build it, 3 evaluate it, 4 document it. The legal aspects which are important to urban planners or politicians will not be described in this paper in order to focus on some aspects such as the duration of travelling for a kind of user as these aspects seem question- ing to many potential users or the appealing character of some paths promenades, for example, and particularly promenades through parks.
The knowledge can originate from experts of the domain, text mining, meta-data of databases, etc. In this case study, various documents and data related to soft mobil- ity were mainly used. The knowledge thus extracted has to be structured. Textual definitions have to be defined by referring to other terms and including notions such as class, relation, etc.
The Fig. This phase can be achieved in parallel with the previous phases. The main classes in OTN are shown in Fig. So re-using OTN is not pertinent for creating an ontology of soft mobility but it can be useful for extending this ontology to other transportation issues such as public transport for example see next section. The criteria include the following: consistency, completeness, concision no redundancy, good degree of granularity , etc. As this case study aims at defining an ontology-based model for promoting soft mobility for the inhabitants, the evaluation phase should include usability tests with end-users.
It means producing definitions formal, non formal to specify the meaning of the terms of the ontology, giving examples, etc. It can also include naming conventions such as the use of upper or lowercase letters to name the terms. In this case study the names of the classes begin with uppercase letters while the names of the properties begin with lowercase letters. Furthermore a knowledge base composed of the source documents associated with the ontology is on-going. That is while our case study focuses on street canyon models.
Many street canyon models have been defined.
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Although different, these models show some common characteristics. Their input parameters are: —— the pollutant source characteristics source location, emitted product, etc. Kruijff, J.
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LaViola, and I. Addison Wesley Longman Publishing Co. Ontologies in urban development projects. Advanced information and knowledge processing, 1. Neale and S. Nichols: Designing and developing Virtual Environments: methods and applications. Dieng, O. Corby, A. Giboin, M. Guarino, M. Carrara and P. Gao, J. Nick and F.
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Omer: Bringing semantics to visualization services. Brodlie, D. Duce, D. Duke, Visualization ontologies. Report of a workshop held at the National e-Science Centre, April , www. Gusev and P. Mitrevski Eds. Voigt, J. Polowinski: Towards a Unifying Visualization Ontology. Voigt, S.
Pietschmann, L. Grammel, K. Meissner: Context-aware recommendation of visualization components. Vaaraniemi, M.