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Urban information integration for advanced e-Planning in Europe
Available online at www.sciencedirect.com
Government Information Quarterly 24 (2007) 736 – 754
Urban information integration for advanced e-Planning in Europe Hongxia Wang ⁎, Yonghui Song, Andy Hamilton, Steve Curwell Research Institute for the Built and Human Environment, Technology House, University of Salford, 2 Lissadel Street, Salford, Manchester M6 6AP, UK Available online 17 July 2007
Abstract Urban planning is a complex task requiring multidimensional urban information (spatial, social, economic, etc.). The need for assistance in performing urban planning tasks has led to the rapid development of urban information systems, especially “e-Planning” systems, with the support of government policy and emerging information and communication technologies (ICT). In order to enhance the capability of e-Planning and to facilitate 3D visualization and rich analysis of complex city problems, it is very important to allow information from the various sources to be shared and integrated. This paper focuses on technical approaches for multidimensional information integration, especially spatial information integration. In particular it describes the Building Data Integration System (BDIS), developed as part of the IntelCities project, which demonstrates the type of multidimensional systems that are likely to be used in future urban information systems. Functionally, the BDIS demonstrates the feasibility of systems to support the multinational collaboration of construction professionals in the procurement and renovation of buildings. For such systems to be accepted in the United Kingdom (UK) and Europe, they need to be developed with regard to current planning information structures and standards in the UK and Europe which are reviewed in this paper. The achievements and further development of multidimensional information integration through the use of innovative urban data modelling techniques are discussed. © 2007 Elsevier Inc. All rights reserved.
⁎ Corresponding author. E-mail address: [email protected] (H. Wang). 0740-624X/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.giq.2007.04.002
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1. Introduction The rapid development of ICT provides new opportunities to improve planning processes and make better use of resources (Huang, 2003; Hamilton, Burns et al., 2005; Hamilton, Wang et al., 2005). Many local authorities have employed new ICT to provide different e-Government services in Europe. For example, the UK Government set a target that by 2008 all services (with exclusions for policy or operational reasons) should be available electronically to citizens and business (Cabinet Office, 2000). e-Planning, as a section of e-Government, can enable easy access to information, guidance and services that support and assist planning applicants, and streamlined means of sharing and exchanging information among key players (Planning-Service, 2004). However, in UK government e-Planning services, there is still a lack of advanced planning applications like interactive maps, 3D visualization, and intelligent support in the e-Planning services (ODPM, 2004). Generally UK e-Planning services compare well with other European countries, although more advanced services are available in a few countries, such as Estonia. Interactive 3D visualization and intelligent systems can help planners to implement scenario modelling, environmental impact assessment, and compliance with planning policies checking. In the Building Data Integration System (BDIS) described in this paper, we consider how a development team, with the project of developing a training centre for a multinational bank, would use building and urban data sets, integrated in a project database, to work collaboratively on a project to select and refurbish the building, possibly as part of a larger urban regeneration plan. Urban planning decision making is a complex process that involves many stakeholders and relies on the multidimensional information (Hamilton, Trodd, Zhang, Fernansdo, & Watson, 2001). There are various stakeholders including urban planners, property developers, politicians, architects, engineers, transport and utility service providers, and individual citizens. The stakeholders rely on many types of information including both formal reports and quantitative data sets such as census and pollution figures (Innes, 1998). The required information in planning is related to the different dimensions of the city. For example, deciding on the right location for a primary school requires geospatial, infrastructure, environmental, housing, and population data. The information is multidimensional as it is always a mix of the spatial, aspatial and nonspatial, a blend of the qualitative and the quantitative, covering a wide range of physical, social, and economic attributes many of which are noncomparable with one other (Harris & Batty, 1993). An integrated multidimension information model is needed to combine the relevant information for effective decision support. This led to the creation of nD (n-Dimensional) urban information model concept (Wang & Hamilton, 2004a; Hamilton, Wang et al., 2005). This work was based on nD modelling research at construction domain at the University of Salford (Lee et al., 2003; Aouad, Lee, & Wu, 2006). The nD urban information model was designed to describe multidimensional urban aspects like 3D urban physical structure, transport, crime, and environmental issues plus the temporal dimension. For an effective system, sharing and assessing the relevant data sets should be easily achieved by stakeholders (system users). The increased access to relevant information aided by the implementation of an
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information system can ultimately lead to increases in the quality of plans, number of alternatives generated, and the quality of decisions (Shiffer, 1992). The BDIS was developed, as part of the IntelCities project (an EU funded project with 75 partners), to be a system that allows easy sharing of data to promote close collaborative working and increased quality and efficiency of work. The BDIS achieved the integration of digitized building data with geospatial information and other types of city data based on the nD urban model concept. It demonstrates the support of intelligent urban planning services which can improve planning practice. The focus of this paper is on the technical approaches to integrate multidimensional urban information, including spatial, for e-Planning, and on the BDIS produced. Section 2 reviews the current information architecture of e-Government from the UK and EU perspectives and discusses the related standards which formed the development background of this research. Section 3 introduces the research work undertaken on the BDIS in the IntelCities project and discusses the development of data structures and data converters to produce a centralized database with a standard interface. The BDIS is designed to assist stakeholders in the urban environment in making sense of the wide variety of urban data and to promote collaboration in the urban development. Finally, this research work is summarized and future work is discussed.
2. e-Government Interoperability Framework (e-GIF) and information standards Implementing e-Government services is increasingly part of government policy throughout the European Union. The European approach to the development of the information society hinged on the twin track of achieving physical access through the communication network and achieving social access and economic objectives through public sector information services (Masser, 2003). The rapid growth of ICT, especially broadband Internet, makes the physical access workable. A series of relevant standards and policies such as e-GIF, INSPIRE, etc., will facilitate the achievement of government information integration and interoperability. This section will review the emerging standards and policies for e-Government, and especially e-Planning, in the UK and the EU, as shown in Table 1. These standards and policies form the context for the IntelCities project and the BDIS created within IntelCities. 2.1. e-GIF in UK and EU The UK government's e-GIF (e-Government Interoperability Framework) is an initiative that aims to define the essential requirements for a Web-enabled government. It defines the technical policies and specifications governing information flows across government and public sectors. The e-GIF is a cornerstone of the UK's e-government strategy. Adherence to e-GIF standards and policies is mandatory. UK's e-GIF adopts specifications that are well supported in the market place. It is a pragmatic strategy that aims to reduce cost and risk for government systems while aligning them to the global Internet revolution. The World Wide Web Consortium (W3C) developed
Names
Organizations
Web sites
e-GIF (e-Government Interoperability Framework) IDABC (Interoperable Delivery of European e-Government Services to public Administrations, Businesses and Citizens) EIF (European Interoperability Framework) eEurope SDI (Spatial Data Infrastructure)
Cabinet Office, UK government
http://www.govtalk.gov.uk/schemasstandards/egif.asp
European Commission
http://ec.europa.eu/idabc/en/home
European Commission European Commission The Global Spatial Data Infrastructure Association INSPIRE (Infrastructure for Spatial Information Europe Commission in Europe) GML (Geographical Mark-up Language) OGC (Open Geospatial Consortium) CityGML SIG 3D, GDI NRW, Germany IFC (Industry Foundation Class) IAI (International Alliance for Interoperability)
http://europa.eu.int/idabc/en/document/3761 http://europa.eu.int/information_society/eeurope/2005/index_en.htm http://www.gsdi.org/SDILinks.asp http://www.ec-gis.org/inspire/ http://www.opengeospatial.org/ http://www.citygml.org/ http://www.iai-international.org/model/ifc(ifcXML)Specs.html
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Table 1 The e-Planning relevant policies and standards
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eXtensible Markup Language (XML) (http://www.w3.org/XML) is adopted in e-GIF as the primary standard for data integration and presentation on all public sector systems. This includes the definition and central provision of XML schemas for use throughout the public sectors. The UK government policy is to use: (1) XML and XML schemas for data integration; (2) UML, RDF, and XML for data modelling and description language; and (3) XSL, DOM, and XML for data presentation. In terms of important spatial information in urban planning, the UK's e-GIF identifies the use of Geospatial Mark-up Language (GML) as the geospatial information standard. Some details of the data integration policies are highlighted in the “policies and technical standards” section of the e-GIF report (GovTalk, 2005). The “Interoperable Delivery of European e-Government Services to Public Administrations, Businesses and Citizens” (IDABC, 2005) is a European program using advances in information and communication technology to encourage and support the delivery of crossborder public sector services to citizens and enterprises across Europe. As a horizontal measure, European Interoperability Framework (EIF) (EIF, 2004) provides a series of recommendations and defines generic standards, offering a comprehensive set of principles for European co-operation in e-Government. The EIF does not explicitly list which standards or data formats should be used when developing government systems. Instead, it refers developers to the interoperability frameworks used by member states. There are many other European projects in this field. For example, the eEurope 2005 Action Plan (e-Europe, 2005) is a common initiative defined by the European Commission (EC). This plan aims at “developing modern public services and a dynamic environment for e-Business through widespread availability of broadband access at competitive prices and a secure information infrastructure.” e-GIF and equivalent European standards are still developing. Published standards in this area do not address enough on spatial information, e.g., the combination of building and urban scale data, although the development of such standards is in the scope of the INSPIRE initiative. This is discussed in Sections 2.2 and 2.3. 2.2. Spatial data infrastructure (SDI) Eighty percent of all public sector information is geo-referenced (Lemments, 2001). Spatially referenced information is increasingly becoming central to advanced e-Planning applications. This is reflected by the extensive development of spatial data infrastructure (SDI) at national, regional, and global levels, i.e., National Spatial Data Infrastructure (NSDI) and Global Spatial Data Infrastructure (GSDI). SDI is a means of understanding global environmental and health challenges, supporting international telecommunication, commerce, and human development and stimulating economic growth and productivity (Holland, 1999). SDI aims to enhance interoperability among geographic components of government activities and to maintain a common inventory of geospatial content and services.
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All countries in Europe and many other countries around the world have developed or are developing their NSDIs (http://www.gsdi.org/SDILinks.asp). Since different governments have different policies for dissemination of information, the NSDI of one country could differ from the NSDI of another country. The UK government's National Geospatial Data Framework (NGDF) aims to develop an over-arching UK framework to facilitate and encourage efficient integration of, and access to, geospatial data (Rhind, 1997). NGDF, as UK's NSDI, emphasizes a framework of standards, metadata, and access rather than the more centralized approach in other countries involving base data sets (Hadley & Elliot, 2000). Infrastructure for Spatial Information in Europe (INSPIRE) is an initiative to “create a legal framework for the establishment and operation of an infrastructure for spatial information in Europe, for the purpose of formulating, implementing, monitoring and evaluating Community policies at all levels and providing public information” (http://www.ec-gis.org/inspire/). INSPIRE focuses on environmental policy but is open for use by other sectors such as agriculture, transport, and energy. INSPIRE will not set off an extensive program of new spatial data collection in the Member States. Instead, it is designed to optimize the scope for exploiting the data that is already available, by requiring the documentation of existing spatial data, the implementation of services aimed at rendering the spatial data more accessible and interoperable, and by dealing with obstacles to the use of the spatial data. INSPIRE will pave the road for a progressive harmonization of spatial data in the Member States of the EU (INSPIRE, 2004). The INSPIRE directive will follow international standards to facilitate the integration of information so that it can be available for use to provide enhanced public services. The INSPIRE philosophy of integrating existing data sets to facilitate more holistic applications is a key motivation for work on the BDIS system and current research building on the BDIS. 2.3. Spatial information standards for advanced e-Planning Efforts to standardize the way in which we describe spatial features are critical to our ability to share government data between different departments, levels of government, and commercial and educational institutions. Standards have been recognized as a key part of e-GIF and SDI in two distinct ways, through the adoption of standards and in setting new standards or extending existing standards (Murray, 2002). There are several important spatial information standards like GML for geospatial information and IFC for building information. These standards, explained below, are essential to the BDIS, described in Section 3 of this paper. Open Geospatial Consortium (OGC)'s Geographical Mark-up Language (GML) (http:// www.opengeospatial.org/) is an XML-based schema for the modelling, transport, and storage of geospatial information. It was developed as a data exchange standard interface by OGC to achieve data interoperability and reduce costly geographic data conversions between different systems. The GML full specification defines “an XML grammar for the encoding of geographic information including geographic features, coverage, observations, topology, geometry, coordinate reference systems, units of measure, time, and value objects” (GML, 2004). There are some efforts to develop application schemas based on GML 3.1. One typical
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example is CityGML, which was developed by the “Special Interest Group SIG3D” from North-Rhine Westphalia in Germany (http://www.citygml.org/). This promising 3D city standard holds not only geospatial information but also supports a semantic model of city objects (Kolbe, Groger, & Plumer, 2005). Industry Foundation Classes (IFC) (ISO/PAS 16739) is a building model standard (http:// www.iai-international.org/). IFC is developed by the International Alliance for Interoperability (IAI) (IFC, 2004). It is a universal model to be a basis for collaborative work in the building industry and consequently to improve communication, productivity, delivery time, cost, and quality throughout the design, construction, operation, and maintenance life cycle of buildings (IFC, 2004). The IFC object descriptions deal not only with full 3D geometry but also with relationships, process, material, cost, and other behavior data. Integrating a CAD model with IFC enables the accurate geometric representation to be integrated with structural and behavior elements and facilitates linking with external applications (Ding, Liew, Maher, Gero, & Drogemuller, 2003). The initiatives described in this section of the paper, provide the much-needed emerging standards of interface and information integration for e-Government services in Europe. As these standards develop they will provide better support for advanced e-Planning systems that will enhance decision making by providing comprehensive and integrated city services to citizens and businesses. This provided the context for IntelCities and the BDIS.
3. IntelCities and Building Data Integration System 3.1. IntelCities project The Intelligent Cities (IntelCities) Project (http://www.intelCitiesproject.com/), from January 2004 until October 2005, was an Integrated Project funded by the European Commission (EC) Information Society Technologies program under framework 6 (11 million euros funding and 75 partners including Rome, Helsinki, Manchester, Marseilles, Nokia, Cisco, IBM, The Research Institute for the Built and Human Environment, University of Salford, and many other research institutes). IntelCities is now established as the European Intelligent Cities Research Centre with partners across the world. The focus of IntelCities is the development of open, secure, interoperable, and reconfigurable e-Government services at the city level to meet the needs of both citizens and businesses. The e-City Platform (e-CP) was developed to underpin the provision of a wide range of innovative applications and multimodal services that integrate inter- and intragovernment administrative, planning, public/business consultation and local government policy development dimensions. As shown in Fig. 1, an integrated multidimensional data repository is the foundation of the e-CP. The project explored related e-Gov standards, such as UK e-GIF and EU IDA (Interchange of Data between Administrations), and ensured that the standards adopted for interface design of the analysis and multidimensional components of the e-CP are compatible with these standards. The analysis within IntelCities of e-Government concepts together with the
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Fig. 1. IntelCities system architecture (IntelCities, 2004).
requirements of citizen and cities have led to the adoption of a distributed service-oriented architecture (SOA) framework (Vankeisbelck, Duchon, & Zarli, 2005). Several e-CP-based service modules have been developed and tested across a set of European pilot cities. The e-CP supports new services as well as the integration of legacy systems for e-Government. In the rest of this section the BDIS is described. The BDIS demonstrates how a system for property development and refurbishment can both make use of, and to contribute to, the urban data repository envisaged in the IntelCities project, as well as being an effective regeneration service. 3.2. Building Data Integration System The Building Data Integration System (BDIS) was produced within Work Package 5 (e-Regeneration) of IntelCities. The project was led by the University of Salford with
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Universitat Politècnica de Catalunya, Barcelona; Labein, Bilbao; Vilnius Gediminas Technical University; and Dr. Pedro Gamito, visiting professor in Real Estate Management ESAI, Lisbon. The BDIS illustrates how digitized historic building data can be integrated with other types of city data to support a multinational team of construction professionals in the procurement and refurbishment of an historic building. As such, it is an example of an integrated intelligent city system. The three main tasks for implementation of the BDIS are • • •
to capture building data using a 3D laser scanning system; to integrate digitized building data with geospatial information and other types of city data; and to build a project database and application system for property development based on integrated data. The implementation of the BDIS was driven by the application scenario described below.
3.2.1. Gaudi Bank scenario and requirement analysis In order to give realism to this research work, the fictitious Gaudi Bank scenario was created. Gaudi Bank, with headquarters in Barcelona and branches in the UK, wants to set up a new training centre in Manchester. Gaudi Bank has located a building in east Manchester, Jactin House, as a potential base. However, they have to make a number of checks and assessments about the building and its location before making any detailed plans. Furthermore, once a suitable building is selected, a plan for its refurbishment needs to be developed. Gaudi Bank employs (either directly or indirectly) a range of professionals to undertake this work. Property developers work on selecting and purchasing buildings in the appropriate location. Construction professionals, such as architects and quantity surveyors, design and plan the refurbishment work. At a basic level, this scenario also involves local government professionals such as local planners. However, cities today work hard to encourage local investment, particularly in regeneration areas, so there would be a wide range of interests in Manchester concerned with supporting Gaudi Bank. Using current methods the Gaudi project team would make many trips to the UK to meet the project partners and collect a wide range of information. For example they would have to collect transport and accommodation information to assess suitability for intended trainees visiting the centre. This information is not always easy to find, and effort is often duplicated as people research the same problem independently. Also sharing information about the refurbishment of a building, and adding information as design and planning progresses, is difficult. Mistakes are made by people using out of date plans that have been superseded. When project information is held on a central database, the latest version of the plan is always easily available. The BDIS also shows how most project information could be accessed through one central point and cross-referenced to make comprehension easier. The system analysis of the BDIS was carried out during two IntelCities workshops (Hamilton, Burns et al., 2005). The information requirement of the Gaudi Bank scenario was investigated. The information required includes not only data about the building itself but also about the surrounding environment information. The Labein group, who advise the Spanish
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Government on the refurbishment of buildings, led this work. The following requirements were determined: • • • •
structural survey of building (construction elements like windows, doors, etc.); surrounding geospatial information like building footprint, road, land, and ownership; local plan (zoning, potential changes of uses, development plans, etc.); and transport, accommodation, and similar information.
Based on the requirement analysis, the structure of the BDIS was designed as shown in Fig. 2. Note that the project database was designed so it could be integrated with the IntelCities eCP of Manchester City. However, full integration was not undertaken within this 18-month project due to time limitation. Thus, the BDIS was built to operate as a freestanding system independent on the e-CP. The implementation of the BDIS is described below. 3.2.2. Data acquisition The first task of the BDIS is to capture the building information by using the 3D laser scanner and acquire the surrounding area's geospatial information and gazetteer, etc. Jactin house in Manchester, as the proposed building for Gaudi Bank's training centre, was scanned, internally and externally, using Riegl LMS-Z210 laser scanner. Using the point cloud
Fig. 2. Building Data Integration System and e-City Platform.
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data captured, a virtual 3D model of the building with an accuracy of 10 mm was produced. It should be noted that the authors are now (March 2007) using the Reigl LMS Z390 with an accuracy of better than 2 mm, making it adequate for all but the most exacting building surveying applications. To be able to identify elements of the building in the virtual 3D model made from the point cloud data of Jactin house, a CAD model was extracted. The identification of building elements such as doors and windows from the CAD model is partially automated using Microstation Triforma software with the Triforma objects (Arayici, Hamilton, Gamito, & Albergaria, 2005). From this work, the IFC building model of Jactin house was obtained with building elements as objects which can be stored in a database. The geo-information of Jactin House surrounding area comes from Ordnance Survey's landline data. Other related information like gazetteer, transport information was acquired from the local authority or other sources. 3.2.3. Data modeling and integration The second task is to integrate digitized building data with geospatial information and other types of city data. However, the integration of digitized building data, usually CAD models, with geospatial information is problematic because the two data types come from different domains and the coordinate system definitions and modelling methods are heterogeneous and incompatible. This was achieved by modelling multidimensional urban data sets and integration based on the nD urban database, described in Section 3.2.3.2. 3.2.3.1. Data modeling. Data modelling is a process of specifying a representation of the data sets and their relationships. This provides a conceptual or implementation view of the data that best supports the information requirements (Date, 1999). An nD urban model was designed based on the multidimensional data set types required by the Gaudi Bank scenario. It includes the following: geometry model, building model, land parcel model, road network model, open space model, and public transport model. Since buildings are the most important element of urban environment, building data sets are essential for the Gaudi Bank scenario. The following discussion mainly focuses on the design of the building data model. Two most popular building models are identified as CAD model and GIS-based model (Wang & Hamilton, 2004b). Currently in the mainstream GIS domain, a building is described as a feature with some attributes like name, age, building type, etc. The geometrical representation of buildings is still limited to 2D footprint, sometimes with height. The buildings can only be visualized as block models. However, it should be noted that recent work in GML and City GML allows 3D modelling based on GIS-derived systems, (see Section 2.3) but currently not with the details of models based on CAD. Detailed building description can be found in CAD domain. Currently various CAD-based 3D city models have been developed by such as Los Angles model (Liggett & Friedman, 1995), Bath model (Bourdakis, 1997), Tokyo (http://www.planet9.com/) just to name a few. The realistic building models can be visualized within many of these 3D city models. However, for most of these models the focus is mainly on the visual impact without enough attribute descriptions of building objects. The designed building model was shown in Fig. 3. The building structure was based on an IFC building model. As introduced in Section 2.3, IFC, as an object-oriented building
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Fig. 3. Building model.
information standard, does not only preserve the full geometric description of building objects, but also the relationships, and the properties of each object. However, IFC is a complicated and construction-focused model. It is necessary to simplify the IFC model used in the construction industry for urban applications. In IFC, there is a single underlying World Coordinate System (WCS) used for building plans. Also there are many local coordinate systems that have direct or indirect links to the WCS. In the BDIS building model, buildings and all the building objects are designed in one WCS. This reduces the coordinate calculation burden efficiently. We employed the British National Grid (OSGB-1936) as our world coordinate system in the BDIS. In BDIS, the building structural objects include buildings, building parts, building stories, building elements. Every object contains information about its geometry and attributes. A building is composed by several building parts. A building can also have several building stories and building elements. Building elements include walls, roof, floors, and windows. The relationships between elements were also described, e.g., a window element can be an opening
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in a wall element. The building attributes and geo-location definition were also included in this model. For the other models included in this nD urban model, the common geometric model describes all the geometric primitive definition which is complied with the OGC's GML standard. The design of other geo-features and thematic models (representing economic, environmental, and social data) is mainly based on the Gaudi Bank application requirement and geospatial data sources from the local authority and Ordnance Survey. 3.2.3.2. nD urban database. In BDIS, a database-based integration method was employed to support the information integration. An nD (n Dimensional) urban database was created based on the nD urban model. MS SQL Server was used to host the database to store semantic-rich 3D building information, geospatial information, and other types of city data. This centralized nD database can accept various data sources and data formats through a series of converters as shown in Fig. 4. An IFC converter was developed to convert the IFC building model into a format acceptable to the nD database. The IFC model of Jactin house is generated using LiDAR technology as discussed in Section 3.1. The IFC document manipulation is implemented by using a freeware IFCsvr ActiveX component (http://cic.vtt.fi/projects/ifcsvr/ifcsvrr200/default.html). The coordinate system of the IFC building model is different from the definition of the geospatial coordinate system. Coordinate conversion was implemented to convert the IFC model coordinates into the geospatial coordinate reference system in the BDIS. Due to the lack of geo-referenced information in the IFC building model of Jactin house, the building footprint matching process has to be undertaken by manually finding corresponding registration points between the IFC model and the geospatial data. A transform matrix was then calculated based on these registration points and the IFC building model coordinate conversion was then implemented automatically. A geospatial data converter was also developed to implement information conversion (Hamilton, Wang et al., 2005). The geospatial data of the Jactin house area is an ESRI's shop
Fig. 4. Technical structure of Building Data Integration System.
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file which comes from OS's landline data. The parcel, building, and road layers of geospatial data were converted into the nD database. Through data modelling, database creation, and data conversion, integration of semanticrich 3D building information with geospatial information and other types of city data was implemented as a unified database. 3.2.4. Prototype system The third task is to implement the prototype system to demonstrate the use of multidimensional urban databases to support intelligent planning services. This prototype is based on the Gaudi Bank scenario and developed using C++ programming language. The building data and surrounding environmental data can be retrieved from the database and presented to users. The stakeholders can get knowledge that normally can only be achieved by manual information collection and processing as currently employed in building surveys. In the BDIS the schedule of doors and windows of Jactin house was extracted from the digital survey information held on the project database as shown in Figs. 5 and 6. 3.3. BDIS to support intelligent city service As one of the deliverables of the IntelCities project, the objective of the BDIS “to illustrate how digitized historic building data can be integrated with other types of city data, using nD modelling, to support integrated intelligent city systems” has been achieved. The nD urban database demonstrates that the approach of data modelling and integration can support
Fig. 5. Digital survey of Jactin House based on the BDIS (left: 3D VRML model of windows and doors of Jactin House; right: windows/doors illustration image).
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Fig. 6. Digital survey report of generated by the BDIS.
advanced e-Planning services. The design and implementation of the BDIS reflected this idea of information sharing and integration. Although the Gaudi Bank scenario was used in the original version of BDIS it could also be configured for any generic building development project. In the final formal review of the IntelCities project, the BDIS was well received as making significant quality and efficiency improvements as an intelligent city service. It is regarded as a good example of the new way of working envisaged by IntelCities. Cities, such as Barcelona and Manchester within IntelCities project, are interested in providing an intelligent service to promote investment in urban development projects.
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While work was in progress on the BDIS system, it was reviewed by a scientist from the Environment Agency of England and Wales (EAEW). Following this review, the architecture we were developing was used in the proposal for the Virtual Environment Planning (VEPs) project (http://www.veps3d.org/), which is currently funded (2005–2008) by EU's INTERREG IIIB North West Europe Programme (2 million Euros) and UK's Office of the Deputy Prime Minister (ODPM) (£250,000). Within VEPs, we have built upon the BDIS to produce the VEPS delivery of 3D local planning integrated environments on e-GIF compliant Web sites that can be easily accessed by all members of the community. We are also developing more effective ways to integrate building (IFC) and urban (GML) data based on OGC's Web service technology.
4. Conclusion In Europe, governments and organizations at different levels are investing many millions of Euros to promote e-Planning services by putting forward a series of standards, regulations, and policies. Given the diversity of standards in spatial data, let alone the variety of other urban data, it is a difficult challenge to consider city development holistically in e-Planning processes. Emerging urban data modelling techniques have great potential to address such a challenge. The e-CP produced in the IntelCities Project demonstrates a future in which all eGovernment applications and services can be made to work together in a seamless and interoperable manner. With the development of much relevant work like INSPIRE and e-GIF, new forms of e-Planning could support a high level and wide range of planning services which can be integrated with other city services to address the needs of a variety of stakeholders in terms of effective collaboration to produce sustainable urban developments. In this paper, the approach of an integrated multidimensional data repository for the e-CP is developed based on an nD modelling database. This is a centralized database, accepting various data sources and data formats through a series of converters. This design reflects the idea of information sharing and integration, e.g., on the one hand stakeholders are able to contribute information in different formats to the database through converters. On the other hand, stakeholders can access the database through a standard and universal database interface. The Gaudi Bank scenario on which the BDIS is based (see Section 3.2) has provided a realistic example of urban development. It can be seen that integrating urban and building data with transport and other relevant data about the city can provide an environment in which property developers could do much of their work on line, saving time and travel. Furthermore, the potential for construction professionals to work together on the renovation of buildings, sharing information collected in a project database, is potentially much more effective than current ways of working. Bovis and other large construction companies already use on line systems for the collaborative production of construction drawings and this experience is producing more effective ways of working. For example, in the Bovis project to build the Bluewater retail park in Dartford, England, which was monitored by one of the authors, the normally common problem of on site work being implemented wrongly by use of out of date and inaccurate drawings was eliminated.
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There are potential benefit and limitation that should be noted in this research. Due to the 18-month project time scale, the BDIS approach has demonstrated the ideas of data integration rather than achieve all of the ideas in a fully functional system. For example, from Fig. 4 in Section 3.2.3.2, the automation of conversion processes is important but some processes of the data conversion are not fully automated yet. This is the subject of current research. The BDIS is implemented as a centralized database. However, it is possible to implement a system which remains “logically centralized” while it is in fact a physically distributed data repository. For example, from one stakeholder's perspective, the data needed are retrieved through a standard interface; however, the actual data can be distributed anywhere in the world. The idea of distributed data but uniform and standard interface is very useful. It is always difficult to collect and manage all the data sets in a physically centralized DB. In particular, when converting object data types into a relational database, such as IFC data, the data model will be very complicated. The approach of a standard interface is a solution because there will be fewer requirements to convert data from one format to another and store it. Instead the data stays where it is, in its native format. The standard interfaces and the converters will act as an “on demand wrapper” of the various data formats and give standard access to all users. There is still much work to be done in converting the advanced research-based systems produced in IntelCities, such as the BDIS, to workable systems to be used in commerce and government. Fortunately, we have procured funding to do this work as part of the VEPS project (see Section 3.3 of this paper) and the technology produced in IntelCities is being refined for use in a large (100 million Euros) regeneration project in Stuttgart involving the main railway into Stuttgart being put underground, making existing railway sidings available for development. In summary, in this paper we have shown how the approach for urban and building data sets integration used in the BDIS can transcend the limitations of current building or urban scalebased systems. The BDIS implementation shows a way forward in the provision of comprehensive urban planning services to business and citizens. Future systems based on this technology have the potential to be more efficient in the provision of information through one point of access; more effective in providing reliable up to date information, and capturing project produced data for reuse; and more efficacious in the quality of collaboration supported for the wide range of interests in urban development. Acknowledgments The authors would like to thank the EC for funding of IntelCities through the Information Society Technologies program as a Framework 6 Integrated Project. The BDIS work presented in this paper could not be achieved without the following IntelCities partners' collaboration and support: The Centre de Política de Sòl i Valoracions, Universitat Politècnica de Catalunya, Spain; Labein, the Spanish research institute based in Bilbao; The Department of Construction Economics and Property Management, Vilnius Gediminas Technical University; and Dr. Pedro Gamito, visiting professor in Real Estate Management ESAI, Lisbon, Portugal.
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The authors would also like to thank EU's INTERREG IIIB North West Europe Programme and UK's Office of Deputy Prime Minister (ODPM) for funding of the VEPs project.
References Aouad, G., Lee, A., & Wu, S. (2006). nD modelling for collaborative working in construction. Architectural Engineering and Design Management, 1, 33−44. Arayici, Y., Hamilton, A., Gamito, P., & Albergaria, G. (2005). Using the 3D laser scanner data capture and modelling to visualise the built environment: Data capture and modelling. 9th International Conference of Information Visualisation, London. Bourdakis, V. (1997). Virtual reality: A communications tools for urban planning. CAAD: Towards new design conventions. Poland. Bialystok Technical University. CabinetOffice (2000). e-government: A strategic framework for public services in the Information Age. London. Breckland Print Solutions Ltd. Date, C. J. (1999). Introduction to database systems (7th ed.). Addison-Wesley Longman Publishing Co, Inc. Ding, L., Liew, P., Maher, M. L., Gero, J., & Drogemuller, R. (2003). Integrating CAD and 3D virtual worlds using agents and EDM (pp. 301–323). CAAD Futures. e-Europe. (2005). eEurope 2005 Action Plan, http://europa.eu.int/information_society/eeurope/2005/index_en. htm. Accessed Feb 8 2006. EIF. (2004). European Interoperability Framework, http://europa.eu.int/idabc/en/document/3761, assessed on 22 Oct 2005. GML. (2004). Geographical Mark-up Language, http://www.opengeospatial.org/. Accessed Jan 21 2005. GovTalk. (2005). e-Government Interoperability Framework. UK government. Accessed May 12 2005. Hadley, C., & Elliott, L. (2000). National Geospatial Data Framework (NGDF) The UK Model. Hamilton, A., Burns, M. C., Revilla, I., Kaklauskas, A., Alejandro, E. M., & Arayici, Y. (2005). Final Report of IntelCities D5.4c Building Data Integration System. BuHu, University of Salford. Hamilton, A., Trodd, N., Zhang, X., Fernando, T., & Watson, K. (2001). Learning through visual systems to enhance the urban planning process. Environment and Planning. B, Planning and Design, 28(6), 833−845. Hamilton, A., Wang, H., Tanyer, A. M., Arayici, Y., Zhang, X., & Song, Y. (2005). Integration of building and urban n-Dimensional data to support intelligent cities. ITcon 10 (From 3D to nD modelling), 55−67. Harris, B., & Batty, M. (1993). Locational models, geographic information systems and planning support systems. Journal of Planning Education and Research, 12, 184−198. Holland, P. (1999). The strategic imperative of a global spatial data infrastructure. Cambridge Conference for National Mapping Organizations, Cambridge, UK. Huang, B. (2003). Web-based dynamic and interactive environmental visualization. Computers, Environment and Urban Systems, 27, 623−636. IDABC. (2005). Interoperable delivery of European e-Government services to public administrations, businesses and citizens, http://ec.europa.eu/idabc/en/home. Accessed February 8, 2006. IFC. (2004). Industry Foundation Class, http://www.iai-international.org/model/ifc(ifcXML)Specs.html. Assessed March 15, 2005. Innes, J. E. (1998). Information in communicative planning. Journal of the American Planning Association, 64(1), 52−63. INSPIRE. (2004). Infrastructure for spatial information in Europe, http://www.ec-gis.org/inspire/. Accessed May 18, 2005. Kolbe, T. H., Groger, G., & Plumer, L. (2005). CityGML—Interoperable access to 3D city models. International Symposium on Geoinformation for Disaster Management, Delft, Netherlands. Lee, A., Marshall_Ponting, A., Aouad, G., Song, W., Fu, C., Cooper, R., et al. (2003). Developing a vision of nDenabled construction.
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Lemments, M. J. P. M. (2001). An European perspective on geo-information infrastructure (GII) issues. The workshop National Geospatial Data Infrastructure: Towards a roadmap for India. New Delhi. Liggett, R., Friedman, S., et al. (1995). Interactive design/decision making in a virtual urban world: Visual simulation and GIS. Proceedings of the Fifteenth Annual ESRI User Conference, Palm Springs, CA, May USA. Masser, M. C. a. I. (2003). Access to geographic information: A European perspective. URISA Journal, 15, 51−59. Murray, K. (2002). New information framework for Great Britain. FIG XXII International Congress, Washington, DC, USA. ODPM. (2004). Planning Website survey 2004: Survey of planning Websites in England and Wales. London. Office of the Deputy Prime Minister & Peter Pendleton & Associates Ltd. Planning-Service. (2004). Electronic Planning (ePlanning), http://www.planningni.gov.uk/Corporate_Services/ eplanning/default.htm. Accessed February 8 2006. Rhind, D. (1997). Overview of the National Geospatial Data Framework. AGI-GIS '97. Shiffer, M. J. (1992). Towards a collaborative planning system. Environment and Planning. B, Planning and Design, 19, 709−722. Vankeisbelck, R., Giraud-Carrier, F., Duchon, J., & Zarli, A. (2005). The IntelCities e-City platform: Framework for a new generation government services. e-Gov INTEROP'05, Geneva, Switzerland. Wang, H., & Hamilton, A. (2004a). The conceptual framework of ND urban information model. 2nd CIB Student Chapters International Symposium, Beijing, China. Wang, H., & Hamilton, A. (2004b). Integrating multiple datasets to support urban analysis and visualization. 4th International Postgraduate Research Conference Salford. Blackwell Publishing.
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Urban information integration for advanced e-Planning in Europe Hongxia Wang ⁎, Yonghui Song, Andy Hamilton, Steve Curwell Research Institute for the Built and Human Environment, Technology House, University of Salford, 2 Lissadel Street, Salford, Manchester M6 6AP, UK Available online 17 July 2007
Abstract Urban planning is a complex task requiring multidimensional urban information (spatial, social, economic, etc.). The need for assistance in performing urban planning tasks has led to the rapid development of urban information systems, especially “e-Planning” systems, with the support of government policy and emerging information and communication technologies (ICT). In order to enhance the capability of e-Planning and to facilitate 3D visualization and rich analysis of complex city problems, it is very important to allow information from the various sources to be shared and integrated. This paper focuses on technical approaches for multidimensional information integration, especially spatial information integration. In particular it describes the Building Data Integration System (BDIS), developed as part of the IntelCities project, which demonstrates the type of multidimensional systems that are likely to be used in future urban information systems. Functionally, the BDIS demonstrates the feasibility of systems to support the multinational collaboration of construction professionals in the procurement and renovation of buildings. For such systems to be accepted in the United Kingdom (UK) and Europe, they need to be developed with regard to current planning information structures and standards in the UK and Europe which are reviewed in this paper. The achievements and further development of multidimensional information integration through the use of innovative urban data modelling techniques are discussed. © 2007 Elsevier Inc. All rights reserved.
⁎ Corresponding author. E-mail address: [email protected] (H. Wang). 0740-624X/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.giq.2007.04.002
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1. Introduction The rapid development of ICT provides new opportunities to improve planning processes and make better use of resources (Huang, 2003; Hamilton, Burns et al., 2005; Hamilton, Wang et al., 2005). Many local authorities have employed new ICT to provide different e-Government services in Europe. For example, the UK Government set a target that by 2008 all services (with exclusions for policy or operational reasons) should be available electronically to citizens and business (Cabinet Office, 2000). e-Planning, as a section of e-Government, can enable easy access to information, guidance and services that support and assist planning applicants, and streamlined means of sharing and exchanging information among key players (Planning-Service, 2004). However, in UK government e-Planning services, there is still a lack of advanced planning applications like interactive maps, 3D visualization, and intelligent support in the e-Planning services (ODPM, 2004). Generally UK e-Planning services compare well with other European countries, although more advanced services are available in a few countries, such as Estonia. Interactive 3D visualization and intelligent systems can help planners to implement scenario modelling, environmental impact assessment, and compliance with planning policies checking. In the Building Data Integration System (BDIS) described in this paper, we consider how a development team, with the project of developing a training centre for a multinational bank, would use building and urban data sets, integrated in a project database, to work collaboratively on a project to select and refurbish the building, possibly as part of a larger urban regeneration plan. Urban planning decision making is a complex process that involves many stakeholders and relies on the multidimensional information (Hamilton, Trodd, Zhang, Fernansdo, & Watson, 2001). There are various stakeholders including urban planners, property developers, politicians, architects, engineers, transport and utility service providers, and individual citizens. The stakeholders rely on many types of information including both formal reports and quantitative data sets such as census and pollution figures (Innes, 1998). The required information in planning is related to the different dimensions of the city. For example, deciding on the right location for a primary school requires geospatial, infrastructure, environmental, housing, and population data. The information is multidimensional as it is always a mix of the spatial, aspatial and nonspatial, a blend of the qualitative and the quantitative, covering a wide range of physical, social, and economic attributes many of which are noncomparable with one other (Harris & Batty, 1993). An integrated multidimension information model is needed to combine the relevant information for effective decision support. This led to the creation of nD (n-Dimensional) urban information model concept (Wang & Hamilton, 2004a; Hamilton, Wang et al., 2005). This work was based on nD modelling research at construction domain at the University of Salford (Lee et al., 2003; Aouad, Lee, & Wu, 2006). The nD urban information model was designed to describe multidimensional urban aspects like 3D urban physical structure, transport, crime, and environmental issues plus the temporal dimension. For an effective system, sharing and assessing the relevant data sets should be easily achieved by stakeholders (system users). The increased access to relevant information aided by the implementation of an
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information system can ultimately lead to increases in the quality of plans, number of alternatives generated, and the quality of decisions (Shiffer, 1992). The BDIS was developed, as part of the IntelCities project (an EU funded project with 75 partners), to be a system that allows easy sharing of data to promote close collaborative working and increased quality and efficiency of work. The BDIS achieved the integration of digitized building data with geospatial information and other types of city data based on the nD urban model concept. It demonstrates the support of intelligent urban planning services which can improve planning practice. The focus of this paper is on the technical approaches to integrate multidimensional urban information, including spatial, for e-Planning, and on the BDIS produced. Section 2 reviews the current information architecture of e-Government from the UK and EU perspectives and discusses the related standards which formed the development background of this research. Section 3 introduces the research work undertaken on the BDIS in the IntelCities project and discusses the development of data structures and data converters to produce a centralized database with a standard interface. The BDIS is designed to assist stakeholders in the urban environment in making sense of the wide variety of urban data and to promote collaboration in the urban development. Finally, this research work is summarized and future work is discussed.
2. e-Government Interoperability Framework (e-GIF) and information standards Implementing e-Government services is increasingly part of government policy throughout the European Union. The European approach to the development of the information society hinged on the twin track of achieving physical access through the communication network and achieving social access and economic objectives through public sector information services (Masser, 2003). The rapid growth of ICT, especially broadband Internet, makes the physical access workable. A series of relevant standards and policies such as e-GIF, INSPIRE, etc., will facilitate the achievement of government information integration and interoperability. This section will review the emerging standards and policies for e-Government, and especially e-Planning, in the UK and the EU, as shown in Table 1. These standards and policies form the context for the IntelCities project and the BDIS created within IntelCities. 2.1. e-GIF in UK and EU The UK government's e-GIF (e-Government Interoperability Framework) is an initiative that aims to define the essential requirements for a Web-enabled government. It defines the technical policies and specifications governing information flows across government and public sectors. The e-GIF is a cornerstone of the UK's e-government strategy. Adherence to e-GIF standards and policies is mandatory. UK's e-GIF adopts specifications that are well supported in the market place. It is a pragmatic strategy that aims to reduce cost and risk for government systems while aligning them to the global Internet revolution. The World Wide Web Consortium (W3C) developed
Names
Organizations
Web sites
e-GIF (e-Government Interoperability Framework) IDABC (Interoperable Delivery of European e-Government Services to public Administrations, Businesses and Citizens) EIF (European Interoperability Framework) eEurope SDI (Spatial Data Infrastructure)
Cabinet Office, UK government
http://www.govtalk.gov.uk/schemasstandards/egif.asp
European Commission
http://ec.europa.eu/idabc/en/home
European Commission European Commission The Global Spatial Data Infrastructure Association INSPIRE (Infrastructure for Spatial Information Europe Commission in Europe) GML (Geographical Mark-up Language) OGC (Open Geospatial Consortium) CityGML SIG 3D, GDI NRW, Germany IFC (Industry Foundation Class) IAI (International Alliance for Interoperability)
http://europa.eu.int/idabc/en/document/3761 http://europa.eu.int/information_society/eeurope/2005/index_en.htm http://www.gsdi.org/SDILinks.asp http://www.ec-gis.org/inspire/ http://www.opengeospatial.org/ http://www.citygml.org/ http://www.iai-international.org/model/ifc(ifcXML)Specs.html
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Table 1 The e-Planning relevant policies and standards
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eXtensible Markup Language (XML) (http://www.w3.org/XML) is adopted in e-GIF as the primary standard for data integration and presentation on all public sector systems. This includes the definition and central provision of XML schemas for use throughout the public sectors. The UK government policy is to use: (1) XML and XML schemas for data integration; (2) UML, RDF, and XML for data modelling and description language; and (3) XSL, DOM, and XML for data presentation. In terms of important spatial information in urban planning, the UK's e-GIF identifies the use of Geospatial Mark-up Language (GML) as the geospatial information standard. Some details of the data integration policies are highlighted in the “policies and technical standards” section of the e-GIF report (GovTalk, 2005). The “Interoperable Delivery of European e-Government Services to Public Administrations, Businesses and Citizens” (IDABC, 2005) is a European program using advances in information and communication technology to encourage and support the delivery of crossborder public sector services to citizens and enterprises across Europe. As a horizontal measure, European Interoperability Framework (EIF) (EIF, 2004) provides a series of recommendations and defines generic standards, offering a comprehensive set of principles for European co-operation in e-Government. The EIF does not explicitly list which standards or data formats should be used when developing government systems. Instead, it refers developers to the interoperability frameworks used by member states. There are many other European projects in this field. For example, the eEurope 2005 Action Plan (e-Europe, 2005) is a common initiative defined by the European Commission (EC). This plan aims at “developing modern public services and a dynamic environment for e-Business through widespread availability of broadband access at competitive prices and a secure information infrastructure.” e-GIF and equivalent European standards are still developing. Published standards in this area do not address enough on spatial information, e.g., the combination of building and urban scale data, although the development of such standards is in the scope of the INSPIRE initiative. This is discussed in Sections 2.2 and 2.3. 2.2. Spatial data infrastructure (SDI) Eighty percent of all public sector information is geo-referenced (Lemments, 2001). Spatially referenced information is increasingly becoming central to advanced e-Planning applications. This is reflected by the extensive development of spatial data infrastructure (SDI) at national, regional, and global levels, i.e., National Spatial Data Infrastructure (NSDI) and Global Spatial Data Infrastructure (GSDI). SDI is a means of understanding global environmental and health challenges, supporting international telecommunication, commerce, and human development and stimulating economic growth and productivity (Holland, 1999). SDI aims to enhance interoperability among geographic components of government activities and to maintain a common inventory of geospatial content and services.
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All countries in Europe and many other countries around the world have developed or are developing their NSDIs (http://www.gsdi.org/SDILinks.asp). Since different governments have different policies for dissemination of information, the NSDI of one country could differ from the NSDI of another country. The UK government's National Geospatial Data Framework (NGDF) aims to develop an over-arching UK framework to facilitate and encourage efficient integration of, and access to, geospatial data (Rhind, 1997). NGDF, as UK's NSDI, emphasizes a framework of standards, metadata, and access rather than the more centralized approach in other countries involving base data sets (Hadley & Elliot, 2000). Infrastructure for Spatial Information in Europe (INSPIRE) is an initiative to “create a legal framework for the establishment and operation of an infrastructure for spatial information in Europe, for the purpose of formulating, implementing, monitoring and evaluating Community policies at all levels and providing public information” (http://www.ec-gis.org/inspire/). INSPIRE focuses on environmental policy but is open for use by other sectors such as agriculture, transport, and energy. INSPIRE will not set off an extensive program of new spatial data collection in the Member States. Instead, it is designed to optimize the scope for exploiting the data that is already available, by requiring the documentation of existing spatial data, the implementation of services aimed at rendering the spatial data more accessible and interoperable, and by dealing with obstacles to the use of the spatial data. INSPIRE will pave the road for a progressive harmonization of spatial data in the Member States of the EU (INSPIRE, 2004). The INSPIRE directive will follow international standards to facilitate the integration of information so that it can be available for use to provide enhanced public services. The INSPIRE philosophy of integrating existing data sets to facilitate more holistic applications is a key motivation for work on the BDIS system and current research building on the BDIS. 2.3. Spatial information standards for advanced e-Planning Efforts to standardize the way in which we describe spatial features are critical to our ability to share government data between different departments, levels of government, and commercial and educational institutions. Standards have been recognized as a key part of e-GIF and SDI in two distinct ways, through the adoption of standards and in setting new standards or extending existing standards (Murray, 2002). There are several important spatial information standards like GML for geospatial information and IFC for building information. These standards, explained below, are essential to the BDIS, described in Section 3 of this paper. Open Geospatial Consortium (OGC)'s Geographical Mark-up Language (GML) (http:// www.opengeospatial.org/) is an XML-based schema for the modelling, transport, and storage of geospatial information. It was developed as a data exchange standard interface by OGC to achieve data interoperability and reduce costly geographic data conversions between different systems. The GML full specification defines “an XML grammar for the encoding of geographic information including geographic features, coverage, observations, topology, geometry, coordinate reference systems, units of measure, time, and value objects” (GML, 2004). There are some efforts to develop application schemas based on GML 3.1. One typical
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example is CityGML, which was developed by the “Special Interest Group SIG3D” from North-Rhine Westphalia in Germany (http://www.citygml.org/). This promising 3D city standard holds not only geospatial information but also supports a semantic model of city objects (Kolbe, Groger, & Plumer, 2005). Industry Foundation Classes (IFC) (ISO/PAS 16739) is a building model standard (http:// www.iai-international.org/). IFC is developed by the International Alliance for Interoperability (IAI) (IFC, 2004). It is a universal model to be a basis for collaborative work in the building industry and consequently to improve communication, productivity, delivery time, cost, and quality throughout the design, construction, operation, and maintenance life cycle of buildings (IFC, 2004). The IFC object descriptions deal not only with full 3D geometry but also with relationships, process, material, cost, and other behavior data. Integrating a CAD model with IFC enables the accurate geometric representation to be integrated with structural and behavior elements and facilitates linking with external applications (Ding, Liew, Maher, Gero, & Drogemuller, 2003). The initiatives described in this section of the paper, provide the much-needed emerging standards of interface and information integration for e-Government services in Europe. As these standards develop they will provide better support for advanced e-Planning systems that will enhance decision making by providing comprehensive and integrated city services to citizens and businesses. This provided the context for IntelCities and the BDIS.
3. IntelCities and Building Data Integration System 3.1. IntelCities project The Intelligent Cities (IntelCities) Project (http://www.intelCitiesproject.com/), from January 2004 until October 2005, was an Integrated Project funded by the European Commission (EC) Information Society Technologies program under framework 6 (11 million euros funding and 75 partners including Rome, Helsinki, Manchester, Marseilles, Nokia, Cisco, IBM, The Research Institute for the Built and Human Environment, University of Salford, and many other research institutes). IntelCities is now established as the European Intelligent Cities Research Centre with partners across the world. The focus of IntelCities is the development of open, secure, interoperable, and reconfigurable e-Government services at the city level to meet the needs of both citizens and businesses. The e-City Platform (e-CP) was developed to underpin the provision of a wide range of innovative applications and multimodal services that integrate inter- and intragovernment administrative, planning, public/business consultation and local government policy development dimensions. As shown in Fig. 1, an integrated multidimensional data repository is the foundation of the e-CP. The project explored related e-Gov standards, such as UK e-GIF and EU IDA (Interchange of Data between Administrations), and ensured that the standards adopted for interface design of the analysis and multidimensional components of the e-CP are compatible with these standards. The analysis within IntelCities of e-Government concepts together with the
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Fig. 1. IntelCities system architecture (IntelCities, 2004).
requirements of citizen and cities have led to the adoption of a distributed service-oriented architecture (SOA) framework (Vankeisbelck, Duchon, & Zarli, 2005). Several e-CP-based service modules have been developed and tested across a set of European pilot cities. The e-CP supports new services as well as the integration of legacy systems for e-Government. In the rest of this section the BDIS is described. The BDIS demonstrates how a system for property development and refurbishment can both make use of, and to contribute to, the urban data repository envisaged in the IntelCities project, as well as being an effective regeneration service. 3.2. Building Data Integration System The Building Data Integration System (BDIS) was produced within Work Package 5 (e-Regeneration) of IntelCities. The project was led by the University of Salford with
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Universitat Politècnica de Catalunya, Barcelona; Labein, Bilbao; Vilnius Gediminas Technical University; and Dr. Pedro Gamito, visiting professor in Real Estate Management ESAI, Lisbon. The BDIS illustrates how digitized historic building data can be integrated with other types of city data to support a multinational team of construction professionals in the procurement and refurbishment of an historic building. As such, it is an example of an integrated intelligent city system. The three main tasks for implementation of the BDIS are • • •
to capture building data using a 3D laser scanning system; to integrate digitized building data with geospatial information and other types of city data; and to build a project database and application system for property development based on integrated data. The implementation of the BDIS was driven by the application scenario described below.
3.2.1. Gaudi Bank scenario and requirement analysis In order to give realism to this research work, the fictitious Gaudi Bank scenario was created. Gaudi Bank, with headquarters in Barcelona and branches in the UK, wants to set up a new training centre in Manchester. Gaudi Bank has located a building in east Manchester, Jactin House, as a potential base. However, they have to make a number of checks and assessments about the building and its location before making any detailed plans. Furthermore, once a suitable building is selected, a plan for its refurbishment needs to be developed. Gaudi Bank employs (either directly or indirectly) a range of professionals to undertake this work. Property developers work on selecting and purchasing buildings in the appropriate location. Construction professionals, such as architects and quantity surveyors, design and plan the refurbishment work. At a basic level, this scenario also involves local government professionals such as local planners. However, cities today work hard to encourage local investment, particularly in regeneration areas, so there would be a wide range of interests in Manchester concerned with supporting Gaudi Bank. Using current methods the Gaudi project team would make many trips to the UK to meet the project partners and collect a wide range of information. For example they would have to collect transport and accommodation information to assess suitability for intended trainees visiting the centre. This information is not always easy to find, and effort is often duplicated as people research the same problem independently. Also sharing information about the refurbishment of a building, and adding information as design and planning progresses, is difficult. Mistakes are made by people using out of date plans that have been superseded. When project information is held on a central database, the latest version of the plan is always easily available. The BDIS also shows how most project information could be accessed through one central point and cross-referenced to make comprehension easier. The system analysis of the BDIS was carried out during two IntelCities workshops (Hamilton, Burns et al., 2005). The information requirement of the Gaudi Bank scenario was investigated. The information required includes not only data about the building itself but also about the surrounding environment information. The Labein group, who advise the Spanish
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Government on the refurbishment of buildings, led this work. The following requirements were determined: • • • •
structural survey of building (construction elements like windows, doors, etc.); surrounding geospatial information like building footprint, road, land, and ownership; local plan (zoning, potential changes of uses, development plans, etc.); and transport, accommodation, and similar information.
Based on the requirement analysis, the structure of the BDIS was designed as shown in Fig. 2. Note that the project database was designed so it could be integrated with the IntelCities eCP of Manchester City. However, full integration was not undertaken within this 18-month project due to time limitation. Thus, the BDIS was built to operate as a freestanding system independent on the e-CP. The implementation of the BDIS is described below. 3.2.2. Data acquisition The first task of the BDIS is to capture the building information by using the 3D laser scanner and acquire the surrounding area's geospatial information and gazetteer, etc. Jactin house in Manchester, as the proposed building for Gaudi Bank's training centre, was scanned, internally and externally, using Riegl LMS-Z210 laser scanner. Using the point cloud
Fig. 2. Building Data Integration System and e-City Platform.
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data captured, a virtual 3D model of the building with an accuracy of 10 mm was produced. It should be noted that the authors are now (March 2007) using the Reigl LMS Z390 with an accuracy of better than 2 mm, making it adequate for all but the most exacting building surveying applications. To be able to identify elements of the building in the virtual 3D model made from the point cloud data of Jactin house, a CAD model was extracted. The identification of building elements such as doors and windows from the CAD model is partially automated using Microstation Triforma software with the Triforma objects (Arayici, Hamilton, Gamito, & Albergaria, 2005). From this work, the IFC building model of Jactin house was obtained with building elements as objects which can be stored in a database. The geo-information of Jactin House surrounding area comes from Ordnance Survey's landline data. Other related information like gazetteer, transport information was acquired from the local authority or other sources. 3.2.3. Data modeling and integration The second task is to integrate digitized building data with geospatial information and other types of city data. However, the integration of digitized building data, usually CAD models, with geospatial information is problematic because the two data types come from different domains and the coordinate system definitions and modelling methods are heterogeneous and incompatible. This was achieved by modelling multidimensional urban data sets and integration based on the nD urban database, described in Section 3.2.3.2. 3.2.3.1. Data modeling. Data modelling is a process of specifying a representation of the data sets and their relationships. This provides a conceptual or implementation view of the data that best supports the information requirements (Date, 1999). An nD urban model was designed based on the multidimensional data set types required by the Gaudi Bank scenario. It includes the following: geometry model, building model, land parcel model, road network model, open space model, and public transport model. Since buildings are the most important element of urban environment, building data sets are essential for the Gaudi Bank scenario. The following discussion mainly focuses on the design of the building data model. Two most popular building models are identified as CAD model and GIS-based model (Wang & Hamilton, 2004b). Currently in the mainstream GIS domain, a building is described as a feature with some attributes like name, age, building type, etc. The geometrical representation of buildings is still limited to 2D footprint, sometimes with height. The buildings can only be visualized as block models. However, it should be noted that recent work in GML and City GML allows 3D modelling based on GIS-derived systems, (see Section 2.3) but currently not with the details of models based on CAD. Detailed building description can be found in CAD domain. Currently various CAD-based 3D city models have been developed by such as Los Angles model (Liggett & Friedman, 1995), Bath model (Bourdakis, 1997), Tokyo (http://www.planet9.com/) just to name a few. The realistic building models can be visualized within many of these 3D city models. However, for most of these models the focus is mainly on the visual impact without enough attribute descriptions of building objects. The designed building model was shown in Fig. 3. The building structure was based on an IFC building model. As introduced in Section 2.3, IFC, as an object-oriented building
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Fig. 3. Building model.
information standard, does not only preserve the full geometric description of building objects, but also the relationships, and the properties of each object. However, IFC is a complicated and construction-focused model. It is necessary to simplify the IFC model used in the construction industry for urban applications. In IFC, there is a single underlying World Coordinate System (WCS) used for building plans. Also there are many local coordinate systems that have direct or indirect links to the WCS. In the BDIS building model, buildings and all the building objects are designed in one WCS. This reduces the coordinate calculation burden efficiently. We employed the British National Grid (OSGB-1936) as our world coordinate system in the BDIS. In BDIS, the building structural objects include buildings, building parts, building stories, building elements. Every object contains information about its geometry and attributes. A building is composed by several building parts. A building can also have several building stories and building elements. Building elements include walls, roof, floors, and windows. The relationships between elements were also described, e.g., a window element can be an opening
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in a wall element. The building attributes and geo-location definition were also included in this model. For the other models included in this nD urban model, the common geometric model describes all the geometric primitive definition which is complied with the OGC's GML standard. The design of other geo-features and thematic models (representing economic, environmental, and social data) is mainly based on the Gaudi Bank application requirement and geospatial data sources from the local authority and Ordnance Survey. 3.2.3.2. nD urban database. In BDIS, a database-based integration method was employed to support the information integration. An nD (n Dimensional) urban database was created based on the nD urban model. MS SQL Server was used to host the database to store semantic-rich 3D building information, geospatial information, and other types of city data. This centralized nD database can accept various data sources and data formats through a series of converters as shown in Fig. 4. An IFC converter was developed to convert the IFC building model into a format acceptable to the nD database. The IFC model of Jactin house is generated using LiDAR technology as discussed in Section 3.1. The IFC document manipulation is implemented by using a freeware IFCsvr ActiveX component (http://cic.vtt.fi/projects/ifcsvr/ifcsvrr200/default.html). The coordinate system of the IFC building model is different from the definition of the geospatial coordinate system. Coordinate conversion was implemented to convert the IFC model coordinates into the geospatial coordinate reference system in the BDIS. Due to the lack of geo-referenced information in the IFC building model of Jactin house, the building footprint matching process has to be undertaken by manually finding corresponding registration points between the IFC model and the geospatial data. A transform matrix was then calculated based on these registration points and the IFC building model coordinate conversion was then implemented automatically. A geospatial data converter was also developed to implement information conversion (Hamilton, Wang et al., 2005). The geospatial data of the Jactin house area is an ESRI's shop
Fig. 4. Technical structure of Building Data Integration System.
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file which comes from OS's landline data. The parcel, building, and road layers of geospatial data were converted into the nD database. Through data modelling, database creation, and data conversion, integration of semanticrich 3D building information with geospatial information and other types of city data was implemented as a unified database. 3.2.4. Prototype system The third task is to implement the prototype system to demonstrate the use of multidimensional urban databases to support intelligent planning services. This prototype is based on the Gaudi Bank scenario and developed using C++ programming language. The building data and surrounding environmental data can be retrieved from the database and presented to users. The stakeholders can get knowledge that normally can only be achieved by manual information collection and processing as currently employed in building surveys. In the BDIS the schedule of doors and windows of Jactin house was extracted from the digital survey information held on the project database as shown in Figs. 5 and 6. 3.3. BDIS to support intelligent city service As one of the deliverables of the IntelCities project, the objective of the BDIS “to illustrate how digitized historic building data can be integrated with other types of city data, using nD modelling, to support integrated intelligent city systems” has been achieved. The nD urban database demonstrates that the approach of data modelling and integration can support
Fig. 5. Digital survey of Jactin House based on the BDIS (left: 3D VRML model of windows and doors of Jactin House; right: windows/doors illustration image).
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Fig. 6. Digital survey report of generated by the BDIS.
advanced e-Planning services. The design and implementation of the BDIS reflected this idea of information sharing and integration. Although the Gaudi Bank scenario was used in the original version of BDIS it could also be configured for any generic building development project. In the final formal review of the IntelCities project, the BDIS was well received as making significant quality and efficiency improvements as an intelligent city service. It is regarded as a good example of the new way of working envisaged by IntelCities. Cities, such as Barcelona and Manchester within IntelCities project, are interested in providing an intelligent service to promote investment in urban development projects.
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While work was in progress on the BDIS system, it was reviewed by a scientist from the Environment Agency of England and Wales (EAEW). Following this review, the architecture we were developing was used in the proposal for the Virtual Environment Planning (VEPs) project (http://www.veps3d.org/), which is currently funded (2005–2008) by EU's INTERREG IIIB North West Europe Programme (2 million Euros) and UK's Office of the Deputy Prime Minister (ODPM) (£250,000). Within VEPs, we have built upon the BDIS to produce the VEPS delivery of 3D local planning integrated environments on e-GIF compliant Web sites that can be easily accessed by all members of the community. We are also developing more effective ways to integrate building (IFC) and urban (GML) data based on OGC's Web service technology.
4. Conclusion In Europe, governments and organizations at different levels are investing many millions of Euros to promote e-Planning services by putting forward a series of standards, regulations, and policies. Given the diversity of standards in spatial data, let alone the variety of other urban data, it is a difficult challenge to consider city development holistically in e-Planning processes. Emerging urban data modelling techniques have great potential to address such a challenge. The e-CP produced in the IntelCities Project demonstrates a future in which all eGovernment applications and services can be made to work together in a seamless and interoperable manner. With the development of much relevant work like INSPIRE and e-GIF, new forms of e-Planning could support a high level and wide range of planning services which can be integrated with other city services to address the needs of a variety of stakeholders in terms of effective collaboration to produce sustainable urban developments. In this paper, the approach of an integrated multidimensional data repository for the e-CP is developed based on an nD modelling database. This is a centralized database, accepting various data sources and data formats through a series of converters. This design reflects the idea of information sharing and integration, e.g., on the one hand stakeholders are able to contribute information in different formats to the database through converters. On the other hand, stakeholders can access the database through a standard and universal database interface. The Gaudi Bank scenario on which the BDIS is based (see Section 3.2) has provided a realistic example of urban development. It can be seen that integrating urban and building data with transport and other relevant data about the city can provide an environment in which property developers could do much of their work on line, saving time and travel. Furthermore, the potential for construction professionals to work together on the renovation of buildings, sharing information collected in a project database, is potentially much more effective than current ways of working. Bovis and other large construction companies already use on line systems for the collaborative production of construction drawings and this experience is producing more effective ways of working. For example, in the Bovis project to build the Bluewater retail park in Dartford, England, which was monitored by one of the authors, the normally common problem of on site work being implemented wrongly by use of out of date and inaccurate drawings was eliminated.
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There are potential benefit and limitation that should be noted in this research. Due to the 18-month project time scale, the BDIS approach has demonstrated the ideas of data integration rather than achieve all of the ideas in a fully functional system. For example, from Fig. 4 in Section 3.2.3.2, the automation of conversion processes is important but some processes of the data conversion are not fully automated yet. This is the subject of current research. The BDIS is implemented as a centralized database. However, it is possible to implement a system which remains “logically centralized” while it is in fact a physically distributed data repository. For example, from one stakeholder's perspective, the data needed are retrieved through a standard interface; however, the actual data can be distributed anywhere in the world. The idea of distributed data but uniform and standard interface is very useful. It is always difficult to collect and manage all the data sets in a physically centralized DB. In particular, when converting object data types into a relational database, such as IFC data, the data model will be very complicated. The approach of a standard interface is a solution because there will be fewer requirements to convert data from one format to another and store it. Instead the data stays where it is, in its native format. The standard interfaces and the converters will act as an “on demand wrapper” of the various data formats and give standard access to all users. There is still much work to be done in converting the advanced research-based systems produced in IntelCities, such as the BDIS, to workable systems to be used in commerce and government. Fortunately, we have procured funding to do this work as part of the VEPS project (see Section 3.3 of this paper) and the technology produced in IntelCities is being refined for use in a large (100 million Euros) regeneration project in Stuttgart involving the main railway into Stuttgart being put underground, making existing railway sidings available for development. In summary, in this paper we have shown how the approach for urban and building data sets integration used in the BDIS can transcend the limitations of current building or urban scalebased systems. The BDIS implementation shows a way forward in the provision of comprehensive urban planning services to business and citizens. Future systems based on this technology have the potential to be more efficient in the provision of information through one point of access; more effective in providing reliable up to date information, and capturing project produced data for reuse; and more efficacious in the quality of collaboration supported for the wide range of interests in urban development. Acknowledgments The authors would like to thank the EC for funding of IntelCities through the Information Society Technologies program as a Framework 6 Integrated Project. The BDIS work presented in this paper could not be achieved without the following IntelCities partners' collaboration and support: The Centre de Política de Sòl i Valoracions, Universitat Politècnica de Catalunya, Spain; Labein, the Spanish research institute based in Bilbao; The Department of Construction Economics and Property Management, Vilnius Gediminas Technical University; and Dr. Pedro Gamito, visiting professor in Real Estate Management ESAI, Lisbon, Portugal.
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The authors would also like to thank EU's INTERREG IIIB North West Europe Programme and UK's Office of Deputy Prime Minister (ODPM) for funding of the VEPs project.
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