Combining GIS and BIM for facility reuse A profiling approach TONG WANG, THOMAS KRIJNEN, BAUKE DE VRIES Wang, T., Krijnen, T., & de Vries, B. (2016). Combining GIS and BIM for facility reuse: A profiling approach. Research In Urbanism Series, 4(1), 185-204. doi:10.7480/rius.4.824 R IU S 4 : G EO -D ESIG N 186 Abstract Lingering vacant facilities deteriorate the condition of an urban environment, and, as a consequence, actuate neighboring companies to leave the area as well. In addition, new development efforts keep depleting scarce land resources. In this paper, a framework is presented to match existing vacant facilities to the requirements of potential customers or owners to promote sustainable redevelopment and reuse. Important attributes for facility reuse are identified from literature. To automatically extract these attributes from models and their surroundings, Geospatial information and Building Information Models (BIM) are combined. In the proposed framework, a profile is created for each existing vacant facility by combining BIM and GIS attributes. As a result, these profiles can be matched to the desired BIM model, which the aspiring users have provided, based on a weighted distance calculation. The framework presents the most suitable vacant facilities to the users in order to promote facility reuse. These facility reuse alternatives are evaluated based on a single monetary metric that represents the effort required to partially or fully accommodate the requirements of the aspiring users, which is reflected in the weighted distance between profiles from existing vacant facilities and the facility desired by the end-user. This framework identifies suitable areas for redevelopment after which a process is started that forms an iterative and comprehensive evaluation dialog between demand and supply parties, on multiple scale levels, including various design alternatives to adapt the existing facility to the desires of the consumer and revitalize the surroundings according to Geodesign principles. A proof-of-concept of the framework is presented together with the conceptual system structure. Evaluation of the attributes and the technical implementation of their extraction from BIM and GIS data show the technical feasibility of the approach. KEYWORDS Facility reuse; Geodesign; GIS & BIM integration; BIM C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 187 1. INTRODUCTION  1.1 Problem statement Unlike regular urban development planning, industrial area develop- ment is mainly led and directed on the level of individual municipalities in the Netherlands, with the lack of participation of private real estate investors. The unrealism in new industrial area demand estimation, financial profitabil- ity of developing new industrial areas and mutual competition among munic- ipalities all lead to the redundantly large supply of very cheap industrial area land plots (Blokhuis, 2010). As a result, a large amount of existing industrial areas are obsolete, with many abandoned facilities. According to the Dutch national industrial area database IBIS, 1052 industrial areas contain signs of obsolescence, equalling 32,230 gross hectares on January 1st, 2013 (Ministerie van Infrastructuur en Milieu (Ministry of Infrastructure and Environment), 2013). On the contrary, the Netherlands is well-known as a densely populated country, where land resources are scarce. Therefore, industrial area redevel- opment and facility reuse are essential, as it can promote regional economies while using resources more efficiently. In the industrial sector, land and facility reuse is not widespread. A lack of knowledge exists on vacant facilities and their potential for meeting the requirements of aspiring future users. Hence, new facilities are built while instead existing ones could have been reused. Reusing existing facilities is a more sustainable way for reducing the amount of disused, neglected and abandoned facilities and construction efforts. For a sustainable future, facility reuse needs to be put on the agenda. It can serve as a main driver for land use redevelopment and suitable reuse strategies can democratize the planning process, which would allow private parties to participate and state their interest in a certain facility and its sur- roundings. Besides these advantages, the facility locations in many cases are historical sites, which are located centrally in large cities. Due to the spatial development of a given area, these buildings can often be heritage-listed and carry the character of a specific time period. This can be attractive to certain types of customers as it provides a sense of identity. From a financial point of view, it is claimed that reusing existing facilities saves 10%-12% investment over building new ones (Shipley, Utz, & Parsons, 2006). Despite of the aforementioned points, land use planners tend to focus on a larger scale with a top-down perspective when dealing with industrial area redevelopment. On such a scale, details about individual facilities easily get lost, as information about this is not easily accessible. Recently, more and more detailed building information models get avail- able, due to technological advances as well as legislation required to get build- ing permits. Incentives are thriving in countries like Singapore (Das, Leng, R IU S 4 : G EO -D ESIG N 188 Lee, & Kiat, 2011) and South Korea (Zeiss, 2014). The availability of these mod- els for detailed information about existing facilities enables new methods to stimulate facility reuse. Research has been conducted on the usage of Geographic Information System (GIS), Building Information Modelling (BIM) and their integration mainly in the domain of Facility Management (FM). Rich and Davis (2010) provide a detailed overview of the application of GIS for facility management and the integration with other applications, including BIM. They conclude that in the field of facility management, combining GIS and BIM is helpful to ensure requirements are met and data that are needed at various stages of FM are captured. In addition, GIS analysis can help to find a suitable location for a new facility (Abudeif, Abdel Moneim, & Farrag, 2015; Panichelli & Gnan- sounou, 2008; Rikalovic, Cosic, & Lazarevic, 2014; Zhang, Johnson, & Suther- land, 2011). However, the implementation level of BIM within existing build- ings is limited (Volk, Stengel, & Schultmann, 2014) which can be attributed to the fact that only recently BIM technology has become more popular. Notable exceptions include attempts to integrate BIM and GIS for emergency response and lifecycle management of a facility and its surroundings (Mignard & Nicol- le, 2014; Shen, Hao, & Xue, 2012). However, research on BIM or GIS or their integration is limited within the scope of facility reuse. In this domain, the integration of GIS and BIM is of importance to connect geospatial information to detailed building level information to make informed decisions about the reuse suitability and possibility of existing vacant facilities.  1.2 Scopes and assumptions For facility reuse, the emphasis not only lies on the physical facilities themselves but also on the surroundings. In this article it is assumed that successful selection of a facility for reuse is more likely when geospatial in- formation and building information are both considered. In this way, an en- tire overview of how one facility functions in its social, geographic and demo- graphic context can be constructed out of regional characteristics, building and encompassing plot attributes. In the scope of this article, a facility is con- sidered to be a commercial or institutional building and its surrounding plot of land. And in one region, there are enough vacant facilities for reuse. In this research, four general types of reuse are identified for an existing vacant facility, namely: occupying the existing facility only partially, occu- pying it entirely, using the encompassing plot for additional developments and, lastly, rebuilding another facility after demolishing the original (Figure 1). The natural resources that have been put into the building itself, makes the building the primary objective for reuse. Hence, the redevelopment pro- cess as outlined in this framework strives to accommodate facility reuse in the second category: completely occupying an existing facility. One could ar- C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 189 gue that searching for other existing facilities that match the demand of the aspiring owner more closely can fulfill the objectives behind the other three types of reuse. 1 2 3 4 Figure 1. Four types of facility reuse.  1.3 Geodesign for better facility reuse Facility reuse requires creative thinking and an integrated mindset of considering design alternatives for a building in conjunction with its sur- roundings. As such, it could benefit from principles outlined in the Geodesign methodology. Geodesign, as Flaxman has proposed and Ervin has amended, is a design and planning method which tightly couples the creation of de- sign proposals with impact simulations, informed by geographic contexts, systems thinking and digital technology (Steinitz, 2012). It is the process of designing in a geospatial environment. Design assignments based on the Ge- odesign concept are performed globally (McElvaney, 2012). An example in an industrial context is the R2G project, which covers the transition of red fields into green fields, or change of abandoned and possibility contaminated areas into thriving public parks (McElvaney, 2012). In these cases, Geodesign pro- cesses and techniques helped to break down large and complex problems into components that could be analyzed. Furthermore, the Geodesign concept and techniques can be used to contribute to a more competitive and user-orien- tated decision process, which can attract investment and stimulate regional sustainable development. In order to guide this process systematically, Steinitz has proposed a practical framework based on empirical research (Steinitz, 2012). This frame- work guides stakeholders by iteratively posing questions about the problem, the area, the process, the assessment results, and consequently creating more informed designs. Hence these guidelines are relevant in the context of facil- ity reuse. After all, in order to understand the mechanisms behind its aban- donment, the facility and its surroundings should be described along with the factors that drive changes on a geographical level. Furthermore, iterative thinking and the evaluation of design alternatives are of vital importance to successful reuse strategies. R IU S 4 : G EO -D ESIG N 190 However, Geodesign is still mainly discussed solely in a geospatial con- text. No specific attention has been given to incorporate building level in- formation. For facility reuse the building level characteristics are essential to include in the design process. By combining GIS and BIM information, a better design can be obtained to satisfy stakeholder requirements. In this way, Geodesign can be an instrumental tool for planners and fa- cility owners, and can also gain a better understanding of the inner environ- ment of the facility, its site and location within its broader spatial and social context. In addition to the established Geodesign methodology, which starts with a specific project area in need of analysis and design, governments could adapt their Geodesign procedures by stimulating potential future users first to identify an area of suitable vacant facilities with the combination of GIS and BIM information. In this way, a collaborative design process, in which the most suitable project area or facility is not always known in advance, can be initiated, and the output of the framework proposed in this paper identifies areas to redevelop. In addition, the framework proposes a singular monetary metric based evaluation method for different design proposal evaluations, which com- plies with the evaluation guideline of Geodesign process proposed by Steinitz (2012). To conclude, the Geodesign framework and tools help to understand the facility and its surrounding, evaluate possible results for various design pro- posals and negotiate among stakeholders for a better final design. The pro- posed method provides a starting point for finding suitable facilities for fur- ther analysis and a simple evaluation paradigm for design proposals.  1.4 Objectives As discussed in the problem statement, facility reuse is of great poten- tial for sustainable development. In this chapter, a framework is proposed that enables public parties to accommodate facility reuse by private parties. It enables aspiring facility owners to match their criteria with characteris- tics of existing vacant facilities and discover those that are most suitable for redevelopment or reuse. A literature study has been conducted to find the most influential attributes for facility reuse and their data sources. In order to obtain such a system, data sources from both BIM and GIS are aggregat- ed to provide stakeholders with a comprehensive overview of the facility and its geographical context. To aggregate different data sources, commonly used formats of GIS science and BIM are reviewed and possible integration methods are proposed. After data integration, a weighted distance calculation is ap- plied, based on various attribute differences between the desired facility and existing vacant facilities. Aspiring owners can select a facility, based on a sin- C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 191 gle monetary metric that is composed of the acquisition costs, and a monetary interpretation of the extent to which the existing facility might not meet all criteria set by the end-user. It is this single monetary metric that guides the evaluation of different facilities. Likewise, options for adapting the existing facility to meet all requirements can be evaluated iteratively. Guidelines from the Geodesign concept steer the conceptualization of the requirements. The effort is mainly directed at industrial facility reuse for promoting a region- al sustainable redevelopment process to use resources more efficiently and provide economically more viable and desirable facilities to its users. Data sources and data integration are discussed. Several tools have been identified to extract attribute values from various heterogeneous data sources. Howev- er, an implementation and evaluation using real stakeholder data is beyond the scope of this paper. In the following sections, a theoretical background for integrating GIS and BIM are presented and the framework and its proof-of-concept imple- mentation are discussed. The technical implementation of the extractions shows the viability of the approach. 2. THEORETICAL BACKGROUND  2.1 GIS and BIM data formats The Industry Foundation Classes (IFC) file format is a commonly used standard in the domain of BIM. It is spearheaded by the buildingSMART consortium and has been ratified as an ISO standard. IFC describes a build- ing model as an assembly of building products, in which the products are composed of geometrical representations, semantic information stored as key-value properties, and relational information about decomposition and topological adjacency. IFC is based on STEP and has an EXPRESS schema defi- nition, which defines the entities along their attributes and relationships that constitute a valid IFC file. IFC files are stored as either ASCII or XML files and are therefore human readable. Besides the definition of the schema, build- ingSMART also standardises on sets of properties, which are plain text, to be used to cover common-use cases. Other formats for describing building models exist, for example gbXML , or proprietary file formats which are native to commercial BIM applications. The former is primarily intended for use within sustainability analysis and is therefore not considered for inclusion into the system. Proprietary file for- mats, for which automated tools to extract data are not available, are not con- sidered either as they would hamper the development of the system. Conversely, in the GIS domain, shapefiles are a commonly used option for describing vector features. Rather than led by an industry wide consorti- um, this de facto standard has organically grown out of an initially proprietary R IU S 4 : G EO -D ESIG N 192 format. Such a GIS database consists of a set of binary files. The geometrical information itself is separated over several files based on their types or layers. These geometrical entries define the location or perimeter of a feature in the world. Metadata records are stored in separate files that provide the seman- tics to these features. Overlap between, and ways to interlink, the different data carriers can be found on the building and site level, where GIS features describe the same building or site in a BIM model. An IFC file has a default decomposition structure, which starts with a project and descends into a site, a building, individual building stories, down to individual building elements. Typically a building does not have a geomet- rical representation of its own, but is geometrically defined as the composi- tion of its elements. In the GIS domain, the building footprint is represented as a polygonal boundary. Attributes can be applied to the polygonal boundary, such as the building height or the functional characteristics of the building it refers to. In IFC the site can have a geometrical representation, but there is no guarantee that it is aligned with any of the footprints in the GIS system or in the cadastral records. The IFC standard primarily intends the site to be used for documenting the construction site. As defined by the IFC standard, a site does have an optional definition of a single geographic reference point, specified by its latitude, longitude and elevation. This is a key factor for being able to link a building model to a feature in a GIS database, as string-based building identifiers are error prone and context specific. In addition, hybrid formats intended for use on an intermediate scale ex- ist. For example, the CityGML format , which is an XML based standard led by the OGC . It lacks the semantic, topological and parametric richness to convey constructed documentation as can be found in an IFC document, but is suit- able for documenting urban environments in three dimensions. The use of CityGML is currently not considered for this prototype, as public datasets on an urban scale are not available within this project.  2.2 Combining versus converting Despite the differences in how data are stored in the GIS and BIM domain, the attributes that are relevant for this prototype system implementation can be extracted into a uniform tabular structure of attributes. This method is dif- ferent from other strategies found in the literature, for example on building an integrated system by converting one format into the other. The transla- tion from IFC to CityGML is discussed in (Isikdag & Zlatanova, 2009; Nagel, Stadler, & Kolbe, 2007). On a building scale the expressiveness of IFC is richer than CityGML in most aspects; such a translation to a large extent boils down to converting the solid volume representation in IFC into merely the visible surfaces for use in CityGML and mapping the classes. Conversion the other way around is more difficult, due to richer semantics required to constitute C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 193 a meaningful IFC, as has also been reported by (De Laat & Van Berlo, 2011; EI-Mekawy, Ostman, & Hijazi, 2012; Isikdag & Zlatanova, 2009) and due to lack of solid volumes. As such, in this paper, we combine GIS and BIM data sources for the purpose of facility reuse without the associated loss of infor- mation of converting data formats.  2.3 Guidelines for integration The presented approach is based on a combined feature extraction meth- od and therefore does not rely on a shared common denominator between the BIM and GIS input formats. This results in a modular system in which inter- dependencies between logic for BIM and GIS processing can be eliminated and both domains can be queried up to their full potential without information loss. On the other hand, the loose coupling, has urged to come up with guide- lines and best practices for modelling seamless inter-linkage between the two input streams in order to reduce manual effort and errors in data curation. The geospatial database is the starting point for implementing the frame- work. BIM models can incrementally be added into the system. In order to be able to associate the BIM model with the geospatial information, the building model needs to have a georeferenced point. In this way, the distinct domains can be integrated to enable the coupled feature extraction. This association takes place on two levels. Firstly, by means of the georeferenced point, dis- tances to important geospatial features can be computed and land use and other relevant geospatial data can be incorporated. However, in order to link to the attributes related to the GIS feature, the BIM geospatial reference point needs to be contained within the polygon of this building in the GIS. There- fore, the link can be established by a point-in-polygon test. If geo-reference is not supplied in the IFC file, interlinking the two needs to be done manually in a graphical user interface. In general the extracted information from the GIS and BIM domain may need to be curated upon ingesting the information into the system. 3. IMPLEMENTATION  3.1 Framework The framework suggests existing vacant facilities to potential custom- ers aspiring to acquire industrial facilities. Prospective owners are asked to provide detailed information, preferably a BIM model, about their ideal con- ception of a building. The framework then composes a vector of quantitative measures that describes aspects of the building and its relation to geospatial resources. Examples of these measures include properties like the floor area and ceiling height of the facility, derived from the document or BIM they pro- R IU S 4 : G EO -D ESIG N 194 vide. Furthermore, distance to, for example, public transport hubs and supply chain partners can be derived from GIS. Lastly, the outcomes of simulation or measurement can be included, for example the indoor lighting levels on working height expressed in lux. Thus, within this framework, for abandoned or vacant facilities, a profile is extracted by combining data from BIM and GIS. These can be matched to the requirements supplied by the prospective owner. A set of the profiles, that resemble the demand vector, can be presented to the prospective users. This set consists of the profiles for which the weighted Euclidian distance to the demand profile vector is relatively small, based on predefined thresholds. As such, they represent vacant facilities that closely match the desired facility as sketched by the client. This framework enables a win-win situation in which, on the one hand, the requirements from the potential users are fulfilled and, on the other hand, existing vacant facilities are matched for redevelopment. The profiling approach presented connects supply and demand for a sustain- able future.  3.2 Attribute identification A list of prominent attributes for facility reuse is identified and presented in Appendix A. They are categorized into building-level attributes, building plot and geospatial characteristics and demographic properties. The identi- fication of the attributes is based on a literature review (Bottom, McGreal, & Heaney, 1998; Geraedts & Van der Voordt, 2003; Glumac, 2012; Korteweg, 2002; Remøy, 2010; Stichting Real Estate Norm Nederland, 1992; Van der Voordt & Van Wegen, 2005). Figure 2. An illustration of the openness of an architectural space, quantifiable by dividing the volume of spatial separation elements (such as walls, columns and beams) by the volume of the spaces itself, to be extracted from the BIM. The architectural openness can be seen as a measure for the flexibility of the facility. Figure 2 illustrates one of these attributes for two distinct facilities. The objective is that all attributes are unambiguously quantifiable and extracted C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 195 automatically without user interpretation from the building model or geospa- tial and statistical information sources. Additionally, attributes are classified according to their level of measurement, whether they are numeric or nomi- nal. Data sources for Dutch cases and references to the tools for automatically extracting or calculating these attributes are also provided in Appendix A. For implementation into other target areas, this list of attributes will likely have to be tailored to this area.  3.3 Distance calculation The vacant facilities span an n-dimensional search space, in which n represents the size of the set of identified attributes. Every vacant facility occupies a point in this space, as does the desired facility, presented to the framework by the prospective owner. The vacant facilities that are close to the desired facility are selected and presented to the client as recommenda- tions. The relation between these facilities can be visualized by means of their attribute values. Key distinguishing attributes can be represented as an axis in an overview to the user. In such way the user can initiate an interactive ne- gotiation process in which the set of vacant facilities is explored. A graphical depiction of this overview is presented in Figure 3. Maintenance costs Floor area Desired facility € 2 300 000 total cost of ownership Proposed facility 1 12km from desired location € 1 200 000 total cost of ownership Proposed facility 2 8km from desired location € 900 000 total cost of ownership Proposed facility 3 13km from desired location € 1 300 000 total cost of ownership Figure 3. A schematic representation of the facility selection process in which several axes, representing the attributes at which the facilities differ, are presented to the user together with the renovation costs. The formula used to calculate the distance between the desired facility and the existing facilities is shown below. ! = #$ &$ ' '( ) $*+ Where d is the weighted distance between an existing facility and the R IU S 4 : G EO -D ESIG N 196 desired facility in the n-dimensional search space, n is the size of the set of identified attributes. Each of the identified indicators is assigned with a weight ω_i according to the stakeholder evaluation. x is the normalized differ- ence between the actual facility value and the desired value for each attribute. Furthermore, f_i (x) is a fuzziness function (Beg & Ashraf, 2009; Chaudhuri & Rosenfeld, 1996; Montes, 2007) that can be used to model asymmetry and vagueness inherent to the attributes. This will be explained in the next sec- tion.  3.4 Fuzzy theory Suppose the client requires a minimal ceiling height of 4 m due to regu- lations and if one were merely using the squared difference (x^2), this would lead to the surprising assumption that a ceiling height of 4.5 m is as prefera- ble as 3.5 m. This is clearly not the case, as the latter violates the regulations. As a second example, suppose the client suggests a minimal floor area of 40 000 sq.m. Surely some tolerance is desirable, so that a facility of 39 800 sq.m is still matched. Both these aspects, the asymmetry and the imprecision, are modelled by the fuzziness function. Note that this function is tailored to the attribute and varies, based on whether the user specifies a minimum or max- imum value. In some cases this function can also simply be a constantω fω_i (x)=1. Figure 4 illustrates a possible fuzziness function for the ceiling height example, where ω is the standard deviation of the existing vacant facility at- tributes’ values. As the normalized difference approaches -ω the weighted dis- tance will approach infinity, rendering this facility unsuitable for selection due to the large distance, whereas when the requirement is met, the function trends towards zero, diminishing the weighted distance. difference between actual and desired p e n a lt y v a lu e f(x) = σ x + σ Figure 4. A possible fuzziness function for a minimum value attribute. C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 197  3.5 Monetary based negotiation As discussed in the previous section, some attribute requirements might not be fulfilled completely by means of the selection process. It is important to realize that the method presented in this paper entails an iterative negoti- ation process between the demand and supply parties. Unmet requirements are not necessarily insurmountable. For example in the case of the attribute sketched in Figure 4, it is easy to see that by architectural remodelling the two spaces can be made more equivalent, a transformation that comes with a monetary cost. Therefore, the selection process lead by the client can be monetary based, with many of the attribute differences represented as mon- etary measures. For each of the suitable vacant facilities suggested to the cus- tomer, a monetary indicator can be composed that reflects the acquisition, renovation and maintenance costs and can also incorporate costs related to geospatial business risks like crime rates and employment conditions. The redevelopment costs can be tailored to a specific client, industry and spatial context. Thus, the prospective owner is able to select amongst the vacant fa- cilities and can compare the associated costs with the projected costs for es- tablishing a newly built facility by means of a unified monetary measure.  3.6 Proof-of-concept In order to prove the concept of our system, Dutch data sources are ex- plored to give insights to future users where to obtain data (See Appendix A). Since GIS is commonly used in the field of Geodesign, calculating and extract- ing data from ArcMap is not discussed here. B-1.1 B-1.2 B-1.3 B-1.4 B-1.5 Net floor area Restroom floor area Architectural openness Building height Ceiling height 1 79.596 3.752 4.568 3.920 2.300 2 97.270 3.348 5.140 5.530 2.480 3 4648.428 16.727 42.623 12.000 3.800 4 2266.660 102.080 8.303 12.000 3.000 5 909.811 206.849 8.403 10.740 2.780 6 5100.000 245.442 46.331 11.700 3.700 7 11728.758 105.826 16.730 20.000 4.000 8 477.682 54.844 7.294 9.450 3.200 9 6935.799 910.335 13.581 10.250 4.680 10 3582.008 384.390 8.703 7.925 4.267 1 2 3 4 5 6 7 8 9 10 Figure 5. A demonstration of automatically extracting attributes from IFC building information models. R IU S 4 : G EO -D ESIG N 198 In order to demonstrate the feasibility of automatically extracting at- tributes from IFC building information models, a list of such attributes is presented for a selection of publicly available IFC models . A graphical in- terpretation of this data is shown as well (Figure 5). This plot uses a mul- ti-dimensional scaling algorithm that tries to preserve relative distances of the samples while embedding their multi-dimensional space into the ren- dered two-dimensional plane. As such, similar buildings are presented close to each other. Attributes pertaining to building areas are either to be found in IfcPropertySets on an IfcBuilding level, or if absent, aggregated over the var- ious IfcSpaces in the model. Ceiling height and building height are deduced from the Elevations of the IfcBuildingStoreys. Architectural openness is com- puted from the solid volumes of the IfcRepresentationItems of IfcSpaces and IfcWalls, which are computed by an open source software toolkit for IFC files, called IfcOpenShell . With the attributes extracted from the confluence of GIS and BIM, they can be incorporated into the proposed system for selection by end-users. Figure 6. A screenshot of the proposed framework outlining the steps involved of using the system. C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 199 Figure 6 shows a screenshot of a proof-of-concept implementation of the system discussed in this article. It outlines the procedure that the end-user will follow when using the system to get suggested facilities based on a digital building model of his required facility. At the first step, the potential users are asked to upload their desired fa- cility BIM model to the system so that several of the attribute values can be automatically extracted by the system. In case no building model is available, the user can complete the list of attributes manually. If the building model is georeferenced, geospatial and demographic information is extracted from the GIS seamlessly, otherwise a geolocation needs to be specified by the user to guide the same process. Secondly, the user can apply weights to these identified attributes and curate the extracted values where necessary. The user is able to indicate whether these values signify minimum, maximum or approximate values, which is reflected in the weighting function explained above. Based on the combined attributes extracted from BIM and GIS, the sys- tem composes the demand profile and calculates the distance with each of the available vacant facilities by applying the formula outlined above. The most similar vacant facilities are presented. The vacant facility information is pro- vided by the supply party of industrial facilities. This includes building in- formation models and geospatial characteristics. Even though policy makers realize the importance of building information models, there is are not many publicly available BIM models. Conclusively, in an iterative design process the user can evaluate the at- tributes once more by assessing their impacts on the projected expenses of acquiring the facility and adapting it to match the initial requirements. Note that not only costs associated to the building itself are estimated, but differ- ences in the geospatial attributes are reflected in the evaluation as well, as they represent risks associated with the demographic and spatial character- istics of the target area. 4. DISCUSSION AND CONCLUSIONS Facing a severe vacancy of industrial facilities, a challenge is evident to solve negligent depletion of land resources. In this paper, a framework is pre- sented to match existing vacant facilities to the requirements of potential customers to promote sustainable redevelopment with possible private in- vestors involved. The concept of Geodesign acts as a framework for iterative evaluation on multiple scale levels and can be formally initiated after stake- holders have used the proposed framework to identify an area for redevelop- ment. In order to facilitate the evaluation of attributes that have an asymmet- ric nature, the framework applies concepts from fuzzy theory. In addition, a simple straightforward monetary based evaluation paradigm is presented for R IU S 4 : G EO -D ESIG N 200 the purpose of facility reuse. The proposed framework complements the set of tools that concurrently Geodesign professionals have at their disposal. To conceptualize this framework, a literature study has been conducted to identify important attributes for facility reuse, both on building level and on regional level. In this way, geospatial information and building information can be seamlessly combined. These attributes are classified based on their scale, source and type. For integrating geospatial information and building information, a theoretical background is illustrated considering data formats, integration possibilities and the preferred way of integration by extracting relevant attributes without interoperability problems. In the appendix, pos- sible data sources and ways to extract or calculate attribute values for Dutch cases are presented. The implementation of extraction proves the feasibility of the approach suggested in this paper. The framework is designed to provide a comprehensive and effective way to stimulate facility reuse. The methodology outlined in this paper is of a gen- eral nature and relevant to various situations. Nevertheless, several aspects are still to be addressed in future research.The way to determine vacancy of a facility varies based on local situations and regulations. It can be estimated by means of employment rate or the usage percentage of floor area, but general unified measures of required detail are not always present. Thus, a pre-con- sultation with supply parties is essential. In general, at this moment, implementing this framework on a global scale is not feasible due to the fragmentation of data sources and regulations. This is not necessarily detrimental for a further investigation of this concept, as aspiring owners of facilities are likely to have a clear target location in mind to house their facility before consulting this system. From the proof-of-con- cept implementation presented in the previous section, it can be seen that this framework and system can be modified and applied in other areas once the data are available. Therefore, it is possible in future research to verify and validate the applicability by means of a case study in a specific area with ac- tual stakeholders, preferably with the help from supply parties and local gov- ernments to acquire the necessary data. Moreover, the Geodesign concept helps in understanding the facilities and their incorporation in their social and geographic context. Following this methodology, the facilities are not isolated from the environment and alter- natives are evaluated based on the importance that stakeholders have defined not only on a building level, but also pertaining to the building plot and its ge- ospatial relations. In addition to the monetary evaluation, a more visual rep- resentation of the difference in attributes can be presented, based on future research that might appeal more to people with a design background. As have been illustrated, the framework not only initiates the Geodesign process by providing possible facilities for reuse, but also provides guidelines for further C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 201 iterative evaluation and assessment of various reuse designs in a geospatial environment. Though current evaluation is solely monetary-based, further research can be performed for more holistic evaluation according to future user requirements. As discussed in the section on data acquisition, common guidelines about BIM and GIS data interoperability can be provided to the future stakeholders so that systems such as these, that build on a combination of the two sources of information, but also other efforts related to BIM and GIS harmonization, can flourish. R IU S 4 : G EO -D ESIG N 202 ACKNOWLEDGEMENTS This study is partially supported by the Chinese Scholarship Council. We would like to thank others who have helped us during this process as well. REFERENCES Abudeif, A. M., Abdel Moneim, A. A., & Farrag, A. F. (2015). Multicriteria decision analysis based on analytic hierarchy process in GIS environment for siting nuclear power plant in Egypt. Annals of Nuclear Ener- gy, 75, 682-692. doi:10.1016/j.anucene.2014.09.024 Beg, I., & Ashraf, S. (2009). Similarity measures for fuzzy sets. Applied Mathematics and Computation, 8(2), 192-202. Blokhuis, E. (2010). Governing Multi-Actor Decision Processes in Dutch Industrial Area Redevelopment. Eindhoven University of Technology. Bottom, C., McGreal, S., & Heaney, G. (1998). The suitability of premises for business use: an evaluation of supply/demand variations. Property Management, 16, 134-144. doi:10.1108/02637479810232934 Chaudhuri, B. B., & Rosenfeld, A. (1996). On a metric distance between fuzzy sets. Pattern Recognition Letters, 17, 1157-1160. Das, J., Leng, L. E., Lee, P., & Kiat, T. C. (Eds.). (2011). The BIM Issue. Building Smart. De Laat, R., & Van Berlo, L. (2011). Integration of BIM and GIS: The Development of the CityGML GeoBIM Extension. In T. H. Kolbe, G. König, & C. Nagel (Eds.), Advances in 3D Geo-Information Sciences SE - 13 (pp. 211-225). Springer Berlin Heidelberg. doi:10.1007/978-3-642-12670-3_13 EI-Mekawy, M., Ostman, A., & Hijazi, I. (2012). An Evaluation of IFC-CityGML Unidirectional Conversion. International Journal of Advanced Computer Science and Applications, 3(5), 159-171. Geraedts, R. P., & Van der Voordt, D. J. M. (2003). Offices for living in: An instrument for measuring the potential for transforming offi ces into homes. Open House International, 28(3), 80-90. Glumac, B. (2012). Strategic Decision Modeling in Brownfield Redevelopment. Eindhoven University of Technology. Isikdag, U., & Zlatanova, S. (2009). Towards Defining a Framework for Automatic Generation of Buildings in CityGML Using Building Information Models. In J. Lee & S. Zlatanova (Eds.), 3D Geo-Information Sciences SE - 6 (pp. 79-96). Springer Berlin Heidelberg. doi:10.1007/978-3-540-87395-2_6 Korteweg, P. J. (2002). Veroudering van kantoorgebouwen: probleem of uitdaging? Nederlandse Geo- grafische Studies McElvaney, S. (2012). Geodesign: Case Studies in Regional and Urban Planning. ESRI. Mignard, C., & Nicolle, C. (2014). Computers in Industry Merging BIM and GIS using ontologies application to urban facility management in ACTIVe3D. Computers in Industry, 65(9), 1276-1290. doi:10.1016/j. compind.2014.07.008 Ministerie van Infrastructuur en Milieu (Ministry of Infrastructure and Environment) (2013). Ibis werklo- caties de stand van zaken in planning en uitgifte van werklocaties op 1 jan. 2013 en de uitgifte in 2012. Montes, S. (2007). Distance between fuzzy sets as a fuzzy quantity. Mathematics, 14, 41-49. Nagel, C., Stadler, A., & Kolbe, T. (2007). Conversion of IFC to CityGML. Paris. Panichelli, L., & Gnansounou, E. (2008). GIS-based approach for defining bioenergy facilities location: A case study in Northern Spain based on marginal delivery costs and resources competition between facilities. Biomass and Bioenergy, 32(4), 289-300. doi:10.1016/j.biombioe.2007.10.008 Remøy, H. T. (2010). Out of office. A Study on the Cause of Office Vacancy and Transformation as a Means to Cope and Prevent. IOS Press. Rich, S., & Davis, K. H. (2010). Geographic Information Systems (GIS) for Facility Management. Solutions, 55. Retrieved from http://www.ifmafoundation.org/documents/public/GIS_WP_FINAL.pdf Rikalovic, A., Cosic, I., & Lazarevic, D. (2014). GIS Based Multi-criteria Analysis for Industrial Site Selection. Procedia Engineering, 69, 1054-1063. doi:10.1016/j.proeng.2014.03.090 C O M B IN IN G G IS A N D B IM FO R FA C ILIT Y R EU SE: A P R O FILIN G A P P R O A C H 203 Shen, W., Hao, Q., & Xue, Y. (2012). A loosely coupled system integration approach for decision support in facility management and maintenance. Automation in Construction, 25, 41-48. doi:10.1016/j.aut- con.2012.04.003 Shipley, R., Utz, S., & Parsons, M. (2006). Does Adaptive Reuse Pay? A Study of the Business of Build- ing Renovation in Ontario, Canada. International Journal of Heritage Studies, 12(6), 505-520. doi:10.1080/13527250600940181 Steinitz, C. (2012). A Framework for Geodesign: Changing Geography by Design. ESRI. Stichting Real Estate Norm Nederland (1992). Real Estate Norm. Van der Voordt, D. J. M. & Van Wegen, H. B. (2005). Architecture In Use: An Introduction to the Program- ming, Design and Evaluation of Buildings. Routledge. Volk, R., Stengel, J., & Schultmann, F. (2014) Building Information Modeling (BIM) for existing buildings. Literature review and future needs. Automation in construction, 38, 109-127. Zeiss, G. (2014). Bridging Demand-Supply Gap. Amsterdam. Zhang, F., Johnson, D. M., & Sutherland, J. W. (2011). A GIS-based method for identifying the optimal location for a facility to convert forest biomass to biofuel. Biomass and Bioenergy, 35(9), 3951-3961. doi:10.1016/j.biombioe.2011.06.006 R IU S 4 : G EO -D ESIG N 204