Abstract
Many planning support systems and, indeed, some ‘smart city’ initiatives begin with time consuming efforts to integrate cross-agency data describing current conditions in sufficient detail to support ‘what-if’ exploration of urban development options. Integrating data from different sources has become increasingly challenged as available datasets, and the relevant urban modeling efforts, become more disaggregated and spatial-temporally detailed. Open data initiatives, with unprecedented amounts of embedded georeferenced information, have made web services and crowdsourcing attractive. However, data from such sources are typically imperfect and their integration is complicated by syntactic and semantic differences. We develop an ontology-based data integration mechanism to fuse data from different sources in generic ways that can utilize semantic information to minimize the labor involved and facilitate updating as new data are acquired. As a test application, we evaluate, filter, adjust, and integrate building information from heterogeneous data sources for use in an agent-based microsimulation model of transportation and land-use dynamics in Singapore. Third-party data about building size, age and use added substantial value to the official datasets generally available from government agencies.
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Balmer, M., Rieser, M., Meiste, K., Charypar, D., Lefebvre, N., Nagel, K., & Axhausen, K. (2009). MATSim-T: Architecture and simulation times. In A. L. C. Bazzan (Ed.), Multi-agent systems for traffic and transportation engineering (pp. 57–78). Hershey, PA: Information Science Reference, IGI Publishing.
Ben-Akiva, M., Koutsopoulos, H. N., Antoniou, C., & Balakrishna, R. (2010). Traffic simulation with DynaMIT. In J. Barceló (Ed.), Fundamentals of traffic simulation (pp. 363–398). New York: Springer.
Benslimane, D., Leclercq, E., Savonnet, M., Terrasse, M. N., & Yétongnon, K. (2000). On the definition of generic multi-layered ontologies for urban applications. Computers, Environment and Urban Systems, 24(3), 191–214.
Birkin, M., & Clarke, M. (1988). SYNTHESIS—A synthetic spatial information system for urban and regional analysis: methods and examples. Environment and Planning A, 20(12), 1645–1671.
Fonseca, F. T., Egenhofer, M. J., Agouris, P., & Câmara, G. (2002). Using ontologies for integrated geographic information systems. Transactions in GIS, 6(3), 231–257.
Frank, A. U. (1997). Spatial ontology: A geographical information point of view. In O. Stock (Ed.), Spatial and temporal reasoning (pp. 135–153). Netherlands: Springer.
Hendler, J. (2009). Web 3.0 Emerging. Computer, 42(1), 111–113.
Horridge, M., Knublauch, H., Rector, A., Stevens, R., & Wroe, C. (2004). A practical guide to building OWL Ontologies using the Protegé-OWL plugin and CO-ODE tools, edition 1.3, University of Manchester. Accessed February 28, 2015. https://mariaiulianadascalu.files.wordpress.com/2014/02/owl-cs-manchester-ac-uk_-eowltutorialp4_v1_3.pdf.
Knublauch, H., Fergerson, R. W., Noy, N. F., & Musen, M. A. (2004). The Protégé OWL plugin: An open development environment for semantic web applications. In S. A. McIlraith, D. Plexousakis, & F. V. Harmele (Eds.), The semantic Web–ISWC 2004 (pp. 229–243). Berlin, Heidelberg: Springer.
Kokla, M. (2006). Guidelines on geographic ontology integration. In Proceedings of the ISPRS Technical Commission II Symposium, Vienna, Austria.
Mars, N. J. I. (1995). What is an ontology? In A. Goodall (Ed.), The impact of ontologies on reuse, interoperability, and distributed processing (pp. 9–19). Uxbridge, Middlesex: Unicom Seminars Limited.
Nathalie, A. (2009). Schema matching based on attribute values and background ontology. In Proceedings of 12th AGILE International Conference on Geographic Information Science (Vol. 1, No. 1, pp. 1–9).
Partridge, C. (2002). The role of ontology in integrating semantically heterogeneous databases. Rapport technique, 5(02). Accessed February 28, 2015. http://www.borosolutions.co.uk/research/content/files/O-02-SE.pdf.
Ramos, J. M., Vandecasteele, A., & Devillers, R. (2013). Semantic integration of authoritative and volunteered geographic information (VGI) using ontologies. In Proceeding of Association of Geographic Information Laboratories for Europe (AGILE) Conference 2013, Leuven.
Salvini, P. A., & Miller, E. J. (2005). ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and Spatial Economics, 5, 217–234.
Teller, J., Keita, A. K., Roussey, C., & Laurini, R. (2007). Urban ontologies for an improved communication in urban civil engineering projects. Presentation of the COST Urban Civil Engineering Action C21“TOWNTOLOGY”. Cybergeo: European Journal of Geography. doi:10.4000/cybergeo.8322.
Tudorache, T., Noy, N. F., Tu, S., & Musen, M. A. (2008). Supporting collaborative ontology development in Protégé. In A. P. Sheth, S. Staab, M. Paolucci, D. Maynard, T. Finin, & K. Thirunarayan (Eds.), The semantic Web—ISWC 2008 (pp. 17–32). Berlin Heidelberg: Springer.
W3C Semantic Web Activity (2013). Accessed February 28, 2015. http://www.w3.org/2001/sw/.
Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314.
Zhu, Y. (2014). Spatiotemporal learning and geo-visualization methods for constructing activity-travel patterns from transit card transaction data. PhD Dissertation, Massachusetts Institute of Technology, DSpace: http://hdl.handle.net/1721.1/93807.
Zhu, Y., & Ferreira, Jr J. (2014). Synthetic population generation at disaggregated spatial scales for land use and transportation microsimulation. In Transportation Research Record, (Vol. 2429, pp. 168–177).
Ziegler, P., & Dittrich, K. R. (2004). Three decades of data integration-all problems solved? In 18th IFIP World Computer Congress, Volume 12, Building the Information Society (pp. 3–12). Dordrecht: Kluwer Academic Publishers.
Acknowledgments
This chapter draws on a portion of the PhD Dissertation of Yi Zhu, submitted to the Massachusetts Institute of Technology (Zhu 2014). We acknowledge the Singapore Land Authority and other public and private entities who have provided the datasets used in this research. We also acknowledge helpful comments and suggestions from the editors and reviewers and partial support of the Singapore National Research Foundation through the Future Urban Mobility program of the Singapore-MIT Alliance for Research and Technology (SMART) Center.
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Zhu, Y., Ferreira, J. (2015). Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations. In: Geertman, S., Ferreira, Jr., J., Goodspeed, R., Stillwell, J. (eds) Planning Support Systems and Smart Cities. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-18368-8_7
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DOI: https://doi.org/10.1007/978-3-319-18368-8_7
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