AN EXPERIMENT ON DISTANCE METRICS USED FOR ROAD MATCHING IN DATA INTEGRATION

Decision makers and researchers need datasets from different sources to analyze, combine, or create new spatial datasets. The same entity may be represented with different geometries, topologies, and attributes in different datasets due to differences in production, such as projection, scale, accuracy, purpose, and date. The geometries, topologies, and attributes of objects are often used when combining and integrating the datasets from different sources. Matching spatial datasets is one of the most important phases of data integration. Many algorithms have been developed to match datasets using several parameters inspired by geometric, topological, and attribute similarities. They generally find the similarities between objects in different datasets and create relations between each object in order to analyze, combine, update, and transfer data. The differences in geometries, topologies, and attributes make the matching process difficult. The research problem is the critical selection of similarity parameters to ensure the satisfactory matching results. The scope of this paper was limited with distance metrics. In this study, it was aimed to determine the suitable distance metrics measured from point to point and from point to line, which are widely used as parameters in road matching. Two road datasets in different databases were automatically matched using these metrics by employing a plugin of an open desktop software. Automatic matching results were compared to manual matching results to determine the success of each matching process. Consequently, it was shown that none of these metrics for road matching was adequate on its own. However, the distance between centroids of roads and Hausdorff distances were more satisfactory.

Keywords:

Distance metrics, matching, integration conflation, road network,

PDF

___

[1] Mustière, S. and Devogele, T., (2008). “Matching Networks with Different Levels of Detail”, GeoInformatica, 12(4): 435-453.
[2] Zhang, M., (2009). Methods and Implementations of Road-Network Matching. Ph. D. Thesis, Technical University of Munich, Münih.
[3] Yuan, S. and Tao, C., (1999). “Development of Conflation Components”, The Proceedings of Geoinformatics'99 Conference, 19-21 June 1999, Ann Arbor, 1-13.
[4] Lynch, M.P. and Saalfeld, A., (1985). “Conflation: Automated Map Compilation—A Video Game Approach”, Auto-Carto VII, 11-14 March 1985, Washington, D.C.
[5] Rosen, B. and Saalfeld, A., (1985). “Match Criteria for Automatic Alignment” Auto-Carto VII, 11-14 March 1985, Washington, D.C.
[6] Lupien, A. E. and Moreland, W.H., (1987). “A General Approach to Map Conflation”, Auto-Carto VIII, 29 Mart- 3 April 1987, Baltimore.
[7] Saalfeld, A., (1988). “Conflation: Automated Map Compilation”, International Journal of Geographical Information System, 2(3): 217-228.
[8] Deretsky, Z. and Rdony, U., (1993). “Automatic Conflation of Digital Maps”, In Vehicle Navigation and Information Systems Conference, October 1993, Ottawa.
[9] Cobb, M.A., Chung, M.J., Foley III, H., Petry, F.E., Shaw, K.B. and Miller, H.V., (1998). “A Rule-Based Approach for the Conflation of Attributed Vector Data”, GeoInformatica, 2(1): 7-35.
[10] Walter, V. and Fritsch, D., (1999). “Matching Spatial Data Sets: A Statistical Approach”, International Journal of Geographical Information Science, 13(5): 445-473.
[11] Kang, H., (2002). Analytical Conflation of Spatial Data from Municipal and Federal Government Agencies, Ph. D. Thesis, the Ohio State University, Ohio.
[12] Xiong, D. and Sperling, J., (2004). “Semiautomated Matching for Network Database Integration”, ISPRS Journal of Photogrammetry and Remote Sensing, 59(1): 35-46.
[13] Samal, A., Seth, S. and Cueto, K., (2004). “A Feature-Based Approach to Conflation of Geospatial Sources”, International Journal of Geographical Information Science, 18(5): 459-489.
[14] Zhang, M. and Meng, L., (2007). “An Iterative Road-Matching Approach for The Integration of Postal Data”, Computers, Environment and Urban Systems, 31(5): 597-615.
[15] Olteanu-Raimond, A.M. and Mustière, S., (2008).“Data Matching—A Matter of Belief”, Proceedings of the International Symposium on Spatial Data Handling, 23–25 June 2008, Montpellier, 501-19.
[16] Olteanu-Raimond, A.M., Mustière, S. and Ruas, A., (2015). “Knowledge Formalisation for Vector Data Matching Using Belief Theory”, Journal of Spatial Information Science, 10: 21-46.
[17] Kim, J.O., Yu, K., Heo, J. and Lee, W.H., (2010). “A New Method for Matching Objects in Two Different Geospatial Datasets Based on the Geographic Context”, Computers & Geosciences, 36(9): 1115-1122.
[18] Li, L., (2010). Design of A Conceptual Framework and Approaches for Geo-Object Data Conflation, Ph. D. Thesis, University of California, Santa Barbara.
[19] Li, L. and Goodchild, M.F., (2011). “An Optimisation Model for Linear Feature Matching in Geographical Data Conflation”, International Journal of Image and Data Fusion, 2(4): 309-328.
[20] Song, W., Keller, J.M., Haithcoat, T.L. and Davis, C.H., (2011). “Relaxation‐Based Point Feature Matching for Vector Map Conflation”, Transactions in GIS, 15(1): 43-60.
[21] Pourabdollah, A., Morley, J., Feldman, S. and Jackson, M., (2013). “Towards An Authoritative Openstreetmap: Conflating OSM and OS Opendata National Maps’ Road Network”, ISPRS International Journal of Geo-Information, 2(3): 704-728.
[22] Yang, W., Lee, D. and Ahmed, N., (2014). “Pattern Based Feature Matching for Geospatial Data Conflation”, GEOProcessing, March 2014, Barcelona.
[23] Bierlaire, M., Chen, J., and Newman, J., (2013). “A probabilistic map matching method for smartphone GPS data”, Transportation Research Part C: Emerging Technologies, 26: 78-98.
[24] Fan, H., Yang, B., Zipf, A., and Rousell, A., (2016). “A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data”, International Journal of Geographical Information Science, 30(4): 748-764.
[25] Kang, B., Scully, J. Y., Stewart, O., Hurvitz, P. M., and Moudon, A. V., (2015). “Split-Match-Aggregate (SMA) algorithm: integrating sidewalk data with transportation network data in GIS”, International Journal of Geographical Information Science, 29(3): 440-453.
[26] Michaud M., MatchingPlugIn Tutorial for Version 0.7.2., [Internet] http://sourceforge.net/projects/jump- pilot/files/OpenJUMP_plugins/More%20Plugins/Matching%20PlugIn/MatchingPlugIn0.7.2.pdf/download, [Accessed on 15.03.2016].
[27] Global Administrative Areas, GADM database, California, USA, [Internet] http://www.gadm.org/, [Accessed on 10.08.2016]
[28] Hacar, M., (2015). Mekânsal Veri Altyapilarinda Geometrik Entegrasyon, M. Sc. Thesis, Yıldız Technical University, İstanbul.
[29] Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.

Sigma Mühendislik ve Fen Bilimleri Dergisi-Cover

ISSN: 1304-7191
Yayın Aralığı: Yılda 4 Sayı
Yayıncı: Yıldız Teknik Üniversitesi

Arşiv