{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T17:49:19Z","timestamp":1732038559137},"reference-count":26,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:00:00Z","timestamp":1568246400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transactions in GIS"],"published-print":{"date-parts":[[2019,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Spatial data conflation involves the matching and merging of counterpart features in multiple datasets. It has applications in practical spatial analysis in a variety of fields. Conceptually, the feature\u2010matching problem can be viewed as an optimization problem of seeking a match plan that minimizes the total discrepancy between datasets. In this article, we propose a powerful yet efficient optimization model for feature matching based on the classic network flow problem in operations research. We begin with a review of the existing optimization\u2010based methods and point out limitations of current models. We then demonstrate how to utilize the structure of the network\u2010flow model to approach the feature\u2010matching problem, as well as the important factors for designing optimization\u2010based conflation models. The proposed model can be solved by general linear programming solvers or network flow solvers. Due to the network flow formulation we adopt, the proposed model can be solved in polynomial time. Computational experiments show that the proposed model significantly outperforms existing optimization\u2010based conflation models. We conclude with a summary of findings and point out directions of future research.<\/jats:p>","DOI":"10.1111\/tgis.12561","type":"journal-article","created":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T07:35:41Z","timestamp":1568273741000},"page":"1152-1176","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Optimal spatial data matching for conflation: A network flow\u2010based approach"],"prefix":"10.1111","volume":"23","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2385-9128","authenticated-orcid":false,"given":"Ting","family":"Lei","sequence":"first","affiliation":[{"name":"Department of Geography and Atmospheric Science University of Kansas Lawrence Kansas"}]},{"given":"Zhen","family":"Lei","sequence":"additional","affiliation":[{"name":"Automation School, Wuhan University of Technology Wuhan China"}]}],"member":"311","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2016.44"},{"key":"e_1_2_6_3_1","volume-title":"GIS conflation using open source tools","author":"Blasby D.","year":"2002"},{"key":"e_1_2_6_4_1","volume-title":"OWS\u20105 conflation engineering report","author":"Brennan P.","year":"2008"},{"key":"e_1_2_6_5_1","volume-title":"Proceedings of the 1995 Geographic Information Systems for Transportation Symposium","author":"Brown J. 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