{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T12:15:45Z","timestamp":1762604145029,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T00:00:00Z","timestamp":1542412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["SATREPS"],"award-info":[{"award-number":["SATREPS"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on origin-and-destination points with crossing lines, which reduce the map\u2019s readability and overall appearance. Today, with the emergence of geolocation-enabled handheld devices with wireless communication and networking capabilities, human mobility and the resulting events can be captured and stored as text-based geospatial big data. In this paper, we used one-week mobile-call-detail records (CDR) and a GIS road network model to estimate hourly link population and flow directions, based on mobile-call activities of origin\u2013destination pairs with a shortest-path analysis for the whole city. Moreover, to gain the actual population size from the number of mobile-call users, we introduced a home-based magnification factor (h-MF) by integrating with the national census. Therefore, the final output link data have both magnitude (actual population) and flow direction at one-hour intervals between 06:00 and 21:00. The hourly link population and flow direction dataset are intended to optimize bus routes, solve traffic congestion problems, and enhance disaster and emergency preparedness.<\/jats:p>","DOI":"10.3390\/ijgi7110449","type":"journal-article","created":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T11:23:27Z","timestamp":1542799407000},"page":"449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Estimation of Hourly Link Population and Flow Directions from Mobile CDR"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2750-3260","authenticated-orcid":false,"given":"Ko Ko","family":"Lwin","sequence":"first","affiliation":[{"name":"Human Centered Urban Informatics, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihide","family":"Sekimoto","sequence":"additional","affiliation":[{"name":"Human Centered Urban Informatics, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9138-6601","authenticated-orcid":false,"given":"Wataru","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Remote Sensing of Environment and Disaster, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"key":"ref_1","unstructured":"Chen, C., Zhang, D., Zhou, Z.H., Li, N., Atmaca, T., and Li, S. 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