{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T03:14:11Z","timestamp":1777086851209,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T00:00:00Z","timestamp":1565654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["2017YFB0504102"],"award-info":[{"award-number":["2017YFB0504102"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771537"],"award-info":[{"award-number":["41771537"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["none"],"award-info":[{"award-number":["none"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Complex natural disasters often cause people to suffer hardships, and they can cause a large number of casualties. A population that has been affected by a natural disaster is at high risk and desperately in need of help. Even with the timely assessment and knowledge of the degree that natural disasters affect populations, challenges arise during emergency response in the aftermath of a natural disaster. This paper proposes an approach to assessing the near-real-time intensity of the affected population using social media data. Because of its fatal impact on the Philippines, Typhoon Haiyan was selected as a case study. The results show that the normalized affected population index (NAPI) has a significant ability to indicate the affected population intensity. With the geographic information of disasters, more accurate and relevant disaster relief information can be extracted from social media data. The method proposed in this paper will benefit disaster relief operations and decision-making, which can be executed in a timely manner.<\/jats:p>","DOI":"10.3390\/ijgi8080358","type":"journal-article","created":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T03:59:26Z","timestamp":1565755166000},"page":"358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Changxiu","family":"Cheng","sequence":"first","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China"}]},{"given":"Ting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China"}]},{"given":"Kai","family":"Su","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1714-779X","authenticated-orcid":false,"given":"Peichao","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9126-229X","authenticated-orcid":false,"given":"Shi","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1007\/s11069-018-3279-y","article-title":"Spatial distribution patterns of global natural disasters based on biclustering","volume":"92","author":"Shen","year":"2018","journal-title":"Nat. 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