{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:10:37Z","timestamp":1766067037898,"version":"build-2065373602"},"reference-count":34,"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":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1508901"],"award-info":[{"award-number":["2018YFC1508901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant challenge. In this paper, a spatial distribution detection method is established using emergency information based on the urgency degree grading of microblogs and spatial autocorrelation analysis. Moreover, a character-level convolutional neural network classifier is applied for microblog classification in order to mine the spatio-temporal change process of emergency rescue information. The results from the Jiuzhaigou (Sichuan, China) earthquake case study demonstrate that different emergency information types exhibit different time variation characteristics. Moreover, spatial autocorrelation analysis based on the degree of text urgency can exclude uneven spatial distribution influences of the number of microblog users, and accurately determine the level of urgency of the situation. In addition, the classification and spatio-temporal analysis methods combined in this study can effectively mine the required emergency information, allowing us to understand emergency information spatio-temporal changes. Our study can be used as a reference for microblog information applications within the field of emergency rescue activity.<\/jats:p>","DOI":"10.3390\/ijgi8080359","type":"journal-article","created":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T03:59:26Z","timestamp":1565755166000},"page":"359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the \u201c8.8\u201d Jiuzhaigou Earthquake"],"prefix":"10.3390","volume":"8","author":[{"given":"Ziyao","family":"Xing","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7608-0797","authenticated-orcid":false,"given":"Xiaohui","family":"Su","sequence":"additional","affiliation":[{"name":"School of Information Science &amp; Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junming","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8726-5858","authenticated-orcid":false,"given":"Wei","family":"Su","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-4973","authenticated-orcid":false,"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chaoxu, X., Gaozhong, N., Xiwei, F., Junxue, Z., and Xiaoke, P. (2019). Research on the application of mobile phone location signal data in earthquake emergency work: A case study of Jiuzhaigou earthquake. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215361"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yates, D., and Paquette, S. (2010, January 22\u201327). Emergency knowledge management and social media technologies: A case study of 126 the 2010 Haitian earthquake. Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in 127 an Information Ecosystem-Volume 47, Pittsburgh, PA, USA.","DOI":"10.1002\/meet.14504701243"},{"key":"ref_3","first-page":"512","article-title":"Social Sensing: A New Approach to Understanding Our Socioeconomic Environments","volume":"127","author":"Yu","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s41324-016-0014-1","article-title":"Risk analysis and visualization for detecting signs of flood disaster in Twitter","volume":"24","author":"Kwon","year":"2016","journal-title":"Spat. Inf. Res."},{"key":"ref_5","first-page":"451","article-title":"Analysis on Macroscopic Anomalies before and after Lushan MS7.0 Earthquake based on Weibo","volume":"8","author":"Xiaohui","year":"2013","journal-title":"Technol. Earthq. Disaster Prev."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"De Longueville, B., Smith, R.S., and Luraschi, G. (2009, January 3). Omg, from here, i can see the flames!: A use case of mining location based social networks to acquire spatio-temporal data on forest fires. Proceedings of the 2009 International Workshop on Location Based Social Networks, Seattle, WA, USA.","DOI":"10.1145\/1629890.1629907"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.dss.2018.04.005","article-title":"Disaster early warning and damage assessment analysis using social media data and geo-location information","volume":"111","author":"Wu","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s11069-014-1217-1","article-title":"Using social media data to understand and assess disasters","volume":"74","author":"Guan","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sakaki, T., Okazaki, M., and Matsuo, Y. (2010, January 26\u201330). Earthquake shakes Twitter users: Real-time event detection by social sensors. Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA.","DOI":"10.1145\/1772690.1772777"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1016\/j.ajem.2013.08.018","article-title":"Competing to the rescue-leveraging social media for cross border collaboration in life-saving rescue operations","volume":"31","author":"Simon","year":"2013","journal-title":"Am. J. Emerg. Med."},{"key":"ref_11","first-page":"807","article-title":"Assessing the Effectiveness of Social Media Data in Mapping the Distribution of Typhoon Disasters","volume":"20","author":"Chunyang","year":"2018","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.ijdrr.2018.02.003","article-title":"Feasibility study of using crowdsourcing to identify critical affected areas for rapid damage assessment: Hurricane Matthew case study","volume":"28","author":"Yuan","year":"2018","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1007\/s11069-016-2484-9","article-title":"A new crowdsourcing model to assess disaster using microblog data in typhoon Haiyan","volume":"84","author":"Deng","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1007\/s11069-015-1918-0","article-title":"Understanding social media data for disaster management","volume":"79","author":"Xiao","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11069-016-2329-6","article-title":"Spatial, temporal, and content analysis of Twitter for wildfire hazards","volume":"83","author":"Wang","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qu, Y., Huang, C., Zhang, P., and Zhang, J. (2011, January 19\u201323). Microblogging after a major disaster in China: A case study of the 2010 Yushu earthquake. Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, Hangzhou, China.","DOI":"10.1145\/1958824.1958830"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.ijinfomgt.2018.02.003","article-title":"Emergency information diffusion on online social media during storm Cindy in US","volume":"40","author":"Kim","year":"2018","journal-title":"Int. J. Inform. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.3390\/ijgi4031549","article-title":"Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery","volume":"4","author":"Huang","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1007\/s11704-016-5198-y","article-title":"The power of comments: Fostering social interactions in microblog networks","volume":"10","author":"Wang","year":"2016","journal-title":"Front. Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gao, Q., Abel, F., Houben, G.F., and Yu, Y. (2012, January 16\u201320). A Comparative Study of Users\u2019 Microblogging Behavior on Sina Weibo and Twitter. Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization, Montreal, QC, Canada.","DOI":"10.1007\/978-3-642-31454-4_8"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Murzintcev, N., and Cheng, C. (2017). Disaster Hashtags in Social Media. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6070204"},{"key":"ref_22","first-page":"1","article-title":"Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment","volume":"8","author":"Resch","year":"2017","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/asi.10193","article-title":"NLPIR: A theoretical framework for applying natural language processing to information retrieval","volume":"54","author":"Zhou","year":"2003","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_24","unstructured":"(2019, August 02). Baidu Map API. Available online: https:\/\/lbsyun.baidu.com\/."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"General Office of the State Council (2019, August 12). National Earthquake Emergency Plan, Available online: http:\/\/www.gov.cn\/yjgl\/2012-09\/21\/content_2230337.htm.","DOI":"10.1515\/9781501511592-003"},{"key":"ref_26","unstructured":"Zhang, X., Zhao, J., and LeCun, Y. (2015). Character-level convolutional networks for text classification. Advances in Neural Information Processing Systems, Mit Press."},{"key":"ref_27","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.1538-4632.1992.tb00261.x","article-title":"The Analysis of Spatial Association by Use of Distance Statistics","volume":"24","author":"Getis","year":"1992","journal-title":"Geogr. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local indicators of spatial association\u2014LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_30","unstructured":"Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability, Chapman and Hall. [1st ed.]."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wubuli, A., Xue, F., Jiang, D., Yao, X., Upur, H., and Wushouer, Q. (2015). Socio-demographic predictors and distribution of pulmonary tuberculosis (TB) in Xinjiang, China: A spatial analysis. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0144010"},{"key":"ref_32","first-page":"233","article-title":"GIS based research on the spatial distribution of population density in illegal buildings in Shenzhen City","volume":"30","author":"Rui","year":"2018","journal-title":"Remote Sens. Land Resour."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1890\/ES14-00384.1","article-title":"Discriminating disturbance from natural variation with LiDAR in semi-arid forests in the southwestern USA","volume":"6","author":"Swetnam","year":"2015","journal-title":"Ecosphere"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Venturini, L., and Di Corso, E. (2017, January 11\u201314). Analyzing spatial data from twitter during a disaster. Proceedings of the 2017 IEEE International Conference on Big Data, Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258378"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/8\/359\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:10:52Z","timestamp":1760188252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/8\/359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,13]]},"references-count":34,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["ijgi8080359"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8080359","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2019,8,13]]}}}