{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:46:59Z","timestamp":1774680419634,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFF0704400"],"award-info":[{"award-number":["2021YFF0704400"]}]},{"name":"National Key Research and Development Program of China","award":["41971351"],"award-info":[{"award-number":["41971351"]}]},{"name":"National Key Research and Development Program of China","award":["42201438"],"award-info":[{"award-number":["42201438"]}]},{"name":"National Key Research and Development Program of China","award":["220100034"],"award-info":[{"award-number":["220100034"]}]},{"name":"National Key Research and Development Program of China","award":["2022M722930"],"award-info":[{"award-number":["2022M722930"]}]},{"name":"National Key Research and Development Program of China","award":["2021KFJJ06"],"award-info":[{"award-number":["2021KFJJ06"]}]},{"name":"National Nature Science Foundation of China Program","award":["2021YFF0704400"],"award-info":[{"award-number":["2021YFF0704400"]}]},{"name":"National Nature Science Foundation of China Program","award":["41971351"],"award-info":[{"award-number":["41971351"]}]},{"name":"National Nature Science Foundation of China Program","award":["42201438"],"award-info":[{"award-number":["42201438"]}]},{"name":"National Nature Science Foundation of China Program","award":["220100034"],"award-info":[{"award-number":["220100034"]}]},{"name":"National Nature Science Foundation of China Program","award":["2022M722930"],"award-info":[{"award-number":["2022M722930"]}]},{"name":"National Nature Science Foundation of China Program","award":["2021KFJJ06"],"award-info":[{"award-number":["2021KFJJ06"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["2021YFF0704400"],"award-info":[{"award-number":["2021YFF0704400"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["41971351"],"award-info":[{"award-number":["41971351"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["42201438"],"award-info":[{"award-number":["42201438"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["220100034"],"award-info":[{"award-number":["220100034"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["2022M722930"],"award-info":[{"award-number":["2022M722930"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["2021KFJJ06"],"award-info":[{"award-number":["2021KFJJ06"]}]},{"name":"China Postdoctoral Science Foundation","award":["2021YFF0704400"],"award-info":[{"award-number":["2021YFF0704400"]}]},{"name":"China Postdoctoral Science Foundation","award":["41971351"],"award-info":[{"award-number":["41971351"]}]},{"name":"China Postdoctoral Science Foundation","award":["42201438"],"award-info":[{"award-number":["42201438"]}]},{"name":"China Postdoctoral Science Foundation","award":["220100034"],"award-info":[{"award-number":["220100034"]}]},{"name":"China Postdoctoral Science Foundation","award":["2022M722930"],"award-info":[{"award-number":["2022M722930"]}]},{"name":"China Postdoctoral Science Foundation","award":["2021KFJJ06"],"award-info":[{"award-number":["2021KFJJ06"]}]},{"name":"Open Fund of the National Engineering Research Center for Geographic Information System","award":["2021YFF0704400"],"award-info":[{"award-number":["2021YFF0704400"]}]},{"name":"Open Fund of the National Engineering Research Center for Geographic Information System","award":["41971351"],"award-info":[{"award-number":["41971351"]}]},{"name":"Open Fund of the National Engineering Research Center for Geographic Information System","award":["42201438"],"award-info":[{"award-number":["42201438"]}]},{"name":"Open Fund of the National Engineering Research Center for Geographic Information System","award":["220100034"],"award-info":[{"award-number":["220100034"]}]},{"name":"Open Fund of the National Engineering Research Center for Geographic Information System","award":["2022M722930"],"award-info":[{"award-number":["2022M722930"]}]},{"name":"Open Fund of the National Engineering Research Center for Geographic Information System","award":["2021KFJJ06"],"award-info":[{"award-number":["2021KFJJ06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Volunteered geographic information (VGI) plays an increasingly crucial role in flash floods. However, topic classification and spatiotemporal analysis are complicated by the various expressions and lengths of social media textual data. This paper conducted applicability analysis on bidirectional encoder representation from transformers (BERT) and four traditional methods, TextRank, term frequency\u2013inverse document frequency (TF-IDF), maximal marginal relevance (MMR), and linear discriminant analysis (LDA), and the results show that for user type, BERT performs best on the Government Affairs Microblog, whereas LDA-BERT performs best on the We Media Microblog. As for text length, TF-IDF-BERT works better for texts with a length of &lt;70 and length &gt;140 words, and LDA-BERT performs best with a text length of 70\u2013140 words. For the spatiotemporal evolution pattern, the study suggests that in a Henan rainstorm, the textual topics follow the general pattern of \u201csituation-tips-rescue\u201d. Moreover, this paper detected the hotspot of \u201cMetro Line 5\u201d related to a Henan rainstorm and discovered that the topical focus of the Henan rainstorm spatially shifts from Zhengzhou, first to Xinxiang, and then to Hebi, showing a remarkable tendency from south to north, which was the same as the report issued by the authorities. We integrated multi-methods to improve the overall topic classification accuracy of Sina microblogs, facilitating the spatiotemporal analysis of flooding.<\/jats:p>","DOI":"10.3390\/ijgi12060240","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T10:52:15Z","timestamp":1686307935000},"page":"240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Applicability Analysis and Ensemble Application of BERT with TF-IDF, TextRank, MMR, and LDA for Topic Classification Based on Flood-Related VGI"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3202-5128","authenticated-orcid":false,"given":"Wenying","family":"Du","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Chang","family":"Ge","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Shuang","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3521-9972","authenticated-orcid":false,"given":"Nengcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6454-2963","authenticated-orcid":false,"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127882","DOI":"10.1016\/j.jclepro.2021.127882","article-title":"Damage Classification and Recovery Analysis of the Chongqing, China, Floods of August 2020 Based on Social-Media Data","volume":"313","author":"Tan","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, Q., Gao, Y., and Chen, Y. (2014, January 29\u201330). Study on Disaster Information Management System Compatible with VGI and Crowdsourcing. Proceedings of the IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), Ottawa, ON, Canada.","DOI":"10.1109\/WARTIA.2014.6976296"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1080\/17538947.2018.1563219","article-title":"Identifying Disaster-Related Tweets and Their Semantic, Spatial and Temporal Context Using Deep Learning, Natural Language Processing and Spatial Analysis: A Case Study of Hurricane Irma","volume":"12","author":"Sit","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"127053","DOI":"10.1016\/j.jhydrol.2021.127053","article-title":"Extracting the Location of Flooding Events in Urban Systems and Analyzing the Semantic Risk Using Social Sensing Data","volume":"603","author":"Zhang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/s11069-018-3427-4","article-title":"Real-Time Identification of Urban Rainstorm Waterlogging Disasters Based on Weibo Big Data","volume":"94","author":"Xiao","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.cageo.2017.11.008","article-title":"Hyper-Resolution Monitoring of Urban Flooding with Social Media and Crowdsourcing Data","volume":"111","author":"Wang","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.cageo.2017.10.010","article-title":"Geo-Social Media as a Proxy for Hydrometeorological Data for Streamflow Estimation and to Improve Flood Monitoring","volume":"111","author":"Abe","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/13658816.2017.1367003","article-title":"Social Media Analytics for Natural Disaster Management","volume":"32","author":"Wang","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1007\/s10796-021-10105-z","article-title":"A Methodology for Automatic Acquisition of Flood-event Management Information From Social Media: The Flood in Messinia, South Greece, 2016","volume":"23","author":"Arapostathis","year":"2021","journal-title":"Inf. Syst. Front."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s12942-020-00214-4","article-title":"Spatiooral Distribution of Negative Emotions on Twitter during Floods in Chennai, India, in 2015: A Post Hoc Analysis","volume":"19","author":"Karmegam","year":"2020","journal-title":"Int. J. Health Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102107","DOI":"10.1016\/j.ipm.2019.102107","article-title":"Automatic Identification of Eyewitness Messages on Twitter during Disasters","volume":"57","author":"Zahra","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Szczepanek, R. (2023). A Deep Learning Model of Spatial Distance and Named Entity Recognition (SD-NER) for Flood Mark Text Classification. Water, 15.","DOI":"10.3390\/w15061197"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, Y.T., Yang, M.D., Han, J.Y., Su, Y.F., and Jang, J.H. (2020). Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information. Remote Sens., 12.","DOI":"10.3390\/rs12040706"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104893","DOI":"10.1016\/j.cageo.2021.104893","article-title":"Disaster Damage Assessment Based on Fine-Grained Topics in Social Media","volume":"156","author":"Dou","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, W., and Xu, C. (2020, January 13\u201315). Microblog Text Classification System Based on TextCNN and LSA Model. Proceedings of the 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), Shenyang, China.","DOI":"10.1109\/ISCTT51595.2020.00090"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116562","DOI":"10.1016\/j.eswa.2022.116562","article-title":"Topic2Labels: A Framework to Annotate and Classify the Social Media Data through LDA Topics and Deep Learning Models for Crisis Response","volume":"195","author":"Wahid","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Han, X., Wang, J., Zhang, M., and Wang, X. (2020). Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17082788"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, P., Shi, H., Wu, X., and Jiao, L. (2021). Sentiment Analysis of Rumor Spread amid Covid-19: Based on Weibo Text. Healthcare, 9.","DOI":"10.3390\/healthcare9101275"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1080\/17538947.2019.1574316","article-title":"Deep Learning for Real-Time Social Media Text Classification for Situation Awareness\u2014Using Hurricanes Sandy, Harvey, and Irma as Case Studies","volume":"12","author":"Yu","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, T., Ye, X., Zhu, J., and Lee, J. (2016). Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm. Sustainability, 8.","DOI":"10.3390\/su8010025"},{"key":"ref_21","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 24). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA."},{"key":"ref_22","unstructured":"Hey, T., Keim, J., Koziolek, A., and Tichy, W.F. (September, January 31). NoRBERT: Transfer Learning for Requirements Classification. Proceedings of the IEEE 28th International Requirements Engineering Conference (RE), Zurich, Switzerland."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1080\/13658816.2017.1406943","article-title":"Mapping Spatiotemporal Patterns of Events Using Social Media: A Case Study of Influenza Trends","volume":"32","author":"Gao","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Han, X., and Wang, J. (2019). Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040185"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cheng, X., Han, G., Zhao, Y., and Li, L. (2019). Evaluating Social Media Response to Urban Flood Disaster: Case Study on an East Asian City (Wuhan, China). Sustainability, 11.","DOI":"10.3390\/su11195330"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","article-title":"FakeBERT: Fake News Detection in Social Media with a BERT-Based Deep Learning Approach","volume":"80","author":"Kaliyar","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"34046","DOI":"10.1109\/ACCESS.2022.3162614","article-title":"A Long-Text Classification Method of Chinese News Based on BERT and CNN","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.eswa.2016.03.045","article-title":"Ensemble of Keyword Extraction Methods and Classifiers in Text Classification","volume":"57","author":"Onan","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, X., and Wu, Q. (2013, January 22\u201325). Micro-Blog Commercial Word Extraction Based on Improved TF-IDF Algorithm. Proceedings of the IEEE International Conference of IEEE Region 10 (TENCON 2013), Xi\u2019an, China.","DOI":"10.1109\/TENCON.2013.6718884"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.ipm.2006.07.011","article-title":"Document Reranking by Term Distribution and Maximal Marginal Relevance for Chinese Information Retrieval","volume":"43","author":"Yang","year":"2007","journal-title":"Inf. Process. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101737","DOI":"10.1016\/j.ijdrr.2020.101737","article-title":"Tracking Spatio-Temporal Variation of Geo-Tagged Topics with Social Media in China: A Case Study of 2016 Hefei Rainstorm","volume":"50","author":"Wu","year":"2020","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kumar, S. (2012, January 4\u20136). Analyzing the Facebook Workload. Proceedings of the IEEE International Symposium on Workload Characterization (IISWC), La Jolla, CA, USA.","DOI":"10.1109\/IISWC.2012.6402911"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, W., and Zhao, J. (2016, January 8\u201310). TextRank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction. Proceedings of the 3rd International Conference on Information Science and Control Engineering (ICISCE), Beijing, China.","DOI":"10.1109\/ICISCE.2016.151"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1049\/cje.2019.12.011","article-title":"A Text Similarity Measurement Based on Semantic Fingerprint of Characteristic Phrases","volume":"29","author":"Shanchen","year":"2020","journal-title":"Chin. J. Electron."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, D., Yuan, Y., Liu, Q., and Yang, Y. (2018, January 13\u201315). Improvement of TF-IDF Algorithm Based on Knowledge Graph. Proceedings of the IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), Kunming, China.","DOI":"10.1109\/SERA.2018.8477196"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Ge, S.S. (2019, January 15\u201318). An Improved Tf-IdF Algorithm Based on Class Discriminative Strength for Text Categorization on Desensitized Data. Proceedings of the 3rd International Conference on Innovation in Artificial Intelligence, Suzhou, China. Part F1481.","DOI":"10.1145\/3319921.3319924"},{"key":"ref_37","first-page":"905","article-title":"Ontology-Based Extractive Text Summarization: The Contribution of Instances","volume":"23","author":"Flores","year":"2019","journal-title":"Comput. Y Sist."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ullah, S., and Al Islam, A.B.M.A. (2019, January 17\u201319). A Framework for Extractive Text Summarization Using Semantic Graph Based Approach. Proceedings of the 6th International Conference on Networking, Systems and Security, Dhaka, Bangladesh.","DOI":"10.1145\/3362966.3362971"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ins.2018.10.006","article-title":"Multi-Co-Training for Document Classification Using Various Document Representations: TF\u2013IDF, LDA, and Doc2Vec","volume":"477","author":"Kim","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"538","DOI":"10.2991\/ijcis.d.200423.001","article-title":"Bi-Gru Sentiment Classification for Chinese Based on Grammar Rules and Bert","volume":"13","author":"Lu","year":"2020","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_41","first-page":"5999","article-title":"Attention Is All You Need","volume":"2017","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101629","DOI":"10.1016\/j.compenvurbsys.2021.101629","article-title":"KE-CNN: A New Social Sensing Method for Extracting Geographical Attributes from Text Semantic Features and Its Application in Wuhan, China","volume":"88","author":"Chen","year":"2021","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Gong, L., and Wang, Y. (2005, January 10\u201312). Extracting Key Sentences from Chinese Text. Proceedings of the 11th Joint International Computer Conference, Chongqing, China.","DOI":"10.1142\/9789812701534_0082"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhao, L., and Chen, J. (2022). Metro System Inundation in Zhengzhou, Henan Province, China. Sustainability, 14.","DOI":"10.3390\/su14159292"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1080\/17538947.2021.1968048","article-title":"Geographic Context-Aware Text Mining: Enhance Social Media Message Classification for Situational Awareness by Integrating Spatial and Temporal Features","volume":"14","author":"Scheele","year":"2021","journal-title":"Int. J. Digit. Earth"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.cag.2013.10.008","article-title":"Public Behavior Response Analysis in Disaster Events Utilizing Visual Analytics of Microblog Data","volume":"38","author":"Chae","year":"2014","journal-title":"Comput. Graph."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5310920","DOI":"10.1155\/2022\/5310920","article-title":"Risk Assessment and Prediction of Rainstorm and Flood Disaster Based on Henan Province, China","volume":"2022","author":"Deng","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1007\/s13351-022-2053-y","article-title":"Assessing 10 Satellite Precipitation Products in Capturing the July 2021 Extreme Heavy Rain in Henan, China","volume":"36","author":"Liu","year":"2022","journal-title":"J. Meteorol. Res."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/240\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:51:58Z","timestamp":1760125918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,9]]},"references-count":48,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["ijgi12060240"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12060240","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,9]]}}}