{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T14:47:21Z","timestamp":1787582841595,"version":"build-2736575974"},"reference-count":83,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008899","name":"University of South Carolina","doi-asserted-by":"publisher","award":["ASPIRE 135400-20-54176"],"award-info":[{"award-number":["ASPIRE 135400-20-54176"]}],"id":[{"id":"10.13039\/100008899","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2028791"],"award-info":[{"award-number":["2028791"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.<\/jats:p>","DOI":"10.3390\/ijgi10050344","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T06:01:33Z","timestamp":1621317693000},"page":"344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0632-624X","authenticated-orcid":false,"given":"Yuqin","family":"Jiang","sequence":"first","affiliation":[{"name":"Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-382X","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenlong","family":"Li","sequence":"additional","affiliation":[{"name":"Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"ref_1","unstructured":"(2020, September 28). WHO Coronavirus Disease (COVID-19) Dashboard | WHO Coronavirus Disease (COVID-19) Dashboard. Available online: https:\/\/covid19.who.int\/table."},{"key":"ref_2","unstructured":"Calgary, O. (2020, September 27). United States COVID-19 Cases and Deaths by State over Time | Data | Centers for Disease Control and Prevention, Available online: https:\/\/data.cdc.gov\/Case-Surveillance\/United-States-COVID-19-Cases-and-Deaths-by-State-o\/9mfq-cb36."},{"key":"ref_3","unstructured":"(2020, September 28). Proclamation on Declaring a National Emergency Concerning the Novel Coronavirus Disease (COVID-19) Outbreak, Available online: https:\/\/www.whitehouse.gov\/presidential-actions\/proclamation-declaring-national-emergency-concerning-novel-coronavirus-disease-covid-19-outbreak\/."},{"key":"ref_4","unstructured":"(2020, September 28). CDC Coronavirus Disease 2019 (COVID-19), Available online: https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/prevent-getting-sick\/social-distancing.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1126\/science.abb4218","article-title":"The Effect of Human Mobility and Control Measures on the COVID-19 Epidemic in China","volume":"368","author":"Kraemer","year":"2020","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1126\/science.abb4557","article-title":"Effective Containment Explains Subexponential Growth in Recent Confirmed COVID-19 Cases in China","volume":"368","author":"Maier","year":"2020","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.ijid.2020.03.031","article-title":"Transmission Potential and Severity of COVID-19 in South Korea","volume":"93","author":"Shim","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10484","DOI":"10.1073\/pnas.2004978117","article-title":"Spread and Dynamics of the COVID-19 Epidemic in Italy: Effects of Emergency Containment Measures","volume":"117","author":"Gatto","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1377\/hlthaff.2020.00608","article-title":"Strong Social Distancing Measures in The United States Reduced The COVID-19 Growth Rate: Study Evaluates the Impact of Social Distancing Measures on the Growth Rate of Confirmed COVID-19 Cases across the United States","volume":"39","author":"Courtemanche","year":"2020","journal-title":"Health Aff."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pan, B., Zheng, Y., Wilkie, D., and Shahabi, C. (2013). Crowd Sensing of Traffic Anomalies Based on Human Mobility and Social Media. Proceedings of the 21st ACM International Conference on Advances in Geographical Information Systems, Orlando, FL, USA, November 2013, Association for Computing Machinery.","DOI":"10.1145\/2525314.2525343"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1166\/jmihi.2017.2148","article-title":"Effectiveness of Social Media Data in Healthcare Communication","volume":"7","author":"Nawaz","year":"2017","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Charles-Smith, L.E., Reynolds, T.L., Cameron, M.A., Conway, M., Lau, E.H., Olsen, J.M., Pavlin, J.A., Shigematsu, M., Streichert, L.C., and Suda, K.J. (2015). Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0139701"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ajic.2018.02.010","article-title":"Social Media and Outbreaks of Emerging Infectious Diseases: A Systematic Review of Literature","volume":"46","author":"Tang","year":"2018","journal-title":"Am. J. Infect. Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e19447","DOI":"10.2196\/19447","article-title":"Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends","volume":"6","author":"Lwin","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110123","DOI":"10.1016\/j.chaos.2020.110123","article-title":"Analysis of Spatiotemporal Characteristics of Big Data on Social Media Sentiment with COVID-19 Epidemic Topics","volume":"140","author":"Zhu","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s00477-013-0739-5","article-title":"Assessing Risk Perception and Behavioral Responses to Influenza Epidemics: Linking Information Theory to Probabilistic Risk Modeling","volume":"28","author":"Liao","year":"2014","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"S375","DOI":"10.1093\/infdis\/jiw400","article-title":"Big Data for Infectious Disease Surveillance and Modeling","volume":"214","author":"Bansal","year":"2016","journal-title":"J. Infect. Dis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ye, X., Li, S., Yang, X., and Qin, C. (2016). Use of Social Media for the Detection and Analysis of Infectious Diseases in China. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5090156"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.ajic.2015.05.025","article-title":"Detecting Themes of Public Concern: A Text Mining Analysis of the Centers for Disease Control and Prevention\u2019s Ebola Live Twitter Chat","volume":"43","author":"Lazard","year":"2015","journal-title":"Am. J. Infect. Control"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1016\/j.ajic.2016.05.011","article-title":"Ebola Virus Disease and Social Media: A Systematic Review","volume":"44","author":"Fung","year":"2016","journal-title":"Am. J. Infect. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1016\/j.ajic.2016.04.253","article-title":"How People React to Zika Virus Outbreaks on Twitter? A Computational Content Analysis","volume":"44","author":"Fu","year":"2016","journal-title":"Am. J. Infect. Control"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e22","DOI":"10.2196\/publichealth.6925","article-title":"Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts","volume":"3","author":"Stefanidis","year":"2017","journal-title":"JMIR Public Health Surveill."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chew, C., and Eysenbach, G. (2010). Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0014118"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Signorini, A., Segre, A.M., and Polgreen, P.M. (2011). The Use of Twitter to Track Levels of Disease Activity and Public Concern in the US during the Influenza A H1N1 Pandemic. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0019467"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Albinati, J., Meira, W., Pappa, G.L., Teixeira, M., and Marques-Toledo, C. (2017). Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data, Association for Computing Machinery.","DOI":"10.1145\/3079452.3079464"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ramadona, A.L., Tozan, Y., Lazuardi, L., and Rockl\u00f6v, J. (2019). A Combination of Incidence Data and Mobility Proxies from Social Media Predicts the Intra-Urban Spread of Dengue in Yogyakarta, Indonesia. PLoS Negl. Trop. Dis., 13.","DOI":"10.1371\/journal.pntd.0007298"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1140\/epjds\/s13688-018-0144-x","article-title":"Inferences about Spatiotemporal Variation in Dengue Virus Transmission Are Sensitive to Assumptions about Human Mobility: A Case Study Using Geolocated Tweets from Lahore, Pakistan","volume":"7","author":"Kraemer","year":"2018","journal-title":"EPJ Data Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Broniatowski, D.A., Paul, M.J., and Dredze, M. (2013). National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0083672"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Allen, C., Tsou, M.-H., Aslam, A., Nagel, A., and Gawron, J.-M. (2016). Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0157734"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lai, S., Ruktanonchai, N.W., Zhou, L., Prosper, O., Luo, W., Floyd, J.R., Wesolowski, A., Santillana, M., Zhang, C., and Du, X. (2020). Effect of Non-Pharmaceutical Interventions for Containing the COVID-19 Outbreak in China. medRxiv.","DOI":"10.1101\/2020.03.03.20029843"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1140\/epjds\/s13688-017-0124-6","article-title":"Are You Getting Sick? Predicting Influenza-like Symptoms Using Human Mobility Behaviors","volume":"6","author":"Barlacchi","year":"2017","journal-title":"EPJ Data Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Souza, R.C., Assun\u00e7\u00e3o, R.M., Neill, D.B., and Meira, W. (2019, January 5\u20138). Detecting Spatial Clusters of Disease Infection Risk Using Sparsely Sampled Social Media Mobility Patterns. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, USA.","DOI":"10.1145\/3347146.3359369"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"140033","DOI":"10.1016\/j.scitotenv.2020.140033","article-title":"Spatial Analysis and GIS in the Study of COVID-19. A Review","volume":"739","author":"Napoletano","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, C., Sha, D., Liu, Q., Li, Y., Lan, H., Guan, W.W., Hu, T., Li, Z., Zhang, Z., and Thompson, J.H. (2020). Taking the Pulse of COVID-19: A Spatiotemporal Perspective. Int. J. Digit. Earth, 1\u201326.","DOI":"10.1080\/17538947.2020.1809723"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.geosus.2020.03.005","article-title":"COVID-19: Challenges to GIS with Big Data","volume":"1","author":"Zhou","year":"2020","journal-title":"Geogr. Sustain."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bragazzi, N.L., Dai, H., Damiani, G., Behzadifar, M., Martini, M., and Wu, J. (2020). How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17093176"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gasser, U., Ienca, M., Scheibner, J., Sleigh, J., and Vayena, E. (2020). Digital Tools against COVID-19: Framing the Ethical Challenges and How to Address Them. arXiv.","DOI":"10.1016\/S2589-7500(20)30137-0"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, Z., Huang, X., Hu, T., Ning, H., Ye, X., and Li, X. (2021). ODT FLOW: A Scalable Platform for Extracting, Analyzing, and Sharing Multi-Source Multi-Scale Human Mobility. arXiv.","DOI":"10.1371\/journal.pone.0255259"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","article-title":"Clinical Characteristics of Coronavirus Disease 2019 in China","volume":"382","author":"Guan","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_40","unstructured":"Huang, J., Wang, H., Fan, M., Zhuo, A., Sun, Y., and Li, Y. (, January July). Understanding the Impact of the COVID-19 Pandemic on Transportation-Related Behaviors with Human Mobility Data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, CA, USA. Available online: https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3412856."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101630","DOI":"10.1016\/j.tmaid.2020.101630","article-title":"Mapping the Incidence of the COVID-19 Hotspot in Iran\u2013Implications for Travellers","volume":"34","author":"Sah","year":"2020","journal-title":"Travel Med. Infect. Dis."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"138705","DOI":"10.1016\/j.scitotenv.2020.138705","article-title":"Investigation of Effective Climatology Parameters on COVID-19 Outbreak in Iran","volume":"729","author":"Ahmadi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Giuliani, D., Dickson, M.M., Espa, G., and Santi, F. (2020). Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy. BMC Infect. Dis., 20.","DOI":"10.2139\/ssrn.3559569"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1038\/s41467-020-18190-5","article-title":"The Use of Mobile Phone Data to Inform Analysis of COVID-19 Pandemic Epidemiology","volume":"11","author":"Grantz","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Keeling, M.J., Hollingsworth, T.D., and Read, J.M. (2020). The Efficacy of Contact Tracing for the Containment of the 2019 Novel Coronavirus (COVID-19). medRxiv.","DOI":"10.1101\/2020.02.14.20023036"},{"key":"ref_46","unstructured":"Cho, H., Ippolito, D., and Yu, Y.W. (2020). Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-Offs. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e417","DOI":"10.1016\/S2589-7500(20)30165-5","article-title":"Effects of Human Mobility Restrictions on the Spread of COVID-19 in Shenzhen, China: A Modelling Study Using Mobile Phone Data","volume":"2","author":"Zhou","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1049\/iet-smc.2020.0037","article-title":"Mobile Crowdsensing Approaches to Address the COVID-19 Pandemic in Spain","volume":"2","author":"Cecilia","year":"2020","journal-title":"IET Smart Cities"},{"key":"ref_49","first-page":"100111","article-title":"Testing on the Move South Korea\u2019s Rapid Response to the COVID-19 Pandemic","volume":"5","author":"Lee","year":"2020","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"ref_50","unstructured":"Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J., Doepfer, D., Sethi, A.K., Reyes, J.F.M., Patz, J., and Yandell, B.S. (2020). Mobile Phone Location Data Reveal the Effect and Geographic Variation of Social Distancing on the Spread of the COVID-19 Epidemic. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/3404820.3404824","article-title":"Mapping County-Level Mobility Pattern Changes in the United States in Response to COVID-19","volume":"12","author":"Gao","year":"2020","journal-title":"SIGSPATIAL Spec."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"170","DOI":"10.12688\/wellcomeopenres.15997.1","article-title":"Anonymised and Aggregated Crowd Level Mobility Data from Mobile Phones Suggests That Initial Compliance with COVID-19 Social Distancing Interventions Was High and Geographically Consistent across the UK","volume":"5","author":"Jeffrey","year":"2020","journal-title":"Wellcome Open Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Huang, X., Lu, J., Gao, S., Wang, S., Liu, Z., and Wei, H. (2021). Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the U.S. during the COVID-19 Pandemic. Ann. Am. Assoc. Geogr.","DOI":"10.1080\/24694452.2021.1904819"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, Z., Lu, J., Wang, S., Wei, H., and Chen, B. (2020). Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.1101\/2020.09.27.20202671"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1038\/s41597-020-00734-5","article-title":"Multiscale Dynamic Human Mobility Flow Dataset in the US during the COVID-19 Epidemic","volume":"7","author":"Kang","year":"2020","journal-title":"Sci. Data"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e2020485","DOI":"10.1001\/jamanetworkopen.2020.20485","article-title":"Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates with Covid-19 Infection Rates in the Us","volume":"3","author":"Gao","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"eabd6989","DOI":"10.1126\/sciadv.abd6989","article-title":"An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in near Real Time","volume":"7","author":"Kogan","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Queiroz, L., Ferraz, A., Melo, J.L., Barboza, G., Urbanski, A.H., Nicolau, A., Oliva, S., and Nakaya, H. (2020, September 28). Large-Scale Assessment of Human Mobility during COVID-19 Outbreak. Available online: https:\/\/osf.io\/nqxrd\/.","DOI":"10.31219\/osf.io\/nqxrd"},{"key":"ref_59","unstructured":"Huang, X., Li, Z., Jiang, Y., Li, X., and Porter, D. (2020). Twitter, Human Mobility, and COVID-19. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cotti, C.D., Engelhardt, B., Foster, J., Nesson, E.T., and Niekamp, P.S. (2020). The Relationship between In-Person Voting, Consolidated Polling Locations, and Absentee Voting on Covid-19: Evidence from the Wisconsin Primary, National Bureau of Economic Research.","DOI":"10.2139\/ssrn.3597233"},{"key":"ref_61","unstructured":"Ghader, S., Zhao, J., Lee, M., Zhou, W., Zhao, G., and Zhang, L. (2020). Observed Mobility Behavior Data Reveal Social Distancing Inertia. arXiv."},{"key":"ref_62","unstructured":"Zhao, J., Lee, M., Ghader, S., Younes, H., Darzi, A., Xiong, C., and Zhang, L. (2020). Quarantine Fatigue: First-Ever Decrease in Social Distancing Measures after the COVID-19 Pandemic Outbreak before Reopening United States. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bisanzio, D., Kraemer, M.U., Bogoch, I.I., Brewer, T., Brownstein, J.S., and Reithinger, R. (2020). Use of Twitter Social Media Activity as a Proxy for Human Mobility to Predict the Spatiotemporal Spread of COVID-19 at Global Scale. Geospat. Health, 15.","DOI":"10.4081\/gh.2020.882"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Porcher, S., and Renault, T. (2020). Social Distancing Beliefs and Human Mobility: Evidence from Twitter. arXiv.","DOI":"10.1371\/journal.pone.0246949"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, Z., Jiang, Y., Ye, X., Deng, C., Zhang, J., and Li, X. (2020). The Characteristics of Multi-Source Mobility Datasets and How They Reveal the Luxury Nature of Social Distancing in the US during the COVID-19 Pandemic. medRxiv.","DOI":"10.1101\/2020.07.31.20143016"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1109\/TCSS.2020.2980007","article-title":"Characterizing the Propagation of Situational Information in Social Media during Covid-19 Epidemic: A Case Study on Weibo","volume":"7","author":"Li","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"16598","DOI":"10.1038\/s41598-020-73510-5","article-title":"The Covid-19 Social Media Infodemic","volume":"10","author":"Cinelli","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"taaa031","DOI":"10.1093\/jtm\/taaa031","article-title":"The Pandemic of Social Media Panic Travels Faster than the COVID-19 Outbreak","volume":"27","author":"Depoux","year":"2020","journal-title":"J. Travel Med."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Bridgman, A., Merkley, E., Loewen, P.J., Owen, T., Ruths, D., Teichmann, L., and Zhilin, O. (2020). The Causes and Consequences of COVID-19 Misperceptions: Understanding the Role of News and Social Media. Harv. Kennedy Sch. Misinform. Rev., 1.","DOI":"10.37016\/mr-2020-028"},{"key":"ref_70","first-page":"e7255","article-title":"Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter","volume":"12","author":"Kouzy","year":"2020","journal-title":"Cureus"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H., and Dai, J. (2020). Mental Health Problems and Social Media Exposure during COVID-19 Outbreak. PLoS ONE, 15.","DOI":"10.2139\/ssrn.3541120"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e19276","DOI":"10.2196\/19276","article-title":"Mining Physicians\u2019 Opinions on Social Media to Obtain Insights into COVID-19: Mixed Methods Analysis","volume":"6","author":"Wahbeh","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Li, D., Chaudhary, H., and Zhang, Z. (2020). Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17144988"},{"key":"ref_74","unstructured":"Koh, J.X., and Liew, T.M. (2020). How Loneliness Is Talked about in Social Media during COVID-19 Pandemic: Text Mining of 4,492 Twitter Feeds. J. Psychiatr. Res."},{"key":"ref_75","unstructured":"(2020, September 28). New York City Department of City Planning PLUTO DATA DICTIONARY September 2020 (20v6), Available online: http:\/\/www.nyc.gov\/html\/dcp\/html\/bytes\/applbyte.shtml."},{"key":"ref_76","unstructured":"Google LLC. (2020, December 02). COVID-19 Community Mobility Report. Available online: https:\/\/www.google.com\/covid19\/mobility?hl=en."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1140\/epjds\/s13688-018-0178-0","article-title":"Tampering with Twitter\u2019s Sample API","volume":"7","author":"Pfeffer","year":"2018","journal-title":"EPJ Data Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1080\/15230406.2018.1434834","article-title":"Understanding Demographic and Socioeconomic Biases of Geotagged Twitter Users at the County Level","volume":"46","author":"Jiang","year":"2019","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_79","first-page":"3","article-title":"Population Bias in Geotagged Tweets","volume":"1","author":"Malik","year":"2015","journal-title":"People"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1609\/icwsm.v8i1.14554","article-title":"A Tale of Cities: Urban Biases in Volunteered Geographic Information","volume":"14","author":"Hecht","year":"2014","journal-title":"ICWSM"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1080\/13658816.2015.1137298","article-title":"Understanding the Bias of Call Detail Records in Human Mobility Research","volume":"30","author":"Zhao","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"20120986","DOI":"10.1098\/rsif.2012.0986","article-title":"The Impact of Biases in Mobile Phone Ownership on Estimates of Human Mobility","volume":"10","author":"Wesolowski","year":"2013","journal-title":"J. R. Soc. Interface"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.1001\/jama.2020.8598","article-title":"COVID-19 and Racial\/Ethnic Disparities","volume":"323","author":"Hooper","year":"2020","journal-title":"JAMA"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/5\/344\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:03:10Z","timestamp":1760162590000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/5\/344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":83,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["ijgi10050344"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10050344","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,18]]}}}