The promise of excess mobility analysis: measuring episodic-mobility with geotagged social media data

References

• Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., … Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734, 1–74. https://doi.org/10.1016/j.physrep.2018.01.001
   Web of Science ®Google Scholar
• Bengtsson, L., Lu, X., Thorson, A., Garfield, R., Von Schreeb, J., & Gething, P. W. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haiti. PLoS Medicine, 8(8), e1001083. https://doi.org/10.1371/journal.pmed.1001083
   PubMed Web of Science ®Google Scholar
• Beveridge, S., & Nelson, C. R. (1981). A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘business cycle.’ Journal of Monetary Economics, 7(2), 151–174. https://doi.org/10.1016/0304-3932(81)90040-4
   Web of Science ®Google Scholar
• Bilsborrow, R. E. (2002). Migration, population change, and the rural environment. Environmental Change and Security Project Report, 8(1), 69–84.
   Google Scholar
• Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 9(1), 247–274. https://doi.org/10.1214/14-AOAS788
   Web of Science ®Google Scholar
• Camacho, K., Portelli, R., Shortridge, A., & Takahashi, B. (2021). Sentiment mapping: Point pattern analysis of sentiment classified Twitter data. Cartography and Geographic Information Science, 48(3), 241–257. https://doi.org/10.1080/15230406.2020.1869999
   Web of Science ®Google Scholar
• Cheng, Z., Caverlee, J., & Lee, K. (2010, October). You are where you tweet: A content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 759–768).
   Google Scholar
• Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition. Journal of Official Statistics, 6(1), 3–73.
   Google Scholar
• Coburn, T. C. (2004). Statistical and econometric methods for transportation data analysis. Technometrics, 46(4), 492. https://doi.org/10.1198/tech.2004.s238
   Google Scholar
• Coleman, D., Georgiadou, Y., & Labonte, J. (2009). Volunteered geographic information: The nature and motivation of produsers. International Journal of Spatial Data Infrastructures Research, 4(4), 332–358.
   Google Scholar
• Da Silva, T. T., Francisquini, R., & Nascimento, M. C. (2021). Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil. Expert Systems with Applications, 182, 115190. https://doi.org/10.1016/j.eswa.2021.115190
   PubMed Web of Science ®Google Scholar
• Finch, C., Emrich, C. T., & Cutter, S. L. (2010). Disaster disparities and differential recovery in New Orleans. Population and Environment, 31(4), 179–202. https://doi.org/10.1007/s11111-009-0099-8
   Web of Science ®Google Scholar
• Fouillet, A., Rey, G., Laurent, F., Pavillon, G., Bellec, S., Guihenneuc-Jouyaux, C., Clavel, J., Jougla, E., & Hémon, D. (2006). Excess mortality related to the August 2003 heat wave in France. International Archives of Occupational and Environmental Health, 80(1), 16–24. https://doi.org/10.1007/s00420-006-0089-4
   PubMed Web of Science ®Google Scholar
• Fussell, E., Hunter, L. M., & Gray, C. L. (2014). Measuring the environmental dimensions of human migration: The demographer’s toolkit. Global Environmental Change, 28, 182–191. https://doi.org/10.1016/j.gloenvcha.2014.07.001
   PubMed Web of Science ®Google Scholar
• Haklay, M. (2013). Citizen science and volunteered geographic information: Overview and typology of participation. In D. Sui, S. Elwood, M. Goodchild (Eds), Crowdsourcing Geographic Knowledge, (pp. 105–122). Springer.
   Google Scholar
• Harvey, D. (1990). The condition of postmodernity (Vol. 14). Blackwell.
   Google Scholar
• Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260–271. https://doi.org/10.1080/15230406.2014.890072
   PubMed Web of Science ®Google Scholar
• Hu, T., Wang, S., Luo, W., Yan, Y., Zhang, M., Huang, X., … Li, Z. (2021). Revealing public opinion towards COVID-19 vaccines using Twitter data in the United States: A spatiotemporal perspective (medRxiv). https://doi.org/10.1101/2021.06.02.21258233
   Google Scholar
• Huang, Q., & Wong, D. W. (2015). Modeling and visualizing regular human mobility patterns with uncertainty: An example using Twitter data. Annals of the Association of American Geographers, 105(6), 1179–1197. https://doi.org/10.1080/00045608.2015.1081120
   Web of Science ®Google Scholar
• Huang, X., Li, Z., Jiang, Y., Li, X., Porter, D., & Gao, S. (2020a). Twitter reveals human mobility dynamics during the COVID-19 pandemic. PloS one, 15(11), e0241957. https://doi.org/10.1371/journal.pone.0241957
   PubMed Web of Science ®Google Scholar
• Huang, X., Li, Z., Jiang, Y., Ye, X., Deng, C., Zhang, J., & Li, X. (2021). 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. International Journal of Digital Earth, 14(4), 424–442. https://doi.org/10.1080/17538947.2021.1886358
   Web of Science ®Google Scholar
• Huang, X., Li, Z., Lu, J., Wang, S., Wei, H., & Chen, B. (2020b). Time-series clustering for home dwell time during COVID-19: What can we learn from it? ISPRS International Journal of Geo-Information, 9(11), 675. https://doi.org/10.3390/ijgi9110675
   Web of Science ®Google Scholar
• Huang, X., Wang, C., Li, Z., & Ning, H. (2019). A visual–textual fused approach to automated tagging of flood-related tweets during a flood event. International Journal of Digital Earth, 12(11), 1248–1264. https://doi.org/10.1080/17538947.2018.1523956
   Web of Science ®Google Scholar
• Huo, Y., Yan, Y., Du, D., Wang, Z., Zhang, Y., & Yang, Y. (2019, September). Long-term span traffic prediction model based on STL decomposition and LSTM. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) (pp. 1–4). IEEE.
   Google Scholar
• Iglewicz, B., & Hoaglin. (1993). How to detect and handle outliers. ASQC Quality Press.
   Google Scholar
• Jackson Hole Travel & Tourism Board. (2020). Jackson hole travel and tourism board annual report. Fiscal year 2019. http://tetoncountywy.gov/DocumentCenter/View/12483/019_Annual_Report_v9_spreads_LRnc
   Google Scholar
• Jiang, Y., Huang, X., & Li, Z. (2021). Spatiotemporal patterns of human mobility and its association with land use types during COVID-19 in New York city. ISPRS International Journal of Geo-Information, 10(5), 344. https://doi.org/10.3390/ijgi10050344
   Web of Science ®Google Scholar
• Jiang, Y., Li, Z., & Cutter, S. L. (2019). Social network, activity space, sentiment, and evacuation: What can social media tell us? Annals of the American Association of Geographers, 109(6), 1795–1810. https://doi.org/10.1080/24694452.2019.1592660
   Web of Science ®Google Scholar
• Jiang, Y., Li, Z., & Cutter, S. L. (2021). Social distance integrated gravity model for evacuation destination choice. International Journal of Digital Earth, 1–15.
   Web of Science ®Google Scholar
• Jiang, Y., Li, Z., & Ye, X. (2019). Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level. Cartography and Geographic Information Science, 46(3), 228–242. https://doi.org/10.1080/15230406.2018.1434834
   Web of Science ®Google Scholar
• Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., Newth, D., & Wu, Y. (2015). Understanding human mobility from Twitter. PloS one, 10(7), e0131469. https://doi.org/10.1371/journal.pone.0131469
   PubMed Web of Science ®Google Scholar
• Kong, Y.-L., Meng, Y., Li, W., Yue, A.-Z., & Yuan, Y. (2015). Satellite image time series decomposition based on EEMD. Remote Sensing, 7(11), 15583–15604. https://doi.org/10.3390/rs71115583
   Web of Science ®Google Scholar
• Kumar, S., Zafarani, R., & Liu, H. (2011, August). Understanding user migration patterns in social media. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 25, No. 1).
   Google Scholar
• Laczko, F. (2015). Factoring migration into the’development data revolution’. Journal of International Affairs, 68(2), 1.
   Google Scholar
• Lambert, J., Drenou, C., Denux, J. P., Balent, G., & Cheret, V. (2013). Monitoring forest decline through remote sensing time series analysis. GIScience & Remote Sensing, 50(4), 437–457. https://doi.org/10.1080/15481603.2013.820070
   Web of Science ®Google Scholar
• Li, Z., Huang, X., Hu, T., Ning, H., Ye, X., Huang, B., Li, X., & Yang, C. (2021a). ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility. PLoS ONE, 16(8), e0255259. https://doi.org/10.1371/journal.pone.0255259
   PubMed Web of Science ®Google Scholar
• Li, Z., Huang, X., Ye, X., Jiang, Y., Martin, Y., Ning, H., Hodgson, M., & Li, X. (2021b). Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data. Scientific Reports, 11, 14694. https://doi.org/10.1038/s41598-021-94300-7
   PubMed Web of Science ®Google Scholar
• Liu, Q., Liu, M., & Ye, X. (2021). An extended spatiotemporal exposure index for urban racial segregation. Cartography and Geographic Information Science, 48(6), 530–545. https://doi.org/10.1080/15230406.2021.1965915
   Web of Science ®Google Scholar
• Liu, Z., Zhang, A., Yao, Y., Shi, W., Huang, X., & Shen, X. (2021). Analysis of the performance and robustness of methods to detect base locations of individuals with geo-tagged social media data. International Journal of Geographical Information Science, 35(3), 609–627. https://doi.org/10.1080/13658816.2020.1847288
   Web of Science ®Google Scholar
• Luo, F., Cao, G., Mulligan, K., & Li, X. (2016). Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago. Applied Geography, 70, 11–25. https://doi.org/10.1016/j.apgeog.2016.03.001
   Web of Science ®Google Scholar
• Martín, Y., Cutter, S. L., & Li, Z. (2020a). Bridging twitter and survey data for evacuation assessment of Hurricane Matthew and Hurricane Irma. Natural Hazards Review, 21(2), 04020003. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000354
   Web of Science ®Google Scholar
• Martín, Y., Cutter, S. L., Li, Z., Emrich, C. T., & Mitchell, J. T. (2020b). Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria. Population and Environment, 42(1), 4–27. https://doi.org/10.1007/s11111-020-00338-6
   Web of Science ®Google Scholar
• Martín, Y., Li, Z., & Cutter, S. L. (2017). Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew. PLoS one, 12(7), e0181701. https://doi.org/10.1371/journal.pone.0181701
   PubMed Web of Science ®Google Scholar
• Martín, Y., Li, Z., Ge, Y., & Huang, X. (2021). Introducing Twitter daily estimates of residents and non-residents at the County level. Social Sciences, 10(6), 227. https://doi.org/10.3390/socsci10060227
   Google Scholar
• Moen, B. (2017). Solar eclipse set tourism record in Grand Teton National Park. Press release. https://skift.com/2017/08/25/solar-eclipse-set-tourism-record-in-grand-teton-national-park/
   Google Scholar
• National Park Service. (2021a). National Park visitation numbers. https://www.nps.gov/aboutus/visitation-numbers.htm
   Google Scholar
• National Park Service. (2021b). The Grand Teton National Park monthly public use report. https://irma.nps.gov/Stats/SSRSReports/Park%20Specific%20Reports/Monthly%20Public%20Use
   Google Scholar
• Olabarria, M., Pérez, K., Santamariña-Rubio, E., Aragay, J. M., Capdet, M., Peiró, R., Rodríguez-Sanz, M., Artazcoz, L., & Borrell, C. (2013). Work, family and daily mobility: A new approach to the problem through a mobility survey. Gaceta sanitaria, 27(5), 433–439. https://doi.org/10.1016/j.gaceta.2012.08.008
   PubMed Web of Science ®Google Scholar
• Omkar, G., & Kumar, S. V. (2017, August). Time series decomposition model for traffic flow forecasting in urban midblock sections. In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 720–723). IEEE.
   Google Scholar
• SafeGraph-Social Distancing Metrics. (2020). https://docs.safegraph.com/docs/social-distancing-metrics
   Google Scholar
• Santos, A., McGuckin, N., Nakamoto, H. Y., Gray, D., & Liss, S. (2011). Summary of travel trends: 2009 national household travel survey (No. FHWA-PL-11-022). Federal Highway Administration.
   Google Scholar
• Santos-Burgoa, C., Sandberg, J., Suárez, E., Goldman-Hawes, A., Zeger, S., Garcia-Meza, A., Pérez, C. M., Estrada-Merly, N., Colón-Ramos, U., Nazario, C. M., Andrade, E., Roess, A., & Goldman, L. (2018). Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: A time-series analysis. The Lancet Planetary Health, 2(11), e478–e488. https://doi.org/10.1016/S2542-5196(18)30209-2
   PubMedGoogle Scholar
• Seem, J. E. (2007). Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1), 52–58. https://doi.org/10.1016/j.enbuild.2006.03.033
   Web of Science ®Google Scholar
• Singhal, A., Singh, P., Lall, B., & Joshi, S. D. (2020). Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons, and Fractals, 138, 110023. https://doi.org/10.1016/j.chaos.2020.110023
   PubMed Web of Science ®Google Scholar
• Stange, H., Liebig, T., Hecker, D., Andrienko, G., & Andrienko, N. (2011, November). Analytical workflow of monitoring human mobility in big event settings using bluetooth. In Proceedings of the 3rd ACM
   Google Scholar
• Wang, S., Zhang, M., Hu, T., Fu, X., Gao, Z., Halloran, B., & Liu, Y. (2021). A bibliometric analysis and network visualisation of human mobility studies from 1990 to 2020: Emerging trends and future research directions. Sustainability, 13(10), 5372. https://doi.org/10.3390/su13105372
   Web of Science ®Google Scholar
• Weaver, S. D., & Gahegan, M. (2007). Constructing, visualizing, and analyzing a digital footprint. Geographical Review, 97(3), 324–350. https://doi.org/10.1111/j.1931-0846.2007.tb00509.x
   Web of Science ®Google Scholar
• Wei, X., & Yao, X. A. (2021). Constructing and analyzing spatial-social networks from location-based social media data. Cartography and Geographic Information Science, 48(3), 258–274. https://doi.org/10.1080/15230406.2021.1891974
   Web of Science ®Google Scholar
• Willekens, F., Massey, D., Raymer, J., & Beauchemin, C. (2016). International migration under the microscope. Science, 352(6288), 897–899. https://doi.org/10.1126/science.aaf6545
   PubMed Web of Science ®Google Scholar
• Wu, L., Zhi, Y., Sui, Z., Liu, Y., & Colizza, V. (2014). Intra-urban human mobility and activity transition: Evidence from social media check-in data. PloS one, 9(5), e97010. https://doi.org/10.1371/journal.pone.0097010
   PubMed Web of Science ®Google Scholar
• Xu, F., Lin, Y., Huang, J., Wu, D., Shi, H., Song, J., & Li, Y. (2016). Big data driven mobile traffic understanding and forecasting: A time series approach. IEEE Transactions on Services Computing, 9(5), 796–805. https://doi.org/10.1109/TSC.2016.2599878
   Web of Science ®Google Scholar
• Zagheni, E., & Weber, I. (2012, June). You are where you e-mail: Using e-mail data to estimate international migration rates. In Proceedings of the 4th annual ACM web science conference (pp. 348–351).
   Google Scholar
• Zarnowitz, V., & Ozyildirim, A. (2006). Time series decomposition and measurement of business cycles, trends and growth cycles. Journal of Monetary Economics, 53(7), 1717–1739. https://doi.org/10.1016/j.jmoneco.2005.03.015
   Web of Science ®Google Scholar
• Zhao, C., Zeng, A., & Yeung, C. H. (2021). Characteristics of human mobility patterns revealed by high-frequency cell-phone position data. EPJ Data Science, 10(1), 5. SIGSPATIAL international workshop on indoor spatial awareness (pp. 51-58). https://doi.org/10.1140/epjds/s13688-021-00261-2
   Web of Science ®Google Scholar
• Zhong, C., Manley, E., Arisona, S. M., Batty, M., & Schmitt, G. (2015). Measuring variability of mobility patterns from multiday smart-card data. Journal of Computational Science, 9, 125–130. https://doi.org/10.1016/j.jocs.2015.04.021
   Web of Science ®Google Scholar
• Zhu, X., & Guo, D. (2017). Urban event detection with big data of taxi OD trips: A time series decomposition approach. Transactions in GIS, 21(3), 560–574. https://doi.org/10.1111/tgis.12288
   Web of Science ®Google Scholar