“Human mobility” is a commonly used but loosely defined term which represents the concept about people’s spatiotemporal occupation and involves interaction among human, society, and surrounding physical environment. Better understanding human mobility is essential for understanding human interactions with surrounding environment and the use of geographic space, which can benefit transportation and urban planning, political decision making, epidemiology, economic development, emergency management, and many other fields. Human activities have been producing massive amount of geospatial data. Recent technology advancements further pushed the volume, variety, and velocity of human mobility to an unprecedented level. How to efficiently process, analyze, and make sense of the massive human movement data remains challenging, especially within dynamic spatial and temporal context.
Our studies along this line aim to develop innovative computing methods, spatial algorithms to effectively analyze big human movement data and reveal human movement patterns that contributes to a better understanding of human activities and their surrounding environment under various circumstances and within different domains, such as transportation, social networks, public health, urban analysis, and emergency management.
Huang X., Martin Y., Wang S., Zhang M., Gong X., Ge Y., Li Z. (2022) The promise of excess mobility analysis: measuring episodic-mobility with geotagged social media data, Cartography and Geographic Information Science, 49(5). 464-478. https://doi.org/10.1080/15230406.2021.2023366
Wei, H., Huang, X., Wang, S., Lu, J., Li, Z., & Zhu, L. A data-driven investigation on park visitation and income mixing of visitors in New York City. Environment and Planning B: Urban Analytics and City Science, https://doi.org/10.1177/23998083221130708
Huang X., Wang S., Zhang M., Hu T., Hohl A., She B., Gong X., Li J., Liu X., Gruebner O.,Liu R., L X., Liu Z., Ye X., Li Z., (2022), Social media mining under the COVID-19 context: progress, challenges, and opportunities, International Journal of Applied Earth Observation and Geoinformation, https://doi.org/10.1016/j.jag.2022.102967
Huang X., Zhao B., Li Z, Bao S., Zhang S. (2022) Black businesses matter: A longitudinal study of black owned restaurants in the COVID-19 pandemic. Annals of the American Association of Geographers. https://dx.doi.org/10.1080/24694452.2022.2095971
Zhang, M., Wang, S., Hu, T., Fu, X., Wang, X., Hu, Y., Halloran B., Li Z., Cui Y., Liu H., Liu Z., Bao, S. (2022). Human mobility and COVID-19 transmission: a systematic review and future directions, Annals of GIS, https://doi.org/10.1080/19475683.2022.2041725
Zeng C., Zhang J., Li Z., Sun X., Yang X., Olatosi B., Weissman S., Li X., (2022) Population mobility and aging accelerate the outbreaks of COVID-19 in the Deep South: a county-level longitudinal analysis, Clinical Infectious Diseases, https://doi.org/10.1093/cid/ciac050
Huang X., Xu Y., Liu R., Wang S., Wang S., Zhang M., Kang Y. Zhang Z., Gao S., Li Z., Hu T. Exploring the spatial disparity of home-dwelling time patterns in the U.S. during the COVID-19 pandemic via Bayesian inference, Transactions in GIS, https://doi.org/10.1111/tgis.12918
Kupfer, J. A., Li, Z., Ning, H., & Huang, X. (2021). Using Mobile Device Data to Track the Effects of the COVID-19 Pandemic on Spatiotemporal Patterns of National Park Visitation. Sustainability, 13(16), 9366. https://doi.org/10.3390/su13169366
Li Z., Huang X., Hu T., Ning H., Ye X., Huang B., Li X., (2021), ODT FLOW: A Scalable Platform for Extracting, Analyzing, and Sharing Multi-source Multi-scale Human Mobility, Plos One, 16(8): e0255259. https://doi.org/10.1371/journal.pone.0255259
Hu T., Wang S., She B., Zhang M., Huang X., Cui Y., …, Li Z., (2021) Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges, International Journal of Digital Earth, https://doi.org/10.1080/17538947.2021.1952324
Li Z., Huang X., Ye X., Jiang Y., Martin Y., Ning H., Hodgson M., Li X., (2021), Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data, Scientific Reports (in press)
Jiang, Y., Guo, D., Li, Z. Hodgson, M., (2021) A novel big data approach to measure and visualize urban accessibility. Computational Urban Science. 1, 10 (2021). https://doi.org/10.1007/s43762-021-00010-1
Martín, Y., Li, Z. Ge, Y., Huang, X. (2021) Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level. Social Sciences, https://doi.org/10.3390/socsci10060227
Huang X., Li Z., Jiang Y., Li X., Porter D. (2020) Twitter reveals human mobility dynamics during the COVID-19 pandemic, PloS One, https://doi.org/10.1371/journal.pone.0241957
Martín Y., Cutter S.L. Li Z, Emrich C., Mitchell, J.T. (2020) Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria. Population and Environment , https://doi.org/10.1007/s11111-020-00338-6
Hu L., Li Z., Ye X., (2020) Delineating and Modelling Activity Space Using Geotagged Social Media Data, Cartography and Geographic Information Science, https://doi.org/10.1080/15230406.2019.1705187
Martín Y., Cutter S.L., Li Z., (2020) Bridging social media and survey data for the evacuation assessment of hurricanes, Natural Hazard Review, 21(2), https://doi.org/10.1061/(ASCE)NH.1527-6996.0000354
Jiang Y., Li Z., Cutter S., (2019), Social network, activity space, sentiment, and evacuation: what can social media tell us?Annals of the American Association of Geographers
Huang X., Wang C., Li Z., (2019) High-Resolution Population Grid in the CONUS using Microsoft Building Footprints: a feasibility study, in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities, November 5, Chicago, Illinois, USA
Dahal, Kumar, Li Z., (2019), Spatiotemporal Topic Modeling and Sentiment Analysis of Global Climate Change Tweets, Social Network Analysis and Mining (in press)
Hu F., Li Z., Yang C., Jiang Y., (2018) A graph-based approach to detect the tourist movement pattern using social media data, Cartography and Geographic Information Science.
Jiang Y., Li Z., Ye X.,(2018), Measuring inter-city network using digital footprints from Twitter users, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on PredictGIS, 11/06/2018, Seattle, Washington, USA (accepted).
Deng, C., Lin, W., Ye, X., Li, Z., Zhang, Z., Xu, G. (2018) Social media data as a proxy for hourly fine-scale electric power consumption estimation. Envrionment and Planning A: Economy and Space.
Jiang Y., Li Z., Ye X. (2018) Understanding Demographic and Socioeconomic Bias of Geotagged Twitter Users at the County Level, Cartography and Geographic Information Science
Martín, Y., Li, Z., & Cutter, S. L. (2017). Leveraging Twitter to gauge evacuation compliance: spatiotemporal analysis of Hurricane Matthew. PLoS one, 12(7), e0181701.
Liu X., Huang Q., Li Z. (2017), The impact of MTUP to explore online trajectories for human mobility studies. Proceedings of the 1st ACM SIGSPATIAL International Workshop on PredictGIS
Huang Q., Li Z., Li J., (2016), Mining Frequent Trajectory Patterns from Online Footprints, 7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS), San Francisco, California, USA.