Bio 

Dr. Zhenlong Li joined the Department of Geography in 2015 after receiving his Ph.D. in Earth Systems and Geoinformation Sciences from the George Mason University (GMU). He holds a B.S. (2006) in GIS from Wuhan University and an M.S. (2010) in Earth System Science from GMU. Dr. Li was named a Breakthrough Star by USC in 2020 and one of the Geospatial World 50 Rising Stars by the Geospatial Media and Communications in 2021. He is also a Peter and Bonnie McCausland Faculty Fellow at the USC College of Arts and Sciences (2020–2023).

He served as the Chair of the Cyberinfrastructure Specialty Group of Association of American Geographers (AAG), Co-Chair of the Cloud Computing Group of Federation of Earth Science Information Partners (ESIP), and the Board of Director of the International Association of Chinese Professionals in Geographic Information Sciences (CPGIS). Currently, he sits on the Editorial Board of 4 international journals including the Geo-spatial Information Science, PLOS ONE, ISPRS International Journal of Geo-Information, and Big Earth Data. He also serves as a peer reviewer for more than 30 international journals.

Research 

Dr. Li’s primary research field is GIScience with a focus on geospatial big data analytics, high performance computing, spatiotemporal analysis/modelling, cyberGIS, and geospatial artificial intelligence (GeoAI).  By synthesizing cutting-edge computing technologies, geospatial methods, and spatiotemporal principles, Dr. Li and his Geoinformation and Big Data Research Lab aim to accelerate spatial information extraction and advance knowledge discovery to support domain applications such as disaster management, climate analysis, human dynamics, and public health. 

He has more than 100 publications, including over 70 peer-reviewed journal articles, most of which have appeared in top journals in GIScience and other related fields (e.g., International Journal of Geographical Information Science, International Journal of Digital Earth, and Proceedings of National Academy of Sciences), 20 articles in books and proceedings, and 4 edited books. He has received external funding support from National Science Foundation (NSF), National Institute of Health (NIH), Federation of Earth Science Information Partners (ESIP), and National Aeronautics and Space Administration (NASA) among others.

Teaching

• GEOG 363: Introduction to Geographic Information Systems
• GEOG 531: Quantitative Methods in Geographic Research
• GEOG 554: Spatial Programming
• GEOG 556: WebGIS
• GEOG 763: Seminar in Geographic Information Science
  
  

Representative and Recent Publications 

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, https://doi.org/10.1371/journal.pone.0255259

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, Nature Scientific Reports, https://doi.org/10.1038/s41598-021-94300-7

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

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, https://doi.org/10.3390/ijgi10050344

Jiang Y., Li Z., Cutter S., (2021) Social Distance Integrated Gravity Model for Evacuation Destination Choice, International Journal of Digital Earth, https://doi.org/10.1080/17538947.2021.1915396

Ning H., Li Z., Wang C., Yang L., (2020), Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation, Annals of GIS, https://doi.org/10.1080/19475683.2020.1803402

Hu L., Li Z., Ye X., (2020) Delineating and Modelling Activity Space Using Geotagged Social Media Data, Cartography and Geographic Information Science, 47(3), 277–288 https://doi.org/10.1080/15230406.2019.1705187

Li Z., Huang Q., Emrich C., (2019) Introduction to Social Sensing and Big Data Computing for Disaster Management, International Journal of Digital Earth, 12(11), 1198–1204.

Hu F., Li Z., Yang C., Jiang Y. (2019) A graph-based approach to detect the tourist movement pattern using social media data, Cartography and Geographic Information Science, 46(4), 368–382, https://doi.org/10.1080/15230406.2018.1496036

Li Z., Huang Q., Jiang Y., Hu F. (2019), SOVAS: A Scalable Online Visual Analytic System for Big Climate Data Analysis, International Journal of Geographic Information Science, 1–22, doi: 10.1080/13658816.2019.1605073

Li Z., Hodgson M., Li W., (2018) A general-purpose framework for large-scale Lidar data processing, International Journal of Digital Earth, 11(1), 26–47

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. DOI: 10.1080/15230406.2018.1434834

Li Z., Wang C., Emrich C., Guo D., (2018) A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods, Cartography and Geographic Information Science, 45(2), 97–110

Li Z., Huang Q., Carbone G., Hu F. (2017), A High Performance Query Analytical Framework for Supporting Data-intensive Climate Studies, Computers, Environment and Urban Systems, 62(3), 210–221

Yang C., Huang Q., Li Z., Liu K., & Hu F. (2017) Big Data and cloud computing: innovation opportunities and challenges, International Journal of Digital Earth 10(1),13–53.

Li Z., Yang, C., Huang, Q., Liu K., Sun, M., Xia, J., (2017). Building Model as a Service for Supporting Geosciences, Computers, Environment and Urban Systems. 61, 141–152.

Martin Y., Li Z., Cutter S., (2017) Leveraging Twitter to gauge evacuation compliance: spatiotemporal analysis of Hurricane Matthew, PLOS ONE, 12(7), e0181701.

Li, Z., Hu, F., Schnase, J. L., Duffy, D. Q., Lee, T., Bowen, M. K., & Yang, C. (2016). A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-based Climate Data with MapReduce. International Journal of Geographical Information Science, 31(1), 17–35