Social Sensing and Big Data Computing for Disaster Management

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 (in press)

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

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.

Huang X., Li Z., Wang C., Ning H. (2019), Identifying disaster related social media for rapid response: a visual-textual fused CNN architecture, International Journal of Digital Earth, 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, DOI: 10.1080/24694452.2019.1592660

Huang, X., Wang, C., Li, Z. (2019) Linking picture with text: tagging flood relevant tweets for rapid flood inundation mapping. Proceedings of the International Cartographic Association, 2019 International Cartography Conference, 15–20 July 2019, Tokyo, Japan

Vayansky I., Kumar S., Li Z., (2019) An Evaluation of Geotagged Twitter Data during Hurricane Irma using Sentiment Analysis and Topic Modeling for Disaster Resilience, in 2019 IEEE International Symposium on Technology in Society (ISTAS) Proceedings, 15 – 16 November, Boston

Singleton S., Kumar S., Li Z. (2018), Twitter Analytics: Are the United States Coastal Regions Prepared for Climate Change? IEEE International Symposium on Technology and Society

Dahal, B., Kumar, S. A., & Li, Z. (2019). Topic modeling and sentiment analysis of global climate change tweets. Social Network Analysis and Mining, 9(1), 24.

Huang X, Wang C, Li Z, and 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.

Huang X., Wang C.,  Li Z. (2018) A Flooding Probability Reconstruction Approach by Enhancing Near Real-Time Imagery with Real-Time Gauges and TweetsIEEE Transactions on Geoscience and Remote Sensing 

Wang C., Li Z., Huang X. (2018) Geospatial assessment of flooding dynamics and risks of the October’15 South Carolina Flood, Congaree River WatershedSoutheastern Geographer(in press)

Huang X., Wang C., Li Z., (2018) A Near Real-time Flood Mapping Approach by Integrating Post-event with Satellite Imagery and Flood-related TweetsAnnals of GIS

Martín, Y., Li, Z., & Cutter, S. L. (2017). Leveraging Twitter to gauge evacuation compliance: spatiotemporal analysis of Hurricane Matthew. PLoS one, 12(7), e0181701.

Li Z., Wang C., Emrich C., Guo D.(2017)  Leverage Social Media for Rapid Flood Mapping: A Case Study of 2015 October Flood in SCCartography and Geographic Information Science, doi: 10.1080/15230406.2016.1271356

Li Z., Wang C., Emrich C., Guo D., (2016). Rapid Mapping of October 2015 South Carolina Flood using Social Media, Remote Sensing and Stream Gauges. In: The South Carolina Deluge: Lessons from a Watershed Disaster, Center for Resilience Studies, Northeastern University (pp. 52-62)

Liu X., Huang Q., Li Z. (2017), The impact of MTUP to explore online trajectories for human mobility studiesProceedings of the 1st ACM SIGSPATIAL International Workshop on PredictGIS

Huang Q., Li Z., Li J., (2016), Mining Frequent Trajectory Patterns from Online Footprints7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS), San Francisco, California, USA.

Wang C., Li Z., Emrich C., Remote sensing of surface wetness dynamics during the October 2015 South Carolina Flood, Congaree River Watershed. In: The South Carolina Deluge: Lessons from a Watershed Disaster, Center for Resilience Studies, Northeastern University (pp. 63-67)

Karami A., Li Z. (2016), Computational Framework for Tracking Reports, Opinions and Feelings of People in Social Media Before, During and After a Natural Disaster: Twitter Case Study in the 2015 South Carolina Flood (pp. 37-38)


Special Issue on Social Sensing and Big Data Computing for Disaster Management” in International Journal of Digital Earth(IJDE)

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