AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: GeoAI for Social Sensing
Type: Virtual Paper
Day: 3/1/2022
Start Time: 3:40 PM
End Time: 5:00 PM
Theme:
Sponsor Group(s):
Geographic Information Science and Systems Specialty Group
, Cyberinfrastructure Specialty Group
, Spatial Analysis and Modeling Specialty Group
,
,
,
,
,
,
Organizer(s):
Song Gao
, Yingjie Hu
, Qunying Huang
, Di Zhu
Chairs(s):
Jinmeng Rao, Department of Geography, University of Wisconsin-Madison
; ,
Description:
The prevalence of mobile devices and location-based services contributes to the explosive growth of social media data and promotes the prosperity of location-based social sensing applications. Such a location-aware big data source provides unprecedented opportunities for diverse studies such as human mobility, human-place interaction, and public health. Benefiting from the development of Geospatial Artificial Intelligence (GeoAI) methods and technologies, researchers are now able to better dig out patterns and connections between people’s activities on social media and geographic locations. However, how to efficiently clean, organize, model, visualize and interpret such massive and heterogeneous data still remains to be a challenge.
This session aims to bring the latest progress and insights on GeoAI-powered social sensing research into the spotlight. This session will be part of the AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS.
Presentation(s), if applicable
Danlin Yu, Montclair State University; Seeing the forest and the trees: holistic view of social distancing on the spread of COVID-19 in China |
Lei Zou, Texas A&M; Social Media for Emergency Rescue: An Analysis of Rescue Requests on Twitter during Hurricane Harvey |
Yifan Wang, University At Buffalo; Vague Cognitive Place Detection via Social Media Data |
Jinmeng Rao, University of Wisconsin - Madison; CATS: Conditional Adversarial Trajectory Simulation for Privacy-Preserved Data Publication |
Di Zhu, University of Minnesota; Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions |
Non-Presenting Participants Agenda
| Role | Participant |
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: GeoAI for Social Sensing
Description
Virtual Paper
Contact the Primary Organizer
Jinmeng Rao - jinmeng.rao@wisc.edu
Session Sponsored By
