This course introduces a broad spectrum of theories, conceptual models, machine learning, and computational modeling that are used in and related to geospatial and social data. Course contents include discussions of, and hands-on exercise with, geo-social data analytics, map-based visualization, community dynamics models, smart cities theories, and GIS-based system development. This course aims to help students grow as IT professionals who can (1) understand critical issues in smart and connected communities (S&CC), (2) combine data-driven approaches in understanding and addressing the problems, and (3) communicate the geographically-embedded social patterns based on data analysis results through visualizations and interactive systems
Theories And Models In Geo-social Data Analytics
Host University
George Mason University
Semester
Spring 2025
Course Number
AIT 722 DL1
Credits
3
Instructor
Lee, Myeong (mlee89@gmu.edu)
Times and Days
7:20-10:00pm
T
Course Information
Prerequisites
Programming (Python or R); Statistics