Research Seminar: Human Oriented Geospatial Data Analytics

Everything takes place in space and time. Geospatial data analytics have become essential techniques in many crossdisciplinary studies, thanks to the wide availability of location-based data and rapid advances in information and computing technologies. From human-oriented perspectives, researchers can learn from traces left by people's activities, emotions, context environments, decision-making, health, and other aspects by integrative use of diverse data sources, including traditional data and location-based big data. Emphasizing the value of cross-disciplinary efforts, I will introduce some of our team's recent interdisciplinary collaborative research addressing human problems and concerns in this sharing. First, I will share the conceptual framework and method development to model and analyze the dynamically changing Covid-19 transmissibility rates. The second study uses mixed methods to understand and visualize the social vulnerability of different people in natural disasters. The third study attempts to understand people's voting trends in political elections through sentiment analysis of social texts and aims to predict election results with social media data mining. The final example explores the impact of urban multi-factor synergy on outdoor exercises of urban residents.

Poster