Introduction

Big spatial and spatial-temporal data have been, and continue to be, collected with global positioning systems, mobile devices, citizen participation, high-resolution remote sensing, census surveys, and volunteered geographic information. The research in our lab focus on big data analytics and visualization through the development of new theory, methodology, tools, and applications to analyze big and complex spatial data, acquire new knowledge, design problem-solving solutions, and support decision/policy making. We are conducting research and working on projects in the following closely related areas (for each you may find related publications and software on this site or in my CV):

  • Developing new computational, visual analytical, and statistical methods to process, analyze, and understand big geospatial and temporal data;
  • Addressing science and engineering problems concerning the environment, society, and humanities such as climate, health, security, transportation, urban planning and environment engineering.
 

To Prospective Graduate Students: We are always looking for talented graduate students. The Department of Geography at University of South Carolina is among the strongest geography programs in the U.S. (top 10 in the NRC survey), where you can work with world-renowned scholars. Please follow the procedure to apply.

This is a smoothed flow map of U.S. county-to-county migration for migrants of age 65-69. [IEEE TVCG publication: “Origin-Destination Flow Data Smoothing and Mapping”]

Live Visual Analytics

 

Where 'Merry Christmas' is Said

This NBC news article is based on our analysis of 400 million tweets of the past four holiday seasons and examine how and where “Merry Christmas” and “Happy Holidays” are used in holiday greetings.  The NBC news article is

The Great American Word Mapper

This Quartz article featured our Digging Into Data project outcome based on billions of tweets collected by our lab. The research identified the top 100,000 words used in these tweets and mapped how often they are used in every county in the continental United States, based on location data from Twitter.