Geo-Social Lab is home to research projects aimed at developing innovative computational and visual tools to analyze massive and complex geo-social networks and geospatial processes that drive our economy, society and the environment.
Our aim is to help address research questions and real-world problems in diverse areas such as human mobility and migration, hazards and disasters, city planning and transportation, spatial and historical demography, global trade, communication and information networks, public discourse and information diffusion, etc.
Geng Tian updates us on how he is doing in China, working on a very cool project - unmanned vehicles delivering food! Click for Geng's blog post!
Kaitlyn Hom and Mark Rifkin, both senior high school students, joined our lab this summer to participate in the Secondary Student Training Program (SSTP) at the University of Iowa.
Angelina Evans worked as an intern in the US Department of Energy's prestigious Science Undergraduate Laboratory Internship (SULI) program in the Geospatial Science and Human Security Division at Oak Ridge National Laboratory (ORNL). Click for Angelina's blog post!
Spatial interactions are the movements of tangible and intangible phenomena such as people, goods, vehicles and information between locations. Spatial interactions form location-to-location networks. Analysis of spatial interaction networks is critical for diverse domains such as migration, epidemics, health care, transportation, and economy. Flow maps are commonly used to visualize spatial interactions and facilitate the understanding of patterns of spatial flows and the corresponding spatial context.
Understand the spatial, temporal and relational (network) aspects of dynamic and geographically-embedded social networks. A dynamic geo-social network evolves (changes) over space and time as the actors of the network move (migrate), new actors are added or removed, and relationships between the actors develop and change over time.
Support a human-centered process for pattern searching and knowledge construction through geovisual analytics and usability evaluation.
Provide a data-driven understanding of the complex system of human society and the physical environment through geographic knowledge discovery enabled by the state of the art machine learning and deep learning methods and their applications in spatial data science
Big data analytics for social media and networking applications, e.g., geospatial semantics, natural language processing, topic modeling, sentiment analysis, machine learning and deep learning methods.
We offer a diverse set of degrees and research experiences for graduate, undergraduate and high school students through the Department of Geographical and Sustainability Sciences, and the Interdisciplinary Graduate Program at Informatics (IGPI), and the Secondary Student Training Program (SSTP) at the University of Iowa.
Graduate teaching and research fellowships, and assistantships are available for competitive students. Before applying, please contact Caglar Koyluwith your brief research interests and CV attached. Competitive students will be invited for a Skype interview. The interview starts with a 6 minute, 40 second, a Pecha Kucha style presentation. Your presentation should focus on your current work, future research goals and interests, and how those intersect with the Lab's research agenda.
In addition to the above research interests, students should have, or be interested in developing, ability in: