Yingjie Hu is a GIScientist. His research interests focus on the space, time, and semantics of geographic information as well as the interactions among the three. Utilizing the powerful spatiotemporal analytics, his research often brings an additional semantic perspective which helps explain the extracted spatial and temporal patterns. Such a semantic perspective can also help understand the perceptions of humans towards places as well as human-place interactions. More specifically, his research involves the following three major areas:
(1) Space, time, and semantics joint data mining: a large amount of text data are becoming available on the Web, and a lot of these datasets are associated with space and time (e.g., location-based social media, travel blogs, and news articles). By applying joint methods, we can discover interesting knowledge on the relations between semantic topics and the corresponding spatiotemporal contexts, as well as the potential influence of such topics on the spatiotemporal behavior of people.
Contact InformationDr. Yingjie Hu
Ph.D., University of California, Santa Barbara
206A Burchfiel Geography Bldg.
Knoxville, TN 37996-0925 Email: email@example.com
(2) The value of geographic information: nowadays, the information available from different sources have surpassed any individual’s capability to manually process. Which information is more important, and which information can wait to be processed later? In some other situations, e.g., in the aftermath of an earthquake, there is a lack of up-to-date information. Which information should be collected first, and which information can be collected later? Answering these questions requires an understanding on the value and semantics of information.
(3) Geographic information retrieval and geoportals: geoportals are important platforms for sharing and reusing geospatial datasets. To help data users efficiently find the right datasets, geoportals need to understand the meaning of both the input queries and the candidate datasets. My research aims at automatically enriching the metadata of the candidate datasets, and enhancing the search functions using semantic methods.