Abstract

Recent years have witnessed an exponential growth of data that link geographic locations with natural language texts (geo-text data for short). Examples include geotagged tweets, geotagged Wikipedia articles, as well as news articles and online reviews that contain place names. These geo-text data offer unique opportunities for discovering a variety of knowledge. In this talk, I present three studies that discover different knowledge by analyzing three types of geo-text data. The discovered knowledge can support disaster response, urban planning, and other applications that benefit our society.

Biography

Dr. Yingjie Hu is an Assistant Professor in the Department of Geography at the University at Buffalo (UB) and the National Center for Geographic Information and Analysis (NCGIA). His major research area is in geographic information science (GIScience), and more specifically in spatial, temporal, and textual analysis, geospatial semantics, and geographic information retrieval. He develops and integrates spatial analysis methods, natural language processing, machine learning and deep learning techniques to discover a variety of knowledge from data for supporting disaster response, urban planning, and other applications. He received his PhD from the University of California Santa Barbara.

 
Yingjie Hu, Ph.D
Assistant Professor, Department of Geography, University at Buffalo