Authors: Cheng Fu*, University of Maryland - College Park, Grant McKenzie, University of Maryland - College Park, Vanessa Frias-Martinez, University of Maryland - College Park, Kathleen Stewart, University of Maryland - College Park
Topics: Geographic Information Science and Systems, Urban Geography
Keywords: Twitter; Natural Language Processing; Big Data; Human Activity Modeling; Urban dynamics
Session Type: Paper
Start / End Time: 1:20 PM / 3:00 PM
Room: Grand Ballroom A, Astor, 2nd Floor
Presentation File: No File Uploaded
Identifying the activities that individuals conduct in a city is key to understanding urban dynamics. It is difficult, however, to identify different human activities on a large scale without incurring significant costs. This study focuses on modeling the spatiotemporal patterns of different activity types within cities by employing user-contributed, geosocial content as a proxy for human activities. In this work, we use linguistic topic modeling to analyze georeferenced twitter data in order to differentiate different activity types. We then examine the spatial and temporal patterns of the derived activity types in three U.S. cities: Baltimore, MD., Washington D.C., and New York City, NY. The linguistic patterns reflect the spatiotemporal context of the places where the social media content is posted. We further construct a method to link what people post online to the activities conducted within a city. We then use these derived activities to profile the characteristics of neighborhoods in the three cities, and apply the activity signatures to discover similar neighborhoods both within and between the cities. This approach represents a novel activity-based method for assessing similarity between neighborhoods.