Understanding Urban Mobility Patterns through Social Media Data - A Washington DC Area Case Study

Authors: Dan Cheng*, George Mason University, Dieter Pfoser, George Mason University
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: Social Media, Urban Mobility, Trajectory, Network Decomposition
Session Type: Paper
Day: 4/7/2020
Start / End Time: 4:00 PM / 5:15 PM
Room: Tower Court C, Sheraton, IM Pei Tower, Second Floor Level
Presentation File: No File Uploaded

Given the important role of online check-in information from social media data (Foursquare, Twitter, etc), we aim to extract meaningful movement information on individuals and understand travel patterns in the urban area. Geo-tagged location information becomes a powerful data source to help with the related study area in the past decade. In this study, we analyze the geo-tagged Twitter trajectories and generate a movement network to summarize the human mobilities. In order to understand travel behavior of people who live in the Washington DC Metropolitan Area, we reveal the structure of the movement network through the k-core decomposition method and extract a hierarchical network structure with three layers: core, bridge and periphery layer. Through the analysis of the three network subsets, we have a better understanding of the global connections and local connections between individuals and locations. The findings in this study could be helpful to improve public transportation services in the Washington DC Metropolitan Area.

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