Authors: Fangda Lu*,
Topics: Geographic Information Science and Systems, Urban Geography, Transportation Geography
Keywords: smartphone position, big data analysis, Twitter, pattern recognition, urban growing
Session Type: Poster
Start / End Time: 1:20 PM / 3:00 PM
Room: Napoleon Foyer/Common St. Corridor, Sheraton, 3rd Floor
Presentation File: No File Uploaded
Understanding the dynamics of the residents’ travel patterns is very important in booming communities. However, lacking tools to monitor the time-resolved location of people makes this research consume both tremendous time and money in the past. The pervasive use of mobile technologies highly facilitates the modeling of urban mobility from different perspectives. The problems trying to explore in this paper are where do the Central Texas residents commute for on weekdays, where do they like to relax on weekends and their travel patterns, as well as which factors influenced their choices. The paper presented an approach that using real-time Twitter data with the position to illustrate the time trends. At the same time, the density maps can be compared with maps of shopping area, tourism sites, and historic sites. It also assesses the impact of terrain causes on Twitter data. The results obtained in this research include the time distribution of tweets, the density maps and cause analysis. The impacts of my obtained results can partially explain the reasons for differences in travel patterns and the lifestyle reflected by travel patterns between two major metropolitans in Central Texas.