Examining fine-scale human mobility extracted from geotagged social media data

Authors: Atsushi Nara*, San Diego State University
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems, Cyberinfrastructure
Keywords: human dynamics, mobility, social media, spatio-temporal data analytics, evacuation
Session Type: Paper
Day: 4/13/2018
Start / End Time: 3:20 PM / 5:00 PM
Room: Bayside A, Sheraton, 4th Floor
Presentation File: No File Uploaded

Effective evacuation during disastrous events is one of the most challenging issues for many local government agencies and large city traffic control centers in U.S. To build an effective evacuation model and response plans, the responsive agencies need to consider the dynamic change of human population and mobility in impact areas as well as social perception from local residents when designing traffic assignment plans, evacuation procedures, and shelter locations. Conventionally, population data come from government cross-sectional episodic census surveys. Those census data represent only the nighttime population distribution based on residential addresses, which hardly reflects dynamic population during a day, on weekdays vs. weekends, or with variations in seasons and holidays. The prevailing use of social media and mobile phone data open unprecedented opportunities to analyze and model human dynamics in space and time, which facilitates capitalizing on crowdsourcing intelligence for hazard information reporting, sharing, and modeling during disastrous events. In this study, we have introduced a framework to utilize geotagged social media data to estimate the hourly population distribution and mobility; however, the model evaluation remains as a key challenge. Specifically, it is important to test the goodness-of-fit of the model estimate to data representing general population since the social media data are known to be biased by the unevenness of the demographic, geographic, and temporal distributions. This research will address the bias issue by comparing the model estimates of dynamic population and mobility derived from social media data to those from general mobile phone data.

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