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ORCID
N/A
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Geography
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2018
Month Degree Awarded
May
Abstract
Understanding human mobility patterns is important for severe weather warning since these patterns can help identify where people are in time and in space when flash floods, tornados, high winds and hurricanes are occurring or are predicted to occur. A GIS (Geographic Information Science) data model was proposed to describe the spatial-temporal human activity. Based on this model, a metric was designed to represent the spatial-temporal activity intensity of human mobility, and an index was generated to quantitatively describe the change in human activities. By analyzing high-resolution human mobility data, the paper verified that human daily mobility patterns could be clearly described with the proposed methods. This research was part of a National Science Foundation grant on next generation severe weather warning systems. Data was collected from a specialized mobile app for severe weather warning, called CASA Alerts, which is being used to analyze different aspects of human behavior in response to severe weather warnings. The data set for this research uses GPS location data from more than 300 APP users during a 14 month period (location was reported at 2 minutes interval, or at based on a 100m change in location). A targeted weather warning strategy was proposed as a result of this research, and future research questions were discussed.
DOI
https://doi.org/10.7275/11934836
First Advisor
Qian Yu
Second Advisor
Brenda Philips
Recommended Citation
Xu, Yue, "A Study on Modelling Spatial-Temporal Human Mobility Patterns for Improving Personalized Weather Warning" (2018). Masters Theses. 677.
https://doi.org/10.7275/11934836
https://scholarworks.umass.edu/masters_theses_2/677
Included in
Applied Statistics Commons, Emergency and Disaster Management Commons, Geographic Information Sciences Commons, Human Geography Commons, Spatial Science Commons, Systems and Communications Commons