Date of Award
Open Access Dissertation
Understanding human mobility patterns is an essence for geography and geographical information science. Although existing studies have found that human mobility patterns are highly predictable, such patterns can be disrupted by events, ranging from sports games to natural hazard caused evacuations. However, traditional data collection methods that heavily rely on self-reported travel behaviors are often delayed and at a small scale, and thus are often not sufficient to reveal the disrupted human mobility patterns. Fortunately, with the development of geolocating-related technologies, multiple platforms are able to capture human mobility data in unprecedented spatiotemporal scales and granularities. These data, such as geotagged social media posts and vehicle travel records, are geospatial and Big in nature, characterized by “3Vs”: volume, variety, and velocity in the Big Data era. However, the quantitative methods used to analyze geospatial big data are lagging behind.
This dissertation contributes to the quantitative methodological developments in human mobility research with geospatial big data. Specifically, this dissertation responds to these three research questions: (1) how to detect spatiotemporal events using human mobility origin-destination data in an urban area? This research question aims to mine human mobility patterns from large volume data; (2) how did COVID-19 affect human mobility patterns over different land use types? This research question compares different data sources and aims to understand how human mobility patterns from various data sources differ; (3) how can aggregated long-term social connections help improve evacuation modeling? This research question leverages the near real-time high velocity data and long-term historical data to understand evacuation behavior. This dissertation not only develops new methods to understand human mobility patterns under a specific event, but also attempts to advance quantitative methodologies that are used to analyze human mobility patterns with geospatial big data. Results of this dissertation demonstrate the potential in mining valuable information from geospatial big data by leveraging various quantitative methods and data sources. Methods developed in this dissertation can be applied to different applications and study areas with various data sources for future research.
Jiang, Y.(2022). Quantifying Human Mobility Patterns During Disruptive Events With Geospatial Big Data. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/6897