Browsing by Subject "Spatial data"

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    Access Across America: Transit 2022 Data
    (2024-10-21) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility Observatory
    These data were created as part of a study that examines the accessibility to jobs by transit across the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's metropolitan areas. These data are part of a longitudinal study. Transit data for additional years can be found in the Accessibility Observatory Data collection: https://hdl.handle.net/11299/200592
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    Data Science for Mining Patterns in Spatial Events
    (2019-05) Tang, Xun
    There has been an explosive growth of spatial data over the last decades thanks to the popularity of location-based services (e.g., Google Maps), affordable devices (e.g., mobile phone with GPS receiver), and fast development of data transfer and storage technologies. This significant growth as well as the emergence of new technologies emphasize the need for automated discovery of spatial patterns which can facilitate applications such as mechanical engineering, transportation engineering, and public safety. This thesis investigates novel and societally important patterns from various types of large scale spatial events such as spatial point events, spatio-temporal point events, and spatio-temporal linear events. Multiple novel approaches are proposed to address the statistical, computational, and mathematical challenges posed by different patterns. Specifically, a neighbor node filter and a shortest tree pruning algorithms are developed to discover linear hotspots on shortest paths, a bi-directional fragment-multi- graph traversal is proposed for discovering linear hotspots on all simple paths, and an apriori-graph traversal algorithm is proposed to detecting spatio-temporal intersection patterns. Extensive theoretical and experimental analyses show that the proposed approaches not only achieve substantial computational efficiency but also guarantee mathematical properties such as solution correctness and completeness. Case studies using real-world datasets demonstrate that the proposed approaches identify valuable patterns that are not detected by current state-of-the-art methods.