Data Science for Mining Patterns in Spatial Events
2019-05
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Data Science for Mining Patterns in Spatial Events
Authors
Published Date
2019-05
Publisher
Type
Thesis or Dissertation
Abstract
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.
Description
University of Minnesota Ph.D. dissertation. May 2019. Major: Computer Science. Advisor: Shashi Shekhar. 1 computer file (PDF); ix, 122 pages.
Related to
Replaces
License
Collections
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Tang, Xun. (2019). Data Science for Mining Patterns in Spatial Events. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/206364.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.