Yuan, May

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6856

May Yuan Is Ashbel Smith Professor of Geospacial Information Sciences and Director of the Geospatial Analytics and Innovative Applications Laboratory. Her research has developed new data models and analysis methods to address an ever growing list of problems, including:

  • Wildfire Risk
  • Tornado Hazard
  • Air Pollution
  • Species Distribution
  • Infectious Disease
  • Hazardous Waste Transport
  • Offender Monitoring


Elected 2020 fellow of the American Association for the Advancement of Science (AAAS) "for her contributions to fundamental and applied geographic information science (GIS), especially for developing new data models and analytical methods to address problems of significant societal concerns."


Recent Submissions

Now showing 1 - 1 of 1
  • Item
    Network-Based Likelihood Modeling of Event Occurrences in Space and Time: A Case Study of Traffic Accidents in Dallas, Texas, USA
    (Taylor and Francis Inc.) Acker, Benjamin; Yuan, May; Acker, Benjamin; Yuan, May
    We propose a novel approach to network-based event likelihood modeling that estimates the probabilities of event occurrences on a network and identifies the influences of site and situation characteristics. Our premise is that the occurrences of events that involve human activities are subject to site and situational characteristics, and an understanding of event occurrences serves the basis for preparation or mitigation. Using data from Dallas, Texas, USA, we take the proposed approach to estimate the likelihood of traffic accidents based on binary (event or nonevent) space–time atoms of 100-m road segments and 1-h intervals. We choose 12 variables representing time, site characteristics, and situational conditions based on literature reviews to develop logistic regression and random forest models. The traffic accident data on even days were used for model construction and data on odd days for model testing. Both models result in comparable accuracy at 84.11% (logistic regression) and 85.42% (random forest) with significant differences in the spatial patterns of how site and situation correlate to traffic accidents. The difference signals the dynamic influence of site and situation characteristics on the event likelihood over time. The proposed approach shall be applicable to other point events on a network. ©2018 Cartography and Geographic Information Society.

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