University of Illinois Urbana-Champaign

Integrating cyberGIS and big data for scalable spatial accessibility analysis

Michels, Alexander

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https://hdl.handle.net/2142/129513

Description

Title
Integrating cyberGIS and big data for scalable spatial accessibility analysis
Author(s)
Michels, Alexander
Issue Date
2025-04-07
Director of Research (if dissertation) or Advisor (if thesis)
Wang, Shaowen
Doctoral Committee Chair(s)
Wang, Shaowen
Committee Member(s)
  • Kolak, Marynia
  • Li, Bo
  • Padmanabhan, Anand
  • Vogiatzis, Chrysafis
Department of Study
Illinois Informatics Institute
Discipline
Informatics
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • cyberGIS
  • spatial accessibility
  • GIS
Abstract
Spatial accessibility describes the uneven spatial distribution of access to vital resources and services like healthcare and critical infrastructure. Access is typically measured using travel time and ratios of supply-to-demand at the population-level to determine a level of access for each spatial unit such as a census tract. While the growth in geospatial big data and advances in cyberinfrastructure-based geographic information science and systems (cyberGIS) have widened the possibilities for spatial accessibility analysis, providing new data sources and research capabilities, the spatial accessibility literature has lagged behind this trend. This dissertation details methodological advancements in the field of cyberGIS-enabled spatial accessibility with three main research objectives: (1) the development of algorithms and methods for scalable travel time and spatial accessibility analyses, (2) the design of innovative approaches to harnessing emerging mobility data sources, and (3) the application of machine learning techniques to emerging big data sources to enhance the comprehension and approximation of travel time within spatial accessibility analyses. Our results underscore the efficacy of cyberGIS-enabled spatial accessibility through novel applications to real-world datasets and case studies. This approach enables researchers and decision-makers to analyze accessibility at larger scales, finer granularities, and with higher accuracy than what is currently achievable with existing methods.
Graduation Semester
2025-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/129513
Copyright and License Information
Copyright 2025 Alexander Michels

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