An integrated cyberGIS and machine learning framework for data-intensive urban analytics
Lyu, Fangzheng
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/124515
Description
Title
An integrated cyberGIS and machine learning framework for data-intensive urban analytics
Author(s)
Lyu, Fangzheng
Issue Date
2024-04-12
Director of Research (if dissertation) or Advisor (if thesis)
Wang, Shaowen
Doctoral Committee Chair(s)
Wang, Shaowen
Committee Member(s)
Chang, Kevin Chenchuan
He, Jingrui
Kolak, Marynia Aniela
Diao, Chunyuan
Department of Study
Geography & GIS
Discipline
Geography
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
GIS
Urban Informatics
Geospatial Artificial Intelligence
CyberGIS
Abstract
This thesis introduces a cyberGIS and machine learning framework for data-intensive urban analytics. Due to the rapid urbanization and global changes, it is critical to understand urban environments and the complexity in the urban systems. The framework bridges the gap between heterogeneous geospatial big data and the urban complex system, proposing a novel framework for urban analytics. Applied across three thesis chapters, the framework aims to model, evaluate and predict urban heat with fine spatiotemporal granularity, (near) real-time, and high precision using heterogeneous urban big data. The first chapter showcases the integration of cyberGIS and machine learning for predicting Urban Heat Island in Chicago, achieving high spatiotemporal granularity at 1 km spatial resolution and 10 minutes temporal granularity. The second chapter aims to conduct (near) real-time evaluation and mapping of human sentiments of heat exposure using Location-based Social Media data using keywork-based natural language processing algorithm and backend supercomputer. The third chapter introduces a scalable video machine learning framework for urban spatiotemporal analysis, showcasing advantages such as integrated factors, applicability to diverse urban issues, handling of heterogeneous geospatial data, adaptable spatiotemporal granularity, and high precision, which is effectively demonstrated in predicting urban heat dynamics. Overall, these chapters highlight the achievements of the proposed cyberGIS and machine learning framework for data-intensive urban analytics, offering fine spatiotemporal granularity, real-time application, and high accuracy. This innovative urban analytics framework contributes to the understanding of urban heat dynamics and provides effective framework for urban analytics.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
