An integrated cyberGIS and machine learning framework for data-intensive urban analytics

Lyu, Fangzheng

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https://hdl.handle.net/2142/124515 Copy

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

Language

eng

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.

Graduation Semester

2024-05

Type of Resource

Text

Handle URL

https://hdl.handle.net/2142/124515

Copyright and License Information

(Copyright 2024 Fangzheng Lyu)

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