2026-09-19T16:09:51Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124515 2026-01-14 col_2142_5131 col_2142_14805 com_2142_5130 com_2142_13460 com_2142_13459 com_2142_8903

Wang, Shaowen Wang, Shaowen Chang, Kevin Chenchuan He, Jingrui Kolak, Marynia Aniela Diao, Chunyuan 2024-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01 The student, Fangzheng Lyu, accepted the attached license on 2024-04-10 at 15:22. The student, Fangzheng Lyu, submitted this Dissertation for approval on 2024-04-10 at 15:25. This Dissertation was approved for publication on 2024-04-12 at 16:40. DSpace SAF Submission Ingestion Package generated from Vireo submission #20350 on 2024-09-16 at 00:43:21 An integrated cyberGIS and machine learning framework for data-intensive urban analytics Lyu, Fangzheng 2024-04-12 Gis Urban Informatics Geospatial Artificial Intelligence Cybergis 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. Text eng https://hdl.handle.net/2142/124515 (Copyright 2024 Fangzheng Lyu) Ph.D. Dissertation Geography University of Illinois at Urbana-Champaign Geography & GIS