{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124515"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124515","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An integrated cyberGIS and machine learning framework for data-intensive urban analytics","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Lyu, Fangzheng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Geography","degree_department":null,"school":null,"contributors":["Wang, Shaowen","Chang, Kevin Chenchuan","He, Jingrui","Kolak, Marynia Aniela","Diao, Chunyuan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Gis","Urban Informatics","Geospatial Artificial Intelligence","Cybergis"],"languages":["en","eng"],"rights":["(Copyright 2024 Fangzheng Lyu)"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124515","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Shaowen","Chang, Kevin Chenchuan","He, Jingrui","Kolak, Marynia Aniela","Diao, Chunyuan"]},{"key":"dc:creator","label":"Author","values":["Lyu, Fangzheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geography"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gis","Urban Informatics","Geospatial Artificial Intelligence","Cybergis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["(Copyright 2024 Fangzheng Lyu)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124515"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An integrated cyberGIS and machine learning framework for data-intensive urban analytics"]}]}],"canonical_facts":{"dc:contributor":["Wang, Shaowen","Chang, Kevin Chenchuan","He, Jingrui","Kolak, Marynia Aniela","Diao, Chunyuan"],"dc:creator":["Lyu, Fangzheng"],"dc:date":["2024-05","2024-04-12"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124515"],"dc:language":["en","eng"],"dc:rights":["(Copyright 2024 Fangzheng Lyu)"],"dc:subject":["Gis","Urban Informatics","Geospatial Artificial Intelligence","Cybergis"],"dc:title":["An integrated cyberGIS and machine learning framework for data-intensive urban analytics"],"dc:type":["text"],"thesis:degree_discipline":["Geography"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}