{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115686"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115686","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Urban informatics: Systems science and machine learning in spatiotemporal analysis","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Chen, Si"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Informatics","degree_department":null,"school":null,"contributors":["Deal, Brian","Wilson, Bev","Cidell, Julie","Wang , Shaowen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:55Z","subjects":["Urban informatics","Machine learning","Model cross-domain adaptation","Interactive interface","Social media"],"languages":["en","eng"],"rights":["Copyright 2022 Si Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115686","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Deal, Brian","Wilson, Bev","Cidell, Julie","Wang , Shaowen"]},{"key":"dc:creator","label":"Author","values":["Chen, Si"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-11"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Informatics"]},{"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":["Urban informatics","Machine learning","Model cross-domain adaptation","Interactive interface","Social media"]}]},{"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 2022 Si Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115686"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Si Chen, accepted the attached license on 2022-04-08 at 16:25.","The student, Si Chen, submitted this Dissertation for approval on 2022-04-08 at 16:26.","This Dissertation was approved for publication on 2022-04-11 at 09:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17611 on 2022-11-11 at 12:18:57","Urban planning as a discipline has struggled with efficient ways to integrate new technologies into planning processes. The main challenges can be traced to some fundamental weaknesses in planning technology design, development, and implementation. For example, a lack of flexibility in tool design and a lack of open and transferable data sources and collection methods. To date, there have also been poor user interactions in both technology development and use. Finally and perhaps most importantly, there has been a distinct inability to reliably replicate and adapt urban decision support models across contexts (from one place to another). There have been many high-quality models built for specific places that cannot be replicated or used anywhere else. Opportunities in big data and smart technologies offer some promise for improving and addressing these weaknesses. Many of these opportunities, however, emphasize advanced information technology, rather than the integration of the technique into actual decision-making processes, making them less useful in the world of practical planning. This dissertation explores ways in which big data, ubiquitous computational technology, and digital social networks, can contribute to addressing the challenges that hamper the adoption of planning technologies and implementation of planning support systems (PSS) more directly. Under the lexicon of ‘Urban Informatics’, this work aims to enable a new generation of planning technologies more readily integrated within the plan-making process and in the process affecting better urban planning decisions in more places."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Urban informatics: Systems science and machine learning in spatiotemporal analysis"]}]}],"canonical_facts":{"dc:contributor":["Deal, Brian","Wilson, Bev","Cidell, Julie","Wang , Shaowen"],"dc:creator":["Chen, Si"],"dc:date":["2022-05","2022-04-11"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Si Chen, accepted the attached license on 2022-04-08 at 16:25.","The student, Si Chen, submitted this Dissertation for approval on 2022-04-08 at 16:26.","This Dissertation was approved for publication on 2022-04-11 at 09:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17611 on 2022-11-11 at 12:18:57","Urban planning as a discipline has struggled with efficient ways to integrate new technologies into planning processes. The main challenges can be traced to some fundamental weaknesses in planning technology design, development, and implementation. For example, a lack of flexibility in tool design and a lack of open and transferable data sources and collection methods. To date, there have also been poor user interactions in both technology development and use. Finally and perhaps most importantly, there has been a distinct inability to reliably replicate and adapt urban decision support models across contexts (from one place to another). There have been many high-quality models built for specific places that cannot be replicated or used anywhere else. Opportunities in big data and smart technologies offer some promise for improving and addressing these weaknesses. Many of these opportunities, however, emphasize advanced information technology, rather than the integration of the technique into actual decision-making processes, making them less useful in the world of practical planning. This dissertation explores ways in which big data, ubiquitous computational technology, and digital social networks, can contribute to addressing the challenges that hamper the adoption of planning technologies and implementation of planning support systems (PSS) more directly. Under the lexicon of ‘Urban Informatics’, this work aims to enable a new generation of planning technologies more readily integrated within the plan-making process and in the process affecting better urban planning decisions in more places."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115686"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Si Chen"],"dc:subject":["Urban informatics","Machine learning","Model cross-domain adaptation","Interactive interface","Social media"],"dc:title":["Urban informatics: Systems science and machine learning in spatiotemporal analysis"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Informatics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}