{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106365"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106365","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"CyberGIS-enabled reproducible agent-based modeling for scalable emergency evacuation","abstract":"Agent-based models represent an effective methodology for studying the complexity of emergency evacuation. However, due to the high computational intensity that increases dramatically with regard to evacuation area size and the number of people to be evacuated, agent-based evacuation models are typically applied to relatively small areas and populations. In order to make agent-based models scalable to large evacuation areas and population sizes for emergency decision support, it is important to not only effectively harness advanced cyberinfrastructure and geospatial big data, but also make modeling workflows accessible and reproducible by researchers and decision makers. In this dissertation research, a novel cyberGIS-based approach to reproducible and scalable modeling of emergency evacuation is developed to encompass 1) systematic design of the approach for examining the reproducibility of scalable modeling scenarios for researchers and decision makers; 2) algorithmic innovation for achieving desirable computational scalability of agent-based evacuation modeling; and 3) novel geospatial big data analytics for modeling fine-scale population distribution that is important to agent-based evacuation modeling. An agent-based evacuation model is developed based on a reproducible cyberGIS science gateway framework named CyberGIS-Jupyter; enhanced by a novel network-partition algorithm for computational scalability; and improved using fine-scale population distributions derived from location-based social media data. The central contribution of this dissertation research is to achieve computational scalability and reproducibility for spatially explicit agent-based modeling to gain new fundamental knowledge of mass emergency evacuation.","abstract_html":"Agent-based models represent an effective methodology for studying the complexity of emergency evacuation. However, due to the high computational intensity that increases dramatically with regard to evacuation area size and the number of people to be evacuated, agent-based evacuation models are typically applied to relatively small areas and populations. In order to make agent-based models scalable to large evacuation areas and population sizes for emergency decision support, it is important to not only effectively harness advanced cyberinfrastructure and geospatial big data, but also make modeling workflows accessible and reproducible by researchers and decision makers. In this dissertation research, a novel cyberGIS-based approach to reproducible and scalable modeling of emergency evacuation is developed to encompass 1) systematic design of the approach for examining the reproducibility of scalable modeling scenarios for researchers and decision makers; 2) algorithmic innovation for achieving desirable computational scalability of agent-based evacuation modeling; and 3) novel geospatial big data analytics for modeling fine-scale population distribution that is important to agent-based evacuation modeling. An agent-based evacuation model is developed based on a reproducible cyberGIS science gateway framework named CyberGIS-Jupyter; enhanced by a novel network-partition algorithm for computational scalability; and improved using fine-scale population distributions derived from location-based social media data. The central contribution of this dissertation research is to achieve computational scalability and reproducibility for spatially explicit agent-based modeling to gain new fundamental knowledge of mass emergency evacuation.","abstract_has_math":false,"creators":["Yin, Dandong"],"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","Cidell, Julie","Chang, Kevin","Ouyang, Yanfeng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:15:05Z","date_published":"2020-03-02T22:15:05Z","updated_at":"2026-07-22T22:24:45Z","subjects":["cyberGIS","agent-based modeling","computational reproducibility","network partition","population modeling"],"languages":["en"],"rights":["Copyright 2019 Dandong Yin"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106365","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Shaowen","Cidell, Julie","Chang, Kevin","Ouyang, Yanfeng"]},{"key":"dc:creator","label":"Author","values":["Yin, Dandong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T22:15:05Z","2022-03-03T10:15:16Z","2019-12-03","2019-12"]},{"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":["cyberGIS","agent-based modeling","computational reproducibility","network partition","population modeling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Dandong Yin"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106365"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Agent-based models represent an effective methodology for studying the complexity of emergency evacuation. However, due to the high computational intensity that increases dramatically with regard to evacuation area size and the number of people to be evacuated, agent-based evacuation models are typically applied to relatively small areas and populations. In order to make agent-based models scalable to large evacuation areas and population sizes for emergency decision support, it is important to not only effectively harness advanced cyberinfrastructure and geospatial big data, but also make modeling workflows accessible and reproducible by researchers and decision makers. In this dissertation research, a novel cyberGIS-based approach to reproducible and scalable modeling of emergency evacuation is developed to encompass 1) systematic design of the approach for examining the reproducibility of scalable modeling scenarios for researchers and decision makers; 2) algorithmic innovation for achieving desirable computational scalability of agent-based evacuation modeling; and 3) novel geospatial big data analytics for modeling fine-scale population distribution that is important to agent-based evacuation modeling. An agent-based evacuation model is developed based on a reproducible cyberGIS science gateway framework named CyberGIS-Jupyter; enhanced by a novel network-partition algorithm for computational scalability; and improved using fine-scale population distributions derived from location-based social media data. The central contribution of this dissertation research is to achieve computational scalability and reproducibility for spatially explicit agent-based modeling to gain new fundamental knowledge of mass emergency evacuation.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Dandong Yin, accepted the attached license on 2019-12-03 at 12:05.","The student, Dandong Yin, submitted this Dissertation for approval on 2019-12-03 at 12:15.","This Dissertation was approved for publication on 2019-12-03 at 16:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14665 on 2020-02-28 at 17:22:59","Made available in DSpace on 2020-03-02T22:15:05Z (GMT). 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The central contribution of this dissertation research is to achieve computational scalability and reproducibility for spatially explicit agent-based modeling to gain new fundamental knowledge of mass emergency evacuation.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Dandong Yin, accepted the attached license on 2019-12-03 at 12:05.","The student, Dandong Yin, submitted this Dissertation for approval on 2019-12-03 at 12:15.","This Dissertation was approved for publication on 2019-12-03 at 16:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14665 on 2020-02-28 at 17:22:59","Made available in DSpace on 2020-03-02T22:15:05Z (GMT). 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