{"id":{"repo_id":"alabama","oai_identifier":"oai:ir.ua.edu:123456789/13792"},"canonical_url":"https://search.dev.ndltd.org/etd/alabama/oai:ir.ua.edu:123456789/13792","repository":{"repo_id":"alabama","name":"University of Alabama","base_url":"https://ir-api.ua.edu/oai/request"},"display":{"title":"Optimizing Green Space–Type Placement to Maximize Overall Land Surface Temperature Reduction","abstract":"The adverse effects of increasing climate change offer a compelling reason for studying optimal green space–type placement responses. Knowledge of these responses is vital to underserved urban communities, which experience the bulk of repercussions due to climate change. Earlier studies have focused on optimizing green space locations for areas in the southwest United States. Researchers in these studies performed analysis of the direct and indirect land surface temperature benefits of neighboring green spaces but did not factor in varying green space types or budget constraints. Optimal green space–type placement can reduce land surface temperature, increase surface permeability, and increase property sale values. The cumulative effects of these work to ease environmental stressors in disadvantaged communities. Since Alabama, located in the Southeast United States, has been experiencing higher land surface temperatures, more flooding events, and has a population highly vulnerable to these changes, we will show how the optimization model formulated herein is useful as the basis for future planning tools. Specifically, this thesis presents a mathematical model that will optimize green space–type placement and do so in a Tuscaloosa, Alabama urban census tract to maximize land surface temperature reduction while constrained by a set budget. These findings will give evidence for a proof of concept tool that informs those responsible for urban areas, and their populations, poised to experience more significant effects from climate change. Planners, government, and business leaders can use this information for active urban landscape management and site-specific green space–type placement to mitigate climate change effects.","abstract_html":"The adverse effects of increasing climate change offer a compelling reason for studying optimal green space–type placement responses. Knowledge of these responses is vital to underserved urban communities, which experience the bulk of repercussions due to climate change. Earlier studies have focused on optimizing green space locations for areas in the southwest United States. Researchers in these studies performed analysis of the direct and indirect land surface temperature benefits of neighboring green spaces but did not factor in varying green space types or budget constraints. Optimal green space–type placement can reduce land surface temperature, increase surface permeability, and increase property sale values. The cumulative effects of these work to ease environmental stressors in disadvantaged communities. Since Alabama, located in the Southeast United States, has been experiencing higher land surface temperatures, more flooding events, and has a population highly vulnerable to these changes, we will show how the optimization model formulated herein is useful as the basis for future planning tools. Specifically, this thesis presents a mathematical model that will optimize green space–type placement and do so in a Tuscaloosa, Alabama urban census tract to maximize land surface temperature reduction while constrained by a set budget. These findings will give evidence for a proof of concept tool that informs those responsible for urban areas, and their populations, poised to experience more significant effects from climate change. Planners, government, and business leaders can use this information for active urban landscape management and site-specific green space–type placement to mitigate climate change effects.","abstract_has_math":false,"creators":["Leisure, Donald Patrick"],"institution":"University of Alabama Libraries","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Senkbeil, Jason","Debbage, Neil"],"advisors":["Curtin, Kevin"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-27T18:44:05Z","subjects":["green space","land surface temperature","spatial optimization","urban heat island"],"languages":["en_US","English"],"rights":["All rights reserved by the author unless otherwise indicated."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["1051663"],"render_values":[{"text":"1051663","href":null,"code":true}]}]},"links":{"outbound_url":"https://ir.ua.edu/handle/123456789/13792","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Senkbeil, Jason","Debbage, Neil"]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Curtin, Kevin"]},{"key":"dc:creator","label":"Author","values":["Leisure, Donald Patrick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-06-14T17:30:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-06-14T17:30:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:publisher","label":"Institution","values":["University of Alabama Libraries"]},{"key":"dc:type","label":"Dc Type","values":["thesis","text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["green space","land surface temperature","spatial optimization","urban heat island"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["All rights reserved by the author unless otherwise indicated."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["1051663"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://ir.ua.edu/handle/123456789/13792"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Electronic Thesis or Dissertation"]},{"key":"dc:description.abstract","label":"Abstract","values":["The adverse effects of increasing climate change offer a compelling reason for studying optimal green space–type placement responses. Knowledge of these responses is vital to underserved urban communities, which experience the bulk of repercussions due to climate change. Earlier studies have focused on optimizing green space locations for areas in the southwest United States. Researchers in these studies performed analysis of the direct and indirect land surface temperature benefits of neighboring green spaces but did not factor in varying green space types or budget constraints. Optimal green space–type placement can reduce land surface temperature, increase surface permeability, and increase property sale values. The cumulative effects of these work to ease environmental stressors in disadvantaged communities. Since Alabama, located in the Southeast United States, has been experiencing higher land surface temperatures, more flooding events, and has a population highly vulnerable to these changes, we will show how the optimization model formulated herein is useful as the basis for future planning tools. Specifically, this thesis presents a mathematical model that will optimize green space–type placement and do so in a Tuscaloosa, Alabama urban census tract to maximize land surface temperature reduction while constrained by a set budget. These findings will give evidence for a proof of concept tool that informs those responsible for urban areas, and their populations, poised to experience more significant effects from climate change. Planners, government, and business leaders can use this information for active urban landscape management and site-specific green space–type placement to mitigate climate change effects."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["electronic"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Optimizing Green Space–Type Placement to Maximize Overall Land Surface Temperature Reduction"]}]}],"canonical_facts":{"dc:contributor":["Senkbeil, Jason","Debbage, Neil"],"dc:contributor.advisor":["Curtin, Kevin"],"dc:creator":["Leisure, Donald Patrick"],"dc:date.accessioned":["2024-06-14T17:30:16Z"],"dc:date.available":["2024-06-14T17:30:16Z"],"dc:date.issued":["2024"],"dc:description":["Electronic Thesis or Dissertation"],"dc:description.abstract":["The adverse effects of increasing climate change offer a compelling reason for studying optimal green space–type placement responses. Knowledge of these responses is vital to underserved urban communities, which experience the bulk of repercussions due to climate change. Earlier studies have focused on optimizing green space locations for areas in the southwest United States. Researchers in these studies performed analysis of the direct and indirect land surface temperature benefits of neighboring green spaces but did not factor in varying green space types or budget constraints. Optimal green space–type placement can reduce land surface temperature, increase surface permeability, and increase property sale values. The cumulative effects of these work to ease environmental stressors in disadvantaged communities. Since Alabama, located in the Southeast United States, has been experiencing higher land surface temperatures, more flooding events, and has a population highly vulnerable to these changes, we will show how the optimization model formulated herein is useful as the basis for future planning tools. Specifically, this thesis presents a mathematical model that will optimize green space–type placement and do so in a Tuscaloosa, Alabama urban census tract to maximize land surface temperature reduction while constrained by a set budget. These findings will give evidence for a proof of concept tool that informs those responsible for urban areas, and their populations, poised to experience more significant effects from climate change. 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