Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach
Abstract Despite the urban heat islands phenomenon has long been recognized as a major urban environmental problem, it was not until recently that this urban phenomenon gained attention from the discipline of urban planning. To integrate the findings of the urban heat islands research into the plann...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-01-01
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| Series: | Journal of Big Data |
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| Online Access: | http://link.springer.com/article/10.1186/s40537-018-0113-z |
| _version_ | 1828324351604162560 |
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| author | Sanglim Yoo |
| author_facet | Sanglim Yoo |
| author_sort | Sanglim Yoo |
| collection | DOAJ |
| description | Abstract Despite the urban heat islands phenomenon has long been recognized as a major urban environmental problem, it was not until recently that this urban phenomenon gained attention from the discipline of urban planning. To integrate the findings of the urban heat islands research into the planning practice, the relationship between land surface temperatures and urban physical and socioeconomic characteristics should be addressed at the planning relevant spatial scale, a land parcel. Using a parcel as a unit of analysis, this study proposed to use a machine learning approach to identify important variables in the formation of urban heat islands in Indianapolis, Indiana. Applying random forest method to planning zones, this study identified planning zone specific urban physical and socioeconomic characteristics that are important for the interpretation of urban heat islands phenomenon of Indianapolis, Indiana. The main contribution of this study is twofold: to integrate urban physical and socioeconomic characteristics into a land parcel for the better interpretation of the result of urban heat islands study into planning practice and to apply machine learning approach to identify highly determinant variables in the formation of urban heat islands. |
| first_indexed | 2024-04-13T19:05:26Z |
| format | Article |
| id | doaj.art-0d83403f58a44688baa0e7f5c7723fb5 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-13T19:05:26Z |
| publishDate | 2018-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-0d83403f58a44688baa0e7f5c7723fb52022-12-22T02:33:59ZengSpringerOpenJournal of Big Data2196-11152018-01-015112410.1186/s40537-018-0113-zInvestigating important urban characteristics in the formation of urban heat islands: a machine learning approachSanglim Yoo0Department of Urban Planning, College of Architecture and Planning, Ball State UniversityAbstract Despite the urban heat islands phenomenon has long been recognized as a major urban environmental problem, it was not until recently that this urban phenomenon gained attention from the discipline of urban planning. To integrate the findings of the urban heat islands research into the planning practice, the relationship between land surface temperatures and urban physical and socioeconomic characteristics should be addressed at the planning relevant spatial scale, a land parcel. Using a parcel as a unit of analysis, this study proposed to use a machine learning approach to identify important variables in the formation of urban heat islands in Indianapolis, Indiana. Applying random forest method to planning zones, this study identified planning zone specific urban physical and socioeconomic characteristics that are important for the interpretation of urban heat islands phenomenon of Indianapolis, Indiana. The main contribution of this study is twofold: to integrate urban physical and socioeconomic characteristics into a land parcel for the better interpretation of the result of urban heat islands study into planning practice and to apply machine learning approach to identify highly determinant variables in the formation of urban heat islands.http://link.springer.com/article/10.1186/s40537-018-0113-zUrban heat island effectBiophysical vulnerabilitySocioeconomic vulnerabilityMachine learningRandom forestVariable selection |
| spellingShingle | Sanglim Yoo Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach Journal of Big Data Urban heat island effect Biophysical vulnerability Socioeconomic vulnerability Machine learning Random forest Variable selection |
| title | Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach |
| title_full | Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach |
| title_fullStr | Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach |
| title_full_unstemmed | Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach |
| title_short | Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach |
| title_sort | investigating important urban characteristics in the formation of urban heat islands a machine learning approach |
| topic | Urban heat island effect Biophysical vulnerability Socioeconomic vulnerability Machine learning Random forest Variable selection |
| url | http://link.springer.com/article/10.1186/s40537-018-0113-z |
| work_keys_str_mv | AT sanglimyoo investigatingimportanturbancharacteristicsintheformationofurbanheatislandsamachinelearningapproach |