Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data
Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature acro...
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MDPI AG
2024-11-01
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| author | Clemir Abbeg Coproski Bingqing Liang James T. Dietrich John DeGroote |
| author_facet | Clemir Abbeg Coproski Bingqing Liang James T. Dietrich John DeGroote |
| author_sort | Clemir Abbeg Coproski |
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| description | Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m<sup>2</sup> aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R<sup>2</sup> coefficients ranging from 0.879 to 0.997 with the majority exceeding an R<sup>2</sup> of 0.95, all with <i>p</i>-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience. |
| format | Article |
| id | doaj-art-6dd18f62c3034d2a97dcb566a720b9e9 |
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| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-6dd18f62c3034d2a97dcb566a720b9e92025-08-20T01:53:40ZengMDPI AGApplied Sciences2076-34172024-11-0114221057610.3390/app142210576Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial DataClemir Abbeg Coproski0Bingqing Liang1James T. Dietrich2John DeGroote3Department of Geography, University of Northern Iowa, Cedar Falls, IA 50614, USADepartment of Geography, University of Northern Iowa, Cedar Falls, IA 50614, USAApplied Coastal Research and Engineering Section, Washington State Department of Ecology, Lacey, WA 98516, USADepartment of Geography, University of Northern Iowa, Cedar Falls, IA 50614, USAThorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m<sup>2</sup> aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R<sup>2</sup> coefficients ranging from 0.879 to 0.997 with the majority exceeding an R<sup>2</sup> of 0.95, all with <i>p</i>-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience.https://www.mdpi.com/2076-3417/14/22/10576urban temperature patternsurban morphologyLiDARmobile sensorsrandom forest |
| spellingShingle | Clemir Abbeg Coproski Bingqing Liang James T. Dietrich John DeGroote Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data Applied Sciences urban temperature patterns urban morphology LiDAR mobile sensors random forest |
| title | Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data |
| title_full | Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data |
| title_fullStr | Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data |
| title_full_unstemmed | Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data |
| title_short | Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data |
| title_sort | monitoring and modeling urban temperature patterns in the state of iowa usa utilizing mobile sensors and geospatial data |
| topic | urban temperature patterns urban morphology LiDAR mobile sensors random forest |
| url | https://www.mdpi.com/2076-3417/14/22/10576 |
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