Version 1
: Received: 9 September 2024 / Approved: 10 September 2024 / Online: 10 September 2024 (10:48:49 CEST)
How to cite:
Abbeg Coproski, C.; Liang, B.; Dietrich, J. T.; DeGroote, J. Monitoring and Modeling Urban Heat Patterns in the State of Iowa, USA Utilizing Mobile Sensors and Geospatial Data. Preprints2024, 2024090785. https://doi.org/10.20944/preprints202409.0785.v1
Abbeg Coproski, C.; Liang, B.; Dietrich, J. T.; DeGroote, J. Monitoring and Modeling Urban Heat Patterns in the State of Iowa, USA Utilizing Mobile Sensors and Geospatial Data. Preprints 2024, 2024090785. https://doi.org/10.20944/preprints202409.0785.v1
Abbeg Coproski, C.; Liang, B.; Dietrich, J. T.; DeGroote, J. Monitoring and Modeling Urban Heat Patterns in the State of Iowa, USA Utilizing Mobile Sensors and Geospatial Data. Preprints2024, 2024090785. https://doi.org/10.20944/preprints202409.0785.v1
APA Style
Abbeg Coproski, C., Liang, B., Dietrich, J. T., & DeGroote, J. (2024). Monitoring and Modeling Urban Heat Patterns in the State of Iowa, USA Utilizing Mobile Sensors and Geospatial Data. Preprints. https://doi.org/10.20944/preprints202409.0785.v1
Chicago/Turabian Style
Abbeg Coproski, C., James T. Dietrich and John DeGroote. 2024 "Monitoring and Modeling Urban Heat Patterns in the State of Iowa, USA Utilizing Mobile Sensors and Geospatial Data" Preprints. https://doi.org/10.20944/preprints202409.0785.v1
Abstract
With cities experiencing faster warming rates than their surroundings and two-thirds of the global population projected to be living in urban areas by 2050, studies that help to describe in detail the spatial temperature patterns in the urban environment have the potential to address concerns regarding cities’ livability. The goal of this research was to examine the spatial pattern of temperature across multiple small to medium-sized cities in the state of Iowa, a relatively rural state. The study utilized mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten urban areas in Iowa during the afternoon, evening, and night on days exceeding 32°C from June to September 2022. Using a Random forest algorithm, and independent variables derived from aerial imagery and LiDAR data, we created predicted surface temperature models that demonstrated R² coefficients ranging from 0.879 to 0.997 with a majority of the temperature models reaching a R² higher than 0.95. These strong modeling results and derived highly detailed raster spatial data supports recent research which help explain the influence of morphometric features on temperature patterns in the urban environment. This study expands on previous research by investigating multiple small to medium sized cities in Iowa.
Keywords
urban heat patterns; air temperature; LiDAR; mobile sensors; random forest
Subject
Environmental and Earth Sciences, Geography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
