Faculty Publications
Document Type
Article
Publication Version
Published Version
Keywords
cross-validation, geostatistical, landscape variables, NDVI, substitute city, time series, trend surface, urban heat island
Journal/Book/Conference Title
Sustainability Switzerland
Volume
17
Issue
9
First Page
1
Last Page
24
Abstract
Elevated urban temperatures are a significant concern across the globe due to their negative health effects and increased energy use. Understanding the spatial variation in urban air temperatures can lead to informed mitigation and planning efforts. Air temperatures for multiple urban areas in the state of Iowa, USA, at three times of the day, were collected using customized sensors mounted on vehicles driven through a variety of landscapes in each urban area. Geographic information systems technology was used to process high-resolution landscape datasets and derive variables that summarize the urban landscape surrounding each temperature measurement point. Five different statistical models: standard regression, trend surface, geostatistical, time series, and random forest, were fitted to nighttime data in the Waterloo–Cedar Falls urban area. We demonstrate that the best method for predicting Waterloo–Cedar Falls nighttime data is to use Waterloo–Cedar Falls data collected at a different time of day. However, when data are not available in the same city for which predicted air temperatures are needed, we explore which substitute city’s data best forecast the target city’s air temperature, via four cross-validation strategies. We find that, when predicting evening and nighttime air temperatures for the Iowa urban areas, choosing the closest-in-population-size substitute city provides the best predicted air temperatures.
Department
Department of Geography
Department
Department of Mathematics
Original Publication Date
4-26-2025
Object Description
1 PDF File
DOI of published version
10.3390/su17093914
Repository
UNI ScholarWorks, Rod Library, University of Northern Iowa
Copyright
©2025 The Author(s)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Language
en
File Format
application/pdf
Recommended Citation
Ecker, Mark D.; DeGroote, John P.; Coproski, Clemir A.; Liang, Bingqing; Darko, John; and Dietrich, James T., "Urban Heat Mapping Strategies for Predicting Near-Surface Air Temperature in Unsampled Cities in Iowa" (2025). Faculty Publications. 6782.
https://scholarworks.uni.edu/facpub/6782
Comments
First published in Sustainability, v17 i9 published by MDPI DOI: https://doi.org/10.3390/su17093914