Urban Heat Mapping Strategies for Predicting Near-Surface Air Temperature in Unsampled Cities in Iowa

Mark D. Ecker, John P. DeGroote (), Clemir A. Coproski, Bingqing Liang, John Darko and James T. Dietrich
Additional contact information
Mark D. Ecker: Mathematics Department, University of Northern Iowa, Cedar Falls, IA 50614, USA
John P. DeGroote: Geography Department, University of Northern Iowa, Cedar Falls, IA 50614, USA
Clemir A. Coproski: Geography Department, University of Northern Iowa, Cedar Falls, IA 50614, USA
Bingqing Liang: Geography Department, University of Northern Iowa, Cedar Falls, IA 50614, USA
John Darko: Geography Department, University of Northern Iowa, Cedar Falls, IA 50614, USA
James T. Dietrich: Applied Coastal Research and Engineering Section, Washington Department of Ecology, Lacey, WA 98502, USA

Sustainability, 2025, vol. 17, issue 9, 1-24

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.

Keywords: cross-validation; geostatistical; landscape variables; NDVI; substitute city; time series; trend surface; urban heat island (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/9/3914/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/9/3914/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:9:p:3914-:d:1643190

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().