Quantifying the spatial aggregation bias of urban heat data

Abstract

Every year, high temperatures send people to the hospital and morgue, and the combination of climate change and urbanization will increase extreme heat exposure. Cities are searching for ways to determine the most affected areas to begin addressing this pervasive issue. While we are living through the “big data” revolution, policy makers are still uncertain about what level of data is most useful. We evaluate the data loss from using data at different spatial resolutions to evaluate heat vulnerability, as both the definition of intra-urban heat and the resolution of the data affect the area identified and targeted for mitigation. Variance-based metrics provide many advantages, but when data is aggregated, these metrics are less able to represent the full range of urban heat. Using the case of Bexar County (home to San Antonio, TX), we find that increasing data aggregation increases both false positive and false negative identification of intra-urban heat islands, leading to unreliable results. Misclassification increases as aggregation increases, indicating that decisions should be made at the finest spatial resolution possible.