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.

中文翻译：

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量化城市热量数据的空间聚合偏差

每年，高温都会将人们送往医院和太平间，而气候变化和城市化的结合将增加极端高温的暴露程度。各城市正在寻找方法来确定受影响最严重的地区，以开始解决这一普遍问题。虽然我们正在经历“大数据”革命，但政策制定者仍然不确定什么级别的数据最有用。我们评估了使用不同空间分辨率的数据来评估热脆弱性所造成的数据丢失，因为城市内热的定义和数据的分辨率都会影响已确定的和有针对性的缓解区域。基于方差的指标提供了许多优点，但当数据聚合时，这些指标不太能够代表城市热量的全部范围。以贝克萨尔县（德克萨斯州圣安东尼奥市所在地）为例，我们发现增加数据聚合会增加城市内热岛的误报和误报识别，从而导致结果不可靠。随着聚合的增加，错误分类也会增加，这表明应该以尽可能最好的空间分辨率做出决策。