Background

Extreme heat in light of climate change is increasingly threatening the health and comfort of urban residents. Understanding spatio-temporal patterns of heat exposure is a critical factor in directing mitigation measures. Current heat vulnerability indices provide insight into heat sensitivities within given communities but do not account for the dynamic nature of the human movement as people travel for different activities. Here, we present a new Dynamic urban Thermal Exposure index (DTEx) that captures the varying heat exposure within urban environments.

Methods

We developed the DTEx to understand human heat exposure patterns in a mid-sized city. This index incorporates the human movement pattern and the heat hazard pattern obtained via novel and advanced techniques. We generated the human movement pattern from large-scale, anonymized smartphone location data. The heat hazard patterns were extrapolated via machine learning models from air temperature data measured through vehicle-mounted sensors. The exposure index was then developed by combining the two parameters using their standard-deviation-classified indices.

Results

Our exposure index varied between 2 and 12, indicating low to high thermal exposures. Several high-temperature spots associated with a large volume of foot traffic are successfully identified through this DTEx. We observed the hottest spots at shopping plazas but not specifically in the urban center. During the selected football gameday, the exposure index surged across most places near the football stadium but was reduced considerably further away.

Discussion

The proposed DTEx is novel because it provides dynamic heat monitoring capability to facilitate heat mitigation strategies at vulnerable locations in urban environments. Combining the mobility data and extensive sensor data generates rich details on the most heat-exposed areas due to human congregation. Such information will be critical for risk communication and urban planning for policymakers. DTEx could also help smart route planning in sustainable cities to avoid heat hazards risks.

中文翻译：

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DTEx：基于人类流动模式的动态城市热暴露指数

背景

鉴于气候变化，极端高温正日益威胁着城市居民的健康和舒适。了解热暴露的时空模式是指导缓解措施的关键因素。当前的热脆弱性指数提供了对特定社区内热敏感性的洞察，但没有考虑到人们为不同活动而旅行时人类运动的动态性质。这里，我们提出一个新的d ynamic城市Ť有源冰箱例posure指数（分特），该捕获的城市环境中的变化的热暴露。

方法

我们开发了 DTEx 来了解中等城市中人类的热暴露模式。该指数结合了通过新颖和先进的技术获得的人体运动模式和热危害模式。我们从大规模、匿名的智能手机位置数据中生成了人体运动模式。热危害模式是通过机器学习模型从车载传感器测量的气温数据中推断出来的。然后通过使用它们的标准偏差分类指数组合这两个参数来开发暴露指数。

结果

我们的暴露指数在 2 到 12 之间变化，表明热暴露从低到高。通过此 DTEx 成功识别了与大量人流量相关的几个高温点。我们在购物广场观察了最热门的地点，但不是特别在市中心。在选定的足球比赛日期间，足球场附近的大多数地方的暴露指数激增，但在更远的地方大幅下降。

讨论

拟议的 DTEx 是新颖的，因为它提供了动态热量监测能力，以促进城市环境中脆弱位置的热量缓解策略。由于人类聚集，移动性数据和广泛的传感器数据相结合，可以在最热暴露的区域生成丰富的细节。这些信息对于决策者的风险沟通和城市规划至关重要。DTEx 还可以帮助可持续城市中的智能路线规划，以避免热危害风险。