Heat stress is a serious risk, which affects groups such as the elderly or patients with chronic diseases in particular, and is especially pronounced in cities. The ageing of society, progressive urbanization and climate change are increasing the risk of people being affected by heat stress. One way to reduce the risk is to adapt everyday behaviour. To encourage and support such a change of behaviour, we propose a two-step approach. The first step is a route planner for pedestrians which can find a route with minimal heat exposure. The second step is a tool that helps the user to select the time of day with minimal heat exposure to venture outside. The route planner is then used to calculate the heat stress and present the optimal route at that point in time. We evaluate our approach for the city of Karlsruhe. Our results show that the combined approach, as well as its individual steps, can reduce heat exposure and therefore the heat stress for typical daily tasks in a European city.

The Urban Heat Island (UHI) effect describes the difference in temperature between cities and their surrounding areas. However, temperature differences within city limits, so-called Intra-Urban Heat Islands (IUHI), affect human health as well as the energy demands in local areas. In order to anticipate and mitigate the resulting impacts of heat through urban planning, a method to reliably detect these local areas is needed. Existing methods from the geo-statistical field can identify these areas. But these statistics, depending on their parametrization, can be unstable in their detection of hotspots, in particular temperature hotspots. In this paper, we propose a modification of the well-known Getis-Ord (G *) statistic, called the Focal G * statistic. This modification replaces the computation of the global mean and standard deviation with their focal counterparts. We define the stability of our approach by introducing a stability metric called Stability of Hotspot (SoH), which requires that hotspots have to be in similar areas regardless of the chosen weight matrix. The results are evaluated on real-world temperature data for the city of Karlsruhe.

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