By providing shade for residents in urban areas, cool spaces have been shown to be essential for mitigating the effects of heat stress. In response, the Municipality of Amsterdam developed a map showing walking distances to these spaces. However, the map lacks key information on
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By providing shade for residents in urban areas, cool spaces have been shown to be essential for mitigating the effects of heat stress. In response, the Municipality of Amsterdam developed a map showing walking distances to these spaces. However, the map lacks key information on capacity, accessibility, and precise distance measurements. This project addresses these gaps by identifying quality indicators for cool places and mapping their locations and quality scores across Amsterdam. Additionally, it establishes methods for computing the shortest and shadiest pedestrian routes to these spaces, enabling efficient routing to and from any given location.
To address the research questions, the following procedures were conducted. First, shade maps of Amsterdam were created for each warm month using the Daily Shadow Pattern tool of the Urban Multi-scale Environmental Predictor (UMEP). Second, cool spaces were identified and evaluated based on accessibility, shading, usability, capacity, heat risk, and Physiological Equivalent Temperature (PET) indicators. Lastly, after obtaining and processing the pedestrian network from the Open Street Map database, shade weight was calculated for each street segment, and cool spaces were incorporated into the network, allowing users to generate datasets of the shortest and shadiest distances to cool spaces, and an algorithm that performs four different routing options: the shortest, the shadiest, and two combinations of the shortest and shadiest paths with different weighting ratios either between two locations or from a starting point to its nearest cool space.
The project produced datasets which provide insights into Amsterdam’s cool spaces, their quality, and the shadiest and shortest routes to these locations. Additionally, the code to make these datasets has been made available on GitHub.
