On hot summer days, many people seek out air-conditioned cafés to cool down and rest – especially older adults and vulnerable groups. However, in densely built urban areas like Vienna's 1st district, there's little reliable data showing which cafés even offer air conditioning. Although OpenStreetMap (OSM) includes an air_conditioning tag, it is rarely used in practice.
To address this gap, we analyzed 139 cafés in the 1st district and discovered that only 6 entries contained AC information. The dataset also had significant gaps in other key attributes like opening hours, addresses, and websites.
The map below shows the initial state of the dataset, with most cafés lacking information on AC availability. Only 4 cafés were marked as having AC, and 2 without – while the remaining 133 had no data. This lack of information limits the usefulness of the dataset for both citizens and urban planners.
We manually reviewed and updated the data by calling each café manually, conducting additional online research, checking websites, and validating locations. Cafés with outdated or unclear data were excluded. As a result, we compiled a cleaned dataset of 120 cafés, with:
The updated map below provides a much more reliable and complete picture. It highlights the distribution of air-conditioned cafés across the city center and offers a useful basis for climate adaptation strategies, heat mitigation planning, or simply finding a cool place to relax in summer.
To visualize the spatial concentration of cafés with air conditioning, we created a heatmap using only cafés where air_conditioning = yes. This helps identify zones with dense offerings of cool indoor locations, which are especially important during heatwaves.
The highest concentrations are found near Stephansplatz and Kärntner Straße, with additional spots near the Ringstraße and University area.
This map can help urban planners and vulnerable groups alike in locating clusters of heat-resilient spaces.
The following map presents the results of a DBSCAN clustering analysis applied to cafés with confirmed air conditioning in Vienna's 1st district. For this purpose, only points with air_conditioning = yes were used. Coordinates were transformed into a projected system (EPSG:32633) to enable accurate distance-based clustering.
We applied DBSCAN with parameters ε = 150 meters and min_samples = 3, which are well-suited for dense urban environments. These parameters allow the detection of meaningful spatial concentrations while ignoring isolated cases.
The results show several notable clusters, especially near Stephansplatz and along Kärntner Straße, where cafés with air conditioning are heavily concentrated. Additional, smaller clusters can be seen around the University area, the Museumsquartier, and further south. Grey points represent noise – cafés that could not be grouped into a cluster – highlighting areas with sparse AC coverage.
This project demonstrates the value of enriching and verifying open datasets to support real-world applications. By manually completing and validating air conditioning information for cafés in Vienna’s 1st district, we have created a more useful resource for both the public and urban planners.
The approach can easily be extended to other districts or cities, helping to identify climate-adaptive indoor spaces in urban areas. Verified information on air_conditioning can also be re-integrated into OpenStreetMap (OSM) to improve the global availability of POI attributes related to heat resilience. In this way, small-scale contributions can scale up to support community mapping, smart city development, and better preparedness for extreme weather events.