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Emergency response plays a critical role in mitigating the impact of disasters and ensuring public safety. Understanding a city's capability for emergency response is vital for effective disaster management and urban planning. This paper describes a comprehensive geospatial dataset that assesses the emergency response capability of cities in Portugal based on their urban infrastructure, accounting for the number of hospitals, police stations, fire department units, and metro/railway stations. These infrastructures are essential for attending to victims, mitigating emergency situations, and performing rescue operations. Besides that, the GeoJSON definitions of all Portuguese cities are also provided in the dataset, which were used to compute the number of the target facilities based on data from OpenStreetMap. The potential applications of this dataset are numerous, ranging from urban planning and resource allocation to disaster response strategy development. Moreover, it indicates where public investments are most required, especially when combined with others continuously updated public datasets with incidents in urban areas.

CityZones is an online tool to support when computing risk zones in a city, which is performed by mathematically evaluating the emergency response capability of an urban area using real data. The tool allows users to define polygonal Areas of Interest (AoI) on a map, which are categorised into low, medium, and high-risk levels. As urban emergencies in general will typically demand the assistance of hospitals (to attend victims), firefighters (for rescue operations and damages reduction), policemen (to control and manage critical situations), and even metro stations (for evacuation), the risk zones within an AoI will be computed according to the distances to such existing infrastructure, which is extracted from the OpenStreetMap database. The coordinates of an AoI are defined at the client-side and submitted to a server as a regular or irregular polygon, which is then stored in a GeoJSON file along with the computed classifications for further processing according to a multi-tier pipeline. Adopting a configurable square-based mathematical model to compute distances and relative risk levels, the risk zones are computed on-demand and exported to CSV files to be used by any application, with an additional representation for web-based visualisation. Moreover, as a practical exploitation of the computed maps, the CityZones tool also suggests ideal positions for sensors-based Emergency Detection Units (EDUs) within an AoI according to the chosen positioning algorithm, assuming that the computed risk zones indicate how severe an undetected emergency would be. While the CityZones tool is not intended to provide a mitigation approach at all, since it does not have knowledge about current emergency situations, it stands as a valuable pre-mitigation tool to be used in the planning phase of emergency management in smart cities, bringing an important contribution for practical applications in this area.