Health is an important societal topic in the Netherlands. In one of the most densely populated countries in the world, healthy living environments reduce diseases, deaths, healthcare costs, and shifts focus from prevention and medicalising on the individual levels to taking care of communities. Still, for example, the air quality in the Netherlands is considered moderately unsafe, resulting in the highest rate of asthmatic children worldwide. The same goes for recordings of diseases related to polluting substances on land and in water. This makes public spaces social determinants of public health. We see similar correlations when it comes to the presence of infectious microbes and parasites, and environmental stress. Especially in world port-cities like Rotterdam unhealthy conditions coincide with unequal socio-spatial patterns. Here the impact on individual health is largely untraceable. Understanding the impact of inherent industrial and human activities on urban areas at the neighbourhood level and crossing it with heterogeneous data sets help us understand the socio-spatial impact of pollution-related and vector-borne diseases on cities. Measuring environmental pollution in public spaces can tell us e.g. more about the impact of air quality on citizens as a group. Statistical time series and cross-sectional data analyses can be applied to generate valid correlations if they are made geo-specific. By using machine learning and AI technologies we cross data on environmental pollution with other heterogenous socio-spatial and temporal data sets. The use of mapping, spatial statistics, and urban narratives including historical data can lead to a better understanding of the lived experience at the local level. Through workshops at the local level and notably in the public spaces of the city, we engage the general public and local decision-makers in discussions on public health using advanced computer models for visualisation. The Rotterdam case study provides insights applicable in other cities internationally.