•MC-SDSS is suitable for site selection and complex decision-making processes.•AHP and FAHP comparison shows their differences in supporting site selection.•Sensitivity analysis is performed to test the robustness of the evaluation.•The development of suitability maps supports the concertation among stakeholders. Integrated evaluation methods have started to gain more and more importance for supporting the decision-making process in the field of urban and territorial planning, contributing to the consideration of the multidimensionality of the elements involved and the multiple feedback of stakeholders. In the context of infrastructure location in cities, spatial multicriteria analysis is becoming increasingly relevant, thanks to its ability to synthesize the full range of aspects, through Multicriteria Decision Analysis (MCDA), and for the consideration of the spatial dimension, thanks to its integration of GIS data. The case study selected to test this mixed method is the new healthcare project for the metropolitan city of Turin (Italy). The decision where to locate a new healthcare facility is highly complex since many aspects have to be considered, spanning from technical to social factors, and from locational to environmental elements. This choice case study aims at highlighting the role of spatial multicriteria analysis to support Decision Makers (DMs) in the identification of suitable locations for urban facilities and shows the potential of the approach for increasing the participation of different stakeholders thanks to a simple visual representation of data and results. In particular, the research makes use of Analytic Hierarchic Process (AHP) and Fuzzy AHP (FAHP). The comparison of the two approaches shows that they can both be used for identifying suitable locations for urban infrastructures. However, this research demonstrates that AHP should be preferred in the case of the high relevance of all the criteria considered, whereas FAHP introduces more uncertainty in criteria weights assignment and reduces the burden of many spatial analyses with GIS.