Facing the problem of small numbers in expert-guided modelling: the case of urban health and planning

This study presents a fuzzy logic-based methodology to capture and synthesize expert knowledge in urban health and planning, addressing challenges posed by small sample sizes. A fuzzy inference engine simulates expert consensus using structured semantic categories and consistency weighting. The approach is tested with variables such as fresh fish providers and postal offices in proximity to residences, analyzing their perceived impact on cardiovascular health. Results show the method’s capacity to extract nuanced insights while avoiding traditional Delphi limitations.