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

Authors

GARCÍA ALONSO, CARLOS, DÍAZ MILANÉS, DIEGO, Daniel, M. , Salvador-Carulla, L.

External publication

No

Means

Facing The Problem Of Small Numbers In Expert-Guided Modelling: The Case Of Urban Health And Planning

Scope

Conference Paper

Nature

Científica

JCR Quartile

SJR Quartile

Publication date

01/01/2025

Scopus Id

2-s2.0-105018450661

Abstract

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. © 2025 IEEE.

Keywords

Computer circuits; Data mining; Fuzzy inference; Information management; Intelligent systems; Knowledge acquisition; Semantics; Urban planning; Consensus models; Decision support system; Decision supports; Expert knowledge; Expert knowledge elicitation; Fuzzy-Logic; Health planning; Support systems; Urban health; Urban health planning; Decision support systems