RT journal article
T1 Fairness-regularized geographically weighted regression for urban accessibility analysis
A1 Ramírez Cobo, Josefa
A1 Montero Lopez, José Ismael
A2 Estadística e Investigación Operativa
K1 lgorithmic fairness
K1 Geographically weighted regression
K1 Spatial accessibility
K1 Urban analytics
K1 Fairness-aware prediction
K1 Urban planning
K1 Spatial data science
AB The increasing use of predictive models in socially sensitive domains has intensified concerns regarding the reproduction of historical biases affecting vulnerable population groups. Although fairness-aware machine learning has developed numerous methodologies to mitigate unfair predictive behaviour, these approaches have been largely concentrated on non-spatial applications such as finance, healthcare, or recommendation systems. In contrast, urban-planning studies have extensively documented socio-spatial disparities related to accessibility to public services and urban opportunities. Consequently, predictive models trained on urban data reflecting such inequalities may perpetuate or intensify existing accessibility gaps.Motivated by real urban-accessibility datasets exhibiting significant socioeconomic disparities, this paper introduces a fairness-regularized extension of geographically weighted regression (GWR). Specifically, the proposed methodology incorporates explicit fairness constraints into the classical locally weighted least-squares formulation in order to regulate predictive disparities between sensitive and non-sensitive population groups. The resulting constrained regression problem can be efficiently solved using standard quadratic-optimization routines. The proposed methodology is illustrated through two urban-accessibility case studies in the city of Seville (Spain) involving Urban Green Spaces and Healthcare Facilities datasets. Numerical experiments reveal a clear trade-off between predictive accuracy and fairness, while also highlighting the spatial redistributive effects induced by fairness constraints.
YR 2026
FD 2026
LK http://hdl.handle.net/10498/39695
UL http://hdl.handle.net/10498/39695
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026