UrbanIneq: an open framework for urban accessibility datasets with potential socio-spatial inequality patterns

Abstract

Urban planning increasingly relies on data-driven approaches to support decision-making. However, the growing adoption of computational and machine learning methods in urban contexts also raises important concerns regarding the reproduction and amplification of existing socio-spatial inequalities, particularly when datasets contain historical biases affecting vulnerable population groups. At the same time, openly accessible and harmonized urban datasets integrating socioeconomic information with spatial accessibility indicators remain scarce in the literature, hindering reproducible urban accessibility and socio-spatial inequality studies.

This paper presents UrbanIneq, an open-source platform designed for the automated retrieval, integration, and analysis of urban datasets across municipalities in Andalusia, Spain. The framework combines publicly available spatial and socioeconomic data with GIS-based accessibility measures to Urban Green Spaces (UGS) and Healthcare Facilities (HCF) at census-section level. UrbanIneq integrates R-based geospatial analysis, a Python FastAPI backend, and a React-based web interface within a fully reproducible and containerized workflow. By providing harmonized and research-ready urban datasets through an interoperable infrastructure, the platform is intended to support researchers and policy makers in the analysis of urban inequalities affecting disadvantaged population groups.