From Hubs to Deserts: Urban Cultural Accessibility Patterns with Explainable AI

TL;DR

This paper introduces a scalable, open-data framework to measure and explain spatial equity in urban cultural infrastructure access using explainable AI, revealing core-periphery patterns and socio-demographic disparities.

Contribution

It develops a novel metric called CIAS and applies explainable AI models to analyze cultural accessibility patterns at fine spatial resolution.

Findings

Core-periphery gradient in cultural infrastructure access

Libraries provide broader coverage in dense, lower-income areas

Non-library infrastructures cluster near urban cores

Abstract

Cultural infrastructures, such as libraries, museums, theaters, and galleries, support learning, civic life, health, and local economies, yet access is uneven across cities. We present a novel, scalable, and open-data framework to measure spatial equity in cultural access. We map cultural infrastructures and compute a metric called Cultural Infrastructure Accessibility Score (CIAS) using exponential distance decay at fine spatial resolution, then aggregate the score per capita and integrate socio-demographic indicators. Interpretable tree-ensemble models with SHapley Additive exPlanation (SHAP) are used to explain associations between accessibility, income, density, and tract-level racial/ethnic composition. Results show a pronounced core-periphery gradient, where non-library cultural infrastructures cluster near urban cores, while libraries track density and provide broader coverage. Non-library accessibility is modestly higher in higher-income tracts, and library accessibility is slightly higher in denser, lower-income areas.