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

arXiv: https://arxiv.org/abs/2511.07475 · HTML: https://mlsanity.com/paper/2511.07475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 8 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling](https://mlsanity.com/paper/2503.08482/) (2025)
- [Are You Misinformed? A Study of Covid-Related Fake News in Bengali on Facebook](https://mlsanity.com/paper/2203.11669/) (2022)
- [From Bad to Worse: Using Private Data to Propagate Disinformation on Online Platforms with a Greater Efficiency](https://mlsanity.com/paper/2306.04883/) (2023)
- [Cultural gems linked open data: Mapping culture and intangible heritage in European cities](https://mlsanity.com/paper/2402.07335/) (2024)

## Abstract (arXiv)

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.