Interpretable machine learning learns complex interactions of urban features to understand socio-economic inequality

Inequality in cities is a phenomenon arising from the complex interactions among urban systems and population activities. Conventional statistics and mathematical models like multiple regression models require assumptions of feature interactions with specified mathematical forms that may fail to fully capture complex interactions of heterogeneous urban components, creating challenges in systematically assessing socio-economic inequality in cities. To overcome the limitations of these conventional mathematical models, in this work, we propose an interpretable machine learning model to capture the complex interactions of urban variables and the main interaction effects on socio-economic statuses. We extract urban features from high-resolution anonymized mobile phone data with billions of activity records related to people and facilities in 47 US metropolitan areas and predict the attributes of urban areas from six income and race groups. We show that socio-economic inequality in cities can be effectively measured by the predictability of trained machine learning models in controlled experiments. We also examine the tradeoff between spatial resolution, sample size, and model accuracy; test the presence of influential features; and measure the transferability of the trained models to identify the optimal values for controlled factors. The results show that metropolitan areas share similar patterns of inequality, which could be moderated by improved polycentric facility distribution and road density. The generality of associated factors and transferability of machine learning models can help bridge data gaps between cities and inform about inequality alleviation strategies. Despite similarities, 50% to 90% of variations among cities are still present, which shows the need for localized policies for inequality alleviation and mitigation. Our study shows that machine learning models could be an effective approach to examine inequality, which opens avenues for more data-centric and complexity-informed planning, design, policymaking, and engineering toward equitable cities.