Uncovering Dominant Social Class in Neighborhoods through Building Footprints: A Case Study of Residential Zones in Massachusetts using Computer Vision
This work addresses urban planners and researchers by providing a method to infer social class from building footprints, though it is incremental as it applies existing computer vision techniques to a new dataset in urban studies.
The paper tackled the problem of predicting zip-code level income from urban form by analyzing figure-ground maps using deep learning, achieving a model that identifies the relationship between social class and urban morphology with interpretable features like building size and density.
In urban theory, urban form is related to social and economic status. This paper explores to uncover zip-code level income through urban form by analyzing figure-ground map, a simple, prevailing and precise representation of urban form in the field of urban study. Deep learning in computer vision enables such representation maps to be studied at a large scale. We propose to train a DCNN model to identify and uncover the internal bridge between social class and urban form. Further, using hand-crafted informative visual features related with urban form properties (building size, building density, etc.), we apply a random forest classifier to interpret how morphological properties are related with social class.