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In urban theory, urban form is related to social and economic status. Thispaper explores to uncover zip-code level income through urban form by analyzingfigure-ground map, a simple, prevailing and precise representation of urbanform in the field of urban study. Deep learning in computer vision enables suchrepresentation maps to be studied at a large scale. We propose to train a DCNNmodel to identify and uncover the internal bridge between social class andurban form. Further, using hand-crafted informative visual features relatedwith urban form properties (building size, building density, etc.), we apply arandom forest classifier to interpret how morphological properties are relatedwith social class.

Uncovering Dominant Social Class in Neighborhoods through Building  Footprints: A Case Study of Residential Zones in Massachusetts using Computer  Vision