  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.
