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

## TL;DR

This study uses deep learning and visual features of urban form to predict and analyze social class distribution in neighborhoods, demonstrating the potential of computer vision in urban socioeconomic research.

## Contribution

It introduces a novel approach combining deep learning and handcrafted features to uncover social class from urban form at a large scale.

## Key findings

- Deep learning effectively predicts social class from urban form.
- Handcrafted features reveal specific morphological properties linked to social class.
- The method demonstrates high accuracy in classifying neighborhoods by income level.

## Abstract

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.

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Source: https://tomesphere.com/paper/1906.05352