Using Data for Housing Equity
Image Source: Nelson, Winling, Marciano, Connolly, et al., Mapping Inequality
Through the digitization of zoning maps in American cities, scholars now have a unique lens and greater insight into analyzing the wealth disparity between different neighborhoods.
Driven by racist housing policies in the first part of the 20th century, it is easy to now visually see how segregation maps directly with less desirable areas for housing.
A unique pattern that emerged was the difference in temperature between wealthier and poorer neighborhoods. The more desirable neighborhoods were invested in tree-lined streets and parks, while the least desirable neighborhoods had little green space and an abundance of cement and asphalt.
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What’s important today is that we can use geospatial data to learn from these historical inequalities. Machine learning is becoming more powerful every day in the ability to layer and analyze multiple data sets.
These technological improvements will be useful in the future when cities are looking at planning neighborhoods with more significant social equity.
WORDS BY
ASSOCIATE CREATIVE STRATEGY DIRECTOR
CHRISTOPHER HALL