Using Machine Learning to Find Environmentally At-Risk Communities

10 Pages Posted: 22 Feb 2019

See all articles by Shiran Victoria Shen

Shiran Victoria Shen

University of Virginia - Woodrow Wilson Department of Politics

Date Written: December 1, 2018

Abstract

Environmental health persists as a genuine concern in many US localities. However, public agencies often face limited capacity and resources to collect comprehensive environmental health data. Inspired by CalEnviroScreen, an environmental health assessment tool used to identify environmentally at-risk communities in California, I calculate pollution burden scores at the census tract level for the entire contiguous United States. Pollution burden is a composite score that encompasses 12 environmental (air, water, waste) indicators. I combine the actual pollution burden indicator data with predicted statistics using machine learning. I create an interactive, publicly accessible National Pollution Burden Map using ArcGIS Online. Although applied to US states, the same approach can also be applied to other regions of the world.

Keywords: environmental health, environmental justice, pollution, machine learning, United States

Suggested Citation

Shen, Shiran Victoria, Using Machine Learning to Find Environmentally At-Risk Communities (December 1, 2018). Available at SSRN: https://ssrn.com/abstract=3329557 or http://dx.doi.org/10.2139/ssrn.3329557

Shiran Victoria Shen (Contact Author)

University of Virginia - Woodrow Wilson Department of Politics ( email )

PO Box 400787
University of Virginia
Charlottesville, VA 22904
United States

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