Understanding the Problem
Lead is a well-documented neurotoxin, and no level of exposure is safe. Our project addresses two key problems:
Using statistical and machine learning models, we analyze available water testing and property data to estimate the percentage of Chicago households exceeding the EPA's proposed revised limit of 10 ppb for lead in drinking water.
Many entries in Chicago's service line inventory are classified as "suspected lead." Our project develops predictive models that classify these service lines more accurately based on property characteristics, location, and water testing data.
Our Approach
We use advanced data science techniques to provide accurate risk assessments and empower Chicago residents.
We combine property data, service line information, and water testing results to create comprehensive datasets for analysis.
Using spatial regression, machine learning, and statistical analysis to predict lead service line locations and contamination risks.
Providing accessible information, risk mitigation strategies, and policy recommendations to help residents protect themselves.