Using Geospatial Visualization Techniques to Uncover Underlying Disparities in Air and Water Quality in the Redlined Areas of New York City

Nxumalo, Neo L.

Using Geospatial Visualization Techniques to Uncover Underlying Disparities in Air and Water Quality in the Redlined Areas of New York City

2026

Old Westbury, N.Y. : New York Institute of Technology, 2026.

Geospatial data
Cartography—Data processing
Geographic information systems
Air—Pollution—Maps
Air quality management
Water quality—New York (State)—New York
Water—Pollution—New York (State)—New York
Water quality management

English

This study is about identifying the potential environmental disparities in neighborhoods in New York City that were redlined. This research aims to identify any potential disparities specifically in the quality of air and water in neighborhoods in New York City. Using descriptive analytics on publicly available data from NYC Open Data, bar charts, time series graphs, and folium maps, were created to uncover any patterns in water quality and air quality. The key findings indicated that the water quality issue seen in all areas of interest may be city wide issues and are barely indicative of any real disparity. Data pertaining to emergency room visits in the areas of interest was a significant indicator of the drastic difference in air quality, with redlined neighborhoods having greater total emergency room visits due to ozone and particulate matter, PM2.5. In addition to the analysis done on air and water quality data, there was also population data that included racial demographic data. This was plotted onto several different choropleth maps and this revealed that the spread of non-Hispanic white people was lower in the redlined areas but higher in some of the greenlined areas such as the Upper East side, Forest Hills and Riverdale. As for the rest of the populations, the highest populations of each group tended to be spread across all four redlined areas with the highest populations being in Jamaica, Queens Bedford Stuyvesant, Melrose, and Harlem.

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Thesis

College of Engineering & Computer Science

Department of Computer Science

Master of Science in Data Science