Google Earth Engine and Neighborhood Change: A New York City Case Study

by

Faculty Sponsor: Karl Boulware

Ruishi Wang

Ruishi is a rising junior (’26) from Beijing, China. At Wesleyan, he studies Economics, Science and Technology Studies, and Data Analysis. In his free time, he likes rock climbing, photography, and eating.

Abstract:

Geospatial data is increasingly used for economic analysis in areas like economic growth, labor markets, gentrification, and segregation. This study leverages Google Earth Engine (GEE) to analyze neighborhood change in New York City. GEE is a cloud-based, open-source platform that facilitates access to and processing of satellite imagery and geospatial data.

Neighborhood change involves shifts in housing demand or supply, impacting demographics and housing quality. This study uses census tracts, census blocks, and NYC OpenData building footprints as units of analysis. While census tracts are commonly used to represent neighborhoods, this study found mixed results using GEE. Some blocks intersect multiple tracts, and building footprints have poor coverage, with only 13 out of 39 structures found in block 3000.

Additionally, historical district maps intersect tracts and blocks inconsistently, suggesting a need to manually create objects for each building to accurately measure neighborhood change.

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