I was fascinated by an article in the New York Times that highlighted how city trees are more likely to exist in wealthier neighborhoods, and I wanted to explore this issue using spatial analysis. I built a demo application using Google Cloud BigQuery and CARTO's BigQuery Spatial Extension to create a tree equity index for approximately 6,000 census block groups in New York City. The index score is based on the number of trees and tree width within each block group, with weights assigned to these features to prioritize quantity over size. I used two SQL queries to generate this index score: one for calculating ACS and street tree data per block group, and another for creating the custom tree index score by standardizing the features using a machine learning preprocessing function, summing them together, capping outliers, and calculating a 0-100 index score. The resulting dashboard shows the tree scores for each census block group, as well as a correlation exploration between the tree scores and median income and racial demographics. The analysis reveals a weak positive correlation between median income and abundance of trees, but no strong correlations with major racial demographics.