This paper outlines the design of a data pipeline addressing the impact of urban tree patterns on urban heat islands (UHI) and describes a validation study using seven site(s) in Baltimore, MD, USA. Cities are experiencing an increasing rise in urban microclimate air temperatures due to the urban heat island effect (UHI) and climate change. Previous studies have identified tree canopy cover as a critical resource in reducing UHI. However, research has not disentangled the neighbourhood effects of tree patch size, shape, fragmentation, composition, or leaf area densities, hereafter referred to as urban tree patterns, have on mitigating UHI. The data pipeline outlined here allows researchers to aggregate multiple data sources into ENVI-met for microclimate simulations across various neighbourhoods and output the simulation results into an analytical python processing workflow for statistical analysis in r. Initial validation results show that potential air temperatures are within acceptable limits of field measurements. Correlations show relationships between five urban tree patterns, landscape class metrics: proportion of tree cover, patch density, largest patch index, edge density, landscape shape index, and effective mesh size. However, only two show binomial logistic regression significance: proportion of tree cover and effective mesh size.