Using Data-Driven Feature Engineering Algorithms to Determine the Most Critical Factors Contributing to the Urban Heat Island Effect Associated with Global Warming

Urban clusters are areas with high building and infrastructure concentration and limited green and natural landscapes. Since buildings, bridges, roads, and other infrastructure absorb heat from the sun and re-emit it to the atmosphere, urban areas are becoming "islands" that have higher temperature compared to rural surrounding areas. This phenomenon is known as the urban heat island (UHI) effect, which is associated with global warming and the heat generated from increased human urban activities. While previous studies examined potential factors that could affect heat-related challenges, little-to-no research prioritized these factors using a data-driven approach. To address this knowledge gap, this paper determined the most critical factors that contribute to UHIs using feature selection- and machine learning-based methods. First, a dataset was collected and developed for all the census tracts in the state of New Jersey in relation to 14 factors that could impact the UHI effect. These factors are considered as independent variables, while the average day UHI intensity for each census tract is considered as the dependent variable. Second, feature engineering algorithms were implemented to extract or identify the prominent factors. Third, the most critical factors were ranked and prioritized. The results compared the prioritization and ranking of the different factors based on multiple feature engineering algorithms so that robust conclusions could be obtained. This study serves as a useful reference for decision-makers involved in addressing the challenges resulting from UHIs by offering a better understanding of the most important factors that need to be better controlled or managed.