Abstract
Despite the urban heat islands phenomenon has long been recognized as a major urban environmental problem, it was not until recently that this urban phenomenon gained attention from the discipline of urban planning. To integrate the findings of the urban heat islands research into the planning practice, the relationship between land surface temperatures and urban physical and socioeconomic characteristics should be addressed at the planning relevant spatial scale, a land parcel. Using a parcel as a unit of analysis, this study proposed to use a machine learning approach to identify important variables in the formation of urban heat islands in Indianapolis, Indiana. Applying random forest method to planning zones, this study identified planning zone specific urban physical and socioeconomic characteristics that are important for the interpretation of urban heat islands phenomenon of Indianapolis, Indiana. The main contribution of this study is twofold: to integrate urban physical and socioeconomic characteristics into a land parcel for the better interpretation of the result of urban heat islands study into planning practice and to apply machine learning approach to identify highly determinant variables in the formation of urban heat islands.
Highlights
The warming trend of US cities becomes significant since the late 1970s and its rate and magnitude of this trend severed during the late 1990s [1,2,3]
As urban heat islands (UHIs) occur as a result of land cover transformation, mainly replacement of natural vegetation and natural land cover by impervious surfaced associated with urban land uses, key questions for the discipline of urban planning include what are the most contributing urban physical characteristics to
This study proposes to (1) integrate remotely sensed land surface characteristics, built area characteristics, and socioeconomic demographic characteristics of the urban area into planning relevant urban scale (2) apply the random forest (RF) method, a machine learning approach, as well as principal component analysis (PCA), a non-parametric data reduction technique widely used by social vulnerability studies, to identify and select important variables to the formation of UHIs, and (3) explain what makes the urban area hotter, how these factors are differentiated depends on the planning zones, and who are the most vulnerable to the heat-related weather event in each planning zones
Summary
The warming trend of US cities becomes significant since the late 1970s and its rate and magnitude of this trend severed during the late 1990s [1,2,3]. The relationships between remotely sensed land surface temperature and physical land surface characteristics have been intensively investigated in many disciplines of physical sciences, such as geography, as an important climatological phenomenon Together with this trend, many social science studies have focused on addressing socioeconomically vulnerable groups of population and regions to the heat-related weather event. Variables for urban heat islands studies As UHIs occur as a result of the land cover transformation, primarily replacement of natural vegetation and natural land cover by impervious surfaced associated with urban land uses, previous empirical UHIs research has mainly focused on the causal relationship between remotely sensed land surface temperature and remotely sensed physical land surface characteristics of urban areas such as elevation and slope [15, 16], land cover types [5, 10, 17, 18], percent impervious surface [5, 15, 19], percent tree canopy cover [5, 7, 19], vegetation abundance indices [16, 18,19,20]
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