A Spatial Lag Model for Global Urban Heat Islands

Authors: Kangning Huang*, Yale University
Topics: Climatology and Meteorology, Urban Geography, Spatial Analysis & Modeling
Keywords: Urban Heat Island, Regional Climate, Urbanization, Spatial Regression
Session Type: Paper
Day: 4/13/2018
Start / End Time: 10:00 AM / 11:40 AM
Room: Napoleon B1, Sheraton 3rd Floor
Presentation File: No File Uploaded

Many studies have showed that the intensity of urban heat island (UHI) effect is positively correlated with city size. As the global urban population is expected to grow by ~2.7 billion, the well-established UHI-size relationship suggests that UHI intensity will likely be strengthened across the world. In addition to its enormous magnitude, contemporary urbanization has another important characteristic, that is the emergence of mega-urban regions (MURs), extended metropolitan areas that encompass multiple cities, such as the Boston-Washington corridor, the Pearl River Delta Metropolitan Area, and the Tokyo-Yokohama region. However, despite the well-documented trends of urban areas merging into MURs, little is known about how the UHI intensities of these nearby cities will affect each other. To explore this spatial dependency, the paper uses spatial regression technique to analyze UHIs of MURs across the world. The extents of urban areas are delineated with the Global Human Settlement Layer dataset, the UHI intensities are measured by MODIS land surface temperature (LST) products, and the spatial lag model is applied to analyze the spatial dependency of UHIs. The spatial regression model that considers the nearby effect of UHIs performs better (r2=0.72) than the non-spatial regression model (r2=0.60) or daytime UHIs. Yet, the performance improved (spatial: r2=0.76, non-spatial: r2=0.75) is not obvious for nighttime UHIs, suggesting little spatial lag effects exist in the evening. Our results suggest that regional coordination effects are necessary in mitigation of UHI effects.

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