Comparison of Different Spatial Temperature Data Sources and Resolutions for Use in Understanding Intra-Urban Heat Variation

Abstract

Ascertaining appropriate heat mitigation strategies requires a thorough knowledge of intra-urban heat variation. Different data sources in various resolutions are available to represent and explain spatial heat distribution. However, choosing appropriate proxies and data resolutions congruent with system dynamics is challenging. Thus, in this study, we investigate the compatibility of specific vulnerability indicators and heat exposure data and the suitability of spatial temperature-related data at a range of resolutions for the representation of spatial temperature variations within cities. For this purpose, we include various types of known and theoretically based vulnerability indicators such as specific street-level landscape features and urban form metrics, population-based and zone-based variables as predictors, and different measures of temperature--air temperature, land surface temperature, and mean radiant temperature-- at a range of resolutions as dependent variables. We examine how different sets of predictors and spatial resolutions can explain spatial heat variation using data from Atlanta, Georgia. The results of this study suggest that lower-resolution data may still satisfactorily represent spatial urban temperature variation caused by landscape elements. We further demonstrate how the effect of specific landscape features and urban form metrics differ based on scale and density context.

Keywords: data resolution, spatial heat variation, vulnerability indicators, street-level features