This github repo deposits the analysis data in GIS and Matlab code to generate the regression analysis results. There are three folders in the data folder: 3D_files, Rasters, and Shade. The 3D_files folder includes the GIS data such as building footprints/fences/tree locations. The Raster data folder includes the MASTER land surface temperature layer (https://sustainability-innovation.asu.edu/caplter/data/view/knb-lter-cap.629.1/), NAIP land classification (https://data.sustainability-innovation.asu.edu/cap-portal/mapbrowse?packageid=knb-lter-cap.623.1), Sky view factors, and nDSM layer. The Shade folder includes the shading layers at different time. The LIDAR data we used for Tempe, Arizona can be found and downloaded at https://geodata-asu.hub.arcgis.com/apps/asu::2014-metro-phoenix-usgs-lidar-data/about.

Park, Y., Zhao, Q., Guldmann, J.-M., & Wentz, E. A. (2023). Quantifying the cumulative cooling effects of 3D building and tree shade with high resolution thermal imagery in a hot arid urban climate. Landscape and Urban Planning, 240, 104874. https://doi.org/10.1016/j.landurbplan.2023.104874

This research was made possible by the Chung-Ang University Research Grants in 2021, the ESRC’s ongoing support for the Urban Big Data Centre (UBDC) [ES/L011921/1 and ES/S007105/1], and the Royal Society International Exchange Scheme [IEC/NSFC/223042]. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions on an earlier version of this manuscript.