Reconstruction of historical datasets for analyzing spatiotemporal influence of built environment on urban microclimates across a compact city

Previous studies have analyzed the relationships between high-rise/high-density environment and microclimates, by either a temporal study or a spatial approach, while a strategy for investigating the spatiotemporal relationship has yet to be developed. This study initiated a set of innovative strategies to map the historical built environment/microclimates of a compact city, with a spatiotemporal approach to analyze the relationships between building structures and urban climates, for developing a sustainable protocol for future urban planning. Three major components were reconstructed, including 1) the annually averaged Land Surface Temperature (LST) for determining the relative temperature across a compact city; 2) 3D building datasets for representing the building morphology; and 3) sets of urban morphological data derived from building datasets for analyzing microclimate and thermal distress. There are high correlations between observed and predicted LSTs (R = 0.64 to 0.89), with mean absolute error (MAE) of annually averaged LST ranging 0.49 °C–2.60 °C, and root mean square error (RMSE) ranging 0.62 °C–2.98 °C. There are low errors for reconstructing building data, in which MAEs and RMSEs of an open space are 0.41 m–1.23 m and 0.78 m - 1.46 m; and for an area with buildings are 0.81 m–3.25 m and 1.06 m - 5.92 m. The spatiotemporal estimation indicated areas with improved air ventilation through years can significantly reduce an additional 0.12 °C - 1.09 °C than the areas without improvement, while areas with an increase in shades through years have 0.6 °C–0.76 °C higher reduction of relative temperature.