Urban morphological data for street canyon model

Published: 30-07-2020| Version 2 | DOI: 10.17632/dmrhmrfv4j.2


Air temperature prediction models were established based on measured meteorological data and urban morphological variables. This dataset was used to conduct sensitivity analysis of air temperature prediction model. Urban morphology variables in this study include : (1) SVF: onsite sky view factor; (2) GnPR: area-weighted average leaf area index within 50 m radius; (3) BDG: building coverage within 50 m radius (%); (4) PAVE: pavement coverage within 50 m radius (%); (5) WALL: total exterior wall area within 50 m radius (m2); (6) H: average building heights within 50 m radius (m); (7) ALB: average surface albedo within 50 m radius. In general, the regression model can be analysed by the control variable method (e.g. BDG =20% for SVF=0.1, 0.2, 0.3, 0.5). However, this kind of analysis is difficult to visualize in the 3D model and is therefore useless to the designer. Aspect ratio (H/W) was used to bridge the gap between mathematical analysis and design analysis. First, 3D models of urban street canyons were built based on the aspect ratio (H/W) at intervals of 0.4 from 0.8 to 4. The street width was set to 30 m, 50 m and 80 m respectively. Then, 3D models were exported to STEVE tool where urban morphology variables (SVF, GnPR, BDG, PAVE, WALL, H, ALB) were calculated based on grid calculation of 50 m radius. Note 1 for Sheet named "Urban Morphology": First, 3D models with different aspect ratios for different street widths were constructed in SketchUp. Then, use the STEVE tool to calculate the urban morphological data (SVF, GnPR, BDG, PAVE, WALL, H, ALB). Note 2 for Sheet named "Cumulative Temperature Increase": Cumulative Temperature Increase (CTI) was determined by the emprical model of the outdoor air temperature in Singapore.