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Case Studies | Urban and Regional Planning | India | Volume 11 Issue 11, November 2022
Analyzing Sky View Factor Calculation Methods for Urban Climate Studies: Case Study Kolkata
Santanu Bajani | Dr. Debashish Das
Abstract: Many studies confirm that urban morphology cannot simply be described by the common indicators like urban canyon ration, ground coverage, density, building height, etc. It varies according to location and functions. The Sky View Factor (SVF) is an important parameter to describe the geometric shape of an urban canyon. This study introduces a new python-based approach to calculate SVF and assess the accuracy with existing SVF calculation methods for defining SVF in urban areas. Photographic Method, which analyses the Sky View Factor from Fish Eye lens images, and Remote Sensing and GIS method, which uses an urban 3D building dataset or DSM and proposed python-based method is chosen for the research. For Remote Sensing and GIS analysis, the existing buildings were considered and trees and street furniture were discarded. The building's footprints are digitized in ArcGIS and building heights measured by laser distance meter. The vector and raster database were created and processed. We used same input dataset to analysis SVF using the python script. The results show that lower values of SVF were obtained with the fish-eye lens analysis compared with the GIS methods and Python script. The mean variation between the SVF values obtained from python script and GIS method is 0.014, and with Fish-eye lens image is 0.079. The photographic method and vector-based GIS method was systematic enough for studying smaller areas having more details. For continuous SVF analysis over raster-based GIS method and python script is preferable. Raster-based python method is time consuming in respect with python script. The study of viewing each method of sky view has been made objective for specific research. Further investigation of the subject is recommended.
Keywords: Sky View Factor, Urban Canyon, Fish Eye Lens, Building Footprints, Python
Edition: Volume 11 Issue 11, November 2022,
Pages: 129 - 137