3D Data Visualization and Analysis Tools for AI Ready City: Space Syntax and Social Media Data
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Abstract
Space syntax is now widely accepted as a set of techniques that can be used to efficiently analyze spatial morphological structure at the city or community level. Segment analysis, a type of space syntax that is typically rendered through two-dimensional vector lines, can show the effectiveness of pedestrian and vehicular accesses to parts of a city. However, analysis of a city’s condition is far too diverse and complex for the use of space syntax alone. Other types of information, such as data from social media, can be integrated to determine and locate problems in the city, or to search for areas with potential for development. These types of data help in analyzing the quality of experience for those using the urban spaces, and they can be obtained by compiling the judgements of actual city dwellers, or by using advanced technologies to create a more realistic virtual reality and letting system users be the judges. The purpose of this research is to develop a 3D model and a virtual reality system capable of displaying the results of 3D urban morphological analysis, using space syntax segment analysis and social media data from urban space users to support the collaboration and communication among architects, designers, urban planners, city policy makers, or other city stakeholders. The virtual 3D model was created by using photogrammetry from aerial photographs, as well as a low polygon model built with referenced data from the photogrammetry model for faster rendering. The area of Thammasat University, Rangsit Center, was used as the prototype area for the AI Ready City.
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