Authors: Mengdi Guo*, University of Hong Kong, Jianxiang Huang, University of Hong Kong, Yiming Sun, City University of Hong Kong, Lishuai Li, City University of Hong Kong, Rong-Jun Shyu, National Taiwan Ocean Univeristy
Topics: Environmental Perception, Urban and Regional Planning, Quantitative Methods
Keywords: Urban Sound Environment, Big-Data, Noise Mapping
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Poydras, Sheraton, 3rd Floor
Presentation File: No File Uploaded
The traditional approach of assessing urban sound environment is through measurement or simulation. In recent decades, the availability of new source and methods of collecting data provided opportunities to study occupant response to the urban sound environment. We use a combined approach to analyze the urban sound environment using geo-tagged twitter data and public nuisance petitions in Greater Taipei Area. Text-mining, machine learning methods, regression analyses, mapping are utilized to analyze the collected dataset. Results show a spatial correlation between sound perception, built environment and socio-demographic attributes, and confirm big-data as a valuable supplement of the traditional approach to monitor and manage the urban sound environment. Findings have implication on urban planning and design.