Tae Hong Park, Johnathan Turner, Michael Musick, Jun Hee Lee, Christopher Jacoby, Charlie Mydlarz, Justin Salamon
Mining Urban Data (MUD) Workshop Proceedings of the EDBT/ICDT 2014 Joint Conference
Publication year: 2014


Noise pollution is one of the most serious quality-of-life issues in urban environments. In New York City (NYC), for example, more than 80% of complaints1 registered with NYC’s 311 phone line2 are noise complaints. Noise is not just a nuisance to city dwellers as its negative implications go far beyond the issue of quality-of-life; it contributes to cardiovascular disease, cognitive impairment, sleep disturbance, and tinnitus3, while also interfering with learning activities. One of the greatest issues in measuring noise lies in two of the core characteristics of acoustic noise itself — transiency and structural multidimensionality. Common noise measurement practices based on average noise levels are severely inadequate in capturing the essence of noise and sound characteristics in general. Noise changes throughout the day, throughout the week, throughout the month, throughout the year, and changes with respect to its frequency characteristics, energy levels, and the context in which it is heard. This paper outlines a collaborative project that addresses critical components for understanding spatio- temporal acoustics: measuring, streaming, archiving, analyzing, and visualizing urban soundscapes with a focus on noise rendered through a cyber-physical sensor network system built on Citygram.

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