Noise management is an ever-growing part of designing future urban landscapes. We’re becoming more aware of the effects of noise pollution on health and quality of life. With that, the pressure on city planners to manage noise more efficiently is becoming more prominent. Current solutions for researching noise are often too expensive and time-consuming and, regarding noise, urban planning relies on intuition rather than on the data. In the thesis, we design and implement a cost-effective, scalable, flexible urban noise analysis system. We have developed affordable measuring units, a user interface, an open-source server for data storage, and a widget for efficient data analysis. We tested and confirmed the utility of the system in several pilot studies.
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