In 2014, noise rated as the top quality-of-life complaint for a New Yorker.xii In order to reduce noise pollution, the New York City Department of Environmental Protection (DEP) and the New York City Police Department (NYPD) mitigate noise issues based on noise complaints, which are mainly reported via 311 service calls.xiii However, there are limitations of evaluating noise by the volume of complaints, especially regarding noise’s impact on quality of life, since “noise pollution” is both a physical and perceptual matter. On one hand, noise is the range of sounds that can be measured in decibel (dB) with sound meters or sensors; on the other hand, noise is sensed very differently by people of various ages and health conditions. Therefore, a proper evaluation of noise for 370 Jay Street requires analysis in both physical acoustic features and demographic facts. In this study, we aim to evaluate the noise impact that the building will have on the surrounding community, and better understand the sources of noise physically and the perception of noise by people psychologically.
(Figure 1: Noise Complaints Comparison by Types, NYC and Zip code 11201.)
In this study, we use 311 complaints data from NYC Open Data, PLUTO data and U.S. Census data. The 311 data contains all city services and complaints calls from 2010 to present including noise complaints. PLUTO (Primary Land Use Tax Lot Output) contains detailed information on lots in NYC such as tax assessments, historic districts, year built, number of units, lot size, etc. U.S. Census data reveals the demographic change of this neighborhood tabulation area and census tracks from 2000 to 2010. Noise is a both a physical and perceptual factor to quality-of-life, so we developed two approaches to measure the impact of noise. Specifically, we aim to capture the dynamic between physical noise generators and people’s perception to urban noise within the study area. Through historical data analytics, we identify the quantitative and qualitative characters, especially the baseline for noise intensity and diversity of the neighborhood around the project.
(Figure 4: Regression between number of new constructions and number of noise complaints .)
Furthermore, by linking 311 and PLUTO data, we can investigate the spatial correlation between noise, building types and land use attributes. Finally, we use sound meter to measure noise on site to set up certain metrics for future studies that would integrate this new data with other indicator of quality-of-life indices such as transportation and energy consumption. Beyond this, we also explored the potential metrics to better predict and understand 311 data on noise by analyzing the spatial pattern of noise complaints and building classes from PLUTO data.
Using Sound Meter, a smart phone application, allowed the team to monitor the sound level on site during randomly selected “peak” and “off-peak” hours. During a weekday noon rush hour, the average ambient sound level was 75.46 dBA with a standard deviation of 3.21 dBA. This is 5 dBA higher than the marginal acceptable general external exposure sound level for residential, school or commercial buildings. xvi We used same method measured noise (loud music) from shops and food trucks, which is displayed in Figure 4.