DOI: 10.5061/dryad.ncjsxkt35
title: Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston

description: Noise pollution in cities has major negative effects on the health of both
 humans and wildlife. Using iPhones, we collected sound-level data at
 hundreds of locations in four areas of Boston, Massachusetts (USA) before,
 during, and after the fall 2020 pandemic lockdown, during which most
 people were required to remain at home. These spatially dispersed
 measurements allowed us to make detailed maps of noise pollution that are
 not possible when using standard fixed sound equipment. The four sites
 were: the Boston University campus (which sits between two highways), the
 Fenway/Longwood area (which includes an urban park and several hospitals),
 Harvard Square (home of Harvard University), and East Boston (a
 residential area near Logan Airport). Across all four sites, sound levels
 averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high
 noise measurements occurred during lockdown as well. The resulting sound
 maps highlight noisy locations such as traffic intersections and quiet
 locations such as parks. This project demonstrates that changes in human
 activity can reduce noise pollution and that simple smartphone technology
 can be used to make highly detailed maps of noise pollution that identify
 sources of high sound levels potentially harmful to humans in urban
 environments.
description: We collected sound measurements within four different urban sites
 in Boston, Massachusetts. Working in small teams of 2-4 people, we used
 the mobile app SPLnFFT to collect sound level data in A-weighted decibel
 readings using smartphones. We exclusively used iPhones for data
 collection for consistency in hardware and software. Before each
 collection, we calibrated each iPhone to the same standard, which was used
 for every collection outing. We recorded the L50 value (the median sound
 level) for each recording because the L50 value is less affected by short
 bursts of loud sound than the mean reading. Recordings ran for
 approximately 20 seconds each. We recorded all sound measurements between
 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and
 only collected data on days without rain, snow, or strong wind to prevent
 inaccuracies due to weather. Within these conditions, we collected sound
 measurements over multiple days and at different times to ensure
 representative data. We followed these procedures for both collection
 cycles (2020 during lockdown and 2021 after lockdown had been lifted). The
 2017 data were collected for an unrelated noise pollution project
 conducted by previous members of the Primack Lab and were not collected
 with the exact parameters established for the 2020 and 2021 collections.
 However, we found these noise data to be valuable given that they could be
 used to compare lockdown sound levels to the soundscape before the
 COVID-19 pandemic. We used R Studio to create sound
 maps from the individual data points in a way that allows for spatial
 visualization of the soundscape before, during, and after the pandemic
 lockdown. To test for statistically significant differences in sound level
 between years, we performed Welch’s t-tests on the raw data for all sites
 comparing lockdown (2020) measurements to pre (2017) and post (2021)
 lockdown measurements. Given the hypothesis that 2020 would have lower
 sound levels at each site, we report the results of one-tailed
 t-tests. 
description: # Data from: Maps made with smartphones highlight lower noise pollution
 during COVID-19 pandemic lockdown at four locations in Boston
 [https://doi.org/10.5061/dryad.ncjsxkt35](https://doi.org/10.5061/dryad.ncjsxkt35) Dataset contents include csv files of all data (each file describes collection year and site of data), R script used to create noise maps, and kml files needed to run the map creation code. ## Description of the data and file structure Each csv file contains the L50 values (median sound level) taken from hundreds of 20 second recordings over multiple collection days. The SPLnFFT application exports the latitude and longitude of where the recording was taken, which is also included in the csv files and is used to create the noise maps. The csv files are used as data frames for the R script to create noise maps for each collection site. The R script contains comments and instructions to clearly indicate each step of the map creation. The kml files are used to create boundaries/outlines of the noise maps from Google Maps. The csv and kml files should be kept in the working directory in order to run the code properly. ## Code/Software We used the “ggmap” package in R Studio to overlay the decibel readings over the Google maps of each site. To compare sound levels between different time periods, we created base grids for the four sites that covered the area of data collection using R packages “sf,” “units,” “ggplot2,” “grid,” and “ggsn”. Rather than using raw data within the grid, we used kriging interpolation to standardize the comparison, avoid bias from collection patterns, and create accurate sound maps. We used the R package “gstat” along with “sp,” “ggplot2,” and “ggmap” to create the comparison maps that demonstrated the difference between the kriging interpolations for the two years. The R script contains comments and explanations at each step for clarity.