Group 7: The Effects of Automobiles on Pollution Within the Mass Ave Community
Mia Huebscher, Ashvika Boopathy, Qianyong Hu
Traffic Along the Zakim Bridge in Boston
Traffic Along the Zakim Bridge in Boston
This project intends to address the struggles facing our team's Service-Learning partner, The Mass Ave Coalition - a nonprofit organization dedicated to collaborating with stakeholders to enhance public health, transportation, and streetscape within the Mass Ave community. Upon meeting with this organization’s representative, Carol Blair, our team discovered that public indifference towards the negative effects of automobiles serves as a primary source of pollution within the communities surrounding Massachusetts Avenue (colloquially referred to as Mass Ave), as it influences many individuals to travel by car rather than greener transportation modes (public transportation, walking, biking, etc.) Therefore, to help the Mass Ave Coalition moderate this source of pollution, our project will be motivated by the question, what is the quantifiable effect of automobile usage on noise and air pollution? Our team chose these specific types of pollution as our primary focus after encountering extensive research that describes their negative impact on human health (some of which can be located in the "Data Sources and Supplemental Information" portion of this webpage). By answering this motivating question, our team hopes to persuade the members of the Mass Ave community to choose greener transportation modes to preserve their health and the beauty of their homes.
The data utilized in this project comes from various sources. As our primary data set, our group aggregated 2019, 2020, and 2021 data offered by the Boston 311 Program, which reports extensive information on every complaint made by citizens around Boston regarding situations in their communities. Our team utilized this data to analyze the extent to which Mass Ave community members feel the effects of pollution caused by vehicles. Within this data set, the categorical data resides in the columns that document the speed in which each complaint was resolved, whether the subject of each complaint has been fixed, the methods used to fix each complaint subject, the subject of each complaint, the reason for each complaint, the type of each complaint, the department responsible for resolving each complaint, photos to visualize the reason behind each complaint, and the method used to report each complaint. The columns of the data that report the public works district, city council district, neighborhood, neighborhood services district, street, and zip code in which each complaint was made also contain categorical data. The quantitative data can be found in the columns that describe the year of each complaint and the latitude-longitude coordinates of each complaint. Lastly, the ordinal data resides in the column that reports the exact date and time each complaint was issued.
Another data set utilized in this project documents the popularity of various transportation modes that Boston citizens use to travel to work. This commute data enabled us to visualize the relationship between vehicle use and air pollution, as we illustrated how rises and falls in car use create fluctuations in air quality. This data set includes four columns, the name of the commute type and location for each data point (categorical data), as well as the year associated with each data point and the number of Boston households using each commute type (quantitative data).
The last data set used in this project reports the daily air quality index values for areas within Suffolk county. These areas include Von Hillern St, Dudley Square Roxbury, and Boston Kenmore Square. To improve this data, we merged it with another data set that includes the latitude and longitude information for these areas. We also restricted this data to exclusively report on the years 2019, 2020, and 2021. The first column in this data set includes the year, which is quantitative data. The second column is the site name, which is categorical data. The third column contains quantitative data showing the PM 2.5 levels for each area. The third and fourth columns include latitude and longitude data which are also quantitative. It is important to note that larger index values indicate poorer air quality conditions and vice versa.
Each of the data sets utilized in this project was filtered to exclusively include data from the years 2019, 2020, and 2021. Our team made this decision to leverage the circumstances of the 2020 Coronavirus pandemic to illustrate the effect that reductions in vehicle usage had on air quality and noise pollution in Boston.
As previously mentioned, our interview with Carol Blair highlighted public dependence on cars, and consequently general indifference towards the effects of automobiles on pollution, as significant sources of pollution within the neighborhoods surrounding Mass Ave. Therefore, our team developed the primary tasks for this project to quantify the negative impact of cars on air and noise pollution and convince members of the Mass Ave community to protect their homes and health by choosing greener modes of transportation. The first task of this project is to examine the effect that fluctuations in the prevalence of various types of commutes have on air quality in Boston. Our second task is to investigate the portion of noise pollution created by automobiles in various neighborhoods surrounding Mass Ave. The last task of this project is to quantify the impact of vehicles on the pollution of streetscapes within Mass Ave communities. The accomplishment of this task will support our primary tasks as they collectively illustrate the adverse consequences of vehicle usage.
To create our first visualization, our team utilized line charts to show the connection between air quality in Boston and the popularity of different commute types throughout 2019, 2020, and 2021. We chose this chart type because we felt it provided an efficient way to visualize how fluctuations in various commute type popularities created variations in air quality over time. To produce the multi-series line chart, which illustrates fluctuations in the prevalence of commute types over time, our team encoded each line with a unique color to simplify differentiation between the data. We next decided to encode our air quality line chart with the color blue, as we felt this could help users remember that this chart presents air quality data. To illustrate the portion of noise complaints created by vehicles, our team developed a stacked bar chart, as we believed this was the best way to enable users to understand this part-to-whole relationship across various neighborhoods surrounding Mass Ave. We further decided to make this stacked bar chart horizontal to situate the bars in closer proximity and allow our users to compare bar lengths easily. By encoding each stacked bar in this chart with data for a single neighborhood, we enable users to identify the areas most affected by noise pollution from cars and hopefully empower residents in these areas to reevaluate their dependence on vehicles. The automotive noise disturbances in this chart are encoded with red, while the data for other types of noise disturbances are encoded with grey, allowing users to focus on understanding the portion of noise pollution created by cars – the intent of this chart.
To create our Altair map visualization, we utilized a light gray background to make our points easily visible. To differentiate between the noise complaint points and PM 2.5 location points, we depicted the PM 2.5 location points as black squares. The noise complaint points are circular and encoded using three colors: teal, orange, and purple. Each color corresponds to the following years: 2019, 2020, and 2021, respectively. An interactive legend is also included for users to examine noise disturbances according to the year selected. Additionally, when the user hovers over the PM2.5 square, a bar chart illustrating the average PM2.5 levels in 2019, 2020, and 2021 appears. We decided to include this interactive feature to show the relationship between the concentration of vehicle noise complaints in a location and PM 2.5 levels in that location. The bar charts are encoded with different colors for each site to allow users to easily distinguish the different data being depicted. Our team decided to encode the data in this visualization as a map to provide users with PM2.5 air quality and vehicle noise pollution information for various neighborhoods while allowing them to visualize the locations of these neighborhoods.
As a supplemental visualization, our team took advantage of Tableau’s intricate mapping capabilities to position our vehicle noise complaint locations atop a street map of Boston. This design choice allows users to visualize the exact locations of vehicle noise complaints within the context of familiar surroundings. To ensure greater visibility of this data, our team encoded the points on this map in bright red.
As our final visualization, our team produced a Sankey diagram linking each neighborhood to various types of complaints regarding Parked Vehicles, Abandoned Vehicles, and the Flow of Traffic. We believe this is the correct type of visualization to encode our data because it enables users to visualize and compare several part-to-whole relationships. For instance, with this Sankey diagram, users can understand how the featured types of vehicle complaints are dispersed across the neighborhoods surrounding Mass Ave. Users can also utilize this visualization to understand the makeup of each neighborhood in terms of the types of complaints filed. Finally, users can easily identify the neighborhoods that experience the most complaints and the types of complaints that residents filed the most. The extensive insights gained from this Sankey diagram enable users to recognize the effect of cars on the pollution of streetscapes within Mass Ave neighborhoods.
The Effect of Various Commute Types on Air and Noise Pollution in Boston
This visualization contains three charts, each included to highlight the effect of various commute types on air quality and noise pollution in Boston. The multi-series line chart located in the top-left corner of this visualization displays the fluctuations in the popularity of various commute types throughout the years 2019, 2020, and 2021. Users can brush over this chart to manipulate the data located in the lower horizontal bar chart and visualize how various commute types affect noise pollution in different Boston neighborhoods. The line chart situated in the upper-right corner of this visualization illustrates the change in air quality throughout the years 2019, 2020, and 2021. It can be viewed in concurrence with the multi-series line chart to analyze the effect of different commute types on air pollution. The air quality line chart contains a details-on-demand feature that allows users to hover over a point on the line and obtain a small visualization showing the fluctuations in air quality by month for the point’s corresponding year. These small visualizations include a straight, red line cutting across the air quality line to represent the average air quality index value for the related year. The horizontal stacked bar chart also possesses a details-on-demand feature to help users understand the type of noise complaint represented by each stack and the amount of each noise complaint type reported in each neighborhood. Our team produced this visualization with the valued assistance of the Altair library in Python.
Note: higher Air Quality Index (AQI) values indicate lower air quality
Locations of PM2.5 Level Collection Stations and Noise Complaints Regarding Vehicles
The map below shows the noise complaints caused by automobiles (circular marks) and the average PM 2.5 levels (square marks) in three areas in Suffolk County. PM 2.5 is an air pollutant caused by vehicles; high levels of PM 2.5 can negatively impact human health. This pollutant can also reduce visibility when found at high levels. Hovering over the gray areas on the map allows the user to view the appropriate Boston neighborhood name they are currently hovering over. The noise complaint points show where noise complaints related to automobiles were filed. Hovering over each point shows the street name where the noise complaint was filed. The legend is interactive; when the user clicks on a specific year, only noise complaints from that year will be shown. When hovering over the square marks, a bar graph appears; the bar graph shows the average PM 2.5 levels for 2019, 2020, and 2021 in the area the user hovered over. Our team produced this visualization with the valued assistance of the Altair library in Python.
Locations of Noise Disturbances Regarding Automobiles Reported Around Massachusetts Avenue
This interactive visualization displays the locations of noise complaints caused by automobiles on top of a street map to provide users with greater clarity. To zoom in and out of the map, users may either use two fingers to scroll up (zoom out) or down (zoom in) or click the designated buttons located in the top left corner. Users may further interact with this visualization to pan the map and highlight specific points. This visualization also includes a hover feature that enables users to identify each complaint's status (open or closed) and the year each complaint was filed. If a complaint is open, no public official has resolved the issue. Our team produced this visualization with the valued assistance of Tableau Public.
Sankey Diagram Mapping Neighborhoods to Complaints Regarding Parked Vehicles, Abandoned Vehicles, and the Flow of Traffic
The Sankey diagram below illustrates the various types of complaints regarding parked vehicles, abandoned vehicles, and the flow of traffic, and the amount of each that residents reported in the neighborhoods surrounding Mass Ave. The nodes on the left of the diagram represent the neighborhoods around Mass Ave, and the nodes on the right signify the types of complaints regarding streetscape pollution. When users hover over a node on the left, they receive the total number of complaints made in that neighborhood throughout 2019, 2020, and 2021 and the number of unique complaint types that residents reported in that neighborhood throughout the same years (outgoing flow count). By hovering over each node on the right of the diagram, users receive information regarding the total number of each complaint type reported throughout 2019, 2020, and 2021 and the number of unique neighborhoods in which each complaint type was filed throughout the same years (incoming flow count). Finally, users can hover over the links connecting the nodes to identify the total number of complaints made between each neighborhood and each complaint type. Our team produced this visualization with the valued assistance of the Plotly library in Python.
Upon analyzing our primary brushing and linking visualization, we found that as the number of households driving alone to work decreased, air quality in Boston increased. Although this increase in air quality was minuscule, its presence supports our team's hypothesis that a commitment by the public to using greener transportation modes could improve air pollution in Boston. According to the United States Environmental Protection Agency, an air quality index (AQI) value of 0 to 50 is satisfactory and poses little to no risk for people. Looking at the air quality line chart in this first visualization, users can see that the average AQI value for each year resides in the upper portion of this interval. They can also hover over each point in this line chart to recognize that the average AQI values for most months also near the top half of this interval. In particular, June of 2021 had an average AQI value that reached dangerously close to the threshold between good and acceptable air quality. Once air quality becomes classified as acceptable, it may begin to pose a risk to individuals, especially those sensitive to air pollution. Therefore, the findings in this visualization could aid the Mass Ave Coalition in its fight to convince the public that greener alternatives to automobiles may improve air quality in Boston and, consequently, public health. This visualization further shows that noise pollution by vehicles creates a considerable problem for residents of various Mass Ave communities, especially Dorchester, as noise disturbance from automobiles makes up a significant portion of noise pollution for this neighborhood. Accompanied by research connecting noise pollution to health problems, this finding could further enable the Mass Ave Coalition to sway members of the public into reevaluating their dependence on cars.
Our second visualization illustrates the locations of noise complaints regarding automobiles and the locations of the areas included in our air quality data (Von Hillern St, Dudley Square Roxbury, and Boston Kenmore Square). From this visualization, users can recognize that most automobile noise disturbances reside in the Dorchester, Back Bay, and Roxbury areas. This finding could help the Mass Ave Coalition better motivate residents of these specific areas to recognize the impact that their cars have on noise pollution. Throughout all three years, noise complaints have mostly been filed in the Dorchester area within Suffolk county. By looking at the PM 2.5 level bar chart for Von Hillern St, which borders Dorchester and South Boston, users can recognize that, for each year in the data, Von Hillern St had higher PM 2.5 levels than Boston Kenmore Square and Dudley Square Roxbury. This finding further demonstrates the effect of cars on air quality, as neighborhoods with higher concentrations of automotive noise disturbances (which likely result from higher concentrations of traffic) correspond to areas with higher levels of the PM 2.5 pollutant.
Our map visualization produced with Tableau Public primarily functions to help users with their comprehension of automotive noise disturbance locations rather than analyze data. Nevertheless, a quick survey of this map allows users to reaffirm the discoveries previously made regarding the high concentration of automotive noise complaints in Dorchester, Roxbury, and Back Bay.
An analysis of our Sankey diagram enables users to quantify the effect of cars on streetscape pollution throughout Mass Ave communities. As seen in the visualization, Dorchester and Roxbury experience the highest number of complaints regarding streetscape pollution. Users can also utilize this diagram to recognize that parking enforcement issues make up a large portion of the featured complaints, indicating that many residents feel frustrated over the large concentration of vehicles that require parking spaces and thereby disrupt the beauty of the city.
In this project, our team created four visualizations to quantify the negative effects of vehicle use on air pollution, noise pollution, and streetscape pollution within the communities of Mass Ave. From our visualizations, we found that public commitment toward greener transportation modes could generate an increase in air quality and ensure that AQI values remain low throughout each year. We also identified Roxbury and Dorchester as neighborhoods with large concentrations of noise pollution and streetscape pollution from cars.
Our team hopes that these findings within our research will support Mass Ave Coalition in its mission to improve the Mass Ave community by limiting the number of individuals dependent on vehicles. In the future, our team hopes to find better data regarding this topic and create additional visualizations to convince more individuals that greener transportation modes are the key to healthy lives and homes.