Introduction
This project will give a visual overview of the New York City MTA subway system. I will be analyzing its ridership system and a little history. My primary goal will be to analyze the weekly, monthly, and yearly ridership. I will visually break down and explain ridership by Borough and subway stations. I will also make a comparison for a more fundamental understanding of the system. I have created and used a survey for my user experience finding ( UX report). Did we know that New York City MTA houses 472 stations? It does, and this ranks its system the largest number of public transit subway stations in the world. Although there are 472 stations, I will only explain 424 stations from that list. I will also combine ridership data for stations whose dataset Is more complex, like a station where transfer passageways are connected. Please note that some stations do not lend themselves to ridership allocation because of its complexity. For example, 14 St A, C, E station is combined with the 8 Av L station. There are a few important facts about the NYC subway station that needs to be highlighted. Let us take a look at them.
Subway facts
- Average weekday subway ridership: 5.5 million
- Our annual ridership: 1.698 billion
- 2019 ridership has an average of 5.5 million weekday subway ridership
- All-time ridership record: 2.067 billion passengers, in 1946
- Number of subway cars: 6,684
- Subway car mileage: The fleet traveled 365 million miles in 2019
- There are seven numbered routes: 1, 2, 3, 4, 5, 6, 7
- There are 15 lettered routes, not including shuttle service: A, B, C, D, E, F, G, J, L, M, N, Q, R, W, Z
- There are three permanent shuttle services: Franklin Avenue in Brooklyn, Rockaway Park in Queens and 42 St in Manhattan.
- Longest rides “With no change of trains: The A train from 207th Street in Manhattan to Far Rockaway in Queens (more than 31 miles).”
- With a transfer: The 2 train from 241st Street in the Bronx, with a transfer to the Far Rockaway-bound A train (more than 38 miles).
- Between stations: The A train between the Howard Beach/JFK Airport and Broad Channel stations in Queens (3.5 miles).
- Stations
- Number of stations: 472
- Number of stations in original Manhattan system: 28 (opened in 1904)
- Most station platforms are between 525 and 660 feet long.
- Types of stations:
- Underground (about 60%)
- elevated
- embankment
- open-cut
- An open-cut station is built below street level, in a trench-like depression, or “cut.” Unlike a station built in a tunnel, most “open-cut” stations are exposed to the outdoors. An example: Cortelyou Road Station in Brooklyn.
- Highest station above ground: Smith-9 St in Brooklyn, 88 feet above street level.
- Deepest station: 191 St in Manhattan, 180 feet below street level.
Material used
- Dataset from NYC MTA official website.
- Excel used to modify the dataset (Better Arranging and removing unwanted information).
- UX used ( Survey – Questionnaire )
- Microsoft word for writing the full presentation and making the edits before adding it to the student workspace “studentwor\k.prattsi.org .”
- Tableau used to present my dataset in a clear and meaningful visual way.
- Carto used for Mapping visuals.
METHODOLOGY
The tools mentioned above were used to accomplish my goal, which is to analyze and explain the MTA ridership system. An essential tool used is the NYC MTA official website. On their website, I was able to gather the data set needed for my visual presentation. See direct link attached https://new.mta.info/agency/new-york-city-transit/subway-bus-ridership-2019
NYC MTA website gives you the option to download open-source information in several different file formats like CSV, Excel, etc. After the retrieval of this information (dataset). I used “Excel;” to catch and adjust minor oversights, like deleting unwanted columns and rows and any other minor adjustment that is needed to enhance my presentation. Excel was also used to create a pivot table with slices to enhance data interpretation. Carto was also used for mapping visual interpretations.
Tableau- is a great way to analyze data real-time, blending data, and data collaboration. I used Tableau to translate queries into a visual representation, generate dashboards, worksheets to enhance my presentation. Link to Tableau website attached https://public.tableau.com/s/.
Carto- is an intuitive location intelligence cloud base platform; I used Carto to analyze my dataset and to interpret the geographical location of all trains.
User Experience Methodology ” Survey.”
The main objective of this survey is to collect data and to get a better understanding of the New York City transportation system. The study will help us to use existing information that is available on their website, as well as the responses provided from the questionnaire. We will seek to analyze the various reasons one will or will not commute using the subway and how they view the transportation system. This survey will highlight several key issues faced by and are experienced by users of the MTA subway system. More than eighty percent (80%) of the respondents have been taking the trains for a long time, and as such, they have a vast amount of experience regarding the subway. Although there were several limitations in the collection of this data, in that the sample size was a smaller percentage of the city’s population, we can still see the varying opinions that these commuters have. We have chosen to plot these responses using graphs and charts, where we will not only see the variances in the responses but will help to outline better how some issues are more widespread than others. With that limitation in mind, we are still able to prove this analysis.
- To describe the reliability of the New York City subway system
- To outline how satisfied riders are with the subway system
- To provide a comparative analysis of the data collected from the survey
Survey respondents were asked to indicate yes, or no on most of the questions asked relating to their levels of satisfaction with the New York City Subway in the areas being evaluated. All questions on the questionnaire also included an open invitation to provide feedback on their likes and dislike of the subway system, and if possible, suggest changes that they would love to see and experience on these trains. One of the main questions on the survey was, do you think that the MTA system is efficient enough for the number of people that ride daily? Responses were split at an even fifty percent (50%) and show how based on the train, commuters may have a different perception of the system reliability. Although there were several limitations in the collection of this data, in that the sample size was a smaller percentage of the city’s population, we can still see the varying opinions that these commuters have. We have chosen to plot these responses using graphs and charts, where we will not only see the variances in the responses but will help to outline better how some issues are more widespread than others. With that limitation in mind, we are still able to prove this analysis.
Process
Because of the tools used to complete my project, representing my dataset visually was easy. I started the process by getting the raw dataset from MTA official website, pulling the needed information to accomplish my task ahead. After downloading the CSV files from the MTA’S website, I introduced the data to Microsoft Excel, which allows me to clean up my dataset, removing unwanted rows and columns. Once all this is finished, I import the data to Tableau and Carto for visual presentation; I also used Excel to create a visual interpretation by way of “Pivot table and slices.” See table above NYC MTA website gives you the option to download open-source information in several different file formats like CSV, Excel, etc. After the retrieval of this information (dataset). I used “Excel;” to catch and adjust minor oversights, like deleting unwanted columns and rows and any other minor adjustment that is needed to enhance my presentation. Excel was also used to create a pivot table with slices to strengthen my data interpretation. Carto and Tableau were also used for mapping visual interpretations.
Results
Fig1 is a visual mapping of the ridership data collected. This was created in Carto and has the ability to filter the ridership records by Borough, Train Station, and the line that represents the train. We can see by filtering of the train line how the trains travel across the Boroughs. The Visual below shows 2-5 line runs from the Bronx to Brooklyn, and the four trains also run from the Bronx to Brooklyn; The F line runs from Queens to Brooklyn, and the L train travels from Manhattan to Brooklyn. We are also able to hover over each stop and see the station detail. Click Fig1 image for interactive display.
Fig1
Daily Ridership Numbers
Per the data collected, the below represents NYC Subway’s daily ridership to help us understand how many people are using the services daily.
Fig1a data set shows us that there are more commuters during the weekdays from Monday to Friday ( weekdays 7/13/2020 to 7/17/2020 and 7/2/20020 to 7/23/2020 ). However, there has been a drastic decrease in commuting this year in comparison to 2019. 2019 numbers are not listed however the numbers can be retrieved from the NYC ridership website. The data below shows the comparative decrease between 2019 and 2020. This decreased range from 73-81.20%; This is the direct cause of COVID. Most people are now working from home or have lost their job; hence they do not need public transportation, thus effecting the ridership numbers and will, in turn, affect the money need to maintain the stations and tracks.
Fig1a
Fig1b is an interactive representation of the estimated daily ridership. Fig1b houses the sum of percentage change from 2019 Weekday/Saturday/Sunday Average for each Total Estimated Ridership Per Day broken down by Date. It also has a line graph that displays the estimated increase in ridership. Data analysts had estimated that the increase would start from Thursdays and decrease on Sundays not undermining that weekdays have the most ridership. Fig1c show the top 10 busiest subway station for 2019. This is ordered by ranking and color and is also interactive by clicking on the image.
Fig1b
Fig1c
Top 10 busiest subway stations in 2019
Reflection
Working with the MTA ridership dataset was very educational. This process has expanded my knowledge of the history of the NYC subway system and its ridership program. Before this project, I did not know that NYC has the largest number of public transit subway stations in the world. NYC has 472 stations. During this project, I was able to identify the top busiest subway station in NYC from the dataset retrieved from MTA’s official website. With this data, I was also able to draw comparisons with prior ridership record. As mentioned earlier, I used different tools to accomplish my desire outcome. For a visual representation, I used Tableau, Carto, and Excel; and for my user experience, I used a survey. Representing my dataset visually helps the audience to understand the data more has our brain responds better to visual presentation than plain text. It is proven that visual representation allows the brain to consume the material with more consummate ease. There were a few limitations like Carto keeps erroring, but was resolved with the help of Professor Chris Sula. However, doing this research was a great experience. In the future, I would be better able to analyze and visualize my dataset because I would have gained more experience with all the tools used. I would also have gained more experience in creating visual presentations. But I thoroughly enjoyed working on this project. My recommendation to NYC subway ridership analytical team is that they would add a financial budgeting column to the ridership dataset; this column should show the estimated sum of money to be collected ( Ridership entry multiply by per rider cost).
Glossary
Ridership: The number of passengers using a particular form of public transportation.
Work cited
MTA Dataset- https://new.mta.info/agency/new-york-city-transit/subway-bus-ridership-2019
Tableau- https://public.tableau.com/s/
MTA UX – http://web.mta.info/mta/planning/data/NYC-Travel-Survey/NYCTravelSurvey.pdf
