Luke Ingram
INFO 250 -- Summer 2023
Final Project
Abstract:
Urban development and transportation infrastructure must move hand in hand to ensure equitable and efficient access for all residents. This visualization dives deep into the landscape of New York City's subway entrance distribution across its diverse boroughs and neighborhoods, catering to a wide range of people, from city planners to tourists.
-
Are subway entrances equitably distributed in relation to neighborhood population densities?
-
Which high-population neighborhoods provide the best subway access for businesses and residents?
-
Are there high-density areas that lack sufficient subway services, or low-density areas that have an excess of subway services?
-
Is there a correlation between the population of a neighborhood and the count of its subway entrances/exits?
Data:
The "Demographics by Neighborhood Tabulation Area (NTA)" dataset provides demographic details for individual NTAs, encompassing official names, unique codes, associated boroughs, and population figures. It offers insights into the age distribution, with a focus on the 65+ demographic, detailing their proportion and poverty rate. The dataset also breaks down the racial and ethnic composition by presenting percentages for Hispanic/Latino, White, Black/African American, Asian, and other racial/ethnic groups.
Source: NYC Department of Aging (official website)
Collection dates: November 2020
The dataset captures detailed attributes of NYC subway stations and their entrances/exits. For each station, it identifies its division, line, name, and geographic coordinates. It enumerates the routes accessible from the station, details of the entrance type, usage (entry/exit), availability of vending machines, staffing details, and ADA compliance notes. Furthermore, each entrance's specific location, including its latitude, longitude, nearby streets, and corner details, are provided. Georeference identifiers for mapping are also included for both stations and their entrances. These Georeferences will be important for the choropleth discussed later.
Source: Ny.data.gov (Official State Government website)
Collection dates: 2022-2023
This dataset is only used to provide the shapefile needed to construct the neighborhood choropleth map. By "neighborhood", we are specifically using NYC's NTA's (Neighborhood Tabulation Area) which are geographic units in New York City, created by the Department of City Planning, that aggregate census tracts to approximate the city's recognized neighborhoods for statistical reporting and analysis. More info can be found here.
Source: NYC OpenData (Official City Government website)
Collection dates: 2010
This dataset provides the shapefile needed for the subway lines component of the neighborhood choropleth map. The shapefile here provides the geospatial data for each subway line.
Source: NYC OpenData (Official City Government website)
Collection Dates: 2017-2018
This dataset provides the shapefile needed to construct the subway stations component of the neighborhood choropleth map. The shapefile here provides the geospatial data for each subway station.
Source: NYC OpenData (Official City Government website)
Collection Dates: 2013-2019
Using QGIS, spatial joins were performed on datasets 3-5. Specifics on this operation can be found here.
The five datasets are delicately joined using the following logic:
Table Reference:
-
subway_lines_revised.shp refers to a spatial join between datasets 3 & 5.
-
geo_export_84... refers to dataset 3.
-
Demographics_by_NTA.csv1 refers to dataset 1.
-
merged_lines_NYC_transit... refers to dataset 2.
-
subway_lines_joined_shp refers to a spatial join between datasets 3 & 4.
The connections (Relationships) between the tables are as follows:
12: Uses the "NTA Code" (Given to (1) from the spatial join) column
23: Uses the "NTA Code" column.
14: Uses the compares the Line1 & Lines_dashed columns. Lines_dashed is added to dataset 2 with the following python script:
df = pd.read_csv('NYC_Transit_Subway_Entrance_And_Exit_Data.csv')
cols = [f"Route{i}" for i in range(1,12)]
df['Lines_dashed'] = df[cols].apply(lambda row: '-'.join([str(x) for x in row if pd.notnull(x) and str(x) != '']), axis=1)
df['Lines_dashed_first'] = df[cols].apply(lambda row: '-'.join([str(x) for x in row if pd.notnull(x) and str(x) != ''])[0], axis=1)
df['Lines'] = df[cols].apply(lambda row: ', '.join([str(x) for x in row if pd.notnull(x) and str(x) != '']), axis=1)
df.to_csv("merged_lines_NYC_Transit_Subway_Entrance_And_Exit_Data.csv")45: Uses the Lines_dashed_first and Rt symbol columns. Lines_dashed_first is added to dataset 2 with the above python script.
The "Stations per 10,000 people" ratio (used in visualizations 1 & 2) is calculated in Tableau using the following formula:
- (SUM([population (Demographics by NTA.csv1)]) / 10000)
The "Number of stations per Neighborhood" (Used in all visualizations) is calculated in Tableau with the following formula:
- COUNT([Objectid (Subway Lines Revised.Shp)])
In this interactive visualization, the color of each neighborhood represents the population. Red implies higher, and green represents lower. This provides the user with a city-wide overview of the stations within each neighborhood, which they can navigate as they need.
The user can scroll and zoom to focus on specific neighborhoods:
The "Filter by Borough" option provides the user with the ability focus on a specific borough. This is important as the boundaries between the boroughs is not explicitly clear.
Note: it is not possible to crop the subway line shapes to the selected borough. This is due to the construction of the line polygons within the shapefile (dataset 4).
The user can also highlight specific subway stations by using the "Highlight by Line" Feature:
Each Neighborhood, station, and line has their own tooltip, which provides specific information about each datapoint. (Shown On click):
Neighborhood: Station:
Line:
In this interactive visualization, The Treemap & bar chart show the differences in the number of stations to 10,000 people ratio (encoded by color). This visualization provides the user with a more comparative look at the distribution of stations within the city.
The User can filter by borough, neighborhood population, and the stations per 10,000 people ratios. This allows for direct comparisons between different areas of the city.
Each leaf in the treemap, and bar on the bar chart has a tooltip, which provides specific information on each datapoint. (Shown on Click)
Leaf: Bar:
This interactive scatterplot is dedicated to answering the important question, "Does a higher population correlate to more subway stations?"
Even though different Boroughs are encoded using the symbols, they can be hard to see, so the user can highlight specific Boroughs using the "Highlight by Borough" feature.
Each datapoint has a tooltip, which provides the user with specific information about each datapoint. (Shown on click).
Findings:
The findings are broken down by research question:
- Are subway entrances equitably distributed in relation to neighborhood population densities?
No, there does not seem to be an equitable distribution of subway stations related to population densities. As there are several high population neighborhoods not on the outskirts of the city, which have little to no subway access. A few examples are, Brooklyn: Canarsie & Flatlands, Queens: Jackson Heights & Flushing.
- Which high-population neighborhoods provide the best subway access for businesses and residents?
The top neighborhood (with a population of at least 50,000) in each borough for subway access are:
-
Manhattan: Washington Heights North.
-
Brooklyn: Bensonhurst West.
-
Queens: Jamaica.
-
Bronx: Bedford Park / Fordham North.
- Are there high-density areas that lack sufficient subway services, or low-density areas that have an excess of subway services?
The first part of this question is answered by the response to the first question. Regarding low-density areas with an excess of subway services, a few neighborhoods stand out:
-
Midtown / Midtown-South Manhattan has 22 subway stations, with a population of less than 30,000. However this may be due to the number of commuters and tourists moving through midtown.
-
SoHo / TriBeCa Manhattan has 17 subway stations, but a population of about 40,000.
-
Is there a correlation between the population of a neighborhood and the count of its subway entrances/exits?
Visualization 3 demonstrates that there is no direct correlation between the population of a neighborhood and the number of subway stations. This reinforces the intuition that deciding where to put subway stations is a process which involves a lot more information than the population of a neighborhood.
Conclusion:
The visualization exploring Our study on New York City's subway distribution has revealed some clear patterns:
-
Subway stations aren't evenly spread according to population density. High-population areas like Canarsie, Flatlands, Jackson Heights, and Flushing have fewer stations than one might expect.
-
On the other hand, areas like Washington Heights North in Manhattan, Bensonhurst West in Brooklyn, Jamaica in Queens, and Bedford Park/Fordham North in the Bronx have good subway access for their populations.
-
Some low-population areas like Midtown and SoHo/TriBeCa have many subway stations, likely because of daily commuters and tourists.
-
Importantly, there's no straightforward relationship between how many people live in a neighborhood and how many subway stations it has.
These findings show that when it comes to placing subway stations, many factors come into play beyond just local residential numbers. As NYC plans for the future, these insights can help in making more informed decisions about transit and infrastructure.