Examining Accessibility in the City of Dreams
By Alisha Mahen, Elana Wolpert and Aditi Kapre
They say that if you can make it in New York City, you can make it anywhere. New York boasts as the land of opportunity, but is it equal opportunity for everyone?
There is something about New York that makes you feel like you can have anything you want, and yet, you can never really achieve it. New Yorkers live at the center of education, innovation, and technology, and still, for many people, there are huge barriers to making use of these services. So, we sought out to understand these barriers by studying New York and various types of accessibility through data.
Our anchor research question is:
Who really accesses the benefits of New York city, and why isn’t it all New Yorkers?
The first way that we approached this question was looking into education, and the ability to stay in school. Dropping out of high school is one huge setback for young people’s academic and career futures today. So, any tool that can help students stay on the road to graduation, is something that helps provide equitable education access.
Access to After School Programs
One such tool is the presence of after school programs in schools. So, we set out to create a series of maps to compare the correlation between the dropout rate and the presence of after-school programs in each borough of New York City.
The purple lines are the boundaries of all school districts in New York City and the red markers are point locations of the sites of all existing after-school programs in the city.
When we did this, we had a lot of available data, which covered after school locations, after school programs, school performance, and drop out rates. However, each part of these data sets didn’t have any common points to join together on the mapping tool we were using (QGIS), so we couldn’t make any full conclusions on the different data sets. The data sets we had included the following:
- The location information on the dropout rates dataset was for boroughs
- The location info for the after- school programs was site based
- And the location info for each school was by “DBN Number”
The attribute available on the base shape layer was Tract Number. Hence, the information we had from each data set, wasn’t compatible with other data sets for us to reach any full conclusions or inferences.
Access to Technology Versus Nature
Second, we tried to understand a different type of New York accessibility. We wanted to understand whether access to technology and internet takes precedence over access to health and nature. So we looked at data regarding the number of trees, hospitals, and Wifi hotspots throughout New York.
In the end, we felt unsure whether the data was representative of the elements we were trying to map. Wifi Hotspots are not exactly representative of internet access, because for example, the map shows Upper East Side, which is a wealthy residential neighborhood with very few Wifi Spots. This neighborhood does have internet access, but in the form of private Wifi, therefore not showing on the data. This example makes this proxy invalid.
The data set for the trees only contains points for street trees in New York, hence there are no trees marked in Central Park. Since we’re using trees as a proxy definition of “nature” and Central Park, and all of the other parks around the city are important points of nature for city dwellers, we felt the tree data was not fully representative of nature in New York City.
In speaking to our professor, she advised that these two data sets could actually be good proxy sets for what we were trying to represent. In theory, one could make inferences about the access to healthcare, tech and nature for every neighborhood and what that looks like based on the demographic of that area. However, we still chose to search for more data that would more concretely describe access in New York.
Access to Quality Education by Race
The final way that we sought out to understand New York’s accessibility was again through education, but this time in understanding who gets access to the best quality education of New York City.
Unfortunately, while New York boasts some of the best education in the world, it is not available to all New Yorkers. America’s long history of identity politics, hierarchical class and racial structures have created an invisible wall that prevents some of New York’s marginalized inhabitants from tasting the fruits of New York’s success.
For the purpose of our maps, we created an averaged mean of the quality of education through the available data set (2016–2017_School Quality Report Results for High Schools) of the following characteristics: Rigorous Instruction, Collaboration amongst Teachers, Supportive Environment, Effective School Leadership, Strong Family-Community Ties, and Trust within the Community.
We mapped this against the student’s economic need index. The Economic Need Index measures the socio-economic circumstances of a school’s population and assigns a score to each school based on the number of students eligible for free lunch or public assistance or who live in temporary housing.
Combining the data of the school quality, children’s race, and economic need index we set out to make inferences about the relationship between race and quality education in New York.
For the following maps, each of the circles represents one school in New York. So, the overall conclusions we can make about New York is that students in New York trend towards high economic need, and medium-high quality education.
Each map below uses a different color to represent a different race at various schools. When reading the maps, it’s best to focus on the color gradient on each map so as to understand how the racial breakdown in a school changes its placement on the quality vs economic need scales.
What the maps mean
On the X axis (Horizontal) the quality percent mean is plotted, and on the y axis (vertical) is the Economic Need index. Each map has plots of all the schools in New York City. For each map, the circles of the highest saturation of that specific color denote the schools with the highest population of students belonging to that race. For eg: For the first map, the dark red circles are the schools that have the highest population of white students.The title on each map shows which racial population the darkest color will denote. The schools that fall in the top right quadrant of each map are the most equitable schools*.
*For the purpose of this evaluation, we’re defining “equitable schools” as the ones that provide the best quality education to the populations that have the highest economic need.
Map 1 (Hispanic): According to the map, the city has a high population of Hispanic students and a majority of them fall in the quadrant with the highest economic needs. However, they seem to be receiving a high quality education as well.
Map 2 (Asian): There is a relatively small population of Asian students in the city. For that population, the concentration seems to be in the 4th Quadrant- Having relatively low economic need but receiving high quality education.
Map 3 (Black): There is a large population of Black students in the city but a majority of the black students fall between Quadrant 1 and 2, indicating that they have high economic needs and are getting mid to high quality education.
Map 4 White: Only a few schools in the city seem to have a high population of White students, and these are primarily in Quadrant 4. This is the population that has a very low economic need and are receiving the highest quality education available in this city.
So, when it comes to quality education access in New York, through our several maps, we can see that New York does have great education, and many students of all races are able to receive a quality education. However, there are clear trends that white students have the lowest economic need of all races and are also receiving the best quality of it, but students of color (Black, Asian, and Hispanic) have a higher economic need, and receive relatively lower quality of education.
Even though the initial reactions of reading these maps may be that New York city has overall high quality education for all students, when looked at deeper with multiple filters, you can see that access to quality education differs by racial identity and economic need.
