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\begin{document}

\title{Access to Public Tennis Courts in NYC}



\author[1]{Sam}%
\affil[1]{NYU Center for Urban Science \& Progress}%


\vspace{-1em}



  
  \date{May 05, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
As a budding data scientist and a longtime fan of tennis, I decided to
analyze access to public tennis facilities in New York City in terms of
various socioeconomic factors. This analysis combines Public Use
Microdata Areas (PUMA), NYC Open Data, Google Maps API geocoding, and
demographic and economic American Community Survey (ACS) data. After
cleaning, merging, and plotting the data, I applied various multivariate
regressions to predict access to public tennis facilities. The analyses
show that NYC does well in terms of providing access to low-income
families while older, white residents have greater access to courts in
many neighborhoods.%
\end{abstract}%



\sloppy


\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/first-plot/first-plot}
\caption{{Plot of every public tennis facility in NYC on top of a map of NYC's
PUMAs. You can tell right away that most facilities are in Brooklyn and
Queens.
{\label{774479}}%
}}
\end{center}
\end{figure}

\section*{Introduction}

{\label{595351}}\par\null

If you ask someone what they think of when they hear the word
``tennis,'' the first few words that might come to mind are: white,
expensive, and old-fashioned. This characterization is by no means
unfounded. The ``modern'' conception of the game was dreamed up by a
British attorney named Henry Gem and a Spanish merchant named Augurio
Perera on well-manicured lawns in 19th century Warwickshire,
England~\cite{life2005}.

\par\null

Governing bodies and independent groups, however, recognize that tennis
has the opportunity and the need to promote greater diversity within the
sport~\cite{inclusion}. And NYC's role as host of the U.S. Open and
America's ``melting pot'' prime the city to to promote more diversity in
American tennis. To assess where the city currently stands in terms of
accessibility to tennis, I decided to find where public tennis courts
exist in NYC (rather than private courts, which typically charge
exorbitantly high rates for court time) and whether socioeconomic
factors play a large role in determining access to courts.

\par\null\par\null

\section*{Data}

{\label{635857}}\par\null

\subsection*{Tennis Court Locations}

{\label{853569}}\par\null

I started by tracking down the locations of public tennis facilities in
NYC. Using the NYC Open Data portal, I acquired data for every public
tennis facility in NYC, including the name, number of courts, and
location~\cite{data}. Some location data was missing so I
geocoded those addresses using a Google Maps API query to fill in the
missing coordinates. By the end of this stage of the data gathering, I
had a dataframe with all 81 public facilities and 592 courts in total.~

\par\null

Next, I used a shapefile of NYC PUMAs to display
accessibility~\cite{dataa}. (I decided to use PUMAs since the
socioeconomic data I would use later on in my analysis were aggregated
at the PUMA level.) To determine access to tennis, I found the center of
each PUMA and then used a maximum distance function to find every public
tennis facility within 3 miles of that centroid (I decided to use 3
miles since, anecdotally, that is approximately how far New Yorkers are
willing to travel for public tennis courts):

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/access-plot/access-plot}
\caption{{PUMAs in NYC color-coded by the total number of tennis courts within 3
miles of the centroid of each PUMA. The locations of each facility are
plotted in red while the PUMA centroids are plotted in white. Notice
that areas near Central Park and Randall's Island have the greatest
access to public tennis courts, which makes sense since those two
locations have tennis facilities with dozens of courts.
{\label{833934}}%
}}
\end{center}
\end{figure}

\par\null

\subsection*{Socioeconomic Data}

{\label{650607}}\par\null

Finally, I acquired demographic and economic ACS data published on the
NYC Department of Planning's data portal~\cite{survey}. The data
includes statistics about age, race, income, occupation, and other
categories:

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/data-demo/data-demo}
\caption{{This is a sample of the socioeconomic data published by the U.S Census
Bureau's ACS. The data is highly detailed and exhaustive. Note~that the
figures included in the 2016 report are the averages recorded for each
category between 2012 and 2016.
{\label{865240}}%
}}
\end{center}
\end{figure}

\subsection*{}

{\label{599830}}

\subsection*{Limitations Regarding
Data}

{\label{599830}}\par\null

A couple limitations should be noted regarding the data used in this
analysis. The ACS data was recorded between the years 2012 and 2016, so
the results of my analysis may not perfectly match with current
socioeconomic facts and figures. However, it is unlikely that major
shifts have occurred in the past two years.~I am also aware that some of
the centroids in my plot of NYC PUMAs may have been skewed by the
geography of each PUMA. But it is reasonable to define PUMA centroids
geographically so that the analysis would not neglect people living in
the farthest corners of each PUMA.

\par\null

\section*{}

{\label{204791}}

\section*{Methodology}

{\label{204791}}\par\null

\subsection*{Plotting Analysis}

{\label{715755}}\par\null

With my data in hand, I was ready to conduct the analysis. First I
created plots displaying accessibility for various demographic groups:\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/demo-plots/new-maps}
\caption{{The above plots show accessibility to public tennis courts in terms of
various demographic categories.~Note that the color gradients were
created on a quantile scale.
{\label{664375}}%
}}
\end{center}
\end{figure}

The top two plots compare access to public tennis courts per 10,000
white and 10,000 non-white residents while the bottom two compare access
per 10,000 residents under 18 years old with residents who are 65 and
older. Residents 65 and older appear to have slightly more access since
most PUMAs have at least 19 courts for those residents while the
majority of PUMAs have between 10 and 18 courts for residents 18 and
under.

\par\null

The difference in access for non-white vs. white PUMA residents is
slightly less obvious but still notable. Although the distribution of
tennis court access does not neatly overlap, we can still see that white
residents generally have more access than non-white residents. Few
neighborhoods in NYC have at least one court per 1000 non-white
residents (upper Manhattan, Astoria, and south Brooklyn) while many
neighborhoods have at least one court for every 1000 white residents and
many more have between .84 and 1.84 courts.

\par\null

\subsection*{Regression}

{\label{598142}}\par\null

To better hone in on correlations between socioeconomic factors and
accessibility to public tennis courts in NYC, I decided to run a series
of regressions. After standardizing the data and testing different
models, I found that many of the models had p values that were far too
high (and therefore should not be accepted assuming even relatively
generous alpha levels) while others provided little explanatory power
due to their low R squared values. Ultimately, the regression with the
best combination of simplicity and explanatory power was the one fitting
an Ordinary Least Squares model based solely on the percentage of
families living below the poverty line (R squared value of .18 with a p
value of .001):

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/regression/regression}
\caption{{The above plot shows accessibility to public tennis courts as a function
of the percentage of families living below the poverty line (each point
is a PUMA). Interestingly, as poverty rises, access to tennis also
increases, which goes against the commonly held conception that tennis
is a sport only for the rich.
{\label{920939}}%
}}
\end{center}
\end{figure}

\par\null

\section*{Conclusion}

{\label{800650}}\par\null

Out of all the regressions, I find the above regression plot to be the
most compelling because of its clarity and simplicity. The model
suggests that as the percentage of poverty increases, accessibility to
public tennis courts also increases, which goes against the commonly
held conception (and my own assumption at the start of the project) that
tennis is a sport only for the rich and therefore that residents in poor
neighborhoods would have little access to tennis courts.

But an R squared value of .18 (i.e. the model explains only 18\% of the
observed data) might lead one to reject any conclusions drawn from the
model. Still, although 18\% may sound like a very small fraction of the
data, we should also consider the fact that the data analyzed in this
project is highly variable and historically contingent. Decisions to
build tennis courts in NYC depended on a host of factors such as the
prior location of parks and the whim of city planners. Similarly, the
socioeconomic characteristics of neighborhoods depend greatly on
historical phenomena and human behavior that is often unpredictable.~~

Given the context of the analysis, a model that explains 18\% of the
data actually starts to look a lot more interesting. But we cannot
ignore the distribution of court access in terms of race and age. Across
the city, there are more courts for white residents and residents aged
65 and older. Overall, it seems the city does well in terms of providing
court access to lower income residents while older, white residents are
better situated to enjoy tennis at public parks.

\section*{}

{\label{779904}}

\section*{Opportunities for Further
Analysis}

{\label{779904}}\par\null

To further determine how NYC is doing in terms of promoting diversity
within tennis, we would need to know the socioeconomic characteristics
of people who actually play tennis in the city. This would involve much
more detailed data collection but it is a project that could be
conducted possibly with the help of sensor technology or data sharing
through the Parks Department's permitting office.

\par\null

Finally, for anyone who is interested, you can find my data wrangling
and analysis on GitHub using the following link:~

\par\null

\url{https://github.com/seeess1/PUI2018\_shb395/tree/master/extracredit}

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