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\begin{document}

\title{The Wealth of Literature}



\author[1]{Marium Sultan}%
\affil[1]{New York University}%


\vspace{-1em}



  
  \date{May 12, 2024}


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\textbf{PUI2017 Extra Credit Project~ \textless{}Marium Sultan, MariumS,
mas1300\textgreater{}}

\par\null

\textbf{Abstract:}

\par\null

Access to libraries is important for everyone . This project aimed to
find if libraries are~ equitably distributed by median household income,
with Public Use Microdata Areas as the geographical unit.

Public libraries provide free access to books, technologies and classes
. I also included special and academic libraries from the NYC Open Data
facilities database, which may not be accessible to all, but are to a
subset of the population.

I used linear regression to check for correlation between income and
amount of libraries by PUMA, and Moran's I to check for clustering in
space.

Results~of spatial autocorrelation suggest that libraries do cluster in
space, and looking at the map that clustering seems to be in middle and
lower Manhattan (Fig. 2). The amount of libraries positively correlates
with the median income of the PUMA it belongs to, but with too low an
R\textsuperscript{2} to draw any meaningful conclusions. Future work can
include quality of library rather than just presence of one.~

\par\null

\textbf{Introduction:~}

\par\null

What sparked this idea is my memory of reading an article saying that
there are no bookstores in the Bronx. The Bronx is known overall as a
lower income than the other boroughs so I wondered if that was the key
factor in this lack
(\href{http://gothamist.com/2016/11/03/no_bookstores_the_bronx.php}{1}).~Steven
Melendez examined over 60 years of bookstore data and noted the
decreases
(\href{http://gothamist.com/2015/01/30/rip_nyc_bookstores.php}{4}).~~I
predicted that neighborhoods with higher median incomes will be more
likely to contain bookstores and libraries. I also wondered if there is
a difference in which neighborhoods contain libraries versus which
contain bookstores, and if one of these two institutions is more
equitably distributed. In this version of the project I only looked at
libraries. I~ decided not to include bookstores because the price of
books is a deterring factor for their accessibility and places with
higher income would allow more business to these institutions by fact.
The data would also have to be webscraped, a skill I have not yet
learnt.~

\par\null

Public libraries provide much more than books. They also have language
classes, technology training programs and job search resources, among
other services
(\href{http://\%C2\%A0https://nycfuture.org/data/libraries-teach-tech-building-skills-for-a-digital-world}{5}).
``Altogether, the Library offers 103,000 free programs annually, serving
everyone from toddlers to teens to seniors''
(\href{http://https://www.nypl.org/help/about-nypl}{2}).

\par\null

I did not find any previous work analyzing if there is equitable
distribution of libraries in New York City.~~

\par\null

\textbf{Data:}

In order to pursue my analysis I collected data on median household
income~and population from American Community Survey, extracted a list
of libraries from the facilities database,~ and found the shapefile
for~Public Use Microdata Areas, the latter two both from NYC Open Data.

I initially intended to use census tract as my unit of analysis but
examining the data I collected by census tracts I realized that numbers
were repeated across regions, even within NYC itself. For example, even
filtering the income data by county to only include the 5 boroughs
census tract 13800 occurred 5 times. At that point I decided to start
over with Public Use Microdata Areas instead as the labels are not
repeated.~~

The ACS data I accessed through an API call after identifying the
correct variables from the long list of possibilities. My previous work
on assessing the distribution of broadband across the city provided the
template for this call.~

The income and population data come from American Community Survey
Estimates (5 yr). Although I did not access the margin of error
information I know that such estimates often have wide margins of error,
calling their validity into account. The library data included pubic,
special and academic libraries and I trust the facilities database to be
fairly comprehensive. I am not sure how fully accessible special and
academic libraries as compared to public ones, but they still add to the
provision of knowledge, at least to a subset of the population.

After doing an inner merge on the income data (which was for the whole
NY state) and the NYC puma shapefile, I did a spatial join with the
libraries dataframe, which had point geometry for each institution.

\begin{verbatim}

#PwL = Puma with Libraries
#facl = Libary subset of Facilities Database, with Point Geometries
#PIncomeMap = Income by Puma, with Polygon Geometries 

PwL = gpd.sjoin(facl, PIncomeMap, how="inner", op='intersects')
\end{verbatim}

This placed the libraries within their PUMA polygons, which allowed for
counting libraries by PUMA.~

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/Screen-Shot-2017-12-14-at-2-11-14-PM/Screen-Shot-2017-12-14-at-2-11-14-PM}
\caption{{Dataset with all relevant variables for analysis. Last 3 are calculated
by me (left to right): hundreds of people, library count divided by
population, and libraries per 100 people
{\label{726002}}%
}}
\end{center}
\end{figure}

\par\null\par\null

\textbf{Methodology:~}

\par\null\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/cMapLibIncome/cMapLibIncome}
\caption{{Libraries plotted as stars over choropleth of median income by PUMA
{\label{429259}}%
}}
\end{center}
\end{figure}

\par\null

\textbf{Conclusions:}

\par\null

I did not find a strong correlation between the amount of libraries in a
certain PUMA and the income of that PUMA. I tried measuring
libraries~per 100 people plotted against income (Fig. 5), the log of
libraries over population against income (Fig .4) , pure library count
against income (Fig. 3), and library count by population~ (Fig. 6). In
all cases the R\textsuperscript{2~}was below .30.~

\par\null\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/libI/libI}
\caption{{Note upward trend. Adjusted R\textsuperscript{2}= 0.211
{\label{609594}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/lineIncomeLog/libOpop}
\caption{{Note upward trend. Adjusted R\textsuperscript{2} = 0.247
{\label{956310}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/lineIncomeLibper100/libper100}
\caption{{Scatterplot of Income against libraries per 100 people, by PUMA.~Note
lack of trend. Adj. R\textsuperscript{2} = 0.281
{\label{998019}}%
}}
\end{center}
\end{figure}

The adjusted R\^{}2 indicates only 28.1\% of the variability is
accounted for by this model (not so good fit).~

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/libandPOP/libandPOP}
\caption{{Scatterplot of population against library count, by PUMA. Adj.
R\textsuperscript{2} = 0.143
{\label{160986}}%
}}
\end{center}
\end{figure}

The amount of libraries correlates even worse with population than with
income.

\par\null

The fact that there is a lower R\textsuperscript{2~}for libraries by
population than the log of library count over population by income,
seems to indicate that income does affect the distribution of libraries
to some degree, although future multivariate regression and feature
selection would have to be done here.~

\par\null

Lastly, I used Moran's I to test for spatial autocorrelation of library
locations, setting an alpha of 0.05 and a null hypothesis that there is
no spatial autocorrelation of libraries in NYC. For Moran's I, when
using the Rook technique, the P value is 2.84e-5.~ For Moran's I, when
using the Queen technique, the P value is 2.015e-5. Both of these are
much less than the alpha of 0.05 so we can reject the null that there is
no spatial autocorrelation.~ Looking at Fig. 1 we can see a clustering
of libraries in middle and lower Manhattan, which may be what this
algorithm is picking up on.~ Both methods showed a positive Z value
which means similar values cluster together.
(\href{http://http://www.statisticshowto.com/morans-i/}{6})

\par\null

\textbf{Future work:}

\par\null

Other work can go back to the original question and include bookstores
in the analysis, I suspect they are more correlated with income than
libraries because they need an influx of sales to maintain themselves.
Sales depend on having disposable income.~

What also matters that I did not add to my analysis is how~ much each
individual library offers. This could include hours, frequency of
classes, types of classes, technologies and size of collection.
Libraries vary greatly in these components and to group them all into a
simple binary of existing or not ignores nuance. Although there isn't a
strong R\textsuperscript{2~}for the presence of libraries and income
there may be for the quality of those libraries.~

\par\null

\textbf{Links:}

\par\null

\url{https://github.com/MariumS/PUI2017\_mas1300/tree/master/ExtraCreditProject}

\par\null

\textbf{Bibliography}

1.~\url{http://gothamist.com/2016/11/03/no\_bookstores\_the\_bronx.php}

2.~\url{https://www.nypl.org/help/about-nypl}

3.~\url{https://www.bustle.com/p/7-reasons-libraries-are-essential-now-more-than-ever-43901}

4.~\url{http://gothamist.com/2015/01/30/rip\_nyc\_bookstores.php}

5.~\url{https://nycfuture.org/data/libraries-teach-tech-building-skills-for-a-digital-world}

6.~\url{http://www.statisticshowto.com/morans-i/}

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