\documentclass[11pt,a4paper]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Mapping the Vulnerability to Flooding of Community-Development
Corporations (CDCs) Affordable Housing Properties in Central and East
Harlem, New York City ~~~~}



\author[1]{Veronica Olivotto}%
\author[2]{Eddy Almonte}%
\affil[1]{Milano School of Policy, Management and the Environment, The New School}%
\affil[2]{Graduate School of Architecture, Planning and Preservation, Columbia University}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
New York City's affordable housing stock is vulnerable to coastal
flooding under current and projected climate scenarios. Flood
vulnerability in this study, was intended as a factor of the exposure of
affordable housing units to current and future floodplains as well as
topographical elevation. Variables of socio-economic vulnerability
included median household income by census tract, expiring affordability
of rent-subsidized housing, and East Harlem's most recent rezoning . The
affordable housing in question is owned by two community-development
corporations (CDCs) of the Northern Manhattan Collaborative (NMC), Hope
and Ascendant based in East Harlem. Using GIS software and publicly
available data from NYC Open Data and Mapluto, large scale mapping was
conducted at the Borough-Block-Lot (BBL) scale to understand the
exposure to coastal flooding of 101 properties owned by Hope and
Ascendant, as well as a Hotspot Analysis~of all the remaining units
included in the NMC (48 more properties). Results show that~Hope
properties may flood more than Ascendants', under both current and
future floodplain projections. A contributing factor is topographical
elevation, where~Hope Properties are at lower median elevation (13.2
feet) than Ascendants' (29 feet) and also lower than the median
elevation of both Central (22 feet) and East Harlem (15 feet). Results
from the hotspot analysis shows that 20 of Hope Properties fall within
Hot clusters of socioeconomic vulnerability, as well as 5 of Ascendant
Properties. Overall the NMC Properties show a higher socioeconomic
vulnerability than all the properties in East Harlem. This result is
important considering that New York City's stock of affordable housing
hosts some of the most vulnerable populations in the city, with less
ability to move elsewhere before or after a flooding event.%
\end{abstract}%

\newpage

\sloppy


\emph{This project was initiated as the final product for the course
`Introduction to GIS' (Fall 2018) at the New School and continued
through the collaboration with Eddy Almonte,~Morgan Stanley Community
Development Fellow at Ascendant Neighborhood Development. A poster
version of the project was presented at the 10 Years of the New York
Panel of Climate Change, at~the Academy of the Sciences of New York City
on March 15, 2019}

\par\null

\subsection*{Introduction}

{\label{628505}}

New York City (NYC) is affected by three types of floods: coastal,
riverine, and inland. Riverine floods are the least common and least
severe type in NYC.~Inland floods (flash floods) are caused by
stormwater. These floods are difficult to predict and manage as their
occurrence is driven by small intense thunderstorms, local land
characteristics, and the complex sewer network. Coastal and riverine
flood risk can be defined using FEMA flood maps for the 100-year storm,
but inland flood risk and vulnerability are difficult to quantify. NYC
manages this risk through several mechanisms including: regulatory codes
and laws, stormwater management improvements, flood-proofing and
elevating buildings, and protecting critical
infrastructure~\hyperref[csl:1]{({New York City Emergency Management}, 2014)}.~

\par\null

NYC has experienced 7.5-foot floods several times in the past decade.
Superstorm Sandy discharged 10- or 11-foot of coastal floods on much of
Manhattan, Brooklyn, and Staten
Island,~\href{http://www.nytimes.com/interactive/2012/11/17/nyregion/hurricane-sandy-map.html}{killing
43 people} and inundating more than 88,000 buildings~\hyperref[csl:2]{(Atlantic, 2017)}.
New York City has 178,000 units of affordable rental housing that are
privately owned by corporations and receive~government assistance
through the U.S. Department of Housing and Urban Development, the
Mitchell-Lama program, or the Low Income Housing Tax Credit program.
According to a research conducted in the aftermath of Sandy
by~\hyperref[csl:3]{({The Furman Center}, 2013)}, this stock was affected by Sandy: 248 subsidized
buildings, with about 24,500 units, are located in areas reached by
Sandy's surge. Fig.1 below shows that subsidized developments are
concentrated in some surge affected areas, such as Coney Island, the
Rockaways, Harlem, and the Lower East Side.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Screen-Shot-2018-11-30-at-3-28-45-PM/Screen-Shot-2018-11-30-at-3-28-45-PM}
\caption{{Sandy Surge Area and Subsidized Rental Properties in New York City
(Source: FEMA Modeling Task Force, Furman Center Subsidized Housing
Information Project, New York City Department of City Planning, New York
State Office of Cyber Security)
{\label{639924}}%
}}
\end{center}
\end{figure}

\par\null

Among NYC's corporations owning~ stock of affordable rental housing are
Hope and
Ascendant.~\href{http://www.hopeci.org/content/property-management}{Hope's
Property Management Department services}
and~\href{http://www.ascendant.nyc/}{Ascendant} Neighborhood Development
(the former referred to as HOPE and the latter referred to as AND from
now onward) are two community-based not-for-profit affordable housing
organization based in East Harlem. The two organizations are part of a
larger coalition of CDCs, called the Northern Manhattan Coalition (NMC),
which in total represents 157 affordable rental properties. Both
organizations advocate for the preservation of affordable housing in New
York City, ``as rapid increases in market-rate residential and
commercial development propel speculation, drive up rents, and encourage
tenant harassment and displacement'' (AND website). Just 22 percent of
rental units in New York City are affordable to~households whose annual
income is below \$30,000. Both organizations also work to strengthening
the neighborhood's social fabric by assisting in the growth and success
of local businesses, by assisting residents to enhance their lives and
incomes, and by sponsoring community programs.

~

\href{http://www.hopeci.org/content/property-management}{HOPE~}owns over
1,300 affordable apartments in more than 78 buildings throughout Central
and East Harlem (Community District 10 and 11). AND owns over 628
apartments in 28 buildings.~ During a meeting at the New School both
Hope and Ascendant, expressed the need for evidence-based mapping to
understand the exposure to flooding to their properties. Following a
meeting with Hope, one of the main problems seems to be `blue sky
flooding', the type of flooding occurring without stormy weather and
influenced by high tides at seas, high ground water table and surface
elevation. There is anecdotal evidence that this type of flooding
affects basements, which are recurrently flooded, but for which Hope has
very little funding for left over after what goes towards regular
building maintenance. Moreover, Hope has been receiving a number
of~\href{http://www.ewa.bh/en/Customer/MeterReading/violations}{violations}
from ConEd, NYC's energy company, because as flooding affects basements,
officers cannot access gas and water readings~ stored there. Because of
these violations, HOPE has been unable to close housing retrofitting
deals with for-profit-banks they partner with. At the same time HOPE has
been an early adopter of green infrastructure, like green rooftops and
gardens, thanks to funding made available by the city. However, green
rooftops address a different problem: they slow down excess rainfall but
they do not slow down water coming from below, or from coastal storm
surges.~

\par\null

Initially this study was going to access data on occurrence of inland
flooding in basements from the building manager
service~\href{https://sitecompli.com/}{SiteCompli}. This website
contains information about the violations that both organization incur
year round since 2012. A close inspection of the data revealed that
violations are filed for issues such as ``sewer backup'' but no further
explanation is given about whether the violation was filed because
inspectors could not access the basement due to flooding. The number of
complaints~ related to sewer backup was also too small to warrant
visualization through GIS mapping (36 complaint between 2010 and 2018).
The fact remains that, so far, violations descriptions are not capturing
important information about whether the violation is issued as a result
of the inability of inspectors to access the basement due to flooding.~
Therefore we based our analysis on coastal flooding alone but we
recommend Hope and Ascendant Property managers to produce a protocol for
collecting more~accurate descriptions of violations occurring as~
consequence of flooding. In the long term, we see a need for quantifying
the hidden recurrent costs due to basement flooding, adding to the
burden of regular housing maintenance costs. ~

\par\null

\subsection*{A Framing for Flood Vulnerability in New York
City}

{\label{680016}}

This project aims at a large scale mapping of the vulnerability to
coastal flooding of both Hope and Ascendant properties. GIS is used for
this purpose. GIS and remote sensing are critical tools for analyzing,
preparing for, mitigating, and responding to natural and human-induced
disasters, including from flooding
events~\hyperref[csl:4]{(Huang, 2009)}\hyperref[csl:5]{(Strobl et al., 2012)}. Geo-spatial technologies
are used to estimate the risk of occurrence of a disaster based on
physical and/or social dimensions. These two dimensions are brought
together in the concept of risk, which is a function of a hazard, the
exposure to it and social vulnerability, defined below
after~\hyperref[csl:6]{(Rosenzweig et al., 2011}; \hyperref[csl:7]{Ka{\'{z}}mierczak and Cavan, 2011)}.~ Although there may be many variables
influencing exposure to floods, here I refer to exposure as the
susceptibility of people, properties and systems to being affected by an
environmental related hazard~\hyperref[csl:8]{(Cutter, 2005}; \hyperref[csl:9]{Cutter, 2012)}, which in this case is
understood to be the current and projected floodplain in Central and
East Harlem, New York City. We furthermore consider as elements of
exposure the number of affordable housing buildings and their
topographical elevation.~ In terms of socio-economic vulnerability, we
considered three variables that can compound the threat of CDCs housing
units to present and coastal flooding in East Harlem. In East Harlem the
percentage of people living below the federal poverty level is slightly
higher (22\%) than the mean for the whole city of New York (20.5\%, U.S.
Census, 2010), and research shows that people living in poverty and who
are socially marginalized have reduced capacity for self-protection in
terms of mitigating flood hazards at home pre-event, evacuating~ in
response to flooding, or returning home or to employment in the
aftermath of a flooding event, accessing social protection such as flood
insurance, hazard mitigation infrastructure, emergency response
information and assistance~\hyperref[csl:10]{(Collins, 2009}; \hyperref[csl:11]{Collins et al., 2012}; \hyperref[csl:12]{Elliott and Pais, 2006}; \hyperref[csl:13]{Maldonado et al., 2015)}.~

\par\null

The second variable of socio-economic vulnerability is related to the
benefits and drawbacks of urban rezoning plans. Changes in land use
zoning and floor area ratio (FAR) permits can have important impacts on
neighborhoods' populations by increasing property costs ~and influencing
housing careers, skewing access to services and utilities, affecting the
distribution of parks and public space. Rezoning can lead to increased
density and waste water pumping needs These impacts can lead to the
progressive displacement of low-income socioeconomic groups but also of
certain land-uses such as industrial land uses and
manufacturing~\hyperref[csl:14]{(Curran, 2007)}. The changing of ~land uses can imply
the loss of economic stability for working-class
communities~\hyperref[csl:15]{(Wolf-Powers, 2005)} and their ability to equally access
opportunities and privileges in society.~ In this study we pay attention
to the urban blocks interested by the East Harlem Rezoning Plan approved
by the New York City Council in 2017, and which is expected to result in
a net increase of approximately 3,500 dwelling units, a substantial
proportion of which are expected to be affordable. However, this portion
may be allotted to for profit developers instead of CDCs, who can
guarantee to keep the rent to a level of affordability in line with the
needs of the poorest segments of society. There is reason to believe
that the East Harlem Rezoning will just hand over more of the affordable
housing stock into the hands of property developers, reframing what
affordability means by potentially increasing the density of market rate
housing or changing the ways in which local people can afford the
amenities in their neighborhood. This is troubling for two reasons: the
share of severely burdened renters, among the~ 921,000~ renter
households in New York City remains steady at 22\%~\hyperref[csl:16]{({Citizens Budget Commission}, 2018)}.
At the same time, over the past decade or so, New York city housing has
been shifting in favor of more middle and high priced rentals, in a
glaring erosion to the inventory of rent regulated
housing~\hyperref[csl:17]{(Stringer, 2018)}.

\par\null

The third variable of socio-economic vulnerability is represented by the
date of expiration of the affordable rent subsidy. In exchange for a
government subsidy, privately owned, publicly subsidized rental housing
corporations agree to keep units affordable to low- and moderate-income
households for a set number of years, typically 30 years. After a
regulatory agreement expires, however, owners can begin to rent their
units at market rates. This, however would not be possible for
corporations like Hope and Ascendant, whose mission is to provide and
maintain affordable housing. Hope and Ascendant, apply for subsidies, at
the federal level, to the U.S. Department of Housing and Urban
Development (HUD) and the city level, to the NYC Housing and
Preservation (HPD). We account the subsidy expiration as a possible
socio-economic vulnerability because, should the subsidy fail to be
renewed the tenants risk rent spikes or evictions. According to a 2016
analysis by the NYU Furman Center, by 2020, the regulatory agreements
governing of 157 federally subsidized residential properties in New York
City are set to expire.~Over the following decade, an additional 593
will follow suit \hyperref[csl:18]{({The Furman Center}, 2016)}.

~

This large scale mapping project aimed to answers the following
questions:~

\begin{enumerate}
\tightlist
\item
  How many Hope and Ascendant properties are in the current and future
  floodplain?
\item
  How do Hope and Ascendant buildings compare with the rest of the
  buildings in Central and East Harlem?
\item
  What is the average elevation of flooded and not flooded BBL under
  different flood scenarios?
\item
  Where are high or low socio-economic vulnerability clusters located
  within East Harlem?
\end{enumerate}

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Screen-Shot-2018-12-18-at-4-46-15-PM/Screen-Shot-2019-03-11-at-3.39.33-PM}
\caption{{A Framing of Flooding Vulnerability in New York City
{\label{452999}}%
}}
\end{center}
\end{figure}

\subsubsection*{~}

{\label{926318}}

\subsubsection*{}

{\label{926318}}

\subsubsection*{Authors' statement of overarching
rationale}

{\label{926318}}

We believe that universities (private and public) should serve citizens'
interests and bridge the disparity in knowledge available to community
groups that have less resources to access tools/software/data to build
effective arguments against both the systemic forces of history
(struggles for affordable housing, better living conditions and the
myriads of events and practices of life) and the - no longer -
extraordinary climate-induced events like coastal and stormwater
flooding.

\par\null

\subsection*{Methods \& Analysis}

{\label{308549}}

In order to map the H\&A properties in current and future floodplain, we
engaged in the following:

\par\null

\begin{enumerate}
\tightlist
\item
  Retrieved property data from Hope (not geo-located and with not
  practical BBL numbers) and from Ascendant (already geo-located but
  with projection issues)
\item
  Re-coded BBL numbers for both properties from scratch through padding
  numbers and concatenation
\item
  Conversion Tool - Convert Exl into Table
\item
  Table join between properties BBL and Mapluto BBLs for Manhattan but
  6/100 properties did not join. I figured out that some addresses that
  I had initially split into two (e.g. 242-244 East 106 Street) should
  be kept as one because splitting them generates a new BBL code that of
  course does not exist. After I joined the address back I solved 5 of 6
  missing BBL which were not joining.
\item
  Selected all null (not joint attributes) and selected inverse to
  obtain only BBLs in CD 10 and 11, where properties are located.
  Exported data selection as shapefile.
\item
  Created 1 field per floodplain (2015, 2020, 2050) set with a short
  integer and all values at 0 (NOT IN FLOODLAIN). From the FEMA 2015 I
  selected by attribute only the 100 year floodplain
\item
  Selected by location - from the BBL with condition of intersecting
  with floodplain 2015, 2020, 2050, 2080. Each time I selected with
  field calculator BBLs that intersected with each floodplain and I
  assigned the intersection at 1 (IN FLOODPLAIN)
\item
  Created another column where I aggregated all numbers to understand
  the BBLs that fall in the floodplain at none, one, two or all
  projections.
\item
  I decided to represent the different floodplains in three colors and
  code the properties (1 Ascendant; 0 Hope) and assign different colors
  to both. Another way I could have done this was to color code the
  chronology of flooding from green to red depending on 3 (floods in
  2020), 2 (floods in 2050), 3 (floods in 2080) but I realized I also
  needed to distinguish the properties. I tried do choose ``Many
  values'' under Symbology and assign both coding for floodplain and
  coding for type of property (distinguishing them through a specific
  pattern applied to a solid color). The result was not satisfying
  because, especially in the overall map the zoom levels are not
  appropriate to show the details of the patterning.
\item
  Exported Attribute table to Excel and performed a yearly rate of
  change analysis on the total number of buildings exposed to different
  floodplains as well as yearly rate of change for each property type. I
  used the following formula:~
  =(new\_value-original\_value)/(original\_value) where~original\_value
  represents the value that the percentage of change is based on,
  and~new\_value represents the value that has changed. I then divided
  what I obtained by the number of years (2080-2015) that is 65
\item
  Created \textbf{Map 1} showing all floodplains and the percentage of
  properties affected by flooding under each scenario
\item
  Created \textbf{4 small multiple maps} showing a zoomed in version of
  Map 1. The zooms were chosen based on where it most flooded in each
  projection.~
\end{enumerate}

In order to compare the percentages of H\&A buildings flooding to those
in Central and East Harlem I engaged in the following steps:

\par\null

\begin{enumerate}
\tightlist
\item
  Repeated steps 6 to 10 to calculate amount of BBLs in both Central and
  East Harlem that are flooded under each projection
\item
  Created \textbf{Map 6} where I show the percentage and yearly rate of
  flooding of all buildings in Central and East Manhattan
\end{enumerate}

\par\null

Finally in order to calculate the average H\&A properties elevation in
relation to floodplain hazard, I:

\par\null

\begin{enumerate}
\tightlist
\item
  Used Zonal Statistics as Table, where under ``Input raster or feature
  zone data'' I selected the H\&A BBLs properties, in~Zone field I
  selected `BBL' under ``Input value raster'' the DEM clipped and mask
  to Central and East Harlem.~ Under statistics type I choose MEAN and I
  repeated the operation to obtain the MEDIAN elevation of the
  properties.~
\item
  Made a Table join on ``BBL'' between the table produced by zonal
  statistics and the table with H\&A properties.
\item
  Opened the table and on MEAN I chose ` Summarize SUM Floodplain' to
  the average MEAN elevation and saved it outside the geo-database as
  text file so I could work it on Excel.~
\item
  The result is \textbf{Map 7} where I color coded different elevations
  (from 10~ to 70 feet) of all properties and relate this to all
  floodplains.
\end{enumerate}

\par\null

In order to demonstrate socioeconomic vulnerability and exposure to
urban change, we:

\par\null

\begin{enumerate}
\tightlist
\item
  Retrieved property data of remaining units from the NMC (46 in total)
  within East Harlem with expiring rent subsidizations from city, state
  or federal programs. We table joined the expiring subsidy data to
  Mapluto using the BBL number.~
\item
  Retrieved shapefiles for census tracts that have a median household
  income of \$30000 or less for a family of four from the ACS 2017 5
  year Estimates. We selected \$30000 because that is the income limit
  for the federal poverty level.
\item
  Retrieved shapefile for the 2017 East Harlem Rezoning from NYC
  Planning; residential and commercial districts within the rezoning
  were up-zoned~ for new higher density development and investments.
  Expansion of the Second Ave Subway also fall within this rezoning.~
\item
  We made a Vulnerability Ranking to visualize property lots that fall
  within various floodplains and exposed to certain socioeconomic
  conditions related to urban change on a temporal scale (EH rezoning
  and low income census tracts). Each indicator is given a score,
  described below, and property lots (BBLs) are assigned a score of
  0-10, with the most vulnerable BBLs assigned at 10. BBLs that fall
  within FEMA 2015, NPCC 2020, 2050 and 2080 floodplains are given a
  score of 1 for each floodplain, with a max cumulative score of 4 if
  they fall within all four. BBLs with units that have affordable
  housing subsidies expiring between 2010-2020 are scored at 3,
  2020-2030 at 2, and 2030 and 2040 at 1. BBLs that fall within the EH
  rezoning are scored at 2, given that rapid urban change and investment
  will likely to occur in 5-10 years. BBLs that fall within a low income
  census tract are scored at 1.~
\item
  Using the Vulnerability Scores, we then performed a cluster analysis
  using the Hot Spot Analysis (Getis-Ord Gi*) tool. The BBLs were
  clustered according to their vulnerability scores, which produced maps
  with cold and hot spots using polygon contiguity conceptualizations.~
\end{enumerate}

\par\null

\subsection*{Results~}

{\label{713159}}

In this project we asked four questions: 1) How many Hope and Ascendant
properties are in the current and future floodplain? How do Hope and
Ascendant buildings compare with the rest of the buildings in Central
and East Harlem? 3) What is the average elevation of flooded and not
flooded BBL under different floodplains? and 4) Where are high or low
socio-economic vulnerability clusters located within East Harlem?

\par\null

To answer question 1, the results in Figure 3 show that under the 2015
FEMA Floodplain 20\% of Hope's buildings flood, while 12\% of
Ascendant's flood. In the NYPCC 2020 projected floodplain 37\% of
Hope's~ and 31\% of Ascendant's properties may flood. Under the NYPCC
2050 projected floodplain 57\% of Hope's and 35\% of Ascendant's may
flood. Finally under the NYPCC 2080 projected floodplain 84\% of Hope's
and 38\% of Ascendant's properties may flood.~Fig. 4 represents the same
data in GIS.~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Screen-Shot-2018-12-17-at-11-54-00-AM/Screen-Shot-2018-12-17-at-11-54-00-AM}
\caption{{Percentage of Hope and Ascendant Properties Affected by Flooding under
current and future floodplain
{\label{271040}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Map2-0-final-final/Map2-final-final}
\caption{{Hope and Ascendant Properties affected by the Current and Future
Floodplain (Building-Block-Lot scale)
{\label{306580}}%
}}
\end{center}
\end{figure}

\par\null

When compared to all the buildings in Central (3133) and East Harlem
(4358), Fig. 5 shows that properties in East Harlem may flood at higher
rates than those in Central Harlem. East Harlem shows percentages
ranging from 29\% of affected buildings in 2015 to 65\% in 2080, whereas
in Central Harlem the range goes from 1\% in 2015 to 5\% in 2080. Fig. 6
represents the same data in GIS.~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Screen-Shot-2018-12-17-at-11-54-39-AM/Screen-Shot-2018-12-17-at-11-54-39-AM}
\caption{{Comparison between all properties and current and future floodplain
{\label{609772}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Map3-final/Map3-final}
\caption{{Buildings in Central and East Harlem affected by current and future
floodplain (Building-Block-Lot scale)
{\label{588544}}%
}}
\end{center}
\end{figure}

\par\null

The table below summarizes all the results so far, adding the yearly
rate of change in flooded buildings. Hope's properties have a higher
yearly rate of change (4.9\%) than Ascendant's (3.6\%) but this may be
due to the fact that Hope has more properties (78) than Ascendant (26)
in the sample.~Aside from 2 properties owned by Ascendant, all the other
properties fall within East Harlem. The yearly rate of change of Hope
properties is also more than double than that of East Harlem (2\%).
Central Harlem's yearly rate of change is much higher (9.8\%), because
in 65 years the percentage of flooded buildings goes from 1\% in 2015 to
5\% in 2080.~ I conclude that Hope Properties may be more exposed to
flooding under current and future floodplain than Ascendant Properties
and may also be more exposed than all the building in East Harlem.

\par\null

~\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
~ & Hope Properties & Ascendant Properties & Central Harlem & East
Harlem\tabularnewline
Total Number of Buildings & 78 & 26 & 4358 & 3133\tabularnewline
\% flooded in 2015 & 20\% & 12\% & 1\% & 29\%\tabularnewline
\% flooded in 2020 & 37\% & 31\% & 2\% & 43\%\tabularnewline
\% flooded in 2050 & 57\% & 35\% & 4\% & 51\%\tabularnewline
\% flooded in 2080 & 84\% & 38\% & 5\% & 65\%\tabularnewline
Yearly Rate of Change & 4.9\% & 3.6\% & 9.8\% & 2\%\tabularnewline
\bottomrule
\end{longtable}

\par\null

The spatial statistics performed on the digital elevation model revealed
that the average elevation of the buildings that may flood in 2015 is
7.9 feet, in 2020 it is 11.6 feet, in 2050 it is 10.8 feet and in 2080
14.4 feet. Those that never flood under any scenario (31 properties)
have a diverse range of elevations, going from 12 feet to 60 feet. This
means that other factors rather than elevation should also be taken into
account, because the mean elevation calculated here takes into account
the building parcel and not streets morphology. Fig. 7 represents the
same data in GIS.~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Map2-DEM-final-final/Map2-DEM-final-final}
\caption{{Mean Elevation of Hope and Ascendant Properties
{\label{352177}}%
}}
\end{center}
\end{figure}

\par\null

The table below compares the elevation results between Borough, H\&A
properties and all the buildings in Central and East Harlem. A
contributing factor to Hope Properties flooding more than Ascendants' is
topographical elevation.~Hope Properties are at lower median elevation
(13.2 feet) than Ascendants (29 feet) and also lower than the median
elevation of both Central (22 feet) and East Harlem (15 feet).

\par\null\par\null

~\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Elevation (in feet) & Manhattan (DCP, 2018) & Hope & Ascendant & Central
Harlem & East Harlem\tabularnewline
Mean & 42.7 & 30 & 13.4 & 23.6 & 17.7\tabularnewline
Median & 27 & 13.2 & 29 & 22 & 15\tabularnewline
Minimum & 15 & 6.2 & 5.6 & - & -\tabularnewline
Maximum & 103 & 66.4 & 43.8 & - & -\tabularnewline
\bottomrule
\end{longtable}

Results from the Hot Spot Analysis, see Figure 8, shows that 541 out of
2844 (19\%) of all East Harlem properties are within the 90-99\%
confidence level. More specifically 176 out of 2844 (6\%) are at 95\%
and 185 out of 2844 (7\%) are at 99\%. This means that 19\% of all
residential units in East Harlem have a high degree of vulnerability to
coastal flooding and the three socio-economic indicators included in
this study.~

\par\null\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image003/image003}
\caption{{Hot Spot Analysis, showing~ clusters of low and high socio-economic
vulnerability
{\label{504887}}%
}}
\end{center}
\end{figure}

\par\null\par\null

NMC properties show higher vulnerability than all properties in East
Harlem. 37 of 157 (24\%) of the NMC properties are within the 90-99\%
confidence level. More specifically 9 out of 157 (6\%) are at 95\% and
13 out of 157 (8\%) are at 99\% confidence level. Among those, 4 out of
28 of AND properties are at 90-99\% confidence level, while of the 19
out of 78 of HOPE properties, are at 90-99\% confidence level. Hope
properties are not just more exposed to coastal flooding but they are
also more vulnerable because their BBLs feature more among those that
are affected by changes in rezoning, rent subsidy expiration and higher
rates of low- income residents. Figure 9 highlights the polygons where
both Ascendant and Hope and other affordable rental housing units owned
by organizations in the NMC are located in relation to the~
socio-economic hot spot clusters.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image002/image002}
\caption{{Polygons of hot socioeconomic vulnerability where CDC owned properties
are located
{\label{675228}}%
}}
\end{center}
\end{figure}

\subsection*{}

{\label{980985}}

\subsection*{Discussion and
Conclusions}

{\label{980985}}

Hope Properties are a peculiar case compared to the percentage of
flooded buildings in East and Central Harlem under current and future
floodplain, they are also more represented within hot spots of
socioecononic vulnerability.

\par\null

This study opens up a series of relevant and timely questions about the
future of private corporation owned affordable housing in NYC. CDCs
owned properties are important for the city because lower-income people
traditionally serviced by NYCHA or shelter housing are now both
overcrowded. Also, as indicated New York city housing has been shifting
in favor or more middle and high priced rentals, in a glaring erosion to
the inventory of rent regulated housing.

\par\null

Through this analysis we show that CDC owned housing may be affected by
not only increasing rates of coastal flooding now and into 2080 but also
by the compounded effect of changes in urban zoning and rent subsidy
expiration, which can both affect the present and future viability of
the CDCs' model. A loss of affordability in the private market is a loss
of affordability for all.~

\par\null

The hot spots indicate the areas that, in the near future, may return a
loss of affordable housing due to the multiple factors here studied.
Cold spots, on the other hand, may be considered for expansion of CDC
owned properties. Although rezoning is complex and may also bring
positive effects, this study points to the need for rezoning plans that
are more sensitive to affordability needs and climate resiliency needs
across East Harlem.

\par\null\par\null

\subsection*{Data Sources}

{\label{702362}}

Hope and Ascendant Properties (Hope and Ascendant, 2018)

Northern Manhattan Collaborative Properties (NMC, 2019)

Tax lot and Community Districts shapefiles (MapPluto, 2018)

2015 100 Year floodplain (`current') (FEMA, 2013)

2020 100 Year floodplain (10'' SLR) (NPCC, 2014)

2050 100 Year floodplain (30'' SLR)(NPCC, 2014)

2080 100 Year floodplain (58'' SLR) (NPCC, 2014)

New York City Digital Elevation Model (1 foot) integer raster (NYC
OpenData, 2018)

2017 East Harlem Rezoning (NYC Department of City Planning)

Satellite imagery and basemaps
(\href{http://www.maps.nyc.gov}{www.maps.nyc.gov})

\par\null\par\null

\selectlanguage{english}
\FloatBarrier
\section*{References}\sloppy
\phantomsection
\label{csl:2}Atlantic, T., 2017. {Climate Change Will Bring Major Flooding to New York Every 5 Years}.

\phantomsection
\label{csl:10}Collins, T.W., 2009. {The production of unequal risk in hazardscapes: An explanatory frame applied to disaster at the {US}{\textendash}Mexico border}. Geoforum 40, 589–601. \url{https://doi.org/10.1016/j.geoforum.2009.04.009}

\phantomsection
\label{csl:11}Collins, T.W., Jimenez, A.M., Grineski, S.E., 2012. {Hispanic Health Disparities After a Flood Disaster: Results of a Population-Based Survey of Individuals Experiencing Home Site Damage in El Paso (Texas {USA})}. Journal of Immigrant and Minority Health 15, 415–426. \url{https://doi.org/10.1007/s10903-012-9626-2}

\phantomsection
\label{csl:14}Curran, W., 2007. {{\textquotesingle}From the Frying Pan to the Oven{\textquotesingle}: Gentrification and the Experience of Industrial Displacement in Williamsburg Brooklyn}. Urban Studies 44, 1427–1440. \url{https://doi.org/10.1080/00420980701373438}

\phantomsection
\label{csl:8}Cutter, S.L., 2005. {Hazards Measurement}, in: Encyclopedia of Social Measurement. Elsevier, pp. 197–202. \url{https://doi.org/10.1016/b0-12-369398-5/00364-9}

\phantomsection
\label{csl:9}Cutter, S.L., 2012. {Hazards Vulnerability and Environmental Justice}. Routledge. \url{https://doi.org/10.4324/9781849771542}

\phantomsection
\label{csl:12}Elliott, J.R., Pais, J., 2006. {Race class, and Hurricane Katrina: Social differences in human responses to disaster}. Social Science Research 35, 295–321. \url{https://doi.org/10.1016/j.ssresearch.2006.02.003}

\phantomsection
\label{csl:4}Huang, Y., 2009. {Rapid flood risk assessment using {GIS} technology}. International Journal of River Basin Management 7, 3–14. \url{https://doi.org/10.1080/15715124.2009.9635365}

\phantomsection
\label{csl:7}Ka{\'{z}}mierczak, A., Cavan, G., 2011. {Surface water flooding risk to urban communities: Analysis of vulnerability hazard and exposure}. Landscape and Urban Planning 103, 185–197. \url{https://doi.org/10.1016/j.landurbplan.2011.07.008}

\phantomsection
\label{csl:13}Maldonado, A., Collins, T.W., Grineski, S.E., 2015. {Hispanic Immigrants{\textquotesingle} Vulnerabilities to Flood and Hurricane Hazards in Two United States Metropolitan Areas}. Geographical Review 106, 109–135. \url{https://doi.org/10.1111/j.1931-0846.2015.12103.x}

\phantomsection
\label{csl:6}Rosenzweig, C., Solecki, W.D., Hammer, S.A., Mehrotra, S., 2011. {Executive Summary}, in: Rosenzweig, C., Solecki, W.D., Hammer, S.A., Mehrotra, S. (Eds.), Climate Change and Cities. Cambridge University Press, pp. xv--xxiv. \url{https://doi.org/10.1017/cbo9780511783142.006}

\phantomsection
\label{csl:17}Stringer, S.M., 2018. {The Growing Gap: New York City’s Housing Affordability Challenge}. New York City Office of the Comptroller.

\phantomsection
\label{csl:5}Strobl, R.O., Forte, F., Lonigro, T., 2012. {Comparison of the feasibility of three flood-risk extent delineation techniques using Geographic Information System: case study in Tavoliere delle Puglie Italy}. Journal of Flood Risk Management 5, 245–257. \url{https://doi.org/10.1111/j.1753-318x.2012.01143.x}

\phantomsection
\label{csl:15}Wolf-Powers, L., 2005. {Up-Zoning New York City{\textquotesingle}s Mixed-Use Neighborhoods}. Journal of Planning Education and Research 24, 379–393. \url{https://doi.org/10.1177/0739456x04270125}

\phantomsection
\label{csl:16}{Citizens Budget Commission}, 2018. {Think Your Rent Is High? Documenting New York City’s Severest Rent Burdens}.

\phantomsection
\label{csl:1}{New York City Emergency Management}, 2014. {NYC’s Risk Landscape: A Guide to Hazard Mitigation}, in: . \url{https://www1.nyc.gov/assets/em/downloads/pdf/hazard_mitigation/nycs_risk_landscape_a_guide_to_hazard_mitigation_final.pdf.}

\phantomsection
\label{csl:3}{The Furman Center}, 2013. {Sandy’s effects on housing in New York City}, in: .

\phantomsection
\label{csl:18}{The Furman Center}, 2016. {The Location of New York City’s Expiring Affordable Housing}.

\end{document}

