This experiment represents a departure from my previous work largely in that it does not focus on gender or global issues. Rather, I decided to explore hyperlocal dynamics of public space and housing vis-a-vis Community Districts (CDs) in New York City.
My decision to focus on CDs can be explained by the amalgamation of my personal experience as an NYC resident of seven years, and my professional ontology. Six of the seven apartments I’ve occupied have been located within two blocks of the East River. After five years in Greenpoint and Williamsburg (North Brooklyn), I was priced out, whiplashed by the unprecedented spike in development. Two years ago, I moved to the Lower East Side (LES). My friends and family live within a five minute bike ride of my apartment building, all within Manhattan CD 3, and I can quickly access the waterfront and seek respite from the concrete built environment.
In broad terms, my professional lens – comprising socio-economic analysis, feminism, deconstructivism, ethics and the like – is inextricably linked with my all-encompassing drive to contribute to a more equal, just society.
Across the East River
The stark juxtaposition of North Brooklyn and LES is a constant presence in my inner dialogue: everything from architecture to population demographics, from waterway access to median income, LES and North Brooklyn are near-opposites. A long-time commuter on NYC Ferry, (even before the hefty investment) to reach the ferry terminal, I presently walk through NYC Housing Authority (NYCHA) projects built in 1940 (only four NYCHA developments are older than this one). But in Greenpoint and Williamsburg, I experienced the pinnacle of the skyscraper boom intimately, by way of walking through active construction sites daily to reach the waterfront. The new tall building luxury developments in Greenpoint and adjacent neighborhoods Long Island City and Williamsburg, include plans for semi-public access to the waterfront, with projects like Greenpoint Landing and The Greenpoint garnering a lot of media attention. These developments are largely driven by landscape design firm James Corner Field Operations and architecture firm Handel Architects. Along with North Brooklyn, Handel Architects also have a hand in an LES upscale development with semi-public space: Essex Crossing. (I was mildly surprised to learn that the firms have a hand in many other private-public space developments lately, including Hudson Yards, the 9/11 Memorial, and the High Line).
This new model of neighborhood redevelopment is defined by city incentives and mandates for private development to include public spaces in its design, as well as affordable housing. Specifically, the Waterfront Access Plans (WAPs) for Greenpoint-Williamsburg were reviewed in 2004, including a series of public hearings and involvement of Brooklyn CD 1, among other city agencies, and approved in 2005. Most of the waterfront was unoccupied, filled with manufacturing facilities and warehouses, with little access to the waterfront and little access to public parks. But also, and critically, little disruption to the daily lives of Brooklyn CD 1 residents during construction, aside from the constant noise.
If there is an inverse process to WAPs, it’s the East Side Coastal Resiliency (ESCR) project. Here in Manhattan CD 3, construction on the East River Park to increase the neighborhood resiliency to rising sea levels and climate change starts any day now. The extremely controversial project, an answer to the destruction of Hurricane Sandy and climate change, entails razing the park to the ground and closing access to over 50% of the park at any given time. The precious park is an integral part of the community in Manhattan CD 3, whose residents are largely low income, and has the highest density of NYCHA housing. New York City estimates state that between 70,000 to 110,000 residents, including 28,000 NYCHA residents, and over 600 residential buildings will be affected by the ESCR project.
Given these pithy dynamics, the subsequent section on the future details what my ultimate analysis goal is and why I was not able to fulfill it in this experiment.
The main questions I asked in this experiment are as follows:
What is the relationship between publicly accessible spaces and community districts? Which CDs have the highest density of public housing? Largest area of park space? What does a visual representation of public space and public housing reveal about New York City?
Web browser used was Version 90.0.4430.93. Datasets were downloaded from the NYC Open Data web portal. Data visualization was conducted with Carto (free version). Colors were selected with coolors.co if not in Carto. This is a Pratt-administered WordPress site hosting the report.
- Parts of the Waterfront Access Map (WAM). Some of the data was corrupt and after multiple attempts to correct it, I pulled two of the datasets that worked, and ended up only using one.
- Publicly Owned Waterfront – Public parks and facilities owned by city, state or federal agencies that provide access to the waterfront.
- GeoJSON file of NYC Parks Department-managed properties, specifically the archived 2019 version, as I ran into issues with the updated one continually.
- GeoJSON URL of NYC Community Districts (without water).
- CSV of Community Districts Indicators Data
- GeoJSON file of NYCHA developments
- CSV of NYCHA Development Data Book
The experiment entailed numerous data manipulations. In order to gain sophisticated analysis that is represented in a streamlined aesthetic, while also maintaining the ability to add widgets and layers of analysis, I tested all of the features Carto offers. The limit of four layers and ten datasets proved to be challenging in a positive way. Using SQL, I connected several tables that I could not further append to analysis within the Carto map interface. I also manually added the boro_cd column to most of the tables. Functioning as a primary key, the string is a three digit code corresponding to the community district.
One of the main visualizations, or rather group of visualizations, that inspired my design is my brother’s design portfolio. He is an urban design professional living and working in New York City for the Lower East Side Business Improvement District. Trained at Parsons School of Design, his maps have a fresh, minimalist aesthetic, and aptly include only relevant information, which informed my design. Broadly, he focuses on community engagement and inclusive public space, and as such, he was a perfect person to consult with about the direction of this experiment substantively, and in terms of visual expression.
My map does not include street names given they are not relevant to the analysis and support the minimalist aesthetic I’m trying to achieve. It also maintains an intuitive color schema. Parks and public space are green. NYCHA developments are purple for contrast and only have a stroke; a fill made it look too busy. The goal is for the audience to quickly grasp the volume and frequency of public space in the city without having to reference other materials first. The CD layer is colored to draw the eye to the densest area as a result of the analysis, and pink certainly stands out. CDs without NYCHA housing are not colored.
Popup colors are consistent with the layer, aside from CDs, wherein I chose a pale green-yellow to indicate a different, more meta level of information than the others.
For the basemap, I chose a dark format to directly challenge the prevalence of whiteness as a default in visual design. The following research is from a 2016 Dutch book about the responsibility of visual designers in visual culture, The Politics of Design.
This anti-colonial choice denounces the racial hierarchy established by Europeans who started to call themselves white around the same time that Africans were being sold as slaves.
Look in your dictionaries and see the synonyms of the word Black. It’s always something degrading and low and sinister. Look at the word White, it’s always something pure, high and cleanMLK Jr. Speech on April 3, 1968
For more on this subject, see the scanned source here.
Results & Interpretation
This map confirms my long standing hypothesis that my community has the highest density of public housing. After styling the community district layer to reflect the density of public housing within its area, this hypothesis was proven true. Clearly, Manhattan CD 1 has the most dense public housing in all the five boroughs. I specifically chose a color scheme that highlighted this finding.
Moreover, by examining the widgets and interacting with the hover pop-ups I added for each layer, the strong relationship between community-reported #1 issue and public space is undeniable. For example, the CDs that name Parks as their biggest issue are ones in closer proximity to more, and bigger, parks.
To note: the biggest limitation with Carto that may taint the widget use is that it can be misleading to lock onto a community issue, for example, and expect to see all CDs revealed with that leading issue. But, because of how I styled the layers, only the CDs with NYCHA housing will be highlighted on the map. The data is sound, the visualization is limited.
An interesting observation gleaned from the map is that a vast majority of lower Manhattan West Side waterfront public space is not a park at all, but publicly owned waterfront and therefore subject to different jurisdiction. There’s also a huge strip of land between East River Park and Battery Park City with the same attribute.
Plans for the Future
On a technical note, I could do more analysis with SQL and use additional software to manipulate the polygons and give them geographic dimensions with cartographic measurements.
For analysis, I’d like to do a more in-depth analysis of affordable housing, new developments and public space, with historical data. Jason M. Barr has a historical dataset (and more) that includes information about NYC skyscrapers that is particularly interesting. The Waterfront Access Plan has spatial data that includes shapes of the zones that encompass aforementioned developments along the North Brooklyn and Southern Queens waterfronts.
What I really wanted to do with this experiment was determine the area (or polygon size) of publicly accessible space, disaggregate it by attribute, specifically, whether it’s public or private property. Then, situate it within the CD and determine the ratio of public space area to community district area. That would be the first dataset.
Second, is population demographics and housing. Rent burden, poverty level, type of residential units and/or building units in the CD. I’d pay particular attention to the quantity, area and type of NYCHA projects and rent stabilized and controlled apartment units. Demographics such as employment, education, citizenship, race and gender would be included.
The ultimate analysis goal would be to establish whether there is a relationship between these factors: do NYCHA residents or others in government-assisted housing schema have more or less access to public or semi-public space? Do socioeconomic factors correlate to a significant degree with public space? Does your class, race or gender make you more or less likely to have adequate access to nature?
These questions can help answer overarching pressure points with NYC’s development priorities. Updating dismal NYCHA apartment units seems to be diametrically opposed to incentivizing private luxury development. It doesn’t have to be that way, and it’s an unsustainable model for the future. Privately owned public spaces can still have their own rules, such as stationing a guard with a watchful eye or closing the space at will. They’re governed by different city codes and zones, but bent by the city just the same as when it needs to literally bulldoze its way into climate change resiliency – to the detriment of our community members.
The technical limits of the free version of Carto, as well as my own deadlines, stopped me from going full throttle in this direction.
While it may seem like a comprehensive project plan, to holistically analyze the public space-affordable housing-community district dynamics, a feminist, systematic analysis is required. The first step would be to collect feminist data, which either hasn’t been published or hasn’t been collected. Not yet, at least.