Computer Animation of Jeremy Bentham’s Panopticon

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“To say all in one word, [the panopticon] will be found applicable, I think,
without exception, to all establishments whatsoever”

– Jeremy Bentham

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Since the 1780s, hundreds of articles discuss Jeremy Bentham’s panopticon. But, no structure was ever built to the exact dimensions Bentham gives in his panopticon letters. Seeking to translate Bentham into the digital age, I followed his directions and descriptions to create an open source, virtual reality computer model of the panopticon.

Below, you can view the animation about this structure. Visit this link to view the panopticon in virtual reality. Or click here to download and edit my model (requires Sketchup).

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Transcription of audio narration:

The panopticon is the form of the ideal prison, designed around 1787 by English philosopher Jeremy Bentham. Over 300 prisons around the world follow this model:

  1. A circle of diameter 100 feet
  2. Around the perimeter of this circle stretch cells
  3. Each cell is 9 feet deep
  4. And 48 per floor
  5. Each cell has a toilet, a bed, and space to work
  6. The cells rise six floors

On every other floor, there is a surveillance corridor, in which a guard may survey two floors of prisoners. The guard watches the prisoners. But the prisoners do not see the guard and do not know when they are watched. And must therefore act as if they were always watched. Three guards each see 96 prisoners, which makes 288 prisoners total.

In the center of the space, there is an auditorium, in which the prisoners may assemble to be lectured. A wall of screens may rise surrounding the chapel. And separating the prisoners from seeing into it, or from seeing each other from across the void of the empty space in middle.

Spiral staircases ascend through the space. And an iron and glass frame rises through the space and vaults over the chapel.

This completes the panopticon, the form of the ideal prison.

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Credits:

Supervised by Max Sternberg
Audio narration by Tamsin Morton
Audio credits from Freesound
panopticon interior ambiance
panopticon exterior ambiance
prison door closing
low-pitched bell sound
high-pitched bell sound
The archives and publications of the UCL special collections

California Waterscape

California Waterscape animates the development of this state’s water delivery infrastructure from 1913 to 2019, using geo-referenced aqueduct route data, land use maps, and statistics on reservoir capacity. The resulting film presents a series of “cartographic snapshots” of every year since the opening of the Los Angeles Aqueduct in 1913. This process visualizes the rapid growth of this state’s population, cities, agriculture, and water needs.

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Music: Panning the Sands by Patrick O’Hearn
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Text from animation is copied below:

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Each blue dot is one dam, sized for the amount of water it captures. Each blue line is one canal or aqueduct. These infrastructure features become visible as they near completion.

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The challenge: to capture and transport water to where water is needed hundreds of miles away. To grow food where there was once desert.

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Notice the sudden growth spurt in construction during the 1930s Great Depression… And again during the 1950s through 1970s.

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The longest aqueducts that run from mountainous areas to the cities mostly deliver drinking water. The shorter aqueducts in the Central Valley mostly bring water to farms.

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Here we see dams in the Sierra Nevada Mountains gradually come on line. Many prevent flooding. Or they seize winter snow and rain for when this water is needed in summer.

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Since the 1970s, construction slows down, but population continues growing.

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In 2010, about six hundred fifty dams and four thousand five hundred miles of major aqueducts and canals store and move over 38 billion gallons per day. This is the most complex and expensive system ever built to conquer water.

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But, how will man’s system cope with climate change?

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2. Research Methodology and Sources

The most important data sources consulted and integrated into this animation are listed here with links:

– Fire Resource and Assessment Program → Land use and urban development maps
(a pdf file imported as transparent raster into QGIS)
– California Department of Water Resources → Routes of aqueducts and canals
(shapefile)
– Bureau of Transportation Statistics → Dam and reservoir data
(csv with lat-long values)
– USGS Topo Viewer → Historic aqueduct route and land use maps
– U.S. Census Bureau → Estimated California population by year

Consult the research methodology and bibliography for complete details.

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Spotted an error or area for improvement? Please email: [email protected]
Download and edit the open source dataset behind this animation.
Click this Google Drive link and “request access” to QGIS shapefile.

3. Source Data on Dams and Reservoirs

^ Created with open data from the US Bureau of Transportation Statistics and visualized in Tableau Public. This map includes all dams in California that are “50 feet or more in height, or with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more.” Dams are geo-referenced and sized according to their storage capacity in acre-feet. One acre-foot is the amount required to cover one acre of land to a depth of one foot (equal to 325,851 gallons or 1.233 ● 10liters). This is the unit of measurement California uses to estimate water availability and use.

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4. Source Data on Aqueducts and Canals

^ Created with open data from the California Department of Water Resources, with additional water features manually added in QGIS and visualized in Tableau Public. All data on routes, lengths, and years completed is an estimate. This map includes all the major water infrastructure features; it is not comprehensive of all features. This map excludes the following categories of aqueducts and canals:

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  • Features built and managed by individual farmers and which extend for a length of only a few hundred feet. These features are too small and too numerous to map out for the entire state and to animate by their date completed. This level of information does not exist or is too difficult to locate.
  • Features built but later abandoned or demolished. This includes no longer extant aqueducts built by Spanish colonists, early American settlers, etc.
  • Features created by deepening, widening, or otherwise expanding the path of an existing and naturally flowing waterway. Many California rivers and streams were dredged and widened to become canals, and many more rivers turned “canals” remain unlined along their path. Determining the “date completed” or “date built” for these semi-natural features is therefore difficult. So, for the purposes of simplicity and to aid viewers in seeing only manmade water features in the animation, this category is generally excluded.

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Those seeking to share this project to their website or organization are requested to contact the author before publication. We will gladly share all source files associated with this animation, provided recipients use this information for non-commercial purposes. Pre-production and data editing were conducted with QGIS and Tableau. Visualization and animation were conducted Photoshop and Final Cut Pro. For this project, we worked from a mid-2014 MacBook Air with 4GB RAM.

24 Hours in the London Underground

This animation visualizes the number of riders in the London Underground over two weeks in 2010. Each dot corresponds to one station. Dot size corresponds to the number of riders passing through each station. Big dots for busy stations. Small dots for less busy stations. Dot color represents the lines serving each station. White dots are for stations where three or more lines intersect. Each dot pulsates twice in a day. Once during the morning commute. And again during the evening commute.
If you like this, please watch my animation of weekday vs. weekend commuting patterns in the NYC subway.

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This animation does not pretend to be scientific. This is the representation of movement – a way to visualize the rhythmic pulsing of people through the London Underground as analogous to the breathing human body. The passage of red blood cells through the body’s veins is analogous to the movement of people through trains. The red blood cells bring oxygen and remove waste from the cells. Each semi-autonomous cell (with nucleus, membrane, etc.) is analogous to a workplace or home (with kitchen, walls, etc). Much like the cars and trains that move people and distribute their wealth from places of work to places of leisure, the red blood cells are the vehicles that link the heart and lungs (i.e. Central London) to the rest of the body (i.e. the London Metropolitan Region). This analogy of human form to city plan is a longstanding theme in urban studies.

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Methodology:

No algorithm or dataset could capture the true complexity of London’s rhythmic breathing during the daily commute. Stations like King’s Cross St. Pancras, Waterloo, and Victoria rank among the busiest because they are multimodal transfer points between long distance trains, taxis, cars, and buses. So, although this animation visualizes these busiest stations with the largest dot size, this does not necessarily mean more people work or live in the vicinity of these stations. Admittedly, aspects of dot size are determined by immeasurable external factors – namely transfers from other transport modes to the London Underground.

This animation is based off of open-access data collected in November 2010. According to transport for London: “Passenger counts collect information about passenger numbers entering and exiting London Underground stations, largely based on the Underground ticketing system gate data.” Excluding London Overground, the Docklands Light Railways, National Rail, and other transport providers, there are 265 London Underground stations surveyed in this data set. For data collection purposes, stations where two or more lines intersect are counted as a single data entry. This is because at complex interchanges of multiple lines (e.g. Paddington), it is difficult to track which of the lines (e.g. Bakerloo, Circle, District, Hammersmith & City) a passenger is boarding. To complicate matters, passengers are often granted free transfers between lines at interchanges.

Every fifteen minutes, the numbers of passengers are counted from gate entry data, that is, four times per hour. This yields 96 time intervals over each 24 hour period. Multiplying the number of time intervals (96) by the number of stations (265), we get the number of data points represented in this animation: 25,440. Each of the stations was also assigned its corresponding latitude and longitude coordinate, so as to appear on the map in its appropriate spatial location. In the data analysis software (Tableau), we assigned each station:

  • A spatial location → derived from latitude and longitude coordinates coordinates
  • A color → according to the lines extant in 2010: Bakerloo, Central, Circle, District, Hammersmith & City, Jubilee, Metropolitan, Northern, Piccadilly, Victoria, Waterloo & City.
  • A size → scaled to reflect the passenger count in each 15 minute interval. The smallest dot corresponds to the rate of: zero passengers per 15-minute interval. The largest dot corresponds to the rate of about 7,500 passengers per 15-minute interval. This is the range applied to dot size: 0<X<7,500 where X represents “passengers/time.”
  • A time of day → each time interval represents one frame in the animation. We exported each frame from Tableau, conducted slight edits to background map opacity and texture, and then stitched the frames back together again – to create a flip book of sorts. With a rate of 12 frames per 1 second, or 96 frames per 8 seconds, a single day with 25,440 data points is compressed into 8 seconds of animation. This 8 second sequence is then looped.

By syncing the audio volume and background color with the data and time of day, the animation becomes more visually legible. The audio volume rises and falls to mirror the growth and contraction of each colored dot. The background color also shifts from black to gray to mirror the time of day. This was achieved by manually adjusting the background opacity in Adobe Illustrator from 100% to 50% for each of the 96 frames – as modeled with a cosine formula. The visualization was created in Tableau with post-production audiovisual editing in Final Cut Pro.

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The eight second sequence played on a loop as a .gif file.

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The Data:


View this infographic in Tableau Public.

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Powered by TfL Open Data. Contains OS data© Crown copyright and database rights 2016.

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Sources:

Lat Long Coordinates for Stations: Bell, Chris. “London Stations.” doogal.co.uk. doogal.co.uk/london_stations.php (retrieved 21 April 2019).
Ridership Statistics: “Our Open Data.” Transport for London. tfl.gov.uk/info-for/open-data-users/our-open-data (retrieved 21 April 2019). To access data, scroll down to the section entitled “Network Statistics,” then click where it reads “London Underground passenger counts data.”
“List of Busiest London Underground Stations.” Wikipedia. en.wikipedia.org/wiki/List_of_busiest_London_Underground_stations (retrieved 21 April 2019).
“London Connections Map.” Transport for London. tfl.gov.uk/corporate/publications-and-reports/london-connections-map (retrieved 21 April 2019).
Audio effects for animation: “Heartbeat.” Freesound. https://freesound.org/search/?q=heartbeat (retrieved 23 April 2019).

NJ Transit Ridership Patterns

The NJ Transit railroad carries nearly 90,000 passengers per day to and from New York Penn Station – the busiest rail station in North America. The majority of these passengers are commuters, who live in bedroom communities dotting northern New Jersey. The construction of these railroads – mostly in the late 19th and early 20th centuries – reveals patterns of urban growth centered around New York City. Like spokes on a wheel, these rail lines converge around Midtown Manhattan. As with many urban rail networks, this growth pattern makes it easier to travel from center to periphery than between towns on the periphery.

Hover over individual dots to reveal corresponding station statistics. Dot color corresponds to train line. White dots are for stations where multiple lines intersect. Dot size corresponds to number of riders per day. Large dots for busy stations, and small dots for less busy stations. For each station, the average number of riders is listed. This average should control for any aberrations in ridership, such as a particularly busy weekend, line closure, or major event in New York. These visualizations are derived from data NJ Transit provided me, here and here.

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The map above shows weekday ridership patterns. Clearly, the movement patterns are New York City and Newark centric – where the jobs are. The next two busiest stations are Secaucus Junction and Hoboken – but these stations are not primary commuter destinations. Rather, they operate as transfer points. Weekday commuters collected from stations along the Pascack Valley, Bergen County, and Main Line are mostly headed to destinations in New York City. And because no trains on these lines arrive in New York City, they must transfer at Secaucus (to another NJT train) or at Hoboken (to PATH / the Hudson River ferries).

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This map shows Sunday ridership. Unsurprisingly, the riders per station are correspondingly less because NJ Transit is mostly a commuter railroad. Stations are on average 66% to 75% less busy on weekends. The thirteen stations along the Montclair-Boonton Line – between Bay Street and Denville – are also entirely closed on weekends and serve no riders. The one line, however, that seems to be only slightly less busy on weekends is the Atlantic City Line – possibly because this line is popular on weekends for people traveling to the casinos and beaches there.

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The infographic above lists weekday ridership by station. Notice the large gap between the first four stations and all the others listed. Keep in mind that a lot of this data implicitly involves double-counting a single passenger. For instance, someone riding from their home near Bay Street (Montclair) to Penn Station (New York) will be counted once in the morning when they clock-in at Bay Street, and then again once they return through New York Penn the same evening.

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Writing Here Is New York in 1949, E.B. White has the following to say about the suburban commuter:

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The commuter is the queerest bird of all. The suburb he inhabits … is a mere roost where he comes at day’s end to go to sleep. Except in rare cases, the man [or woman] who lives in Mamaroneck or Little New or Teaneck, and works in New York, discovers nothing much about the city except the time of arrival and departure of trains and buses, and the path to a quick lunch…. About 400,000 men and women come charging onto the Island each week-day morning, out of the mouths of tubes and tunnels…. The commuter dies with tremendous mileage to his credit, but he is no rover…. The Long Island Rail Road alone carried forty million commuters last year, but many of them were the same fellow retracing his steps. (p.18-21)

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View a similar data visualization project for the New York City subway and for the London Underground.
View this project or download the data from Tableau Public.
Also of interest might be this set of 60 photos comparing New York Penn Station past and present.

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Geography of Incarceration

 

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Between 1 January 2013 and 31 December 2017, the New York Police Department (NYPD) made 102,992 arrests for the possession, sale, and/or use of marijuana. 1 While only 25.5% of New Yorkers are Black, 67.5% of marijuana arrests are of Blacks. Similarly, 18 out of 20 marijuana arrests are of male individuals, even though only 13 out of 20 marijuana users are male. 2 Males more than females and Blacks more than others are arrested for marijuana. While these two aspects of the “War on Drugs” are widely known, less discussed is the clustering of marijuana arrests in specific hotspots.

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Race

Percentage of New Yorkers who identify as this race 3

Percentage of marijuana arrests of individuals belonging to this race

White

44.0%

11.2%

Black

25.5%

67.5%

Asian/Pacific Islander

12.8%

4.2%

Other

17.7%

17.1%

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These arrests are disproportionately of Black males between the ages of 18-44 from low-income communities, even though this demographic represents less than 10% of the city’s population. Why should this matter? Arresting individuals for using a relatively harmless and non-addictive drug is expensive for the taxpayer. According to the Drug Policy Alliance, the city spent $75 million on marijuana arrests and prosecution per year 4 – money that could have been put to more effective use on education, awareness, etc. This policy also unfairly targets the individuals to whom the consequences of arrest, incarceration, and bail are highest.
The common argument, and the grounds on which marijuana was initially made illegal, is that marijuana is a “gateway drug.” Marijuana supposedly introduces and later encourages individuals to experiment with more dangerous and addictive substances. Whether or not this is true, the arrest and punishment of individuals for marijuana may incur the equal risk of serving as a “gateway crime” to the legal system.

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Click here to view this pie chart in more detail.

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Below are three maps of neighborhood “hotspots” for marijuana arrests. The income of every block is indicated on a red to green color scale from low to high. Population of Latinos and Blacks per square mile is also indicated; unsurprisingly, these groups cluster in low-income neighborhoods. On this base map is the geo-referenced address of every arrest for marijuana possession or sale from 2013 to 2017. Of particular note is the tendency for marijuana arrests to occur in low-income neighborhoods. For instance, Manhattan’s 96th Street represents an income divide between the wealthy Upper East Side and the comparatively poorer Harlem. Drawing a “thin blue line” down 96th, we also identify an unspoken policing boundary. Marijuana arrests are significantly less likely to happen in the majority white neighborhood south of 96th than in the majority black neighborhood north, even though both neighborhoods are of comparable population density. According to the UCLA: “Despite roughly equal usage rates, Blacks are 3.73 times more likely than whites to be arrested for marijuana.” 5 Similarly, the wealthy and majority white neighborhood of Riverdale in the Bronx has few arrests in comparison to the poorer and majority black West Bronx, even though these two neighborhoods are less than mile apart.

 

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Research Methodology and Sources

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Note that on the above map, there are numerous low-income neighborhoods without any drug arrests. This is largely because these areas have little to no population, such as Central Park or La Guardia Airport.

This project was assembled entirely publicly-available data. I began by downloading anonymized microdata on the race, crime, gender, and age of every individual arrested by NYPD, as well as the address where this individual was arrested. Of the approximately 1.7 million arrests in this spreadsheet, I filtered out the marijuana crimes. The colored basemap indicating per capita income and race by city block is extracted from Tableau Public, the mapping software I use. The infographics presented above can be explored or downloaded at this link. Arrest data is from NYC Open Data at this link.

  1. Marijuana arrests represent 5.98% of all arrests made during this time period.
  2. From “Statista,” accessed 15 January 2019, link to statistic.
  3. From the United States Census Bureau, 2010 statistics on NYC demographics, link to report, link to database.
  4. From the Drug Policy Alliance, accessed 15 January 2019, link to press release, link to report.
  5. From the American Civil Liberties Union, accessed 18 January 2019, link to article.

New York City Subway Ridership

Last updated October 23, 2019

Could the movement of people in the New York City subway system be visualized as rhythmic breathing?
Linguistically, we often describe cities in relation to the human body. Major roads are described as “arteries” in reference to blood flow. The sewers are the city’s “bowels.” Central Park is the “city’s lungs.” At various times in history, key industries like textiles or finance, were described as the “backbone” of this city’s economy. Cities are complex organisms. But, this wordplay makes the giant metropolis somehow more human and familiar.
The 424 subway stations and 665 miles of track are analogous to the human circulatory system. Every weekday, the subway carries 5.4 million people, mostly to and from work (c.2018).  This movement during the daily commute is highly ordered, structured, and rhythmic – as Manhattan’s population swells during the daily commute and then contracts by night. Each passenger symbolizes the movement of a single blood cell, operating as one cellular unit in a complex system. With each paycheck, the oxygen of capitalism flows from the heart of Manhattan to the cellular homes in the outer boroughs.
Commuting patterns are analogous to the rhythmic expansion and contraction of the human body while breathing. By contrasting weekday and weekend ridership patterns, we detect the city’s respiratory system.

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sounds of breathingheartbeat, and subway are from freesound.org

In this animation based on subway ridership statistics by station:
● Dots are color-coded according to the subway lines they serve.
● White dots are for junctions between two or more lines of different color.
● Dot size corresponds to the number of riders entering each station within a 24-hour period.
● Larger dots are for busier stations. Smaller dots are for less busy stations.
Maybe the visual language of data can address this deeper need to humanize and soften the concrete jungle.

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Also published by the Gothamist on 22 January 2019.
If you like this, please see my animation of ridership patterns over 24 hours in the London Underground.

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Where in the world is modernism?

What if the nationality of every artist represented in the Museum of Modern Art’s collections could be mapped to illustrate the Museum’s geographic diversity through time? Watch the data visualization below of 121,823 artworks at MoMA.

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Introduction

“The Museum of Modern Art (MoMA) acquired its first artworks in 1929, the year it was established. Today, the Museum’s evolving collection contains almost 200,000 works from around the world spanning the last 150 years. The collection includes an ever-expanding range of visual expression, including painting, sculpture, printmaking, drawing, photography, architecture, design, film, and media and performance art.

“MoMA is committed to helping everyone understand, enjoy, and use our collection. The Museum’s website features 79,870 artworks from 26,215 artists. This research dataset contains 135,804 records, representing all of the works that have been accessioned into MoMA’s collection and cataloged in our database. It includes basic metadata for each work, including title, artist, date made, medium, dimensions, and date acquired by the Museum. Some of these records have incomplete information and are noted as ‘not Curator Approved.’

“The Artists dataset contains 15,757 records, representing all the artists who have work in MoMA’s collection and have been cataloged in our database. It includes basic metadata for each artist, including name, nationality, gender, birth year, death year, Wiki QID, and Getty ULAN ID.” – from MoMA’s website.

I have downloaded this dataset as a spreadsheet, imported the data into a visualization software called Tableau Public, and then proceeded to dissect this data to answer the following question:

What can big data reveal about the history of curating and the growth of museum collections?

The results are presented below in three case studies with accompanying infographics. Hover over the graph or toggle the buttons to explore the data in depth.

If you liked this analysis, please see my animation about the collecting history of the Metropolitan Museum of Art.

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Case Study One:

Geographic and Gender Diversity

The map below visualizes the nationalities of ~15,757 artists whose work is displayed at MoMA. There are 121,823 data points below. The data can be browsed by year or by department. This illustrates the constantly evolving geographic breadth of collections. Beginning in the 1930s, over 80% of artworks were from the four key countries of the US, UK, France, and Germany. Beginning the 1960s, the museum acquired some of its first works from Latin America and Japan. And, post-1991, the museum acquired the bulk of its collections from Russia and China. Recent years have seen a slight growth in African art.

An important distinction: This map does not show where each artwork was made. Rather, it shows where each artist is from. Nationality and national identity are, depending on the artist, an important influence shaping the unique perspective artists bring to their work.

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The bar chart below shows the gender distribution of artworks by date. On the horizontal axis: the date acquired. On the vertical axis: the number of artworks acquired in this year. Each bar is divided into three colors: Blue for artwork by a male artist. Pink for art by a female artist. Grey for art where the gender of the artist is not known.

This data can be explored by year and by department. Across departments, male artists comprise the large majority of holdings. The departments with the greatest number of works by female artists: Photography and Drawings. The department with the least female representation: Prints & Illustrated Books. The department with greatest number of works where the artists’ gender is unknown: Architecture & Design. However, across departments, the representation of female artists has slightly increased over the past few decades from around 0% to somewhere closer to 20%.

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Case Study Two:

Do newer acquisitions tend to be smaller?

The two graphs below plot the relationship between year produced, year acquired by MoMA, and the dimensions of each artwork (width in cm). I’ve plotted 12,250 points. They are color coded with the same blue, pink, and grey system as the previous chart.

In the first graph, we see how new artworks are becoming progressively larger and larger. In 1929, the year of MoMA’s founding, the width of the average work being produced was less than 100cm. Today, the average width of newly produced works in the collection is around 400cm – and is steadily increasing.

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In the second graph, we see how MoMA’s new acquisitions are becoming progressively smaller, even though newly produced artworks are larger than before. In 1929, the average width of a new acquisition was over 300 cm. Today, the width is less than 150cm.

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Contemporary artists seem to be working in ever larger dimensions – at least the contemporary artists whose work MoMA acquires. But, newer acquisitions tend to be smaller. Does this reverse correlation indicate that the growing costs of buying and storing art have priced MoMA out of larger artworks? What is the relationship between size and the decision whether or not to acquire a work?

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Case Study Three:

Is the scope and definition of modernism expanding to include older artworks?

The challenge facing any museum dedicated to modern art is: keeping up-to-date. Modern art is constantly being produced. Like any leading museum, MoMA is:

  • growing its collection of newly-produced contemporary works

  • while also enhancing its collection of older works

  • and expanding the geographic and national representations of artists and artworks

The graph below compares the relationship between production year and acquisition year for 7,797 items. The red trend line is the average of the acquisition (horizontal) and production (vertical) axes. Dot color indicates gender. Dot size indicates the number of works by this artist acquired in this year.

In 1929, most new acquisitions were produced in the 1920s – modernism was a new movement and a new idea. Today, new acquisitions range in date from the late 1800s to the early 2000s – the definition of modernism has grown to encompass both newer and older works. But, the average date of new acquisitions is between 1950 and 1960. There is modern art recently produced, and then there is modern art that is not as new but can reveal the history and birth of “modernism.” This is, so to speak, the history of the present.

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Modernism is not a geographically limited phenomenon. With globalization and the march of capitalism, the area effected by modernity is growing. And as new regions of the world come into contact with modern technology, materials, and ideas, the qualities of their respective art and the practices of their artists will change. Cultural institutions, particularly museums dedicated to modern art, are positioned to curate these global trends through the kinds of works they acquire and display in their galleries. More broadly speaking, the kinds of stories museums and curators can tell about history may reflect the geographic, gender, and temporal strengths (or weaknesses) of their collections.

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Links to Resources

The original datasets can be viewed or downloaded below:

  • MoMA’s dataset from GitHub is free to download here. It is published with the following license: Creative Commons Public Domain (CC0). The information presented above reflects this dataset as of 17 October 2018. New entries after this date are not included as these infographics are not updated in real-time.
  • The dataset, derived from MoMA’s, is also free to download here from Tableau Public.
  • These infographics are not affiliated with MoMA. MoMA does not endorse the conclusions of the authors, who themselves take sole responsibility. The conclusions presented below are limited by the scope of MoMA’s published metadata.
  • This author is aware that, according to some definitions, gender is not a binary. Yet, the colors pink and blue code for traditional gender norms. This color palette is for visual clarity; it does not represent an endorsement or rejection of this gender binary.
  • I have created similar data visualisations analysing:

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Big Data and Historic Preservation in New York City

What can a data analysis of New York City’s landmarks reveal about trends in the historic preservation movement?

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The video above is a visual history of landmarks preservation in New York City.

All records are downloaded from NYC Open Data. Soundtrack is from freesound.org

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Introduction

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There is an ongoing debate between in New York City between developers seeking to rebuild the city in the image of global capitalism and preservationists seeking to slow the rate of change and protect the appearance of the city’s many and distinct neighborhoods. This debate plays out every year in the hundreds of buildings  and structures that are added to (or rejected from) the Landmarks Preservation Commission’s running list of landmarks (LPC). Once added, landmarked buildings cannot be modified without first seeking approval from the city. And, to date, there is no process for de-listing a landmark once added – unless (sometimes intentional) decay by neglect requires demolition. This aspect of preservation is particularly contentious for developers because the legal barriers of preservation law are permanent, binding, and affect all current occupants and future owners.

Historic preservationists are the arbiters of taste. The sites they preserve will become the aesthetic lens through which future generations will appreciate the city’s past. The sites they do not preserve or neglect to protect from demolition will be lost to history – no longer a living testimony to vanished builders, architects, and immigrants. On the individual scale, preservation is about protecting structures of value. On the larger scale, preservation is part of a larger historical debate: Which aspects of the past are worth preserving? And what kinds of narratives can historians tell about cities, based on the material evidence that survives?

In this debate, there are many factors driving preservation: fear of losing heritage, fear of change, well-intentioned activists in the spirit of Jane Jacobs and NIMBYism, or concerned scholars and public servants who see something unique in the sites they add. The objective of this paper is to assess arguments made in favor of or against historic preservation through an analysis of publicly-available landmark records from the New York City Open Data website. We identified two datasets, both containing ~130,000 spreadsheet entries for every single LPC listing. The first dataset is entitled “Individual Landmarks” 1 and describes the date entered in the LPC database, the address, lot-size, the geographical coordinates of every single structure, etc. The second dataset is entitled “LPC Individual Landmark and Historic District Building Database” 2  and includes the construction date, original use, style, and address of all structures. We downloaded these two datasets as .csv files, imported them into mapping software called Tableau Public, merged them into a single file, and then conducted a data analysis – the results of which inform all the statistics presented here and drive the conclusions drawn in the following pages.

From this research methodology, we identify heretofore hidden trends in historic preservation. Firstly, we identify contextual preservation and historic districts as a means to protect the human scale of neighborhoods. Secondly, we identify a marked and potentially unjustifiable preference of preservationists for protecting pre-1945 structures. And thirdly, our data hints at the strength of market forces and developers in shaping the scope of preservation.

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Case Study One:

Distribution of Landmarks over the Five Boroughs

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Above is a tree map of the distribution of the 128,594 landmarks across the five boroughs. This includes both buildings and non-buildings, like street lamps, parks, statues, etc. The size of each rectangle corresponds to the number landmarks within one historic district. Or, in the case of the largest rectangle for each borough, the box represents the number of individual landmarks outside historic districts for that borough. The size of the box reflects the number of buildings within each district – the larger the box, the more buildings within that category. Each historic district is color-coded by borough and grouped alongside all the other districts within that borough. Manhattan. Brooklyn. Queens. Bronx. Staten Island.

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125,594 records above

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At first glance, we notice several trends. The densities and locations of preserved districts do not correspond to the most densely populated areas. For instance, Manhattan, with population only 19.3% of the citywide total, 3 has 30.46% of the landmarks. By comparison, Staten Island, with only 5.55% of the population, has 16.24% of landmarks – the greatest per capita number for all five boroughs. Or, the Bronx with 17.06% of people has only 5.36% – the lowest per capita. Given that the land area of Bronx (42.47 mi²) is comparable to Staten Island (58.69 mi²), and given that their histories are equally rich, then does the Bronx objectively have fewer landmarks worth preserving? Or, do preservation trends follow patterns of economics and race – with economically advantaged neighborhoods having stronger legal and political leverage to maintain and restore the appearance of their architectural heritage?

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Manhattan Brooklyn Queens Bronx Staten Island
% of NYC population in this borough 19.30% 30.72 27.36 17.06 5.55
% of NYC landmarks in this borough 30.46% 25.65 21.98 5.36 16.24

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Historic preservation does not operate off of a tabula rasa with objective standards and processes for listing, despite appearances to the contrary. There is an undeniably spatial pattern to urban growth and income inequality with a city segregated into districts by age of construction, race, and income. Historic preservation may operate on this unequal economic fabric.

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128,212 records above

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Case Study Two:

Contextual preservation?

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One of the most common criticisms of the preservation movement is that it limits economic development by preventing the demolition of older structures and their replacement with larger and more desirable new ones. Additionally, historic preservation is linked to a lengthy (and expensive) approvals process that developers must pass through. A committee of historians reviews each application and suggests revisions to ensure that new development is either a) “contextually” respectful of its neighbors if involving construction on vacant land or b) preserved the existing fabric if involving rehabilitation of an already landmarked building. 4

Developers often claim that historic preservation discourages development and reduces the potential of land to be profitably developed. To support this, developers will acknowledge that there doubtless are structures worth preserving, but that the same legal protections extended to genuinely historic structures are also extended to their less-significant neighbors. This criticism of preservation applies to vacant parcels within historic districts or more modern buildings that are surrounded by historic ones. Our data does not support this claim.

Within the city’s unequal fabric with pockets of concentrated, wealth, poverty, and history, we identify three general categories of protected buildings. First, there are individual landmarks, such as bridges, large railroad stations, statues, or street furniture. While aesthetically and historically important, these individual sites are rarely adjacent to other landmarks. Also, new development can occur adjacent with few restrictions on zoning. No approval from the LPC is necessary – only construction permits and variances as needed. The case for preserving these structures is strong, as application for each was individually made and individually approved on a case-by-case basis by city government and often with approval from the landowner at time of designation. Grand Central Station and Saint Patrick’s Cathedral are two examples. These structures, on account of their height, size, or appearance are genuine landmarks and place-makers in defining neighborhood identity.

Second, there are historic districts, comprising continuous stretches of smaller buildings. This includes structures of various age, use, function, and size. Preservation here is justified on the grounds that 1) the individual structures are historically unique or “significant” and 2) the relationships between these structures and the human-level streetscape they form are worth preserving. Here zoning and use restrictions may be restrictive as the majority of historic districts fall within mostly residential neighborhoods. Height limits are also stricter with the frequent stipulation that new additions must be setback from the main façade line and under one story. From the text of the 2018 city-wide zoning ordinance, zoning aims: “to protect the character of certain designated areas of historic and architectural interest, where the scale of building development is important, by limitations on the height of buildings.” 5

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Third, there are contributing and vacant parcels within these historic districts. The protections applied to category two buildings are extended to category three on the grounds that development on these less important sites will affect the quality and aesthetics of adjacent structures. The best example of this kind of contextual preservation comes in the form of a series of structures. Most may retain their original appearance, but a few interspersed between post-date the neighborhood’s age, are built in a different style, or suffered from demolition before the area was preserved. Above are two examples of these kinds of contributing structures.

If ever a case is made against historic preservation, the flaws seem greatest with this form of contextual preservation because these structures are preserved and their modification legally obstructed solely on grounds of their location. Additionally, there are numerous vacant lots within historic districts, where the argument could be made that the legalities of preservation disincentive the kind high-density development that is preferable to developers. However, an analysis of our dataset reveals that non-designated structures comprise less than 15% of all items within historic districts. The data is broken down on the table below, by borough and for the city as a whole:

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Borough . Manhattan Brooklyn Queens Bronx Staten Island

NYC

Totals

Designated structures

(individual and districts)

32,376 28,680 25,560

17,325

 

5,344 109,285
Non-designated structures within historic districts 6,465 3,783 2,626 3,118

1,512

 

17,504
Number of vacant parcels within historic districts 40 457 74 444 29 1,044
Percentage of buildings in historic districts that are non-designated and/or vacant 16.731% 13.713% 9.5541% 17.054% 22.38% 14.74%
Borough totals 38,881 30,920 28,260 20,887 6,885

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This yields 128,594 6 protected buildings (designated and non-designated). According to NYC’s public database, there are 857,271 structures total in the city. 7 Meaning that protected buildings comprise slightly less than 14% of all structures in the city. In addition, the non-designated and vacant parcels within historic districts comprise less than 2.16% of the city’s fabric. These values stand in contrast to comparable world cities like Paris and London, which are millennia older and have protected a greater percentage of their historic fabric. Below, for instance, are two comparative maps of the conservation areas (green) in the Westminster area of London 8 versus those in Lower Manhattan and Brooklyn (purple). 9

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Case Study Three: Keeping up to pace?

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When the first batch of 2,312 historic structures was landmarked in 1965, their average year of construction was 1882 – representing an 83-year gap during which these structures were not protected. In 2018, the average construction year of newly landmarked structures is 1908, representing a 110-year gap. Thus in the 53 year life of the landmarks movement from1965 to 2018, the average age of a building when landmarked has increased by 37 years.

The more recent inclusion of modernist skyscrapers, like the Lever House (1982) and Seagram Building (1989), may give the impression that the criteria for what qualifies as aesthetically important and worth preserving has expanded. Our data does not support this conclusion, because while recent years have seen newer landmarks granted legal status, the rate of designation has not kept up with the rate of construction and, in fact, has fallen behind.

The graph below illustrates the date a structure was registered on the horizontal axis measured against its construction date on the vertical axis. Every single protected structure is plotted on this graph by color. Individual dots represent individual sites. The black trend line indicates the only moderate increase in the numbers of modern structures receiving protection.

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5,451 records above

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Is historic preservation falling behind, even though the rate of construction and population has increased? Or, is the city no longer building the kinds of structures deemed worthy of preservation? This 16-year gap could be a fluke, or it could be indicative of larger trends.

To qualify for landmark status in NYC, a building must be older than 30 years or older than 50 if added to the National Register. From a publication by the The Trust for Architectural Easements: “LPC property must be at least 30 years old – no exceptions – whereas a National Register property must be at least 50 years old, unless it is found to be of exceptional significance, in which case there is no age limit at all.”  10 When the LPC was formed in 1965, none of the buildings from 1935 to 1965 would have qualified for designation. Today, as of 2018, any building from before 1988 could qualify. However, less than 5% of all listed structures date from the 43 years from 1945 to 1988 – a significant time in this metropolis’ history as it transitioned from an industrial economy to the world’s financial center and a major hub for tourism.

The graph below illustrates the age range of all landmarks and the distribution of landmarks by year. The horizontal axis corresponds to years, and the vertical axis represents the number of landmarks built in that year that are now included in LPC listings. Clearly, the vast majority falls within the 90-year span of 1850 to 1940, with few landmarks falling outside this range. The peak is in 1895 with 13,275 records from this year alone – a surprising anomaly. The rise and falls on this graph may also correspond to roughly 20-year periods of boom and bust recessions, along with corresponding halts to new construction. The shortage of pre-1850 sites is easily explained by the vagaries of time and the relatively smaller size of the city before 1850. But, the chronic shortage post-1940 may hint at a broader historical oversight or change in the way new buildings are designed and age.

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93,691 records above

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The LPC was created partially in response to the demolition of New York Penn Station in 1963. And, it was an attempt to prevent further destruction of aesthetically significant buildings, many of which had already been lost to progress and urban renewal. By the 1960s, urban renewal was winding down and New York was entering the prolonged recession of the 1970s and 80s, during which the rate of urban renewal and highway construction ground to a halt. In this light, the LPC originated as a post-facto response to demolition that had been going on for decades.

Despite the history of the LPC, must land marking occur after destruction has begun? There are doubtless hundreds of post-war buildings of significance – that have not yet been identified or deemed worthy. The question is not: Should we list these buildings? Rather, the question should be: Why are we not listing these buildings before they are threatened? And why should LPC status be limited to buildings older than 30 years? The demolition of the city’s American Folk Art Museum by MoMA in 2014 is one example. 11 The Temple of Dendur and its custom-built exhibit hall is another instance of an interior landmark completed pre-1988 and potentially eligible for LPC status.

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Case Study Four:

How might the preservation movement reflect economic patterns?

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As land values increase, and as it becomes increasingly unsustainable to develop land with single-family residential structures and townhomes, newer buildings are more likely to be commercial, mixed-use, and multi-family. However, the historic preservation movement exhibits a preference toward land-marking residential structures. The table below illustrates the types of buildings preserved, their quantity, and the percentage of the total number of preserved buildings this quantity represents. The buildings are listed below by their original functions. So, a building designed as a factory but more recently converted to residential is still listed as “industrial.”

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Type of Building Quantity Percent of Total
Residential 35,575 27.66%
Civic 16,920 13.16%
Street Furniture 13,943 10.84%
Commercial 4,574 3.56%
Infrastructure 2,490 1.94%
Transportation 2,145 1.67%
Institutional 2,026 1.58%
Religious 1,509 1.17%
Mixed Use 1,324 1.03%
Vacant 1,178 0.92%
Military 759 0.59%
Industrial 436 0.34%
Outbuildings 12 32,391 25.19%
All other uses 14,970 11.64%
Totals 128,594 100%

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The most salient figure in the above table is the disproportionate representation of residential and civic buildings that are preserved. For instance, as of 2018, Manhattan has 39,172 landmarked items. Of these landmarks, 35% (= 13,816) are residential use, 9% (= 3,443) are commercial, and 1.5% (= 650) are mixed-use. Mixed use, in this case, is defined by commercial on the lower level and offices or residential on upper floors. However, there are more commercial and mixed-use buildings in Manhattan than there are residential buildings. 13 So, the percentages of landmarked buildings are not representative of the percentage of residential versus commercial and mixed-use buildings that exist. In short, our data supports the conclusion that residential buildings seem more likely to receive landmarked status than commercial structures.

The numbers of landmarked civic structures strengthens the above conclusion. New York City owns 14,000 properties 14 across five boroughs. This MAS estimate does not include public monuments, statues, civic buildings built by the city and later sold, or civic buildings originally built for private use but acquired by the city. Yet, there are 16,920 landmarks designated as serving “civic” functions, including 11,726 landmarked items relating to hospitals and 571 related to armories. In fact, among all the 440 types of landmarks in this city, civic-related structures have the highest rates of landmark status and the rate of preservation closest to 100%.

What explains these inequalities? One explanation could be that civic sites, particularly those built in the early 20th century tend to be high quality, well built, and designed to articulate the civic values of democracy and government through the beauty of the neoclassical style. Therefore, these buildings are more likely to be deemed worthy of preservation. But, this interpretation is doubtful because there is little factual basis to assume that civic structures are “better than” commercial and mixed-use.

A more believable explanation could be that civic and residential structures are easier to landmark than commercial. The maintenance and upkeep of civic structures is managed by government and elected officials, who are responsible to voter complaints and community pressure. And, the public can threaten to vote out of office any leaders who neglect historic, city-owned properties. Additionally, there are few reasons for developers or residents to object to land-marking civic sites, as legally protecting these structures adds more red tape, not to city residents, but to the future bureaucrats who restore these sites. Again, this is speculation.

Still yet, there is a stronger factor influencing preservation. Civic structures are not subject to market pressures, and city-owned buildings do not have to help their occupants make a profit. For instance, the cost of rehabbing a historic public school building might more expensive than just demolishing and rebuilding it new, but the city is under less pressure to demolish the structure because, fortunately, city government is not run like a profit-driven corporation. And, so historically valuable but functionally outdated city buildings may be more likely to be landmarked and restored than demolished, as illustrated by the unequal distribution of building types in our data.

By contrast, commercial and residential structures are subject to strong market pressures favoring demolition. An old factory that has outlived its designed lifespan and is no long suitable for modern-day production line assembly will be abandoned or demolished if it cannot be converted. But, the process of conversion may require completely gutting the structure, environmental remediation, and a lengthy approvals process. If the cost of renovation is more expensive than the income the renovated structure can bring in, then there will be greater pressure to demolish than to preserve the fated structure. City-owned libraries and hospitals face less of this kind of pressure.

Our data also reveals a spatial concentration of residential buildings in historic districts. For instance, most of Manhattan’s residential landmarks are concentrated within historic districts in the Upper West, Upper East, and skyscraper valley between Midtown and Downtown. Residential sites are more likely to be collectively landmarked as part of districts. As illustrated in the table below, 94.93% of residential landmarks citywide fall within historic districts, and only 5.07% are outside these districts:

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Residential All Other Types
Within historic districts 35,029 = 94.93% 61,124 = 66.66%
Individual landmarks outside historic districts 1,872 = 5.07% 30,569 = 33.34%
Total Number 36,901 = 100% 91,693 = 100%

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What explains the disproportionate protection of residential structures? One possible motivating factor could be income-levels in historic neighborhoods and associated protectionism. The map on the following page overlays the locations of historic districts over 2018 block-level census data for income levels and length of residence. Our analysis reveals a spatial overlap between historic districts and areas with higher incomes and longer-term residents. For instance, the average length of residence for residents in the Brooklyn Heights historic district is between 17.1 and 48 years and incomes range between $51,500 and $289,000, while the rest of Brooklyn averages between 10.3 and 12.8 years and under $51,500 income. Similar patterns play out in the Greenwich Village and the Upper West Side. In short, residents in historic neighborhoods seem more likely to stay-put, and length of residency may be a proxy for measuring the degree to which residents are invested in maintaining the physical appearance and improving their community. From this data, we posit that the relationship between historic preservation and length of residency is too strong and too consistent across the five boroughs to be correlation. There may be causative factors at play between income, emotional investment in one’s community, and preservation, yet this remains to be conclusively confirmed by future data.

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Click map to launch interactivity − opens in new tab.

Individual landmarks in red outside historic districts in brown tend to be commercial structures.
There is no immediately identifiable relationship between the siting of commercial landmarks,
and the income levels of their adjacent community.

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The spatial relationship illustrated above is surprising for another reason: gentrification. Normally, gentrification in the past 20 years is associated with rising income levels and the displacement of existing residents. The physical appearance of historic neighborhoods should also make them more desirable for gentrification. However, the average length of residency is longer in historic than in non-historic districts, even though income (and presumably rent, too) are higher in historic districts. That is, neighborhoods with historic preservation more often have high and rising incomes with long length of residency. This seems contradictory because high-income areas should be more likely to push out longer-term tenants from the pre-gentrification era.

By contrast, neighborhoods without the benefit of historic preservation more often have high incomes and lower length of residency, meaning a high turnover rate. The Williamsburg neighborhood is one example with incomes over $51,500 (similar to Brooklyn Heights) but length of residency under 10.3 years. Additional research should examine if rent-stabilized apartments are more likely to be concentrated in historic districts. There is the possibility that the legal barriers of preservation make it more difficult for developers to push out existing residents, gut an old building, and then rebuild it to charge higher rent. Unfortunately, New York City Open Data has no information on the spread or geographical clustering of rent stabilized apartments.

These possible relationships between historic preservation and gentrification need to be confirmed by further analysis.

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Conclusion: The Future of Historic Preservation

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There are limits to our data – these statistics cannot reveal the intricacy of historic sites, the unique identity of each, or the reasons why each justify (or do not justify) protection. But, this data can reveal big picture trends in preservation, its biases, and some of its problems. While these trends are not visible from walking the street or looking at individual sites, they become visible through the lens of data. This data may also reveal causative relationships between income, length of residency, and the political strength of preservationists.

From this data-driven analysis, we can make deduct several conclusions:

  1. Historic preservationists prefer to landmark and protect pre-WWII buildings, even though numerous post-war examples may qualify. As a result, there are a disproportionately high number of pre-war buildings with landmark status, and comparably few post-war landmarks – less than 5%. Similarly, the rate at which landmarks are designated has not kept up with the pace of new construction.
  2. The market pressures to demolish civic structures are weaker than the market pressures to demolish commercial and residential. As a result, a disproportionately high percentage of city-owned or institutional buildings are preserved, and a disproportionately low percentage of commercial and industrial.
  3. Tangent to the previous point, a disproportionately high percentage of landmarks are for residential use and fall within residential districts. This may indicate that landmarks preservation is a strategy for neighborhood protectionism – that is, an effort by residents to ensure that the appearance of their community is not changed due to new development. Neighborhoods of lower-density old buildings, like the West Village, retain their popularity, charm, and high property values thanks to strong legal barriers against change that could lead property values to depreciate. While these barriers may discourage and prevent developers from reaping larger profits by building higher and larger, they also ensure that existing residents’ investment in their condos or homes will remain more stable.
  4. The, economic success of New York on a global scale and its continuing construction boom has led to the demolition of many non-residential commercial landmarks that might have otherwise qualified for landmark status had New York not been as successful. In the words of Professor Kenneth Jackson: 15

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History is for losers. By that I mean, cities which have chosen to preserve all their historical monuments and locations usually do so because no one else wants the land to develop. Modern progress has passed them by. New York’s history doesn’t litter the streets visually, it can be hard to find sometimes, but that is because the city is an economic winner on a global scale.

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New York is indeed a winner “on a global scale,” with Wall Street as a symbol of America’s economic power, the United Nations as a symbol of political power, and the city’s over three million foreign born as representative of power of immigration and globalization to shape a city. But, this progress comes at a historic and aesthetic cost – the consequences of which are reflected in the dark and sterile skyscraper canyons of Midtown, the worsening congestion in cars and subways, and (more pressingly) this city’s fragility when faced with ecological pressures, such as flooding, hurricanes, and climate change. At the level of historic preservation, this progress comes at the cost of losing New York’s distinctive architectural heritage to the force of globalized change. The Gilded Age mansions on Fifth Avenue and the built-to-last-forever Penn Station are gone, as are the picturesque skylines and distinctive ethnic neighborhoods of Berenice Abbott’s 1930s photographs. The New York of today is different – whether it is architecturally poorer for progress can only be judged in retrospect. Historians prefer not to speak of what-ifs when writing about history, but would it have been possible to accept the benefits of progress without sacrificing history? This, however, is a question beyond the limits of data to contemplate.

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Links to Resources

The original datasets can be viewed or downloaded below:

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Footnotes

This author is not affiliated in any way with NYC Open Data, LPC, or the New York City government.

  1. “Individual Landmarks,” New York City: Open Data, https://data.cityofnewyork.us/Housing-Development/Individual-Landmarks/ch5p-r223 (retrieved 5 November 2018).
  2. “LPC Individual Landmark and Historic District Building Database” New York City: Open Data, https://data.cityofnewyork.us/Housing-Development/LPC-Individual-Landmark-and-Historic-District-Buil/7mgd-s57w (retrieved 5 November 2018).
  3. New York City’s 2017 population estimate is 8.623 million.
  4. More on this topic: Rachel Mollie Levy, “Contextual Zoning as a Preservation Planning Tool in New York City,” (Master’s diss., Columbia University: Graduate School of Architecture, Planning, & Preservation, 2015) https://academiccommons.columbia.edu/doi/10.7916/D8HD7TVM (retrieved 5 November 2018).
  5. “General Purposes of Residence Districts,” in The Zoning Resolution: Web Version, (published by New York City Zoning Department, 2018), pp.252-53. https://www1.nyc.gov/assets/planning/download/pdf/zoning/zoning-text/allarticles.pdf (retrieved 5 November 2018).
  6. The total for all five boroughs is 127,833. Including landmarks not registered in any borough, like Ellis Island, the total is 128,954.
  7. New York City Planning Department, “Spatial Data Properties and Metadata,” from MapPLUTO, (published to the web, 2018), pp.5 https://www1.nyc.gov/assets/planning/download/pdf/data-maps/open-data/meta_mappluto.pdf?v=18v1 (retrieved 5 November 2018).
  8. “Conservation Areas,” City of Westminster, https://www.westminster.gov.uk/conservation-areas (retrieved 5 November 2018).
  9. Published by NYC Zoning Department, “NYC_Historic_Districts_2016,” ArcGIS 9geographic information system), https://data.cityofnewyork.us/Housing-Development/Historic-Districts/xbvj-gfnw (retrieved 5 November 2018).
  10. Anthony W. Robins, “Differences between Landmarks Commission Designations and National Register Listing,” in Similarities and Differences between Landmarks Preservation Commission Regulation and Donation of a Preservation Easements, (Prepared for The Trust for Architectural Easements, 2009), pp.10, http://architecturaltrust.org/~architec/wp-content/uploads/2013/06/1a-2009-0512-Robins-Report.pdf (retrieved 5 November 2018).
  11. Michael Kimmelman, “The Museum With a Bulldozer’s Heart,” The New York Times, January 14, 2014, https://www.nytimes.com/2014/01/14/arts/design/momas-plan-to-demolish-folk-art-museum-lacks-vision.html (retrieved 5 November 2018).
  12. “Outbuildings” mostly include garages, stables, street furniture, and accessory structures, generally small. This category skews our results. Since many accessory structures were turned into residential structures, the actual percentage of residential dwellings should be slightly higher than 27.66%.
  13. Manhattan has more residential than commercial landmarks even though more people work here than live here. On weekdays, 3.1 million people work in Manhattan, while only 1.6 million live here.
  14. “New York City owns or leases 14,000 properties around the five boroughs—a public asset roughly the size of Brooklyn.” From: “Public Assets: Mapping the Sixth Borough of New York,” The Municipal Art Society of New York, https://www.mas.org/initiatives/public-assets/ (retrieved 5 November 2018).
  15. “Quotes from Kenneth Jackson,” CULPA, http://culpa.info/quotes?professor_id=97 (retrieved 5 November 2018).

The “Spiky” Geography of Art History

…according to the Metropolitan Museum, NYC

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According to its founding mandate: “The mission of The Metropolitan Museum of Art is to collect, preserve, study, exhibit, and stimulate appreciation for and advance knowledge of works of art that collectively represent the broadest spectrum of human achievement at the highest level of quality.”
Over the past few years, the Metropolitan Museum has catalogued over 25% of its holdings online. This represents ~590,000 objects, covering over 5,000 years of human history from 17 curatorial departments. The diversity of objects in a museum’s collection (and the amount of contextual information known about these objects) may reflect the kinds of narratives a museum can curate about artistic and global history. This animation charts the provenance and year of production of every single object that is catalogued on the Metropolian Museum website, whenever this information is known.
The geography of art history is, in some ways, “spiky.” Certain regions, particularly cities, are home to diverse and famous artistic output. Thomas Friedman similarly describes globalization as being spiky and concentrated in big cities. Other regions are comparatively less productive and less often collected. Either this reflects museum curator’s historic bias against Africa, Latin America, etc. in favor of Europe. Or, this might reflect a more fundamental historical reality: If geography guides artistic production and privileges regions with good geography, like areas surrounding the Mediterranean, then landlocked and inaccessible regions with poor geography will have less “exciting” artistic output.
If you liked this, please see my analysis and animation of the Museum of Modern Art’s collection history, where I seek to answer the question Where in the world is modern art?

 

 

In this animation, each colored dot indicates one geographical location represented by art in the Met’s online collection. The dot’s location indicates where this object was created. The dot’s size corresponds to the number of objects from this location. The time each dot appears corresponds to the year this object was created. Collectively this animation reveals the potential geographical and temporal preferences of the Met’s online inventories for objects collected in the common era (the year 1 c.e. to present-day). The dots above are assumed to be a relatively accurate sample size.

However, there are many objects in the collections with known provenance but unknown production date. Figure 1 below illustrates objects with known provenance and known year. Figure 2 shows objects with known provenance, regardless of whether year is known. The data-set in figure 2 has approximately double the number of objects, but these are concentrated in the same regions as objects in figure 1. This is because objects with known year also tend to have known provenance. Hence, figures 1 and 2 exhibit similar tendencies.

 

Art objects from ancient cultures like China, Egypt, and Sumeria frequently have known provenance but unknown year of production. This year might be estimated to the level of century with the help of carbon dating and through comparison with similar objects whose date is known for certain. Were the dates of these ancient objects known for certain, they could have been included in the animation above, thereby increasing the size and density of dots in under-represented regions. In this case, the animation would have resembled figure 2.

There is one more interpretive problem: Does this visualization reveal more about the diversity of the collections, or the preferences for which objects are selected for inventory online? For instance, does the statistical absence of objects from East Asia, in comparison to France, mean that the Met collects objects from East Asia less actively and in fewer quantities? Or, does this absence merely mean that fewer objects from the East Asian collections are selected for display on the museum website?

Metadata for this animation was downloaded here from the Met Museum’s website, then edited as a spreadsheet in excel and visualized in Tableau Public. This data was published by the museum staff in the public domain under a Creative Commons license. I am also publishing this visualization as an interactive map; it is open source and free to download  at this link.

 

Zoning and Affordable Housing in Newark

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In the summer of 2017, I helped oppose the gentrification and rezoning of the Ironbound neighborhood of Newark. The area was zoned for buildings no higher than eight stories, which was respectful of the small and community scale of the existing structures. City officials, however, proposed rezoning a large section of the Ironbound for 18-story structures – four times taller than any other structure in the immediate area.

Motivated by profit, a large parking corporation and other landowners lobbied the city to increase the maximum allowable height – thereby increasing the value of their land and threatening the existing community with gentrification. The small streets and infrastructure of the Ironbound would not have been resilient or large enough to support such a large increase in density.

To oppose this ill-devised proposal, I created a computer simulation of how the neighborhood would appear, were the proposal passed. This computer simulation and the proposed legislation were also the subject of a Star Ledger article by human-interest reporter Barry Carter. I am providing the link to this article here. This computer simulation was also watched by members of the City Council and the property owners effected by this legislation. I also spoke five times before the City Council and at community meetings to oppose this project and argue for development in Newark that is genuinely sustainable and genuinely respectful of the existing community and the city’s people.

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Computer Simulation

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Speech before the City Council on Tuesday, September 19

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The text of this speech is transcribed below.

I’d like to speak on why opposing MX-3 is consistent with supporting inclusionary zoning.

To my knowledge, 7 members of the City Council voted in favor of inclusionary zoning. This is an important move to protect our city most vulnerable residents and to preserve affordable housing in our downtown.

MX-3 and upzoning will jeopardize this important piece of legislation.

Why?

inclusionary zoning kicks in when (firstly) developers build structures over 30-40 units and (secondly) they request a variance to build this structure.

But, when an area is zoned for larger and taller structures developers can build more and larger structures WITHOUT requesting a variance to build larger. And when developers do not need to request a variance for height, it is less likely they will need to include affordable housing in their project.

In effect, MX-3 will remove the requirement to build affordable housing in the effected area. When zoning is overly generous to developers and zoning permits overly large scale, develops do not need variances. And when developers don’t need variances, they do not have to built affordable housing.

In addition, since MX-3 could be expanded to anywhere within a half mile radius of Penn Station, it is quite possible that MX-3 could be expanded in the future. In effect, this would eliminate the requirement for developers to build affordable housing in this area. Upzoning does not benefit affordability.

Secondly, what is sustainability?

Sustainability and transit-oriented development is not just about a short distance to Penn Station. It is not just about green roofs or any type of development.

Sustainability is about affordable housing that we the people can afford to live in. We don’t want luxury condos for the 1% in the MX-3 area. We want development that our residents and you can afford.

All of us can agree that WE ALL WANT DEVELOPMENT. But, we want development that is 1. Affordable 2. Respectful of the Ironbound community. And 3. Respectful of our city’s diversity and history.

MX-3 is none of these things. It is about landbanking and benefiting the 1% wealthiest outside our city. I encourage you to strike down MX-3 and to encourage instead an open dialogue with the community about SUSTAINABLE and AFFORDABLE development in our city.

Developers should come to Newark and development should happen. But, we should not upzone entire sections of our city, in effect removing the requirement for affordable housing, undermining the inclusionary zoning we just created, and jeopardizing the recent master plan we created with public participation.