• This website includes dozens of videos, hundreds of essays, and thousands of drawings created over the past twenty years. Search to learn more about the history of buildings, places, prisons, Newark, New York City, and my PhD research on spatial inequality.

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Tag Archives: data visualization

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California Waterscape: time-lapse history of water supply

  15 June 2019 , ,
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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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 georeferenced 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 ● 106 liters). This is the unit of measurement California uses to estimate water availability and use.

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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.

 

Method and Sources

The most important data sources consulted are listed below:

This map excludes the following categories of aqueducts and canals:

  • 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 numerous to map 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 into “canals” remain unlined along their path. Determining the construction date for these semi-natural features is therefore difficult. So, for the purposes of simplicity and to aid viewers in seeing only manmade water features, these water features are excluded.
Download and edit the open source QGIS dataset behind this animation.
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Here Grows New York City

  22 May 2019 , , , ,

Music: “The Language of Cities” by Maserati

1. The Animation

Here Grows New York visually animates the development of this city’s street grid and environment from 1609 to the present day, using geo-referenced road network data, historic maps, and geological surveys. The resulting short film presents a series of “cartographic snapshots” of the built-up environment at intervals of every 20 to 30 years in history. This process highlights the organic spurts of growth and movement that typify New York’s and most cities’ development through time. The result is an abstract representation of urbanism.
Featured in:
– Laughing Squid   March 12, 2019
– Viewing NYC   March 14
– silive.com   March 14
– Open Culture   April 17
– Columbia Data Science Institute   May 1
– Library of Congress Blog   May 2
– Kottke.org   May 6
– NYNJ.com   May 13
– 6sqft   May 13
– UK Daily Mail   August 28
– LangweileDich.net   June 11, 2020
– Wikipedia
– New Amsterdam History Center   December 1, 2020
– Bunk History   July 2021
– Camden Town, 2023 textbook by Westermann Group
– Inspired by Cambridge University’s London Evolution Animation and this map of Barcelona

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2. The Interactive Map

The results of this animation are transformed into this fully interactive map

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3. Research Method

Several hundred maps in the digital archives of the New York Public Library and Library of Congress were analyzed to assemble this film. About 25 of these maps were then selected, downloaded, merged, stretched, and warped in a single document to align with each other. This provided a consistent scale and allowed easier comparison of differences between maps of different date. As the source files were all in different colors, scales, and designs, we created a single base map with unified graphics. The redrawing not only permits correcting errors in less accurate old maps but also provides a graphic representation that is consistent over time. This coherence allows the rate and trends in urban growth to be read more easily and compared between eras.
Click here to read the research methodology and list of maps consulted.
Or watch the video tutorial below of the workflow and software behind the animation

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4. Conclusions and Analysis

This data visualization informs our analysis of the history of the New York City grid. This analysis reflects on the question: What can the built environment of Manhattan’s streets reflect about the evolving social and economic priorities of city planners and leaders? The long phases of urban growth and shifting transportation modes created distinctive road networks in Manhattan. The predominance of different forms of transport during each era also prompted changes to the location and dimensions of streets in response. Manhattan illustrates the evolution of these road networks over four centuries of near continuous growth. A plot can describe a street grid, as well as its builders’ story. This paper aims to tell this second plot, a story of urbanism.
Click here to read the conclusions as part of a working paper
written with Professor Kenneth T. Jackson.

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5. Credits and Appendix

This project would not have been possible without the support, mentorship, and patience of my parents, Anne Mabry and Zemin Zemin Zhang. Nor would this project have been possible without the historical expertise of Columbia University professors Kenneth T. Jackson (History Department) and Gergely Baics (Urban Studies). Thanks is also extended to those who reviewed and critiqued this project in its early stages, including Chris Kok, Wright Kennedy, Dan Miller, and the Center for Spatial Research at Columbia’s Department of History. Most importantly, I thank my dog ChoiChoi.

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Anyone may reuse or republish this content, so long as credit is provided with the link back to this page. If you email [email protected], I will gladly send along the graphics, maps, and source files associated.

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24 Hours in the London Underground

  21 April 2019 ,

Audio effect: Heartbeat from Freesound

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Through analyzing 25,440 data points collected from 265 stations, this animation visualizes commuting patterns in the London Underground over two weeks in 2010.
Each colored dot is one underground station. The dots pulsate larger and smaller in mathematical proportion to the number of riders passing through. 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.
By syncing the audio volume with the density of riders and the background color with the time of day, the animation becomes acoustically legible. The audio volume rises and falls to mirror the growth and contraction of each colored dot during the daily commute.

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The rhythmic pulsing of commuters is analogous to the breathing human body. The passage of red blood cells from the lungs to the organs is analogous to the movement of people to and from the city’s own heart: the downtown commercial district. This analogy of human form to city plan is a longstanding theme in urban studies.
See my film about commuting patterns in the NYC subway.

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The Data

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Method

No single data set could capture the complexity of a metropolis like London. 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. For data collection purposes, stations where two or more lines intersect are counted as a single data entry. This is to avoid double-counting a single passenger who is just transferring trains in one station en route to their final destination.

Every fifteen minutes, the numbers of passengers entering the system are tallied. This yields 96 time intervals per day (4 x 24). 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 station was assigned:

  • A location on the map of latitude and longitude
  • A color according to the lines extant in 2010: Bakerloo, Central, Circle, District, Hammersmith & City, Jubilee, Metropolitan, Northern, Piccadilly, Victoria, Waterloo & City.
  • A circle scaled to reflect the number of passengers moving through. Stations range in business from a few hundred passengers to over 100,000 per day.
  • A time of day: each 15-minute interval becomes one image in this film. Overlaying these 96 “snapshots” of commuter movement creates  a time-lapse animation. Thus, a single day with 25,440 data points is compressed into a mere 8 seconds.

Sources

Station Coordinates: Chris Bell. “London Stations.” doogal.co.uk (link)
Ridership Statistics: Transport for London. “Our Open Data.” (link)
Click on the section “Network Statistics” to view “London Underground passenger counts data.”

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

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Railroad commuting patterns in New Jersey

  8 March 2019
View my data visualizations of New Jersey’s suburban growth here.
Created with data from NJ Transit on weekday and weekend rail ridership.
Or download my data from Tableau Public.
NJ Transit carries over 90,000 commuters per day to and from New York Penn Station, the busiest rail station in the Western Hemisphere. The construction of this rail network in the nineteenth and early twentieth centuries was focused around New York City. Like spokes on a wheel, these rail lines radiate from the urban center.
Hover over stations to view 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 daily riders is listed.

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The map above shows weekday ridership patterns. Movement is centered around the employment hubs of Newark and New York Penn Station. The next two busiest stations are Secaucus Junction and Hoboken, but these two stations are not destinations. Instead, they are transfer points for commuters en route to New York City. Commuters collected from stations on the Pascack Valley, Bergen County, and Main Line are almost all headed to New York City, but they must transfer at Secaucus (to another NJT train) or at Hoboken (to PATH / Hudson River ferries).

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This map shows Sunday ridership. On average, stations are 66% to 75% less busy on weekends. The thirteen stations along the Montclair-Boonton Line – between Bay Street and Denville – are also closed on weekends because ridership is so low. However, the only line that is almost as busy on weekends as it is on weekdays is the Atlantic City Line. This is likely because trains on this line serve weekend tourists to the New Jersey Shore and Atlantic City casinos.

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Notice the large difference between the first four stations and all others listed. Keep in mind that a lot of this data double-counts a single passenger. For instance, someone riding from their home to work will be counted once in the morning, and again in the evening.

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Writing Here Is New York in 1949, American writer E.B. White has this to say about suburban commuters:

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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 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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Northeast Corridor railroad time-lapse

  2 February 2019

Audio effects from Freesound; music is Metamorphosis by Philip Glass

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The Northeast Corridor is the busiest passenger railroad in North America. This drone flight follows a high-speed Acela train making this 456 mile journey from Washington D.C. to Boston via Baltimore, Wilmington, Philadelphia, Trenton, Newark, New York City, Stamford, New Haven, and Providence.
This animation was created from Google Earth satellite imagery. I traced the Northeast Corridor route onto the ground, and I then programmed the computer to follow this route. I then added the inset map, sound effects, and clock in post-production.
The above animation is condensed. View the full and uncut 28 minute flight here.
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Geography of Marijuana Arrests

  15 January 2019 , ,
Update March 2021: Marijuana is now legal in NY state.

 

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The New York Police Department (NYPD) made 102,992 arrests in 2017 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, 90% marijuana arrests are male, even though only 65% marijuana users are male. 2 Males more than females and Blacks more than others are arrested for marijuana in disproportionate numbers.

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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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2017 data

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Click table to view in detail

NYPD marijuana arrests are disproportionately of Black males between the ages of 18 and 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 taxpayers. According to the Drug Policy Alliance, the city spends $75 million on marijuana arrests and prosecution per year. 4 This is money that could have gone to education, parks, and community programs. Marijuana policy targets our country’s poorest people of color.
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 becoming a “gateway crime” to the legal system. With a prison record from a marijuana arrest, a person of color may have more difficulty finding employment and re-entering society – ironically pushing them to desperation and possibly new and greater crimes than their initial arrest.

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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 income. The 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.
Marijuana arrests tend to happen 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 Street, 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 and likely comparable rates of marijuana use. 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 Method

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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 LaGuardia Airport. Controlling for population density, marijuana arrests still target communities of color.
This project was assembled from public data. I downloaded anonymized microdata on the race, crime, gender, and approximate 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 data set, 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 here. Arrest data is from NYC Open Data here.

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Endnotes

  1. Marijuana arrests represent 5.98% of all NYPD arrests in 2017. ↩
  2. From “Statista,” accessed 15 January 2019, link. ↩
  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. ↩
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New York City Subway Ridership

  19 December 2018 ,
Created with data from the MTA.
Published by Gothamist on 22 January 2019.
Related: my data visualization of London Underground commuting patterns.

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The visual language of data addresses a deeper need to humanize and soften the concrete jungle.

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Sounds of breathing, heartbeat, and subway from Freesound

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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.
Movements through the New York City subway are analogous to rhythmic breathing.
People often describe cities in relation to the human body. Major roads are called “arteries” in reference to blood flow. The sewers are the city’s “bowels” in reference to our own digestive systems. Central Park is the city’s “lungs.” At various times in history, key industries like garments and finance were described as the “backbone” of New York’s economy. Although cities are complex organisms, 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 pre-coronavirus, the subway carried 5.4 million people, mostly commuters. This daily commute is 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 red blood cell. With each paycheck, the oxygen of capitalism flows from the heart of Manhattan to the cellular homes in the outer boroughs.
Commuting patterns mirror 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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Interactive Map

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Research Method

In this video lecture, I walk you through how I manipulated MTA and NYC open data
to create this animation.

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The Metropolitan Transit Authority (MTA) publishes statistics on weekday and weekend (Saturday + Sunday) ridership for all 424 stations. These statistics, updated yearly, are public and can be analyzed to track trends in urban growth. I downloaded the MTA data and assigned each station a geographical coordinate (latitude + longitude) so that the data points would appear at their corresponding map locations.

I have a love-hate relationship with the New York City subway. At rush hour, it is crowded, hot, and slow. From years of riding its squeaky trains, it’s given me a ringing tinnitus sound in my ear. Despite its flaws, the subway is one of the few urban spaces where all social classes and ethnicities mix, where their separate lives are momentarily shared. Rich or poor, everyone rides the subway. I hope this animation renews appreciation for this engineering and the people behind it.

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Sources

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Where in the world is modernism?

  17 November 2018
What if the nationality of every artist represented in the Museum of Modern Art’s collections were mapped to illustrate the museum’s evolving geographic diversity through time? Watch the data visualization below of 121,823 works 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 downloaded this dataset and dissected it with this question in mind:
What trends might this dataset reveal about the history of curating and the growth of a museum’s collections?
In the three interactive features below, hover over the graphs to explore the data in depth.

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1. Geographic and Gender Diversity

This map visualizes the nationalities of ~15,757 artists whose work is displayed at MoMA. There are 121,823 data entries displayed below. The data can be browsed by year or by department. This illustrates the 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. Post-1991, the museum acquired the bulk of its collections from Russia and China. Recent years have also seen a slight growth in collections of 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 represent the clear majority. 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 zero to somewhere around 20%.

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2. 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 that newly produced paintings are becoming progressively larger. In 1929, the year of MoMA’s founding, the width of the average painting being produced was less than 100cm. Today, the average width of newly produced paintings is around 400cm – and is steadily increasing.

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In the second graph, we see that 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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In other words, while artists seem to be working in ever larger dimensions, MoMA seems to be acquiring ever smaller paintings from these artists. Have the growing costs of buying and storing art priced MoMA out of acquiring larger artworks? What is the relationship between size and the decision whether or not to acquire a work?

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3. Is the scope and definition of modernism expanding?

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 data entires. Dot size indicates the size of the acquisition (i.e. number of pages or number of paintings from said artist). The red trend line indicates the linear relationship between when a work was produced (vertical axis) and when it was acquired by MoMA (horizontal axis). The vertical gap between the trend line and the upper reaches of the graph indicates the time elapsed between when the work was produced and when it was acquired. With time, the number of years elapsed between production and acquisition has grown.
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 early nineteenth century through present day. The temporal definition of modernism is growing, with origins that stretch ever further back in time.

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Modernism is not geographically restricted. With globalization and the march of capitalism, the world is becoming more modern and interconnected. As new regions adopt modern technology, materials, and ideas, the character of art and artists will change. Cultural institutions, particularly modern art museums, are positioned to curate these global trends through the kinds of works they acquire and display. However, the kinds of stories museums and curators can tell are limited by the size and diversity of the collections available.

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Related Data Projects

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Sources

Download MoMA’s data from GitHub. The analysis 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.

Download my analysis of this data and the infographics above from Tableau Public.

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A History of Historic Preservation in New York City

  4 November 2018 , ,

Data analysis of NYC landmarks since 1965 reveals trends and biases in the landmarks preservation movement.

Developed with urban historian Kenneth Jackson at Columbia University’s Department of History

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A visual history of landmarks preservation in NYC. Data from NYC Open Data. Music from Freesound.

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Introduction

There is ongoing debate between in NYC 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. Several factors drive historic preservation: fear of losing heritage; fear of change; historians, public servants, and well-intentioned activists in the spirit of Jane Jacobs. This debate has played out every year since 1965 through the hundreds of 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. Landmarks preservation is contentious for developers because the protections of preservation law are permanent and affect all current and future owners. Preservation law further restricts significant rebuilding, even if demolition and rebuilding are lucrative for the property owner.
Historians decide the future of the city’s built environment. The sites they preserve will become the architectural lens through which future generations will appreciate the past. The sites they approve for demolition will be lost to history. Preservation is a response to larger historical questions: Which aspects of the past are worth preserving? How should the city balance the economic need for development with the cultural need for history?
This paper will assess the landscape of historic preservation through analysis of publicly-available landmark records from NYC Open Data. We identified two datasets, both containing ~130,000 spreadsheet entries for every single LPC listing from 1965 to 2019. The first dataset is titled “Individual Landmarks” 1 and includes the structure’s address, lot-size, and date landmarked. The second dataset is titled “LPC Individual Landmark and Historic District Building Database” 2  and includes the construction date, original use, style, and address of all structures. We downloaded both datasets as .csv files, imported them into a visualization software called Tableau, merged them into a single map, and then analyzed the data. The results of inform the conclusions presented here. This analysis is broken into four case studies:
  1. Distribution of Landmarks over the Five Boroughs
    Assesses where landmarks preservation is densest or least dense by neighborhood.
  2. Contextual Preservation?
    Analyzes how protecting a landmark limits redevelopment of neighboring properties of less aesthetic value
  3. How does the preservation movement reflect economic patterns?
    – Factor affecting the preservation of city-owned structures
    – Factors affecting the preservation of residential structures
    – Relationship between preservation and gentrification?
  4. Keeping up to pace?
    Questions the degree to which landmarks preservation succeeds in protecting recently-built landmarks
From this data, hidden trends and biases in historic preservation become visible. Firstly, we identify a higher-density of landmarks in certain (and usually higher income) neighborhoods. Secondly, we identify a marked preference among historians for protecting structures pre-1945. (Is there so little in the city’s recent architectural history that is worth preserving?) And thirdly, our analysis hints at the strength of market forces and developers in shaping the scope and definition of preservation.

Read More

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  1. “Individual Landmarks,” NYC 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” NYC Open Data, https://data.cityofnewyork.us/Housing-Development/LPC-Individual-Landmark-and-Historic-District-Buil/7mgd-s57w (retrieved 5 November 2018). ↩
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The Geography of Art History

  14 October 2018
According to the Metropolitan Museum of Art

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Related: Data analysis and visualization of 120,000 works in the Museum of Modern Art

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In this film, each colored dot indicates one location represented by art in the Met’s online database. Dot location indicates artwork provenance. Dot size indicates the number of objects from this place. The time each dot appears corresponds to the year this work was created. This data is assumed to be an accurate sample size.

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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 visualization charts the provenance and year of production of every single object that is catalogued on the Metropolitan Museum website, whenever this information is known.
The geography of art history is uneven. 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 studied. Either this reflects museum curators’ historic bias against Africa, Latin America, and the “Global South” 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 artistic output.

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Art objects from ancient cultures like China, Egypt, and Sumeria frequently have known provenance but unknown year of production. Unfortunately, they are therefore excluded from this visualization. There are many objects in the collections with known provenance but unknown production date. Figure one illustrates objects with known provenance and known year. Figure two shows objects with known provenance only.

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Figure 1: All art with known provenance AND known year.
Figure 1: All art with known provenance AND known year.
Figure 2: All art with known provenance
Figure 2: All art with known provenance

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The original data was downloaded here from the Met Museum’s website.
This visualization and interactive map are free to view and download here.