New York City in a Box

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This pop up model in a recycled metal box (measuring 8 inches wide by 15.5 long and 2.5 deep) reveals a miniature world of New York City architecture and landmarks when opened. About 30 buildings made from hand cut paper and tin are spread across a flat ground of painted streets. Each building is made from a single sheet of paper that is cut and folded like origami to create different shapes and sizes. A hand cranked lever operates a hidden mechanism of chains and gears hidden beneath. These gears move the magnetized trains and airplanes through the city. The video below shows this mechanism exposed.

Click here to read an article featuring this project.

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Hand-crank and music box recording courtesy of Freesound.

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).

Northeast Corridor Drone Flight

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The Northeast Corridor is the busiest railroad in North America by passenger traffic. 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 using the Google Earth Pro desktop application. We began by tracing the full route of the Northeast Corridor onto three-dimensional satellite imagery of the world. We then programmed our computer to follow this route while running a screen-recording to capture the progress. Finishing edits were then made in Final Cut Pro, including the addition of the inset map at bottom, the speedometer and clock at upper left, and edits to the pacing and sound effects. The time and distance markers are calculated using Google Maps.

The above animation is annotated, click here to view the uncut 28 minute drone flight.

Audio effects are courtesy of Freesound.org.
Piano accompaniment is Metamorphosis by Philip Glass
performed by YouTube user: “Coversart”

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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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.

 

Columbia University

A Map of Campus

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This drawing depicts every building, window, tree, and architectural detail on campus as visible from an imaginary perspective 500 feet above the intersection of 110th and Amsterdam and looking northwest toward campus. The number of windows on each facade and details are faithful to reality. There are about 2,000 windows in this image and at least 50,000 individual lines. The image measures 26 by 40 inches and is framed in my room on campus. The personal objective of this project was to create a souvenir through which to remember my formative experiences and time at Columbia. I draw the closed world I find at Columbia so that, years from my graduation date, I can look at this image and reflect on the formative four years I spent here.

The perspective in this image was formed by using Google Earth satellite photos combined with information extracted from Google Maps street view. To read an interview and article about this project: click here.

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Ink Drawing of Columbia University. Measures 26 by 40 inches. Click image to launch full resolution.

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Columbia Campus

Ink Drawing of Columbia University. Measures 26 by 40 inches. Click image to launch full resolution.

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Columbia in a Box

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Before my first day as a Columbia College first-year, I assembled a miniature model of Columbia’s campus out of pleated paper and cardboard. This creation, featuring most of Columbia’s Morningside Campus, folds out of a vintage cigar-box that measures a mere 5 by 9 inches, and 3 inches deep. The model was made by taking flat sheets of paper, etching the silhouettes of the campus structures onto each sheet, decorating these sheets with windows and architectural details, and then cutting out the silhouettes and folding each into the shape of the structure. Each building is made with no more than one sheet of folded paper.

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Timelapses of Morningside

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This project features six time-lapse sequences of Columbia University’s Morningside Campus. I placed a camera horizontally above my desk as I drew and painted each watercolor. Painting is meditative for me. Each painting is an opportunity to reflect on my formative years at Columbia University.

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Ink Sketches of Campus

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The Columbia College Alumni Association commissioned the four ink drawings below. These images will be featured in the University’s June 2020 invitation to the alumni reunion and advertising materials. Not yet released.

 

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How to Run a Canal

The film featured below illustrates the opening and closing sequence of an early canal lock: The Duke’s Lock on the Oxford Canal.

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“The Oxford Canal is a 78-mile (126 km) narrow canal in central England linking Oxford with Bedworth, near Coventry. Completed in stages between 1770 and 1790 during the English Industrial Revolution, it connects to the River Thames at Oxford and is integrated with the Grand Union Canal. The canal was for approximately 15 years the main canal artery of trade between the Midlands and London; it retained importance in its local county economies and that of Berkshire.

“Today the canal is frequently used in weekend and holiday narrowboat pleasure boating, as seen above with rented narrowboats passing through Duke’s Lock, No. 44.”

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– adapted from Wiki.

Evolution of the English Country House

 

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This four minute animation traces the evolution of English country house design from the period 1660 to 1715, which was broadly defined by the arhcitectural style of the English Baroque. Roughly between 1660 (near the end of the English Civil War) and 1715 (with the beginning of the Georgian monarchy from Germany), English Architecture witnessed a profound shift in country house design from the compact and square-ish form of the fortified Elizabethan and late-medieval country house to the more open and less compact plan of the Baroque and later Palladian country house. This shift too in design followed a new embrace of the aesthetic relationship between country house and its surrounding, bucolic landscapes. The objective of this animation sequence is to visually illustrate these aesthetic and architectural changes. Click to watch the video above, or watch the slideshow automatically play below.

This animation sequence is part of the progression to my degree in Architectural History & Theory from Oxford and Columbia University.

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Music: Franz Schubert_ Piano Trio in E Flat, Op. 100. Link to soundtrack.
Link to powerpoint presentation here.
Creative Commons permission is granted to download and circulate this video for non-commercial purposes, provided attribution is given to Myles Zhang.

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