The Heart Deconstructed
A look at how We Were Strangers Once Too went from concept to reality
In the fall of 2016, the Urban Design Forum and the Times Square Alliance’s arts organization invited The OCR to submit a proposal for the Times Square Heart, an annual sculpture placed in the middle of one of the busiest places on Earth around Valentine’s Day each year. Genevieve Hoffman and I took the lead on this design challenge, hoping to bind the goals of the office with the sentiment of the city. What we decided to propose was a reaction to what we perceived to be a turbulent and confrontational year — one filled with xenophobic campaign rhetoric and horrible violence, which culminated in the potential to, among other things, alienate millions of people with an anti-immigration agenda. We sought to address the RFP with these things in mind; we wanted to think about ‘love’ in a way that wasn’t traditionally romantic, but instead would celebrate one of the major factors that make New York City one of the greatest cities in the world: its diversity and history of welcoming immigrants.
One of the goals here at The OCR is to foster data literacy among those who aren’t normally exposed to it. Any member of the office or anyone in our immediate network of data enthusiasts can list off their favorite visualizations, interactives, installations, or infographics, but stray a few degrees outside of that network and you’ll find that most people aren’t familiar with non-standard ways of presenting facts and figures. We saw this project as an opportunity to bring relevant data into the public spotlight.
In combing through a couple of different datasets, one that stood out was the American Community Survey (ACS) 1-Year estimates of the foreign-born population from 2015 [table B05006]. The ACS is a part of the US Census Bureau, but unlike the 10-year all-inclusive census, the ACS is an annual random sampling of residents with more in-depth questions. The ACS goes beyond the basic gender, age, and race questions of the US Census and expands on more detailed issues like housing, education, health insurance, employment, and, if applicable, a person’s country of origin. The ACS provides 1, 3, and 5 year estimates in its data, with the 1 year being the most current. Since we wanted to reflect on the current state of the city, we decided to go with the 1-year 2015 numbers. In culling the data to only New York City, we saw that the numbers revealed a story: an estimated 3.2 million of the city’s 8.5 million residents were born outside of the United States.
To give this narrative a name, Jer Thorp, co-founder of The OCR, and renowned titling genius, found an apt quote by Barack Obama that encapsulated the intent of the project:
Scripture tells us that we shall not oppress a stranger, for we know the heart of a stranger — we were strangers once, too. My fellow Americans, we are and always will be a nation of immigrants. We were strangers once, too.
Because the Times Square Heart traditionally involves some form of a heart we had to figure out a way to use this ACS dataset in a way that would allow us to stay true to the numbers, retain the visual form of a heart, create an ‘interactive’ and engaging experience for the public, and, above all, be durable, fairly inexpensive, and easily built (we would have about a month and a half for the design and fabrication from start to finish). After some deliberation, we arrived at the idea of using a 1 point perspective to form a heart with the data driving the actual shading of the heart.
In a similar way to other works of perspective art [for example Georges Rousse] the visual components would all line up to form a coherent image from one particular vantage point. However, the tricky part about using actual data is that the components were all predefined — their scale and size were predetermined and so they would have to be placed in such a way as to strategically generate the image.
Throughout this entire process, there was always a back-and-forth between driving the design with the data and driving it with materiality and reality. To push forward too far in one direction might mean having to force-fit the other. Given the time, budget, and size constraints, we opted to use 4” outer diameter steel pipes as the primary vehicle for displaying the story. They were relatively cheap, readily available, and could be powder coated. These would be fixed in a vertical position and spaced in a way so that people could meander through the ‘forest’ of data. The challenge of actually securing the poles to withstand the forces of gravity and the brutality of thousands of curious tourists was left to the good folks at Twoseven who did the majority of the structural design, fabrication, and installation.
Getting the data to play well with the poles was quite the challenge. Knowing that the footprint we had to work with (something around 15’ x 15’) and small budget would allow for something like 25–35 poles, we had to find a way to apply the information in a consistent and legible fashion. The first step was to pick which lines in the data we would actually use. The ACS table gave us a little more than we needed, chiefly the summary of regions, such as Southern Europe, or Africa, and a few categories that were marked as ‘Other’ like ‘Other Western Africa.’ In the end, we kept the ‘Other’ categories but ignored the summation groups like ‘Europe.’ The biggest challenge, however, was working with countries such as the Dominican Republic: 382,346, China: 331,465, and Jamaica: 170,211, which had much larger population estimates when compared to countries like Grenada: 19,186, Latvia: 1,965, and Iraq: 765. Using a linear scale would be easier to comprehend than geometric/logarithmic/etc., but would mean that we would have to scale everything one way or another. Either the largest ones would shrink to fit on one pole and we’d lose legibility of the smallest, or we would scale it all up and retain the legibility of the smallest populations but make a 30’+ pole to contain the largest. Given those initial options, we chose a scale factor of 1” = 2,000 people and split up the largest populations among several poles which were positioned in the close proximity to one another.
One problem with the chosen scale factor is that a few countries, Cameroon: 421, Eritrea: 284, and Laos: 170, would have been so small that they would not have printed well on the poles. These countries had to become footnotes on the explanative posters around the piece.
The color of each country’s band was determined by comparing the data from its population estimates in 2015 to that of 2010. We used four shades of red to depict whether the country’s population estimate had increased or decreased between the two years. The darkest red was used for countries that had a 25% or more decrease in its population, whereas the lightest pink was for countries with a 25% or more increase. Not only did this add some visual texture to the sculpture, but also enriched the story with an additional layer of information.
Finding a layout
First, the basic platform mass was generated and the viewpoint was placed. Duffy Square is not a very level place, in fact, there is about a foot of height difference between the viewing point and the furthest corner of the platform, so choosing the target eye level had to take this into account. To accommodate people of all heights, the eye level was chosen to be 5’-2” above the viewing spot.
Next, a bit of margin was added between the poles and the topmost platform edge to give people a chance to comfortably step up to it: 2’ in the back and 1’ in the front. With these borders in place, a heart outline was placed at the rear guideline edge and then lofted back to the viewpoint to form a volume.
From here, the mass was brought into Grasshopper and a series of vertical lines were randomly generated over the platform footprint to intersect it. These lines were made as potential options for the Processing program to select from when trying to lay the data out. The idea behind this was to find which areas of the vertical lines would form the heart shape — to get the x and y positions of the lines and the z points of intersection. These lines were exported as a text file.
Processing was used to do all of the heavy lifting in terms of interpreting and positioning the data. The program first ingested the manually-culled raw numbers and then the lines that were exported from Grasshopper. Through a simple brute force method, it tried its hand at shuffling and then laying out each data point across the virtual canvas. It followed a few rules, like ensuring that there was a minimum distance between poles, that the split countries were close to one another, and that there weren’t any significant gaps in the perspective of the heart image.
Each proposed layout was derived from a random seed. The program was run over 10 thousand times and saved out 149 ‘useable’ layouts until one, with the seed of 4996123, was ultimately (manually) chosen for its aesthetic color configuration and assumed walkability factor.
From here the textures for each pole were saved out as pdfs. These files, of which there was one per pole, were the actual height and circumference of the pole with all of the correct colors and text laid out. The positions of the poles were exported along with the countries on each pole as a way to double check the layout.
The last thing to do was bring it all back into Rhino and verify that it worked as expected. The Processing program exported line segments for the individual colors in a format that a Grasshopper script could easily ingest. These new lines were piped and baked by color into separate layers. Then the Rhino camera was moved to the viewing point and voila!
Getting it built
When all of the drawings were completed and the final design iterations were done, OCR’s project manager, Kate Rath, spearheaded the collaboration between The OCR, the Times Square Alliance, and the two fabricators to make it a reality. The powder coaters digitally powder coated all 33 poles (and then some..) with the pdf image files and the amazing staff at Twoseven took it from there.
In the end, we feel that we had successfully taken one more step in promoting data literacy to some unsuspecting non-data people while simultaneously showing our support for the timely but unfortunate subject of immigration. We implemented a very simple system of representation without any sort of complex methodology for how to read it. It was just a straightforward display of data in the heart of one of the most immigrant-rich cities in the world.