Stochastic Cities: Four Ways of Arranging 3,750 Images of Chicago

María Urigoitia Villanueva

Reviewed by Shannon Mattern

23 May 2018

The next time you hear some­one talk­ing about algo­rithms, replace the term with God” and ask your­self if the mean­ing changes. Our sup­pos­ed­ly algo­rith­mic cul­ture is not a mate­r­i­al phe­nom­e­non so much as a devo­tion­al one, a sup­pli­ca­tion made to the com­put­ers peo­ple have allowed to replace gods in their minds, even as they simul­ta­ne­ous­ly claim that sci­ence has made us imper­vi­ous to reli­gion.1

Algo­rit­mos, the Span­ish word for algo­rithms, was the title of one of my math­e­mat­ics books in mid­dle school. I remem­ber being fas­ci­nat­ed by this term whose mean­ing or pow­er I did not ful­ly grasp: a mag­ic word or spell with which to open a uni­verse of pos­si­bil­i­ty. Twen­ty years lat­er, in an unex­pect­ed twist, the mys­ter­ies behind it became the focus of my inter­est in archi­tec­ture and design.

Accord­ing to Google Books Ngram View­er, use of the word algo­rithm” peaked first in 1991 and again in 1996 (around the same year I was using the math book). Then, after a brief decline, its use began to grow, and it has been doing so at a fast pace since 2006. No day goes by when we do not hear about the impacts of algo­rithms on our lives — most evi­dent­ly, through our inter­ac­tions with online plat­forms, such as Google and Face­book. We live in a time of seem­ing­ly lim­it­less trust in these math­e­mat­i­cal process­es. We allow them to con­trol vast areas of our exis­tence, accept­ing their out­comes as if absolute truths. We treat algo­rithms as objec­tive tools, with blind faith in their math­e­mat­i­cal cer­tain­ty — a form of belief sub­con­scious­ly ingrained in human minds since the Enlight­en­ment through the ide­al of a math­e­sis uni­ver­salis. How­ev­er, the process that deter­mines so much infor­ma­tion — from rec­om­men­da­tions on Netlflix and Ama­zon, to ide­al com­muter routes, to our suit­abil­i­ty for jobs or rela­tion­ships — is any­thing but objective.

Even though the Greeks already warned us about the per­ils of assum­ing tech­no­log­i­cal knowl­edge as supe­ri­or, tech­nol­o­gy is still framed as fair and faith­ful in ser­vice to human beings, the right approach to any prob­lem we seek to resolve. How­ev­er, as Ed Finn has stat­ed, aside from the most sim­plis­tic cas­es, we will nev­er know how algo­rithms know what they know.”2 There­fore, trust­ing them with run­ning our world seems nei­ther ratio­nal nor wise. Yet, what Finn calls the com­pu­ta­tion­al space of imag­i­na­tion aris­es in that very uni­verse of the unknown, a place where we can chal­lenge and rein­vent our rela­tion­ship with tech­nol­o­gy. Instead of look­ing for the most opti­mized results, embrac­ing the space of the unknown allows us to see algo­rithms not as tools that auto­mate design process­es, both affirm­ing and lim­it­ing our agency, but rather as col­lab­o­ra­tive oth­ers in the con­text of cre­ative devel­op­ment. A human-machine feed­back loop that sparks imag­i­na­tion and not just the stan­dard­iza­tion most archi­tec­tur­al soft­ware allows.

Sto­chas­tic Cities explores that com­pu­ta­tion­al space of imag­i­na­tion by rear­rang­ing an aer­i­al image of Chica­go using a com­bi­na­tion of com­put­er vision and machine-learn­ing algo­rithms, not with the inten­tion of find­ing an ide­al con­fig­u­ra­tion but as an exper­i­ment to query unex­pect­ed asso­ci­a­tions. The orig­i­nal orthoim­age rep­re­sents the city from Divi­sion Street in the north to 28th Street in the south, and from Racine Avenue in the west to the end of Navy Pier in the east. It was cut in 3,750 squares fol­low­ing the orthog­o­nal grid so char­ac­ter­is­tic of Chica­go. Each piece was then processed by a clas­si­fi­er, a neur­al net­work trained in this case on the Ima­geNet dataset, one of the first large-scale image data­bas­es avail­able online, that learned to iden­ti­fy to which cat­e­go­ry each new obser­va­tion belonged. This clas­si­fi­er extract­ed” the mul­ti­ple fea­tures of each square, vari­ables rep­re­sent­ing the rela­tions between pix­els, that were inter­pret­ed and grouped by a t‑SNE (t‑Distributed Sto­chas­tic Neigh­bor Embed­ding) algo­rithm. These asso­ci­a­tions were then pro­ject­ed back onto the orig­i­nal grid.

A t‑SNE algo­rithm is a tech­nique for dimen­sion­al­i­ty reduc­tion, which is very use­ful in the visu­al­iza­tion of high-dimen­sion­al datasets. It con­sists on reduc­ing the num­ber of ran­dom vari­ables that are under con­sid­er­a­tion — in this case the ones pro­vid­ed by the clas­si­fi­er — by allow­ing the algo­rithm to find pat­terns in the data, bring­ing to the fore­ground the prin­ci­pal con­nec­tions present. As its name indi­cates, the process is sto­chas­tic — mean­ing, one of its func­tions is ini­tial­ized ran­dom­ly. As a result, each run could out­put a unique visu­al­iza­tion.3 In Sto­chas­tic Cities, the process was run four times. Although the over­all com­po­si­tions of the result­ing images were dis­tinct, the asso­ci­a­tions between and among the com­po­nents fol­lowed a con­sis­tent log­ic, maybe sim­i­lar to the one we would obtain if we asked a human who had nev­er seen a city from above to orga­nize those 3,750 images.

The four runs gen­er­at­ed sim­i­lar effects, such as a vis­i­ble shift in scale, per­haps because the parks were not joined as one, like the water, but rather were scat­tered along the shore, as hybrid links between nat­ur­al and built envi­ron­ments. The new images of Chica­go appear to be orga­nized by typol­o­gy, pre­sent­ed as a lim­it­less city that has lost its cen­ter. A gener­ic city at first sight, a mix of pre­dictabil­i­ty and arbi­trari­ness in the treat­ment of met­ro­pol­i­tan sys­tems fos­ters uncan­ny sit­u­a­tions, such as the way piers enter the water in an accen­tu­at­ed embrace, or the con­sis­tent recon­struc­tion of McCormick Place as a vor­tex of turn­ing roads, not to men­tion the impos­si­ble con­nec­tions of diag­o­nals and curves that gen­er­ate mag­i­cal in-betweens paired with seam­less jux­ta­po­si­tions of build­ings that total­ly erase the mosa­ic qual­i­ty of the grid in the sec­tions they occupy.

What does the algo­rithm know? Not that McCormick Place is the largest con­ven­tion cen­ter in North Amer­i­ca, nor that the Chica­go Riv­er had its flow reversed. It knows only what it has been taught, and that mix of speci­fici­ty and alter­nate log­ic nur­tures an unex­pect­ed urban­ism. Back and forth, through these oper­a­tions, the mar­gin becomes blur­ry between what the com­put­er sees” and what our eyes read in the out­puts. This reflex­iv­i­ty is present in the real dynam­ics of urban­ism as well as in the space of com­pu­ta­tion­al imag­i­na­tion, where the rela­tion­ship between humans and machines becomes that of col­lab­o­ra­tors in a spec­u­la­tive, gen­er­a­tive venture.

Look­ing again at the new images of Chica­go, I dis­cov­ered a choice the algo­rithm had made, one I had not noticed before and that sur­prised me. A lit­tle square of water, iso­lat­ed between roads and away from sim­i­lar units, suf­fered that same fate in each of the four iter­a­tions: a lone­ly pond that, giv­en the mag­i­cal ratio­nal­i­ty of the images, my mind could not explain. I will sure­ly nev­er know why the algo­rithm chose to place that unit in that way, but I embrace that. In remain­ing beyond the scope of my under­stand­ing, the mys­ter­ies of the algo­rithm make evi­dent the col­lab­o­ra­tive nature of our shared work.

María Urigoitia Villanueva, Chicago, Iteration 1, Stochastic Cities (2018).

María Urigoitia Villanueva, Chicago, Iteration 2, Stochastic Cities​ (2018).

María Urigoitia Villanueva, Chicago, Iteration 3, Stochastic Cities​ (2018).

María Urigoitia Villanueva, Chicago, Iteration 4, Stochastic Cities​ (2018).


By Shan­non Mattern

Visu­al artists, writ­ers, and per­form­ers have long exploit­ed the gen­er­a­tive poten­tials of rules and codes. Just think of Sol Lewitt, Bernd and Hilla Bech­er, or the OuliPo. Today, cre­ators like Alli­son Par­rish and Dar­ius Kaze­mi are using algo­rithms as cre­ative part­ners or col­lab­o­ra­tive oth­ers” in their own prac­tices. But what hap­pens when we apply sim­i­lar modes of cyborg-pro­duc­tion in realms of design that shape the mate­r­i­al world we live in — a world that has the poten­tial to deter­mine access to oppor­tu­ni­ty, pub­lic health, and equi­ty? What does it mean to make an algo­rith­mi­cal­ly designed chair or hos­pi­tal or region­al coastal resilience plan? What do we do when our design part­ner won’t, and can’t, artic­u­late, the log­ic by which it deter­mines what’s most salient in the landscape?

We don’t quite know how an algo­rithm under­stands a city. Its read­ing seems pri­mar­i­ly for­mal, and its par­tic­u­lar brand of for­mal­ism is deter­mined by the types of images — satel­lite imagery, Street View — that have trained it. Does our algo­rithm see, can it know, on-the-ground human expe­ri­ences or eco­log­i­cal forces or his­tor­i­cal lay­ers of seg­re­ga­tion? When we run images of our city grid through a clas­si­fi­er, how does it deter­mine which fea­tures are most salient? How does a dimen­sion­al­i­ty reduc­tion” algo­rithm deter­mine which vari­ables are super­flu­ous? The answers to these ques­tions depend upon what our algo­rithm thinks a city is, and what it’s for. These are ques­tions about tele­ol­o­gy, ontol­ogy, and pol­i­tics — which, when so much is at stake in any form of spa­tial plan­ning, would ide­al­ly pre­cede ques­tions about method­ol­o­gy and cre­ative process.



Ian Bogost, The Cathe­dral of Com­pu­ta­tion,” The Atlantic, Jan­u­ary 15, 2015. https://​www​.the​at​lantic​.com/ tech­nol­o­gy/archive/2015/01/the-cathe­dral-of-com­pu­ta­tion/384300/


Ed Finn, What Algo­rithms Want: Imag­i­na­tion in the Age of Com­put­ing (Cam­bridge, MA: The MIT Press, 2017), 185.


For more detailed infor­ma­tion on how the t‑SNE algo­rithm works, refer to Lau­rens van der Maaten’s web­site and the papers linked there: https://​lvd​maat​en​.github​.io/​tsne/.


María Urigoitia Vil­lanue­va is an archi­tect and artist whose prac­tice focus­es on estab­lish­ing new rela­tion­ships with machine learn­ing sys­tems. She grad­u­at­ed with hon­ors from the Escuela Téc­ni­ca Supe­ri­or de Arqui­tec­tura de Madrid in 2013 and sub­se­quent­ly worked with Zhubo Design Ltd. (Shen­zhen, Guang­dong, Chi­na) and estudio.entresitio (Madrid, Spain). While at the lat­ter firm, she col­lab­o­rat­ed on Between the Earth and the Sky,” the win­ning entry in Colom­bi­a’s Nation­al Muse­um of Mem­o­ry com­pe­ti­tion (2015). A recip­i­ent of a la Caixa” Foun­da­tion fel­low­ship, she earned an MFA at the School of the Art Insti­tute of Chica­go with a con­cen­tra­tion in Design for Emerg­ing Tech­nolo­gies (2018). Email: murigo@​artic.​edu

Shan­non Mat­tern is a Pro­fes­sor in the School of Media Stud­ies at The New School in New York. Her writ­ing and teach­ing focus on archives, libraries, and oth­er media spaces; media infra­struc­tures; spa­tial epis­te­molo­gies; and medi­at­ed sen­sa­tion and exhi­bi­tion. She is the author of three books: The New Down­town Library: Design­ing with Com­mu­ni­ties (2006), Deep Map­ping the Media City (2015), and Code and Clay, Dirt and Data: 5000 Years of Urban Media (2017), all pub­lished by the Uni­ver­si­ty of Min­neso­ta Press. Mat­tern has writ­ten sev­er­al dozen jour­nal arti­cles and book chap­ters and writes a reg­u­lar, long-form col­umn about urban data and medi­at­ed infra­struc­tures for Places, a jour­nal focus­ing on archi­tec­ture, urban­ism, and land­scape. She con­tributes to pub­lic design and inter­ac­tive projects and exhi­bi­tions and, from 2006 to 2009, direct­ed the 600-stu­dent Grad­u­ate Pro­gram in Media Stud­ies at The New School. Email: MatternS@​newschool.​edu