Real-Time Cities: an Introduction to Urban Cybernetics Harvard Design School: SCI 0646900

1. Courtnay Saunders Exercise #1: Case Studies in Sensing and Data Collection Real-Time Cities: an Introduction to Urban Cybernetics Harvard Design School: SCI 0646900 Spring 2014

2. Narrative Title These selections offer examples of data collection that is highly visual in nature, whether it be the content of the input or the means in which the output is communicated. ‘United Colors of Dissent’ offers an interesting solution to political intervention by allowing for the anonymous expression of ideas on a public platform, and has the potential to serve as a robust public forum to ascertain the political preferences of a general public. ‘Locals and Tourists’ and ‘Phototrails’ utilize photographs that have been submitted by individuals on popular photo-sharing platforms to identify behavioral patterns in cities. ‘Public Face I’ and ‘Reflex’ are conspicuous representations of real-time data that are sustained by the engagement of individuals, though the data points are not autonomously submitted. The result is an ambient reflection of a city’s inhabitants transmitted via artfully rendered materializations that reveal the physical or emotional state of a location at that specific time. 1 | United Colors of Dissent, OrkanTelhan and MahirYavuz 2 | Locals and Tourists by Eric Fischer 3 | Phototrails by NadavHochman, Lev Manovich and Jay Chow 4 | Public Face I, Julius Von Bismarck 5 | Reflex, rAndom International

3. 1 | United Colors of Dissent, OrkanTelhan and MahirYavuz “’United Colors of Dissent’ is a data- driven performance designed for live public interaction using mobile phones and public displays. Participants collectively respond to a series of questions in their preferred language using a web-based voting interface running on their mobile phones. At every question, UCoD builds real-time graphics based on the answers and features them both on the phones and the displays. The performance intends to capture the linguistic and socio-cultural profile of difference communities in urban environments by creating real-time visualizations that can map the prejudices, assumptions, and biases we may have about each other.” (http://info.ucod.org/) It was created by interdisciplinary artists OrkanTelhan and Mahir M. Yavuz. Project Video: http://vimeo.com/78300301

4. 1 | Sensing the City

5. 1 | United Colors of Dissent, OrkanTelhan and MahirYavuz How was the data collected? The data is submitted by users. Why was the data collected? What is interesting about the data? The data was collected to allow for public expression of socio-political leanings. What stories about the urban dynamics can the collected data tell? It conveys similarities and differences across cultures What sort of questions about urban dynamics can be answered by looking at the data? The data set relays how individuals feel about the cities in which they reside How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? The system is inherently limited, in that it only receives user-submitted data.

6. 1 | United Colors of Dissent, OrkanTelhan and MahirYavuz How are particular patterns highlighted through techniques for tagging the data in order of their importance? The data are automatically separated into groups according to language, nationality, opinion, etc. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? The questions asked of the data submitters are binary. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data is static in regards to the individual responses as once submitted they do not change, but dynamic in terms of how it is articulated in the visualization. Who is the target audience of the data presentation? The data submitters themselves, and any viewers of the project. What are their goals when approaching the data presentation? What do they stand to learn? Revealing the sentiments of urban interactors, though it would be more informative if the data were collected from a wider group.

7. 2 | Locals and Tourists by Eric Fischer “Locals and Tourists uses geotagging data from the photo-sharing websites Flickr and Picasa to visualize the different areas frequented by locals and tourists in New York, London and 124 other cities, including Taipei, Sydney, Berlin and San Jose, CA. After harvesting millions of data points in the form of photographs, Eric Fischer links them by photographer and date and then plots them on a city’s OpenStreetMap grid. A photographer with many shots of the same city and a long photo history can be assumed to be a local and is represented in blue, and someone whose photos are taken within a limited time period is assumed to be a tourist and represented in red; photographers whose status can’t be determined are represented in yellow.” (http://www.moma.org/interactives/exhibitions/2011/talktome/objects/146200/) Project Video:

8. 2 | Locals and Tourists, New York and London by Eric Fischer

9. 2 | Locals and Tourists, New York and London by Eric Fischer How was the data collected? The data was collected by analyzing individual photographs and dividing them into the groups ‘tourist’ and ‘local’ according to individual behavioral patterns. The geo-tagged location of each photograph was then recorded using OpenStreetPlan so that the differences in behavior amongst the two groups could be represented visually. Why was the data collected? What is interesting about the data? It was collected to examine the differences in photographs taken by tourists versus locals. What stories about the urban dynamics can the collected data tell? It conveys the patterns of two distinct types of urban inhabitants. What sort of questions about urban dynamics can be answered by looking at the data? Which places in the city are populated by whom. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? By applying an algorithm to ascertain whether a photographer is a local or a tourist, and then by abstracting the data.

10. 2 | Locals and Tourists, New York and London by Eric Fischer How are particular patterns highlighted through techniques for tagging the data in order of their importance? The only pattern is the location of the two groups; after tagging the ‘identification’ of a photographer, the data is only tagged by location. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? The process eliminates any opportunity for unnecessary details. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? It is static in that the data is sampled from an existing reserve of photographs. If the project is ongoing, the data may change due to the addition of more photographs. Who is the target audience of the data presentation? It is more of an art project than a relevant describer of urban dynamics, but perhaps could be useful to businesses that pertain to tourists. What are their goals when approaching the data presentation? What do they stand to learn? It seems that their goal is the elegant elocution of the observed data set.

11. 3 | Phototrails by NadavHochman, Lev Manovich and Jay Chow “Phototrails is a research project that uses experimental media visualization techniques for exploring visual patterns, dynamics and structures of planetary-scale user-generated shared photos. Using a sample of 2.3 million Instagram photos from 13 cities around the world, we show how temporal changes in number of shared photos, their locations, and visual characteristics can uncover social, cultural and political insights about people’s activity around the world. Project Video: http://www.youtube.com/watch?v=BKumxhehfVM

12. 3 | Phototrails by NadavHochman, Lev Manovich and Jay Chow

13. 3 | Phototrails by NadavHochman, Lev Manovich and Jay Chow How was the data collected? By analyzing Instagram photographs and categorizing them using metrics relating to color, time, metropolis, or spatial locality in relation to similar photos. Why was the data collected? What is interesting about the data? An attempt to harness the value of ‘visual big data’ What stories about the urban dynamics can the collected data tell? Time patterns in photo-taking, color variance in cities, the movement of individuals in between taking photographs and the density of photo-taking in specific places. What sort of questions about urban dynamics can be answered by looking at the data? Behavior of Instagram users over the course of the day, the popularity of visually resonant locations within a city. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? It is limited in that only geo-tagged photographs would apply to many of the visualizations. Otherwise the algorithms are capable of dealing with a huge set of information.

14. 3 | Phototrails by NadavHochman, Lev Manovich and Jay Chow How are particular patterns highlighted through techniques for tagging the data in order of their importance? The project articulates different types of data sets through visualizations that correspond to different patterns; they seem to be weighed in equal importance. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? The original question was ‘what can be gained through analyzing visual data’ and the team was successful in conveying a number of iterations. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data is static as the photos are already present, yet dynamic if the project is ongoing. Who is the target audience of the data presentation? Those interested in visual patterns in cities, perhaps instagram, organizations interested in the flux of populations in cities according to time and frequency of photographs. What are their goals when approaching the data presentation? What do they stand to learn? Solving the issue of how visual data might be analyzed. They were fairly thorough in this endeavor.

15. 4 | Public Face I, Julius Von Bismarck “The project Public Face I (Stimmungsgasometer) is about a smiley on a huge screen from which one can read the average mood of Berlin citizens. The system allows [for the reading] of emotions out of random peoples faces. The faces are analyzed by sophisticated software (contributed by the FraunhoferInstitut). The obtained mood data are then stored on a server and processed by the smiley on the screen to visualize the emotions in real time.” (http://juliusvonbismarck.com/bank/index.php?/projects/stimmungsgasometer/) Project Video: http://vimeo.com/27441271

16. 4 | Public Face I, Julius Von Bismarck

17. 4 | Public Face I, Julius Von Bismarck How was the data collected? The data is aggregated using software that analyzes mood in the facial expressions of passersby, probably caught on camera. Why was the data collected? What is interesting about the data? To create this project. The method of visualization is the most interesting part. What stories about the urban dynamics can the collected data tell? The general well-being of Berlin. What sort of questions about urban dynamics can be answered by looking at the data? How the collective mood changes. Perhaps there is a correlation between emotion and weather. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? It is limited in that the algorithm only analyzes those faces the camera is subject to witnessing.

18. 4 | Public Face I, Julius Von Bismarck How are particular patterns highlighted through techniques for tagging the data in order of their importance? They are highlighted by the emoticon. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? Because this is an artistic project there is no superfluous data, unless there are certain faces the algorithm cannot read (because they are covered?). Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data is dynamic, and changes as new faces are assimilated. Who is the target audience of the data presentation? The city of Berlin What are their goals when approaching the data presentation? What do they stand to learn? Exposing the well-being of Berliners in a highly conspicuous way. It would be interesting if the moods of the emoticon were further researched to expose correlations.

19. 5 | Reflex, rAndom International “Reflex transforms inanimate architecture into the habitat of an organism manifested in light. Individual illuminations respond collectively to the movements of those passing by, tracking and mirroring their presence in light. The behaviour of the installation emulates the collective decision making processes employed by creatures in the natural world such as flocking birds, shoaling fish and swarming bees. Visually striking, these natural phenomena are essential to the survival of a species. Reflex calls into consideration the collective behaviour we as humans demonstrate each day in order to efficiently exist in contemporary urban settings. For one year, ‘Reflex’ invited the public to enter into a discourse with the inanimate through light and their own movement, as they went about their daily routine on the other side of the glass.” (http://random-international.com/work/reflex/) Project Video: http://vimeo.com/23255773

20. 5 | Reflex, rAndom International

21. 5 | Reflex, rAndom International How was the data collected? In this case, I see the data as the passersby themselves, and time-lapse film representation of the window as its visualization. The data is directly correlated to what is happening at that moment in on Euston St. Why was the data collected? What is interesting about the data? To produce the LED output and provide an interesting solution for the visual component of Wellcome Trust’s lobby. What stories about the urban dynamics can the collected data tell? By watching the time-lapse of the window one can understand the population density of this particular sidewalk over the course of time. What sort of questions about urban dynamics can be answered by looking at the data? The population of a given street, and judging by how often people pause to observe, how rushed / curious are citizens of London. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? The ability of the installation to collect the data is equal to the ability of the sidewalk to host the individuals.

22. 5 | Reflex, rAndom International How are particular patterns highlighted through techniques for tagging the data in order of their importance? There are no tagging techniques for this particular data set. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? There are no unnecessary details, everything is accepted and represented in the LED structure. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? Dynamic and real-time. Who is the target audience of the data presentation? Passersby and those interested in the project post-contemporaneously. What are their goals when approaching the data presentation? What do they stand to learn? Inviting reflection in the urban space. I believe the visual documentation that is the time-lapse film was a happy accident.