The Image of the Data City: Perception in Shared Information Spaces

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Presentation from the workshop People Centered Smart Territories: Design, Learning and Analytics, - October 16, 2013 - at the Smart Cities Exhibition, Bologna Italy

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  • Today I wish to call attention to a crucial new question for contemporary city design.
    The question is this:
  • In a city of data, how legible should our algorithms be?
  • Decades ago, city planners and designers idealized the legible city, a polis that facilitated a clear cognitive map for residents.
  • This eventually gave way to the semiotic city, a polis where humans traded cognition for a collection of signs.
  • With GPS and mobile devices came the annotated city, a geospatial web in which data destinations could appear at any point in space.
  • Today, we live in the algorithmic city, in which different data appears to each of us at the same point in space, depending on our own histories, preferences, and networks.
  • Legible Cities required design for perception.
  • Semiotic Cities required design for signification.
  • Annotated Cities required design for storage and recall.
  • Algorithmic Cities requires design for …. Perception again.
  • In a city of data, how legible should our algorithms be?
    I will spend more time describing the question than providing answers. I do this in part because my collaborators and I are only beginning the process of addressing these problems. I also am here today seeking new partners in our effort, as I believe these new problems to be especially conducive to international and transnational research.
  • More than half a century ago, Kevin Lynch revolutionized the architecture of cities through asking a new kind of question: how can a city be more legible?
    This word legible suggests the city to be a kind of text - think of the legibility of a typeface for example. But in his pioneering study of urban legibility, Lynch treats the city plan not as a text, but as an image.
  • Through interviews with passersby, Lynch determined some of the key elements necessary to the design of cities for which inhabitants can form a clear mental image.
    Such a clear mental image, argues Lynch, is necessary to a high quality of life, and especially one wherein citizens are able to imagine the possibility of a shared public space.
  • The elements Lynch considered necessary to forming a clear mental image of a city are, to review:
  • Paths – channels along which inhabitants move
  • Edges – linear boundaries, breaks, or barriers
  • Nodes – junctures or points of convergence, entryways and exits
  • Districts – sections of the city one can be inside or outside of
  • Landmarks – points of reference that are useful and visible from afar
  • According to Lynch, one should aim to organize a city around clear creation of such elements, or augment the city as necessary to facilitate mental imaging.
    Since Lynch's early book, some have suggested that his emphasis on the perception of urban space betrayed a short-sighted preoccupation with the cognitive, and an overlooking of the role of the semiotic in the city, where we often navigate as much by signage as by sight. More recently, Lev Manovich pointed out that we don’t need signs or cognitive maps, for with GPS technology, do we not drive to a point in space without paying attention to how we got there?
    I would like to argue that perception and cognition are vital to even the apprehension of the augmented city, the algorithmic city. I will do so today through revisiting Lynch's five elements in light of our new algorithmic technologies.
  • Paths – Our mobile maps create different channels of flow for each person, for each time.
  • Edges – Edges appear where we know access to data or power ends, or where we have no access because of paywalls, RFID-enabled access, or passwords. They also appear where we know we might enter into unacceptable conditions of surveillance.
  • Nodes – Our nodes travel with us anywhere in the form of FB, Google, Yelp, Tripadvisor, Foursquare: customized, location-specific portals to our surroundings.
  • Districts – The new districts form through your choices of what data streams to follow, or through the access denied or granted by proprietary digital services. Multiple districts appear in the same geography, based on what you’re a fan of, or what you’ve purchased.
  • Landmarks – In dataspace, your landmarks are always visible, but specialized just for you. You’re always just so many blocks away from the point where your friends are, where there’s a sale going on, where there’s an incident to avoid.
  • In all of these, algorithms play a key role. An algorithm takes as inputs your personal data, other outside factors, and outputs results just for you. An algorithm may produce your results through identifying you with a profile : (i.e. Most people who were last in Rome before they arrived in Bologna would like to see paintings). Or, if more robust, the algorithm may produce hyper-personalized outputs, such as only showing you results from your own social network.
    Now these algorithms could very well produce a highly legible city without you ever knowing what they’re up to. But two questions remain:
  • 1 – Is there ever a time when we need to be aware of these algorithms at work?
  • 2 – If each of us moves through his or her own, algorithmically-driven legible city, do we in fact share the same city, the same public? The algorithmic city might be a visible, imageable city, but is a shared image?
  • Both of these questions require some attention to that matter which Lynch found so central years ago – perception. We need to study how people perceive algorithms at work, and in doing so we need to determine how necessary algorithm legibility is to their function in shared urban space.
    At the Center for People and Infrastructures, we are taking some initial steps toward answering this question by studying how in fact people understand these algorithms.
    For example,
  • [explanation of Feedvis studies here]
  • [explanation of Feedvis studies here]
  • Based on these early user studies, we hope to form a basis of recommendation for how visible our algorithms should be, and how they might best facilitate a sense of shared online and offline space.
    These aren't yet to the point of application in the city and locative media technologies, but those are our next steps.
  • Imagine, for example,
    How an interactive map might make visible not only the possible paths you might take from the standpoint of efficiency, but also the paths you might be expected to take based on your purchase or travel history – a sort of predictive credit report to reveal what the databases and algorithms think you might decide to do.
  • Or imagine a location-specific search portal for Google or Facebook that shows you not only what you might enjoy in a neighborhood, but also what you might enjoy if you had fulfilled a different demographic profile for age, race, or gender.
  • Though the privacy implications for our lives in the algorithmic city remain to be examined and worked through, many new design potentials await us in our quest to keep our shared spaces humane, dynamic, and perceptually sound.
    That’s just what we’re aiming for, and I hope some of you will join us.
  • The Image of the Data City: Perception in Shared Information Spaces

    1. 1. Center for People and Infrastructures Coordinated Science Laboratory | University of Illinois, Urbana Champaign USA The Image of the Data City Perception in Shared Information Spaces Presenter : Kevin Hamilton Associate Professor and Co-Director CoKarahalios, Langbort, Sandvig Authors :
    2. 2. How legible should our algorithms be?
    3. 3. LEGIBLE CITY SEMIOTIC ANNOTATED ALGORITHMIC image credit: The Image of the City (1960, Lynch)
    4. 4. LEGIBLE SEMIOTIC CITY ANNOTATED ALGORITHMIC image credit: Learning from Las Vegas (1977, Venturi, Izenour, Scott Brown)
    5. 5. LEGIBLE SEMIOTIC ANNOTATED CITY ALGORITHMIC image credit: Anne Galloway
    6. 6. LEGIBLE SEMIOTIC ANNOTATED ALGORITHMIC CITY image credit: Google
    7. 7. LEGIBLE perception signification storage + recall perception image credit: The Image of the City (1960, Lynch)
    8. 8. LEGIBLE SEMIOTIC perception signification storage + recall perception image credit: Learning from Las Vegas (1977, Venturi, Izenour, Scott Brown)
    9. 9. LEGIBLE SEMIOTIC ANNOTATED perception signification storage + recall perception image credit: Anne Galloway
    10. 10. LEGIBLE SEMIOTIC ANNOTATED ALGORITHMIC image credit: Google perception signification storage + recall perception
    11. 11. How legible should our algorithms be?
    12. 12. Image of the City Kevin Lynch, 1960
    13. 13. Image of the City Kevin Lynch, 1960
    14. 14. Image of the City Kevin Lynch, 1960
    15. 15. Image of the City Kevin Lynch, 1960
    16. 16. Image of the City Kevin Lynch, 1960
    17. 17. Image of the City Kevin Lynch, 1960
    18. 18. Image of the City Kevin Lynch, 1960
    19. 19. Image of the City Kevin Lynch, 1960
    20. 20. Image of the City Kevin Lynch, 1960
    21. 21. image credit: digitalurban.org
    22. 22. image credit: Phandroid
    23. 23. image credit: aat.org.uk
    24. 24. image credit: Sobees
    25. 25. your data algorithm other variables device output
    26. 26. Should algorithms be invisible?
    27. 27. Do we still share the same city?
    28. 28. Center for People and Infrastructures Coordinated Science Laboratory | University of Illinois, Urbana Champaign USA
    29. 29. Center for People and Infrastructures Coordinated Science Laboratory | University of Illinois, Urbana Champaign USA The Image of the Data City Perception in Shared Information Spaces Presenter : Kevin Hamilton Associate Professor and Co-Director CoKarahalios, Langbort, Sandvig Authors : Contact : kham@illinois.edu

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