Sensor Networks for Smart Cities

SMARTERCITIESthroughhumanDATAAnne Galang :: ENGL 794 :: TRANSMEDIA

RECAP: Smart Cities and Big DataSensor networks address challenges of growing cities:Traffic congestionSpace homes and public spaceWater and energy useCarbon emissionsTighter city budgetsAging infrastructure

Big Data/Open dataWere collecting a lot of data!Open data accessibility to public sparks innovation and allows citizens to have a meaningful interaction with the information that surrounds them

ObservationsFrom efficiency and sustainability recent focus on human-centered solutionsUser-friendly interfacesIncreased focus aesthetics, designFocus on quality of life

Projects tend to be driven by private companies and municipal governmentsMore recently researchers and artists

While initial focus of smart technology and data use within cities was driven by need for efficiency and sustainability, recent focus on human-centered approaches

3Limitations of Top-down projectsAnthony Townsend critiques current applications of context-aware (sensor) systems:

[These top down visions] fail to describe scenarios in which location-aware technologies will add meaning and understanding to human life

Into this breach have stepped artists who are co-opting this new [media] to raise fundamental questions about the nature of public space and surveillance

Source:

Anthony TownsendLocative-Media Artists in the Contested-Aware City.Leonardo, Vol. 39, No 4. Pacific Rim New Media Summit Companian (2006), pp. 345-347. JSTOR. Accessed March 6, 2013.

4Grassroots systemsBottom-up context-aware systemsWeb based content-tagging systemsPhotosharing such as Flikr, InstagramTagging places: Open Street Map, FourSquareAllow users to dynamically create context using open vocabularies

[optional slide]5Precedent projectsExamples of projects making use of grassroots context-aware systems.6

San Francisco Emotional Map. Christian Nold 2007.7San Francisco Emotion MapFrom the project web site:

The San Francisco Emotion Map involved a total of 98 participants exploring San Franciscos Mission District neighborhood using the Bio Mapping device Nold invented. The project invited the public to go for a walk using the device, which records the wearers physiological response to their surroundings. The results of these walks are represented on this map using colored dots and participants personal annotations. The San Francisco Emotion Map is a collective attempt at creating an emotional portrait of a neighborhood and envisions new tools that allow people to share and interpret their own bio data.

We Feel Fine[screen shots]

We Feel Fine

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We Feel Fine10

We Feel Fine

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We Feel FineExplores human emotionsCrawls web for blog entries with the term I feel/I am feelingData collected:Feeling (happy, sad, depressed, etc.) Age (in ten year increments - 20s, 30s, etc.) Gender (male or female) Weather (sunny, cloudy, rainy, or snowy)Location (country, state, and/or city)Date (year, month, and/or day)Updated every 10 minutes

Some thoughtsEmotional MapWearable sensorsData from individuals reactions to urban surroundingsSmall sample size requires specialized equipmentWe Feel FineLarge sample sizeTaps into existing emotional data that individuals are posting online. (Human Big Data)How to apply this model to an urban setting?

Critical spacean urban neigborhood is determined not only by geographical factors, but also by the image that its inhabitants have of it

Chombar de Lauwe (qtd in Debords Theory of the Derive)PsychogeographyThe sudden change of ambiance in a street within the space of a few meters; the evident division of a city into zones of distinct psychic atmospheresthese phenomena all seem to be neglected.

Guy Debord, Introduction to a Critique of Urban Geography Imageability[The Image of the City ] considers the visual quality of the American city by studying the mental image of that city which is held by its citizens.

Nothing is experienced by itself, but always in relation to its surroundings, the sequences of events leading up to it, memory of past experiences.

-Kevin Lynch, The Image of the CityGoals of my projectExplore the link between immediate (urban) surroundings and emotionHow neighborhoods within a city differ in psychic atmosphere/imageabilityHow an individual interprets those surroundings ones image of the neighborhoodPoetics of dataTapping into grassroots generated dataHuman-centered data to add meaning and understanding to human life

19Visible CitiesVisible Cities(See PDF)

I chose the title Visible Cities for my project as a play on the Invisible Cities theme of our class blog and some of our key course readings.

With this project I wanted to make visible some of the otherwise unseen/intangible elements of city neighborhoods.

Visible CitiesPsychogeographyThe project explores the relationship between atmosphere (physical surroundings), mental image (how those surroundings are interpreted) and emotion in an attempt to map the psychogeography of neighborhoods.

Visible CitiesNYC and distinct psychic atmospheresI selected NYC, particularly Manhattan, as it is a city of clear-cut neighborhoods, each with a distinct physical atmospheres and each calling to mind different characteristics. The neighborhoods of Manhattan are a striking example of Debords distinct psychic atmospheres within the same city.The photo galleries illustrate the sudden change of ambiance between neighborhoods within close physical proximity.Photos are curated from Flickr, Tumblr and Instagram. I selected photos that I thought representative of the hundreds of images from each neighborhood posted through these apps.

Visible CitiesMental imageWord clouds represent the collective mental image of a neighborhood i.e., what comes to mind when people think of Harlem, Greenwich Village, etc.These word clouds were generated by searching Tumblr tags of each neighborhood name (for example, #harlem, #uppereastside), and running these search results, along with the neighborhoods Wikipedia entry, through a word cloud generator.

Visible citiesEmotionThe feelings map is a mockup of what the emotional map can look like based on geotagged Twitter and blog entries posted from a mobile app.The feeling words are taken from real Twitter posts that I found searching for key phrases I feel, I am feeling, and I am that were geotagged within each respective neighborhood. However, the representations on the map are fictionalI placed coloured dots randomly on the neighborhood map for this mockup as the exact coordinates of each post were not available.My Visible City(See PDF My Visible City)The data in this mockup is all fictionalI mocked these screens up to illustrate the possibilities of psychogeographically mapping a city at the individual level based on personal data. Much of this data is already collected through an individuals mobile phone through social media apps, transit apps, etc. It may be a matter of aggregating, consolidating and visualizing the data.26