t17 urbics backchannel part ii
TRANSCRIPT
Urban Informatics
Measuring Livability Across City Spaces
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Chris Zimmerman – Copenhagen Business School
Kjeld Hansen – IT University of Copenhagen
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*41 Followers
*21 Unique Tweeters
*11 New Twitter Users
*99 Total Tweets (Monday – Sunday)
*29 Instagram + 16 Twitter Photos
*0 Foursquare Checkins (forwarded)
*16 #topical tags - 6 @people
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*Christopher Choa – Urban Planner
* It starts with people
*Cities are manifestations of collaboration
* Trade and exchange
*Plan for structure and serendipity – A kind of ‘loose-tight’
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* Opportunities to Interact with People
* The main attraction
* The Human Scale
* Complex and vibrant
* Diversity and sensory experience
* Invitations to Use Space
* Private - Public
* Exclusive - Inclusive
* “Soft” Metrics
* Consider the perspective of the study
* Process over perception
* Integration of complexity – how does place integrate with space
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Mobile Sensors
Social Noise
Open Data
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#copenhagen
#coffee
#walking
#stroget
#canal
#biking
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Micro-blog/photo
33 likes
2 retweets
1 comment
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*Lovett (2011:31-32): Signal, or “signal to noise ratio,” indicates that there is value in the message. This excludes someone tweeting what they had for lunch, which is definitely noise. Typically, signal can be viewed as sharing information, tagging with a hashtag, making a reference via a hyperlink, or generally contributing to the conversation.
*Noise: “… consumes your time and energy without offering much payout.”
*Signal: “… offers streamlined information that’s relevant to your cause.”
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*Ubiquitous and pervasive computing: Mobile
and social media enables computing to be
woven into the social fabrics of our lives.
*The internet has taken a participatory turn:
Social data is added because it adds value to
the individual as well as overlaying purposes.
*Digital positivism: Epic Fail! Is it real, if it’s
not on social media.
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*Participants are both individuals and organisations.
*Participation is mutually constitutive.
*Potentially participants are both…
*Users
*Participants
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Endorsements that can be measured:
1. The ‘Like’ (Facebook, YouTube, etc)
2. The Check-in (Facebook, Foresquare)
3. The Photo (Instagram, Flickr, etc)
4. The Review (Yelp, TripAdvisor)
5. The Geo-located Post (Twitter, Facebook, and
many more)
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Endorsement Ratios of
a University Campus –
Why do they differ?
> Local understanding is
needed.
Source - Note: Statistics for
Facebook Places is in BETA. We are
currently only monitoring places
that have received over a minimum
number of check-ins.
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*Copenhagen Airport (3 separate entries in the Top 25, and
more than a quarter million check-ins in the top slot alone)
* International travel is often a positive experience, an adventure,
people like to document. It also doesn’t hurt that we have a
sensational airport.
* Also this hotbed for international people, whose exposure to say
Americans who have adopted twitter and foursquare more, might
mean there is a higher level of tweets and check-ins respectively.
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*Tivoli and Christiania (more likes than check-ins)
* Two of the very top tourist attractions when you read about Copenhagen in any guidebook (bringing up the question again, what proportion of these social currencies are tourists vs locals?)
*The like is making a statement, it associates you with something. You become affiliated, endorsing the place wholeheartedly and on an ongoing basis. You are tying yourself to an idea of a place that you like. Whereas the check-in makes a different statement “Dave was here”, usually because he wants to be and maybe even wants people to know it.
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*Fitness Centres (4 in the top 25, and all have a
dominance of check-ins – routines, inability to
like more than once)
*Many other comparisons can be made between
shopping centers (Fields vs Fisketorvet) or
between warm weather activities :Why does
Bakken have more check-ins and Amager
Strandpark more likes?)
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*2012 - A year in Check-Ins
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*http://trendsmap.com/?ll=55.6763_12.5681&z
=10
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Tool Primary Usage @ # Additional features
Socialbro Twitter
communities @ # Custom listing, scheduling
analysis, topic filtering
Followerwonk Full social graph
listing @ Cross-world profiles, follower
mapping
Sentiment140 Sentiment analysis # E-mail alert service
(upon request)
Topsy Topic and mention
comparison @ # E-mail alert service
TweetCharts Demographics &
follower stats @ # Topic and mention analysis
TwitterVenn Cross-world
Analysis # Topic visualization
Gramsfeed Instagram mapping #
Cloud.li Language @ # Filter-out feature
