apps delivering information to mass audiences · –hash tags (e.g. #iphone, #uksnow, #twitter etc...
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Apps
Delivering Information to Mass Audiences
UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS
Scott Adams 1995
1. Richard Milton
2. Steven Gray
3. Oliver O‟Brien
Centre for Advanced Spatial Analysis (UCL)
The Mobile GIS Toy Box
3.5” 9.7” 9” 13.1”
Mobile Technologydevices on the move
LOCATION
GPS, WiFi, 3G, AGPS, DTV
SOFTWARE
Apps: Apple, Android
Apple Map Kit (Apple Developer)
Android Maps
DATA
data.gov.uk, london.gov.uk, Census, real-time data, navigation, APIs
DATA MINING
Twitter, GPS Tracking, Geo Analytics and other forms of data generation
Web Apps
Google Maps Javascript/Flash,
Streetview
Google Maps (Mobile), Bing,
Yahoo Maps
W3C Navigator
An Ontology of Apps
• Right Move, Prime Location
• ASBOrometer
• Met Office
• ESRI ArcGIS on iPad
• Google Earth
• TOTeM tags
• Layar Augmented Reality Browser (not on iPad)
• Navigator Apps
ASBOrometer Apphttp://www.asborometer.com/
Uses static data taken from http://data.gov.uk
ABSO rating for
current locationGraph of ASBOs over
time for this location
ASBO density map
Navigator Applications,
UKMO
Plane Finder AR
Plane Finder AR (iPhone
and Android) uses
Automatic Dependent
Surveillance Broadcast
(ADS-B)
Real time data
Layar is a general-purpose
AR application framework
Acrossair Tube Finder App
The MapTube Website
Frameworks and Geo Analytics
• “Appcelerator” Titanium+Geo (Fortius One, GeoIQ and
Geocommons)
• See where and how your App is used
Map showing
“pizza” twitter
searches and
census
population
Finally
MobVis: A Visualisation System for Exploring
Mobile Data (Shen and Ma 2008)
Figure 3: Network with person, position and
hangout places.
• “There‟s an App For That”
• “Reality Mining”
MIT Media Lab: http://reality.media.mit.edu
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Crowd Sourcing Geographical Data through TwitterSteven Gray - CASA, University College London
• Collects data from Twitter (mainly Geo-located Tweets)
• 30km radius from centre of each city
• Search for trends, specific topics using
– Hash tags (e.g. #iPhone, #uksnow, #twitter etc )
– Individual Words (e.g. CASA)
– Groups (e.g. Carling Cup Final)
• First Experiment – Friday 22nd Jan to Monday 25th Jan• Area – London (All Tweets within M25)
• 378,000 Tweets Captured
• 60,000 Geo-located Tweets
Capturing Geo-location Data from all over the world
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Analysis of London Weekend Tweets
New York London Paris Moscow
Tweets collected using Tweet-o-Meter over a week in an urban area. We build
Interactive City Landscapes showing density of geo-located „Tweeters‟ that provide
their actual location and message through the Twitter API
New City Landscapes compared
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New York
15
London
16
London zoomed
London Zoomed
17
London zoomed
London Zoomed
Temporal Twitter Data
#uksnow - Aggregated Results
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• Real-time Geographic survey tool.
• Up to 50 questions per survey
• Up to 50 answers per question
• Live stats and graphs
• Geographic Regions:
– Worldwide Countries
– European Countries
– UK Counties
– UK Postcode
– Drop Pins
– London Borough
– London Wards
• Frequently updating regions
BBC Radio 4 Mapping the Credit Crunch
What single factor is hurting you most
about the credit crunch?- Mortgage or Rent
- Petrol
- Food Prices
- Job Security
- Utility Bills
- Not Affected
(Cyan)
(Blue)
(Light Green)
(Green)
(Pink)
(Red)
Foursqaure „check-in‟ HotSpots around LondonAnil Bawa-Cavia - http://urbagram.net/archipelago/
Foursqaure „check-in‟ HotspotsAnil Bawa-Cavia - http://urbagram.net/archipelago/
• Always a danger sharing too much location data
– Collects data from Foursquare and Twitter (Accounts Linked)
– Users have profile location set in Twitter
– Foursquare “checkins” are displayed in realtime scrolling list
It leaves one place you're definitely not... home
The New Demographics of Travel in London
– Visualising Transport for London Data
Oliver O‟Brien, CASA
UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS
Tube Station Exits/Entries – Data
• Available on
TfL‟s website
• Year-on-year
• Exits vs Entries
• Weekdays split
into 5 intervals
Can infer the demographics of the users of each station
Commuters Reverse commuters
Tourists Weekend recreational users
Party goers Early morning shift-workers
Can also spot areas
with changing
populations or new
tourist attractions
Tube Station Exits/Entries – Map
ENTRY
Weekday
AM peak
ENTRY
Sunday
EXIT
Weekday
AM peak
ENTRY
Weekday
evening
Tube Station Exits/Entries – Map
Change in total entries/exit numbers between 2006-9 for the
Jubilee/Metropolitan and Northern Line stations in NW London
Barclays Cycle Hire Scheme – Data
• Also available
on TfL‟s website
• Dock-level data
• Near real-time
• Clustering
Clustering of full/empty patterns may reveal the demographics of the area and
the people who work or live in the area
Long-hour work zones Regular work zones
Busy areas at night University students?
Barclays Cycle Hire Scheme – Clustering
Preliminary hierarchical clustering
based on average half-hour values
across a week
Courtesy of James Cheshire
Normal work locations?
Long-hour work locations?
Barclays Cycle Hire Scheme – Mobile Apps
• Mobile applications for smartphones (e.g. iPhone,
Android) are critical to using a popular bike hire
scheme
– Allow discovery of nearby docks at journey‟s end
– Allow discovery of the nearest available spaces
• The applications have therefore been very
popular, numerous implementations have been
made
All examples are of
free applications
on the iPhone
TfL and the London Data Store
• Data released by TfL for public reuse at
http://data.london.gov.uk/
Summary
• Apps – delivering information to mass audiences
– Data sources rapidly becoming more accessible to
commercial and “volunteer” developers, both for
application development and analysis
– Thriving volunteer development community creating
often free applications to display demographic
information to users and collect it from them
– Powerful and flexible “app stores” on smartphones
allow for application reach by a mass market
Workshop Session
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