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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)
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The Mobile GIS Toy Box
3.5” 9.7” 9” 13.1”
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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
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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
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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
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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
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The MapTube Website
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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
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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
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• 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
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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
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London
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London zoomed
London Zoomed
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London zoomed
London Zoomed
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Temporal Twitter Data
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#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
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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)
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Foursqaure „check-in‟ HotSpots around LondonAnil Bawa-Cavia - http://urbagram.net/archipelago/
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Foursqaure „check-in‟ HotspotsAnil Bawa-Cavia - http://urbagram.net/archipelago/
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• 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
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The New Demographics of Travel in London
– Visualising Transport for London Data
Oliver O‟Brien, CASA
UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS
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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
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Tube Station Exits/Entries – Map
ENTRY
Weekday
AM peak
ENTRY
Sunday
EXIT
Weekday
AM peak
ENTRY
Weekday
evening
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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
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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?
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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?
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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
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All examples are of
free applications
on the iPhone
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TfL and the London Data Store
• Data released by TfL for public reuse at
http://data.london.gov.uk/
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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
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Workshop Session