1. Richard Milton 2. Steven Gray 3. Oliver O Brien Centre for Advanced Spatial Analysis (UCL)

Transcription

1 UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS Apps Delivering Information to Mass Audiences 1. Richard Milton 2. Steven Gray 3. Oliver O Brien Centre for Advanced Spatial Analysis (UCL) Scott Adams 1995

2 The Mobile GIS Toy Box

3 Mobile Technology devices on the move LOCATION GPS, WiFi, 3G, AGPS, DTV SOFTWARE Apps: Apple, Android Apple Map Kit (Apple Developer) Android Maps DATA Web Apps Google Maps Javascript/Flash, Streetview Google Maps (Mobile), Bing, Yahoo Maps W3C Navigator 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

4 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

5 ASBOrometer App ABSO rating for current location Graph of ASBOs over time for this location Uses static data taken from ASBO density map Navigator Applications, UKMO

6 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

7 The MapTube Website

8 Frameworks and Geo Analytics Map showing pizza twitter searches and census population Appcelerator Titanium+Geo (Fortius One, GeoIQ and Geocommons) See where and how your App is used

9 Finally There s an App For That Reality Mining MIT Media Lab: MobVis: A Visualisation System for Exploring Mobile Data (Shen and Ma 2008) Figure 3: Network with person, position and hangout places.

10 Crowd Sourcing Geographical Data through Twitter Steven Gray - CASA, University College London 10

11 Capturing Geo-location Data from all over the world 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 22 nd Jan to Monday 25 th Jan Area London (All Tweets within M25) 378,000 Tweets Captured 60,000 Geo-located Tweets

12 Analysis of London Weekend Tweets 12

13 New City Landscapes compared 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 York London Paris Moscow

14 New York 14

15 London 15

16 London zoomed London Zoomed 16

17 London zoomed London Zoomed 17

18 Temporal Twitter Data

19 #uksnow - Aggregated Results

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21 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

22 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)

23 Foursqaure check-in HotSpots around London Anil Bawa-Cavia -

24 Foursqaure check-in Hotspots Anil Bawa-Cavia -

25 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

26 UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS The New Demographics of Travel in London Visualising Transport for London Data Oliver O Brien, CASA

27 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

28 Tube Station Exits/Entries Map ENTRY Weekday AM peak ENTRY Sunday EXIT Weekday AM peak ENTRY Weekday evening

29 Tube Station Exits/Entries Map Change in total entries/exit numbers between for the Jubilee/Metropolitan and Northern Line stations in NW London

30 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?

31 Barclays Cycle Hire Scheme Clustering Normal work locations? Long-hour work locations? Preliminary hierarchical clustering based on average half-hour values across a week Courtesy of James Cheshire

32 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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34 All examples are of free applications on the iphone

35 TfL and the London Data Store Data released by TfL for public reuse at

36 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

37 Workshop Session