Evaluating the Utility of Geo-referenced Twitter Data as a Source of Reliable Footfall Insight Guy Lansley, UCL Presented at the American Association of Geographers Annual Meeting, Tampa, FL. (10/04/2014)

1. Evaluating the Utility of Geo-referenced Twitter Data as a
Source of Reliable Footfall Insight
Guy Lansley
Department of Geography, University College London
@GuyLansley
g.lansley@ucl.ac.uk
Web: http://www.uncertaintyofidentity.com

2. Outline
• Spatial representativeness of Twitter
• Gridded Twitter density of the UK
• Inferring bespoke travel catchments

3. 3
Context
• Twitter could pose as a useful source of temporal population data at a very small area
geography
• These data can be used to predict how the population negotiate travel around cities at
an small area aggregate level
• Previous research has found geo-located Twitter data sourced from the UK to be
over-representative of young, White British adults, and there is also a higher
penetration amongst males
• The content of the Tweets poses an interesting area of research into location activity
characteristics

4. Data available through the Twitter API
• User Creation Date
• Followers
• Friends
• User ID
• Language
• Location
• Name
• Screen Name
• Time Zone
• Geo Enabled
• Latitude
• Longitude
• Tweet date and time
• Tweet text

5. Twitter #Hashtags
• Most common London football club hashtag

6. 6
• 20:00 – 0:00 (Twitter)• 10:00 – 16:00 (Twitter)
• Work day population (2011
Census)
• Residential population aged
16 and above (2011 Census)
Dispersal of activity (LSOA level)
• Difference (Twitter 2013)
• Difference (Census 2011)

7. 7
Twitter over-representation
Day Night
Twitter
Census

8. 8
Geo-located social network data
• Twitter activity by land use category
– Generalised Land Use Database
Residential
Non-Domestic
Transport
Green Space
Water
Other
From Longley, Adnan & Lansley (Forthcoming)

9. Temporal map of geo-located Tweets recorded on selected weekdays during the winter of 2012/13
Twitter: Weekday activity in London
A video of this slide can be found at:
https://vimeo.com/88076916

10. 10

11. 11
Note: the words “Greater” and “London” have been removed
Tweet content
• Content reflects
– Place
– Land use
– Activity
– Sentiment
– Language
• Content also reflect time and
date
• Words can be aggregated to
make a definitive classification
of topics
Retail Nightlife Eating out Entertainment Outdoor Tourism Transport Work Home
140 170 156 103 73 163 63 45 23

12. 12
• E.g. Day-time catchment
1. Identify the unique ID of users
frequently transmitting from a
particular location at a given time or
date range
2. Request their other activity through
Twitter’s API, filter by time/date
3. Aggregate
Specific time catchments
The Twitter work-day time catchment of Bishopsgate
Activity at Bishopsgate during weekdays (2013)

13. 13
Waterloo St PancrasVictoria Paddington
London Bridge Liverpool Street Kings Cross Euston

14. 14
Inferring a residential catchment based on Twitter data
• First, extract the unique ID’s
of users have tweeted from
inside the building
• Request these users’ other
Tweets for a given time/date
range
• Create a customer
catchment by identifying all
Tweets sent from domestic
land uses at a given time
• E.g. ASDA in Clapham
Junction The Twitter residential catchment of ASDA
Supermarket at Clapham Junction

15. 15
• Twitter users are not a representative sample of the British
population
• Sample size
• Precision of geo-location varies between handheld devices
• Signal availability
• Tweets do not always reflect the place where they are transmitted
• Demographic characteristics & home address are not recorded
• Ethics
Limitations of Twitter Data

16. Conclusion
• Twitter poses as a useful dataset for representing geo-temporal population movements in
London
• Although the Twitter sample is not entirely representative of the total population
• Twitter data can provide a useful insight into the residential and travel geographies of
individuals which can be utilised to understand the characteristics of places and how they
interact with their wider environment
• The Tweet content also poses an interesting opportunity for further location insight

17. Any Questions?
Thank you for Listening
Guy Lansley
Department of Geography, University College London
@GuyLansley
g.lansley@ucl.ac.uk
Web: http://www.uncertaintyofidentity.com
For a work in progress visit: http://www.uncertaintyofidentity.com/Twitter_Grid_Maps/Mapping.aspx