1. Twitter as a novel source of mobility indicators
Nigel Swier
Data Science for Local Government - Oxford
27 September 2016 1

2. Background
ONS Big Data Project: This is one of four pilots exploring the use of big data for official statistics
Users tweeting from a smartphone have an option to provide a GPS location
300,000-plus such tweets sent daily within GB
Data is accessible
Can these data be used to infer residence and mobility patterns?

3. Age Distribution of UK Twitter Users

4. Data Acquisition
All geolocated tweets sent within Great Britain between (1 April 2014 to 31 October 2014)
Combination of Twitter API and procured data (GNIP)
81.4 million tweets
Stored as JSON files in MongoDB

5. Distribution of user activity

6. Persistence across time
Users with geolocated tweets on just one day not shown
User frequency count

7. Geo-located Twitter volumes by Device Type Great Britain, 15 August to 31 October 2014

8. Lots of activity in different places but where does this person live?

9. DBSCAN DBSCAN (Density Based Spatial Clustering Algorithm with Noise)
i = distance (radius)
minpts = minimum points to define a cluster
Developed by Ester et al (1996)

10. Most likely lives here: “Dominant Residential Cluster”
Cluster_id
Northing
Easting
Count
Type
60033_1
105?31
530?02 28
Residential
60022_2
104?41
530?94 4
60033_6
182?46
532?10 13
Commercial
60033_13
104?56
531?17 3
60033_15
179?30
533?95
60033_21
165?47
532?51
Most likely lives here:
“Dominant Residential Cluster”
Raw Data
Cluster Centroid
Noise

11. Time of day profile by address type

12. Geolocated penetration rates* by local authority
* Dominant residential cluster with date range of at least one month

13. Student mobility

14. Conclusions
Twitter may be useful for identifying short-term mobility patterns
DBSCAN can identify anchor points and AddressBase can classify them
Results are indicators NOT estimates - may be possible to produce new de-facto based population statistics
Twitter could help inform public policy but we need to be extremely alert to source changes.

15. Further Research
Developing methods for inferring socio-demographic characteristics
Development of an estimation framework (including a benchmarking survey)