BigDataEurope organized its first webinar on the 21st September 10h00-11h00 (CET) to introduce the BigDataEurope project, in particular the domain of Smart, Green, and Integrated Transport. The presentation was created and presented by Philippe Crist from OECD.

1. Mobility Data:
Changes and
Opportunities
Philippe Crist
Senor Reseacher and Administrator

2. What we did What we foundWhyMobility Data: Changes and Opportunities
BigData

3. What we did What we foundWhyMobility Data: Changes and Opportunities
Data Analysis Pipeline
Acquisition
Recording
Extracting
Cleaning
Annotation
Storage
Integration
Aggregation
Representation
Visualisation
Analysis
Modeling
Interpretation
Reinterpretation
Deletion
Human input
Interpretation
Heterogeneity
Volatility
Scale
Velocity, timeliness
Traceability, privacy
ValueRepresentativeness
Data/analysis issues

4. What we did What we foundWhyMobility Data: Changes and Opportunities
Big data has not done away with the need for
statistical rigour since big data is not only
prone to many of the same errors and biases
in smaller datasets, it also creates new ones

5. What we did What we foundWhyMobility Data: Changes and Opportunities

6. What we did What we foundWhyMobility Data: Changes and Opportunities
1 914
29%
4 474
67%
293
4%
2013
6681
5 629
61%
2 854
31%
685
8%
2019
9168
Smartphone Feature/basic phone Mobile PC/Router/Tablet
Global Mobile Subscriptions (millions)

7. What we did What we foundWhyMobility Data: Changes and Opportunities
latitude
longitude
Human mobility is unique

8. What we did What we foundWhyMobility Data: Changes and Opportunities
latitude
longitude
time
Human mobility is unique

9. What we did What we foundWhyMobility Data: Changes and Opportunities
latitude
longitude
time

10. What we did What we foundWhyMobility Data: Changes and Opportunities
latitude
longitude
time
People’s patterns of movement in space and time are repetitive and
predictable. These trajectories are powerful identifiers – like fingerprints

11. What we did What we foundWhyMobility Data: Changes and Opportunities
+
>1m
MAC address (WiFi)
Automatic image recognition (video)
Facial recognition/tracking
5-10m 5-50m 100-300m 100m to kms
A-GPS (GPS+Cell tower)
Hybrid GPS (GPS+WiFi)
GPS (GNSS) Cell tower triangulation
Mobile telecom
cell (tower)
Location Sensing Technologies and Precision

12. What we did What we foundWhyMobility Data: Changes and Opportunities
z-axis
x-axis
y-axis
z-axis
x-axis
y-axis
Mode detection from accelerometer signals

13. What we did What we foundWhyMobility Data: Changes and Opportunities

14. What we did What we foundWhyMobility Data: Changes and Opportunities
Mobile
telecoms
tower

15. What we did What we foundWhyMobility Data: Changes and Opportunities
Mobile
telecoms
tower
Mobile
telecoms
service cells

16. What we did What we foundWhyMobility Data: Changes and Opportunities
Mobile
telecoms
tower
Mobile
telecoms
service cells
Precise location fix

17. What we did What we foundWhyMobility Data: Changes and Opportunities
Mobile
telecoms
tower
Mobile
telecoms
service cells

18. What we did What we foundWhyMobility Data: Changes and Opportunities
4 co-located data points
within an anonymised
track sufficient for 95%
re-identification rate

19. What we did What we foundWhyMobility Data: Changes and Opportunities

20. What we did What we foundWhyMobility Data: Changes and Opportunities
Data Use and Privacy: New Perspectives
Traditional Approach Emerging New Perspectives
Data actively collected with data
subject and data user awareness.
Data from machine-to-machine
transactions and passive collection –
difficult to notify individuals prior to
collection.
Personal data is predetermined,
well-identified and binary
(personal/not personal).
Personal data dependent on combinatory
techniques and other data sources or
may be contextual and dependent on
social norms.
Data collected for a predetermined
specific use and for a duration in line
with that use.
Social benefits, economic value and
innovation come from co-mingling data
sets, subsequent uses and exploratory
data mining.
World Economic Forum, 2013

21. What we did What we foundWhyMobility Data: Changes and Opportunities
Data Use and Privacy: New Perspectives
Traditional Approach Emerging New Perspectives
Data accessed and used principally
by the data subject.
Data user can be the data subject, the
data controller and/or third party data
processors.
Individual provides consent without
full engagement or understanding.
Individuals engage in meaningful
consent, understand how data is used
and derive value from data use.
Data privacy framework seeks to
minimise risks to individuals.
Data protection framework focuses more
on balancing individual privacy with
innovation, social benefits and economic
growth.
World Economic Forum, 2013

22. What we did What we foundWhyMobility Data: Changes and Opportunities
Privacy by design

23. What we did What we foundWhyMobility Data: Changes and Opportunities
Policy insights:
Privacy integrated into technologies at the outset
New models for public-private data-sharing
Transport authorities will need to audit data they use