The #ISDE12 conference hosted the #DIO forum on July 7th. This panel presentation highligted the importance of active mobility data.

1. Data for
Sustainable Mobility
Martin Loidl | martin.loidl@sbg.ac.at
ISDE12: Data Driven Spatial Intelligence
Salzburg, July 7th 2021

2. 2
“… only the measurable aspects count. […]
some modes of transport are very measurable.”
Koglin & Rye (2014: 216)

3. 3

4. 4
https://www.google.
at/maps

5. 5
Focus on motorized modes.

6. 6
Pedestrians and cyclists are marginalized in
the data sphere
“Both modes [walking, cycling] have long lacked
predictive planning tools and are often [...] merely
treated as a combined remainder left over after
modelling motorised modes. This perpetuates a lack
of predictive data not similarly experienced in
planning public transport provision.”
Aldred et al. (2019: 156)

7. 7
Modernism
Concepts,
methods,
models
Measure-
ments
Planning
Tech-optimism:
car as indicator for
prosperity
Transport models
with a focus on
motorized traffic
(car, PT)
Data acquisition
for motorized
modes
Car-centric
urban/transport
planning and
implementation
verify
realizes
supports
inspired by Koglin & Rye (2014)

8. 8
In the 1960s planners viewed the car as the travel mode of the future, and swaths of the city were destroyed
to make way for motorised traffic. Photograph: Fotocollectie Anefo/Society for the Nationaal Archief
https://www.theguardian.com/cities/2015/may/05/amsterdam-bicycle-capital-world-transport-
cycling-kindermoord

9. 9
Data for providing an evidence base
 Evaluation
 From single use cases to representative
conclusions
 Monitoring (KPIs)
 Visibility » promotion of sustainable
mobility

10. 10
What do we expect?

11. 11
https://www.visualcapitalist.com/how-much-data-is-generated-each-day/

12. 12
https://visitingnegev.com/wp-content/uploads/2018/12/floodseinavdatelion-
1024x683.jpg
https://upload.wikimedia.org/wikipedia/commons/thumb/0/05/En-avedat-upper-
entrance-canyon-b.JPG/1920px-En-avedat-upper-entrance-canyon-b.JPG
“The context for geographic research has shifted
from a data-scarce to a data-rich environment”
Miller & Goodchild (2015: 349)

13. 13
https://www.zigerlig.ch/fileadmin/user_upload/Bilder/therm
o-hydrograph1.png
https://de.wikipedia.org/wiki/Hygrograph#/media/Datei:Hygrograph_-_Kolkata_2012-01-
23_8701.JPG
Automation Digitalization

14. 14
https://fdn.gsmarena.com/imgroot/news/19/05/pixel-3a-teardown/-727/gsmarena_001.jpg
Miniaturization of sensors
 Ubiquitous internet
 IoT
 Data streams
Data from sustainable mobility?

15. 18
What do we actually have?

16. 19
“More than half of the countries (18 out of 30)
mentioned difficulties when collecting active
modes data. In countries with a systematic data
collection structure, difficulties due to under
reporting and bias or partial data were
mentioned. In seven countries the lack of
systematic and consistent data collection is
mentioned, whilst three countries mention a
complete lack of data.”
Steenberghen et al. (2017: 15)

17. 20
 Full count
 Point location
Stationary counting
systems

18. 21
 Representative
 High effort
 Snapshot
Mobility surveys
Steenberghen
et al. (2017)

19. 22
 Network data
 High temporal and
spatial resolution
 Additional (socio-
demographic) data
 Not representative
 Skewed
Tracking apps

20. 23
Blind spot?
By Ske. - See below, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php
?curid=92925

21. 24

22. 25
Challenges (for GIScience)

23. 26

24. 27
Lack of sound concepts for data
triangulation.
 Data fusion
 Mixed-methods

25. 28
 Availability and
accessibility
 Representativeness
 Data triangulation
 Ethics and privacy
 Organisation
 https://mobilitylab.zgis.at
 martin.loidl@sbg.ac.at
twitter.com/gicycle_
Political implication
Contribution to SDG