Data for Sustainable Mobility
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The document discusses the challenges and limitations of data collection for sustainable mobility, particularly emphasizing the marginalization of non-motorized transport modes like walking and cycling. It identifies issues in data acquisition and the reliance on motorized transport models which hinder predictive planning for sustainable transportation. Furthermore, it highlights the need for better data triangulation and mixed-methods approaches to address these gaps in understanding sustainable mobility.
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“… only the measurable aspects count. […]
some modes of transport are very measurable.”
Koglin & Rye (2014: 216)](https://image.slidesharecdn.com/2021-06-30-isde-data-intelligence-panel-210707123719/85/Data-for-Sustainable-Mobility-2-320.jpg)
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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)](https://image.slidesharecdn.com/2021-06-30-isde-data-intelligence-panel-210707123719/85/Data-for-Sustainable-Mobility-6-320.jpg)
Data for Sustainable Mobility
- 1. Data for Sustainable Mobility Martin Loidl | [email protected] 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
- 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?
- 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)
- 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
- 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 [email protected] twitter.com/gicycle_ Political implication Contribution to SDG