A presentation from the workshop on the role of VGI in advancing science which was part of the GIScience 2010 conference, 14 September 2010

1. Geographical Citizen Science
Dr Muki Haklay
Department of Civil, Environmental and Geomatic
Engineering, UCL & Co-founder ‘Mapping for
Change’
Povesham.wordpress.com
MappingForChange.org.uk

2. Oh, No! Why do we need another term?
What’s wrong with Volunteered
Geographic Information (VGI),
Crowdsourced Geographic information,
User Generated Geographic Information or
any other similar term that is already in
use?
Well, terms are useful to
highlight specific aspects that
might be otherwise overlooked...

3. Outline
• Contexts for VGI:
– Web 2.0
– Geographical Information production
– Citizen Science/Citizen Cyberscience
• Geographical Citizen Science
– Practical challenges: coverage, quality, motivations
– Conceptual challenges: trust and elitism,
heterogeneity, uncertainty, lab and reality

4. Non-spatial Crowdsourcing/ User Generated
Content
Non-spatial applications - anywhere, anyone,
anytime. It is possible to collaborate, update and
keep in touch regardless of where you are.

5. Spatially Implicit Crowdsourcing/User
Generated Content
Spatially Implicit applications – place matters:
if you’re not there, you can’t take a picture

6. Spatial Crowdsourcing/User Generated Content

7. Context I: Web 2.0 and VGI
• Demographic and technological trends:
education, broadband & computer use,
applications for sharing media, social networking
• Identifying VGI in spatially explicit and implicit
applications, understanding the wider trends that
influence it

8. VGI in geographic information production
(Image source: OpenStreetMap)

9. Context II: Geographic data collection and
VGI
• ‘Crowdsourcing’ – reducing costs of production
through free labour
• Active vs. Passive Crowdsourcing
• Ensuring standards and integration with existing
systems

10. Citizen Science
Volunteer rainfall observer Rick
Grocke checks the rain gauge at
Tanami Downs cattle station in the
Northern Territory of Australia Source: Audubon Cal.
Source: WMO–No. 919

11. Citizen Science
Source: BioScience 58(3) p. 195
Source: BioScience 58(3) p. 192

12. Non-spatial citizen (cyber)science

13. Context III: Geographical Citizen Science and
VGI
• The goal of the project is the production of
scientific knowledge
• Role of participants – from passive volunteering
resources (CPU cycles) to ‘distributed thinking’
(identifying galaxies)
• Participants have an interest in the topic, some
become experts

14. Hanny van Arkel. “The Dutch schoolteacher and Queen admirer who discovered
Hanny‟s Voorwerp”.

15. Aspects of Geographical Citizen Science
• Coverage – which part of the world can we
expect to cover?
• Participants – who is likely to participate, for
how long and what will motivates such a person
to continue and contribute?
• Culture – how established science view citizen
science activities?

16. Geograph Panoramio Picasa Web Flickr
Source: Vyron Antoniou, UCL

17. Geograph Panoramio Picasa Web Flickr
Source: Vyron Antoniou, UCL

18. Spatial pattern
• Not only urban areas are
captured, but also national
parks are emerging as
popular places to capture
information

19. England – March 2008
Completeness can be
tested in comparison to
official datasets from
the Ordnance Survey

20. England – March 2010

21. Change in completeness Mar 2008 – Mar 2010

22. England – March 2010
• The test for completeness
with attributes checks that
roads and streets names
have been completed
• Until the release of
Ordnance Survey data in 1st
April 2010, this was a good
indication for ground survey
of an area

23. Change in completeness with attributes

24. Population and completeness
Length of OSM / Length of Meridian 2 <= 0.20 Length of OSM / Length of Meridian 2 > 1.00
(both datasets with attributes) (both datasets with attributes)
16000 7000
Number of 'Untouched' Grid Cells
14000
Number of 'Complete' Grid Cells
6000
12000
5000
10000
OSM 0308 4000 OSM 0308
8000 OSM 0309 OSM 0309
OSM 1009 3000 OSM 1009
6000
2000
4000
2000 1000
0 0
<= 14.00 15.00 - 40.00 41.00 - 137.00 138.00 - 820.00 821.00+ <= 14.00 15.00 - 40.00 41.00 - 137.00 138.00 - 820.00 821.00+
Population / 1km Grid Square Population / 1km Grid Square

25. Prosperity and completeness

26. Geographical Citizen Science – geographic
patterns
• Highly populated and central places
• Tourist attractions: national parks and coastal
zones
• Bias towards affluent areas (and participants)
• Potential for rapid coverage of large areas by
dedicated volunteers

27. Cultural problems
• Citizen Scientists are perceived as untrustworthy
– No quality control
– Elitism – demarcation of ‘professional scientists’ from
‘amateurs’
• Evaluation mechanisms assume unskilled workers
such as classifying each object three times (in
Citizen Cyberscience)
• But volunteer effort can improve the data

28. Positional accuracy in complete tiles
Mean 9,57m
Standard Deviation 6,51m
Y-axis: Length difference between OSM and Meridian 2
X-axis: Average positional accuracy in the Tile
Average positional accuracy is 9.57m with St. Dev. = 6.51m
Source: Vyron Antoniou, UCL

29. Positional accuracy in incomplete tiles
Mean 11,72m
Standard Deviation 7,73m
Y-axis: Length difference between OSM and Meridian 2
X-axis: Average positional accuracy in the Tile
Average positional accuracy is 11.72m (almost 22.5% less accurate)
with a greater St. Dev. = 7.73m
Source: Vyron Antoniou, UCL

30. Positional accuracy for complete
and incomplete tiles
Source: Vyron Antoniou, UCL

31. Time for a paradigm shift?
Standard GI view VGI/Citizen Science
From expectation for a To explicit acceptance of
universal and heterogeneity
homogenous coverage
From expectation of high To acceptance that modern
level of knowledge instrumentation and
enthusiasm are enough
From attempting to To accepting the
replicate laboratory uncertainty of reality
conditions in the field
From elitism To participatory science

32. ‘Extreme’ Citizen Science
„Normal‟ Citizen „Extreme‟ Citizen
Science Science
Users Educated, usually Everyone, regardless
with some domain of level of literacy
knowledge
Location Affluent, populated Everywhere
and popular
Role Data collection and Shaping the problem,
entry analysing the data
Mode of Crowdsourcing Collaborative and
work participatory science

33. ‘Extreme’ Citizen Science
• Citizens can participate in defining the problems,
envisioning possible projects and participate in
the analysis as in the UK EPSRC project SuScit
• Can (and should) participate in the discussion of
the results of what they’ve collected
• Often, they are best placed to analyse the data

34. Noise Mapping

39. Sound Readings - Normal Flight Operations Sound Readings - Period of No Flights
90 90
80 80
70 70
60 60
50 50
dBA
40 40
30 30
20 20
10 10
0 0
00:00 04:48 09:36 14:24 19:12 00:00 04:48 00:00 04:48 09:36 14:24 19:12 00:00
Eyjafjallajökull

42. Conclusions
• Geographical Citizen Science
– Great potential for data collection, analysis as well as
awareness
– Requires careful consideration of the limitations that
are imposed by participants locations, motivations and
availability
– ‘Cyborg’ view of participants with their personal
knowledge, mobile phones, and sensors
– Change in mindset: heterogeneity, collaboration, skill
development
Povesham.wordpress.com MappingForChange.org.uk