2. Observing from afar or joining the action:
OSM and GIScience research
Muki Haklay
‘Extreme Citizen Science’ group (ExCiteS)
Department of Civil, Environmental and Geomatic
Engineering
UCL
m.haklay@ucl.ac.uk / twitter: @mhaklay
http://povesham.wordpress.com

3. Content
• OpenStreetMap and academic research
• Using, researching and working with
OpenStreetMap
• What university researchers can and can’t do for
OpenStreetMap
• Some suggestions for engagement with
OpenStreetMap

4. OpenStreetMap and academic publications
Number of publications indexed by Google Scholar
600
527
500
400
300
274
200
132
100
47
0 13
2006 2007 2008 2009 2010
• ISI Web of Knowledge: 10; Elsevier Scirus: 66

5. More germane to the topic of this paper is the OpenStreetMap, which is a free editable
map of the whole world. Operating as a WIKI, OpenStreetMap allows users to view,
edit and use geographical data in a collaborative way from anywhere in the world.
According to the website:
OpenStreetMap is a project is a project aimed squarely at creating and providing free
geographic data such as street maps to anyone who wants them… (www.openstreetmap.org,
accessed January 2006)
... Although innovative and attractive for its low cost and democratic principles, such an
approach would have two notable limitations: (1) it might take years to cover the
world on an “all volunteer” basis, and (2) there would be little ability to impose
standards or to undertake a comprehensive independent data verification. Still, as
these technologies advance, and the community of users grows, it is worth considering
this as a possible model for roads data compilation.

6. …Calling it volunteered geographic information (VGI) captures what
is perhaps its most important aspect…
For example, OpenStreetMap (www.openstreetmap.org) is building a
public-domain street map of the entire world through volunteer effort.
Each contributor develops a map of his or her local streets using GPS
tracking; and individual contributions are assembled and reconciled into a
single patchwork. Extensive metadata is incorporated, since each piece of
the patchwork may have different levels of accuracy and may have been
acquired at different dates. Some level of expertise is required in the
use of GIS and the project’s software, in the basic principles of
geographic measurement, and in the project’s system for
classifying streets.

7. Types of research with OpenStreetMap
• With OSM – Working close to OSM community to
identify research needs and follow them (Wiki
Research Ideas page)
• About OSM – Learning about OSM, and the
community as part of VGI. Comparing it to other
projects
• Using OSM – Using the dataset to create new
applications and explore scientific issues

8. Data quality – first and major area of research
• The ‘wikipedia problem’:
– We know little about the people that collect it, their
skills, knowledge or patterns of data collection
– Loose coordination and no top-down quality assurance
processes
• Significant to ‘buy in’ from users of the map and
thus to the making OSM meaningful project
• Academic research provide the credibility and
access to reference datasets, in addition to
expertise in spatial data quality

9. Positional accuracy and completeness
• Tests in the UK, France, Germany, Switzerland
and Greece demonstrated that it OSM data is
accurate:
– UK: Zulfiqar (2008) Basiouka (2009), Ather (2009),
Haklay (2010)
– Greece: Kounadi (2009)
– France: Girres and Touya (2010)
– Germany: Zielstra and Zipf (2010), Mondzech and
Sester (2011) Ludwig, Voss and Krause-Traudes (2010)
– Switzerland: Ueberschlag (2010)
• Usually mix between positional accuracy,
attribute accuracy and completeness

10. Thomas Koukoletsos
Using data matching to evaluate completeness

11. Thomas Koukoletsos
Using data matching to evaluate completeness

12. Thomas Koukoletsos
Using data matching to evaluate completeness

13. Thomas Koukoletsos
Using data matching to evaluate completeness

14. Thomas Koukoletsos

15. Thomas Koukoletsos

16. Thomas Koukoletsos

17. Thomas Koukoletsos

18. Byron Antoniou
Self contained quality indicators

19. Data quality – current understanding
• Quality can be as good as the best datasets
• Heterogeneity / patchwork is key – no global
quality measure
• Rural / Urban gap in quality and completeness
• Evaluation methods are robust and work across
cultures, some been automated
• Intrinsic indicators are critical (Linus law, edits) –
important area for further research

20. Nama Budhathoki
Participants’ characteristics
Female Above 50 Below 20
(3%) years years
(10%) (4%)
41-50
years 20-30
(22%) years
Male (32%)
(96%)
31-40
years
(32%)
Doctoral High
degree School or
Post- (8%) lower
Some Other societal studies:
graduate (5%)
College
degree (17%)
• Parker (2010)
(21%) • Gerlach (2010)
College/ • Coleman et al. (2010)
University
degree
(49%)

21. Use cases: emergency and humanitarian
situations
Source: Maron (2010).

22. Other areas of activity
• 3D models and routing applications (Zipf et al.)
• Neocartography and geovisualisation (Chilton,
O’Brien)
• Semantics, ontologies (Bishr, Antoniou)
• Technical applications (Mooney)
• Trust and general quality (van Exel)
• Some questions on long held views: data quality,
ontologies, top down processes

23. ‘Critical friendship’
2008 2011

24. 200
OSM coverage in England, by
180
deprivation index
160 Mar ‘11
140
Mar ‘10
120
Oct‘09
100
Mar ‘09
80
Average of PCTMeridianOSM0308
Average of PCTMeridianOSM0309
60
Average of PCTMeridianOSM1009
Average of PCTMeridianOSM0310
Mar ‘08 Average of PCTMeridianOSM0910
Average of PCTMeridianOSM0311
40
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98

25. 140
OSM (with attributes) coverage in England,
120 by deprivation index
100
80
60
40
Average of PCTMeridianOSM0308A
20 Average of PCTMeridianOSM0309A
Average of PCTMeridianOSM1009A
Average of PCTMeridianOSM0310A
Average of PCTMeridianOSM0910A
Average of PCTMeridianOSM0311A
0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98

26. But it doesn’t have to be this way (Tom
Chance work) …

27. OSM use for academic research
Space Syntax analysis (choice 2km)

28. Surbiton – Integration at 800m

29. Important open questions
• Cultural differences and the impact on mapping
• Impact of imports, and the rate and nature of
updates of well mapped areas
• Spatial and temporal patterns of engagement and
contribution, role of ‘silent’ mappers
• ‘Tyranny of place hypothesis’ and the impact of
specific individual on mapping an area
• Internal culture and impact on gender, exclusion
and long term engagement
• Legal studies – licence use cases and applications
• Usability (see Weber and Jones 2011)

30. Limitations of academic research / collaboration
• Short attention span – the incentives are for
novelty, not routine
• Publications are important, so research
ideas are welcomed
• Academic institutes can provide power
and network (up to a point), not money
• Enthusiastic students are available at
specific time cycle
• Across the world, match funding for
business, so introductions needed
• Academic careers have thematic focus

31. ‘Code of engagement’ for OpenStreetMap
research
• Rule 1 – even if you are just going to use the
data, do some mapping, and understand the
process. Join a mapping party.
• This will help you avoiding misinterpretations
such as ‘the data is collected by users from the
GPS trails’

32. ‘Code of engagement’ for OpenStreetMap
research
• Rule 2 – Read. OSM Books, Wiki, Blog and mailing
lists.

33. ‘Code of engagement’ for OpenStreetMap
research
• Rule 3 – Explore the data. There’s plenty of it –
quantitative and qualitative. Then talk with
someone in the community to check that you’ve
got it right.

34. ‘Code of engagement’ for OpenStreetMap
research
• Rule 4 – Open Access. Put outputs in Open
Access repository, publish in Open Access
journals & blogs.

35. ‘Code of engagement’ for OpenStreetMap
research
• Rule 5 - Open Knowledge. Publish and share the
data that you’ve processed, and ideally the code
so other people can use it for their purposes.
• Rule 6 - You have a responsibility to your
academic field, and the OpenStreetMap
community can deal with criticism – be a critical
friend.

36. ‘Code of engagement’ for OpenStreetMap
research
• Rule 7 - Teach. Students are some of the most
likely participants. It’s
also fun for them.
Source: Harry Wood 2010

37. ‘Code of engagement’ for OpenStreetMap
research
• Maintain links with the OSM community – it will
pay off and will help you to identify new research
directions
• Also maintain links within the VGI research
community – even if the term is awkward, the
research is valuable
• Explore comparisons and parallels – it’s
important to learn what is going on in other
projects

38. Conclusions
• OpenStreetMap is becoming a significant resource
in GIScience research – studying it, using it and
improving it
• There are many open questions that are
important to both the community and the
researchers. Continuous dialogue is the key
• We should use
http://wiki.openstreetmap.org/wiki/Research
more