The document discusses who contributes to OpenStreetMap and why. It finds that most mappers are male (96%), between 20-40 years old, educated, and employed in commercial jobs. The top motivations for mappers are having local knowledge represented accurately on the map and contributing to a valuable public resource. "Instrumentality of local knowledge" is a key driver of contributions. Serious mappers who contribute more are motivated more by monetary factors, local knowledge representation, and their self-view than casual mappers. Most mappers say commercial company involvement would not decrease their contributions.

1. Who map in OpenStreetMap
and Why?
Nama Budhathoki, McGill University
Muki Haklay, University College London
Zorica Nedovic-Budic, University College Dublin
State of the Map 2010– Atlanta, USA, 14-15 August, 2010

2. Looked from the traditional mode of
production, it is a puzzle (Benkler
2005, 2006)
Understanding this question lies at the
heart of the science of volunteered
geographic information (Goodchild 2007)

3. Research questions
•Who are those mappers?
•Why do they map?
•What contributory pattern do mappers
demonstrate?

4. Theoretical framework for VGI
motivational study
• Unique ethos • Recognition
• Learning • Career
• Fun • Reciprocity
• Instrumentality • Community
• Recreation • Monetary
• Meeting self need • Socio-political
• Altruism • More………...
Clary et al. (1998), Clary and Synder (1999); Stebbins (1982), Gould et al. (2008);
Wasko and Faraj (2005), Lee et al. (2008), Hertel et al. (2003), Shah (2006), Hippel
and Krogh (2003), Nov (2007),

5. Methodology
• Analysis of Planet.OSM to identify
patterns of contribution
• Qualitative analysis of talk-pages
• Survey of globally distributed contributors

6. Who are the mappers?
Above Below 20
Female 50 years years
(3%) (10%) (4%)
20-30
41-50 years
years (32%)
(22%)
Male 31-40
(96%) years
(32%)
Gender Age
Doctoral High
degree School or <1 year
(8%) lower Some (26%)
Post- (5%) College None
graduate (17%) (49%)
1-5
degree years
(21%) (15%)
College/
University >10 6-10
degree years years
(49%) (3%) (7%)
Education GIS Experience
N=426

7. Occupation Employment
Self Other
employed (3%) Local Non-profit Other
(15%) govt. (2%) (3%)
Student (6%)
Retired (17%) Federal
(2%) govt.
(7%)
Employed Commercial
(63%) Academia (71%)
(11%)
Place In percent (%)
Home 96
Office 18
Mobile 13
Public libraries 0
Internet cafes 0.3
Others 0.6

8. Motivations
Being an author of books which are using maps, I am not
able to pay royalty fees to map companies like google or
teleatlas.
It's a lot of fun, and it's nice to see your work appear 1-2
hours after it's done available to the whole world :)
I love to see the area around where I live accurately mapped
(and updated in a timely manner). I get enormous
satisfaction out of this entire process as well as know that
I'm contributing towards a valuable resource that others
can use. I also enjoying exploring on my bike new areas
that I'm mapping - I've discovered some cool suburban
places that I never new existed - often within meters of
roads that I drive down regularly.

9. Perceived Motivations
Motivational construct Mean SD
Project goal 6.14 .77
Altruism 5.73 .83
Instrumentality of local knowledge 5.58 .81
Learning 5.29 .95
Self need 5.2 1.19
Social/Show off 4.04 1.00
Monetary 2.14 1.06

10. Difference in perceived motivations between
serious & casual mappers

11. Hypothesis Development
Motivational Factors
H1: Project goal
H2: Altruism
H3: Local knowledge
Node
H4: Learning
Longevity Contribution
H5: Self need
H6: Show-off
Frequency
H7: Monetary
H8: Mapping party

12. Contributory Pattern (Europe)

13. Contributory Pattern (Africa)

14. Contributory Pattern (Asia)

15. Contributory Pattern (North America)

16. Contributory Pattern (South America)

17. Contributory pattern in OSM
Registered users
117,000
Mappers Non-mappers
33,452 (29%) 83,548 (71%)
• 44% are one-timers
• 5% have contributed more than 10,000 nodes
• 0.6% have contributed more than 100,000 nodes
Source: www.openstreetmap.org , downloaded from http://downloads.cloudmade.com/
(Accessed on April, 2009)
34

18. Continent level
One-time contributors >100 Node
>1000 Node >10000 Node
>100000 Node
80
70
Mappers (in %)
60
50
40
30
20
10
0
Africa Asia Europe North South
America America

19. Hypothesis Testing
Main hypotheses Sig value (Pillai’s Sub-hypotheses Unstandardized Sig-value
trace) parameter estimates
H1: Project goal 0.030* Node (H1a) -0.615 0.012*
Longevity (H1b) -0.328 0.093
Frequency(H1c) -0.362 0.005*
H2: Altruism 0.080 Node (H2a) -0.440 0.049*
Longevity(H2b) -0.072 0.689
Frequency(H2c) -0.206 0.080
H3: Instrumentality 0.000* Node(H3a) 2.011 0.000*
of local knowledge Longevity(H3b) 1.275 0.000*
Frequency(H3c) 1.038 0.000*
H4: Learning 0.877 Node(H4a) 0.054 0.794
Longevity(H4b) -0.064 0.697
Frequency(H4c) 0.001 0.995
H5: Self need 0.977 Node(H5a) 0.022 0.868
Longevity(H5b) -0.009 0.936
Frequency(H5c) 0.015 0.837
H6: Show off 0.454 Node(H6a) -0.263 0.180
Longevity(H6b) -0.215 0.171
Frequency(H6c) -0.105 0.311
H7: Monetary 0.724 Node(H7a) 0.097 0.593
Longevity(H7b) -0.033 0.822
Frequency(H7c) 0.046 0.633
H8: Mapping party 0.486 Node(H8a) 0.710 0.242
Longevity(H8b) 0.029 0.953
Frequency(H8c) 0.239 0.454

20. Serious mappers
Motivations Sig. Value
Monetary 0.035*
Learning 0.922
Instrumentality of local knowledge 0.008*
Project Goal 0.574
Altruism 0.200
Show-off 0.110
Self need 0.625
Community importance 0.622
Identity 0.595
Self view 0.012*
Socio-political agenda 0.794

21. How will the involvement of commercial
companies affect your contribution to the
project?
80
70 75.6%
60
50
40
30
20
10
7.3% 12.1% 5%
0
It will increase my I will decrease my It will not affect my I will stop
contribution contribution contribution contributing

22. Summary and implications
• Instrumentality of Local knowledge as a
key motivator of contribution
• Representation of local area
• Accuracy of map
• Self efficacy
• Fun
• Those who have higher monetary
motivation, local knowledge, and self view are
likely to be serious mappers.

23. Summary and implications
• Why cann’t those with other motivations can’t
make good contribution?
• Learning materials
• Ease of use of the system
• Social network

24. Thanks for listening!
Feel free to contact me for more information:
namabudhathoki@gmail.com
http://budhathoki.wordpress.com