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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
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)
Research questions
 •Who are those mappers?

 •Why do they map?

 •What contributory pattern do mappers
  demonstrate?
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),
Methodology
• Analysis of Planet.OSM to identify
  patterns of contribution
• Qualitative analysis of talk-pages
• Survey of globally distributed contributors
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
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
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.
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
Difference in perceived motivations between
serious & casual mappers
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
Contributory Pattern (Europe)
               600000

               500000
No. of Nodes




               400000

               300000

               200000

               100000

                   0

                        0   100   200   300      400   500   600
                                   No. of Days
Contributory Pattern (Africa)
           25000


           20000
No. of Nodes




           15000


           10000


               5000


                 0

                      0   20   40       60   80   100
                               No. of Days
Contributory Pattern (Asia)
               250000


               200000
No. of Nodes




               150000


               100000


               50000


                   0

                        0   100       200       300   400

                                  No. of Days
Contributory Pattern (North America)
           400000

           350000

           300000
No. of Nodes




           250000

           200000

           150000

           100000

               50000

                  0

                       0   50   100       150       200   250   300
                                      No. of Days
Contributory Pattern (South America)
               90000

               80000

               70000

               60000
No. of Nodes




               50000

               40000

               30000

               20000

               10000

                  0

                       0   20   40       60        80   100   120
                                     No. of Days
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
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
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
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
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
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.
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
Thanks for listening!

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

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Sotm us 2010 (nama r. budhathoki)

  • 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) 600000 500000 No. of Nodes 400000 300000 200000 100000 0 0 100 200 300 400 500 600 No. of Days
  • 13. Contributory Pattern (Africa) 25000 20000 No. of Nodes 15000 10000 5000 0 0 20 40 60 80 100 No. of Days
  • 14. Contributory Pattern (Asia) 250000 200000 No. of Nodes 150000 100000 50000 0 0 100 200 300 400 No. of Days
  • 15. Contributory Pattern (North America) 400000 350000 300000 No. of Nodes 250000 200000 150000 100000 50000 0 0 50 100 150 200 250 300 No. of Days
  • 16. Contributory Pattern (South America) 90000 80000 70000 60000 No. of Nodes 50000 40000 30000 20000 10000 0 0 20 40 60 80 100 120 No. of Days
  • 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