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Gender differences in OpenStreetMap contributor activitiy, editing and tagging behvaiour
- 1. Gender differences in OpenStreetMap contributor activity, editing and tagging behaviour Dr Zoe Gardner Dr Peter Mooney Dr Liz Dowthwaite Professor Giles Foody GISRUK Conference17th – 20th April 2018 University of Leicester
- 2. Background
- 3. OpenStreetMap and the male participation bias • OpenStreetMap (OSM) is a crowdsourced mapping platform created in 2004 to enable the creation of an editable map of the world. • 6 years in to the OSM project = 96.2% male participation bias (Budhathoki 2010) • Subsequent studies replicated these findings (e.g. Schmidt and Klettner (2013); Stephens (2013)) • Critical GIS has questioned the democratic potential of crowdsourced mapping, to truly represent the interests of the ‘crowd’ – particularly those of women • However, these assertions are theoretical as real impacts of the participation bias on the geospatial data remain largely untested.
- 5. Surveying OSM users • August 2017: Online survey of OSM users in English to several talk lists (de facto language of OSM) • 326 responses generated (33 excluded). 255 Men / 38 Women (n=293) • Demographic data (gender, age, education, country of residence, nationality) AND, critically, OSM username • Access to ‘How did you contribute to OSM’ user statistics • 19 HDYC variables included in the study 0 50 100 150 200 250 300 Users Respondents by gender Women Men
- 6. How did you contribute to OSM (HDYC)?
- 8. Describing the sample – distribution curves Distribution curves for Number of days active and changesets replicate the ‘long tail effect’ characteristic of crowdsourcing projects (Haklay 2016) i.e. a small proportion of the community conduct the greatest proportion of contributions
- 9. Table 1: Median ranking values for overall edits to Nodes, Ways and Relations Men Women Median Range Median Range Nodes 5486 20 - 341070 21382 744 – 398667 Ways 4696 70 – 270072 18566 657 – 350051 Relations 3762 2 – 277779 24211 665 – 354915 • Range values suggest some of the male contributors are among the most active contributors • Most active female contributors in our sample are ranked considerably lower • Both male and female median values still in the top 1% of contributors (4.2million)
- 10. Results
- 11. Results: HDYC variables Behaviour HDYC variable Activity Days active Changesets Editing Nodes created Nodes modified Nodes deleted Ways created Ways modified Ways deleted Relation created Relations modified Relations deleted Tagging Amenity Building Highway Landuse Leisure Name Natural Address 19 variables 3 types of editing behaviour
- 12. Results 1: Gender differences in Activity Finding 1: Men are statistically more active than their female counterparts 158.94 66.86 0 20 40 60 80 100 120 140 160 180 1 Median number of days active Women Men 156.26 84.83 0 20 40 60 80 100 120 140 160 180 1 Median number of changesets Women Men
- 13. Results 2: Gender differences in ‘Editing’ (Raw data) Finding 2: Men contributed statistically more edits in each of the 9 categories Category Men Women Nodes created 109343 8469 Ways created 11835 1403 Relations created 133 2.5 Nodes modified 29157 939 Ways modified 11320 109 Relations modified 412 0 Nodes deleted 9265 1278 Ways deleted 994 378 Relations deleted 23 4
- 14. Results 2a: Gender differences in Editing – Object focus Finding 2a: Nodes dominated object focus (over ways and relations) for both groups (men and women)
- 15. Results 2b: Gender differences in Editing – mode preferences Finding 2b: Men demonstrate a higher propensity to modify data than women, who show more confidence in deleting
- 16. Results 3: Gender differences in tagging behaviours Median tagging values Category Men Women Amenity 2 1 Building 33 67 Highway 36 23 Landuse 3 1 Leisure 1 0 Name 14 7 Natural 1 0 Address 4 1
- 17. Results 3: Gender differences in Tagging Finding 3: Men demonstrate higher levels of variance in their preference for tagging categories
- 18. Summary
- 19. Summary This paper has examined differences in the way men and women edit OSM according to the ‘hdyc’ stats. Main findings: 1. Men are statistically more activities than their female counterparts; 2. Men demonstrate higher values than women for the modification of data; 3. More nuanced differences in tagging activities: men demonstrate a higher level of variance in the features they edit Further work: gendered contributions in humanitarian mapping activities in Malawi (Gardner & Mooney, 2018, forthcoming @ AGILE 2018 Conference, June)
- 20. Thank you to my sponsors, Pascal Neis and of course my respondents! Questions? e: zoe.gardner@nottingham.ac.uk : @DrZoeGardner
Editor's Notes
- Share some preliminary results of a recent online survey of contributors to the crowdsourced mapping platform OpenStreetMap. Part of a broader research project funded by the University of Nottingham and EPSRC of contributions by a sample of globally distributed OSM users which is attempting to identify impacts of the male participation bias in OpenStreetMap. We have used the survey data to access pre-collated OSM user data to make some broadbrush observations about gendered volunteered geospatial information specifically in OSM.
- OSM created in 2004 to enable an editable map of the world Six years after the inception of the online mapping project OpenStreetMap (OSM), Nama Budhathoki identified a 96.2% male participation bias in OSM. This was supported in subsequent studies which looked specifically at gender dimensions in crowdsourced mapping and which also observed a strong bias towards male participation in the project. Since then, critical GIS scholars have challenged the notion of OSMs democratic potential to represent the interests of the wider ‘crowd’. Discourses of gender and VGI propose that the crowdsourced map is a reflection of the geospatial interests of the people that create it, and given the participation bias strongly in favour of men, it is their interests that are represented while those of women are repeatedly excluded by the process. However, thus far there has been very little in terms of empirical work looking at the impacts on the data of the participation bias. So this broader study is a response to this need.
- In August 2017, an online demographic survey of OSM contributors was conducted. The link to the survey was distributed globally via both the OSM user diaries and five English language talk mailing lists (English being the accepted de facto language of OSM). Prize draw of 60 Amazon vouchers as incentive to participate 326 responses, 33 excluded on the grounds of duplication, invalid username or user having no edits in OSM 293 remaining responses. 38 were from women and 255 from men. Graph. 13:2 male to female ratio. So compared to Nama’s study which received a 24:1 male to female response ratio this survey attracted a greater response from the female OSM cohort five demographic indicators plus username Important because: enabled gender to be assigned to a series of tagging and editing contributions, openly accessible via pascal neis' wiki page ‘how did you contribute to OSM’ (hdyc)
- we have used 19 different variables from this site.
- Given the 4.2 million registered users the responses rate was perhaps small but representative sample Distribution of contributions (days active and changesets) replicated ‘long tail effect’ characteristic of crowdsourcing projects – small number of user contributing greatest proportion of activity and vice and versa
- If we look further at our sample of users in terms of how they are ranked for the editing nodes, ways and relations, The lower range values suggests our male respondents are amongst some of the highest ranking contributors, whereas female respondents are ranked considerably lower. And without the user data for he higher rankings we don’t know whether there are highest ranking female contributors or not. However, given a community of 4.2 million users these rankings can still be deemed relatively high as these median values are still in the top 1%.
- 19 different variables from the how did you contribute user page divided into 3 different measures of contributor behaviour: Activity, Editing and Tagging. Used the median values for each variable
- Looking at our first set of results: Activity was measured on number of days active + number of changesets. Looking first at the graph on the left which charts the median values for the number of days active we can see that men are active on almost two and a half times as many days as women Looking at the right hand graph, again, Men contribute almost twice as many changesets as their female counterparts And if we interrogate this further what this means is that not only are men more active than their female counterparts but on the days that women are editing they contribute a greater number of changesets during that period of activity. Studies which have explored the criteria which influence participation in VGI, find that these include competing demands on time (including the difference in caring responsibilities) and the perceived necessity of advanced GIS skills (Steinmann et al., 2013; Schmidt et al., 2013).
- So, moving on to editing behaviours it starts to get slightly more interesting with differences observed through a couple of different variables or groups thereof. The raw data shows here that in all nine of the categories, men made more edits so men are more active as OSM editors of nodes, ways and relations interesting outliers here with women contributing very little in the modification and deletion of relations split the results into two different typologies. Object focus and ‘mode’ of editing
- So, for object focus, ‘nodes’ dominated the objects of interest (over ways and relations) And these graphs show approximately 85% of activity for both groups focused on this object with the remainder, around 15% focused on the editing of ways but less than 1% focused on relations
- 1. Creating of edits (over modifying and deleting) dominating the mode of editing for both groups 2. But the stand out feature here is that men were much more likely to ‘modify’ objects than women although women did demonstrate higher confidence values in the deletion of edits.
- Firstly what we can see here are much lower values for tagging editing Secondly, again, reinforcing the common trend throughout the analysis men contribute many more tagging edits than women and this was statistically significant and again reinforces the results for activity and editing that men make more contributions. However, once we chart these values more nuanced differences are revealed:
- However, when charting the median values for tagging, more variation in the preferences of the two groups was revealed. And the big difference here is that women are much more likely to add labels in the ‘buildings’ category than men, whereas the greatest volume of men’s tagging was in the ‘highway’ category. However, men and women demonstrate similarities in their preference for these two categories combined: The six remaining categories constituted only 9% of female tagging, whereas the same proportion for men totalled 26%, demonstrating a higher level of variance in the topographical features that men chose to edit.
- As well as demonstrating that men are significantly more active than their female counterparts in terms of the number of days active and volume of contributions made, men and women demonstrate variance in their of modes of editing, with men demonstrating higher levels of modifying than women. Differences are also observed in gendered preferences for tagging categories for topographical features, with women demonstrating a preference for buildings whereas men show greater variance in their feature tagging preferences. Further analysis on these empirically observed variations has been conducted in the context of gendered contributions in humanitarian mapping activities in Malawi which Peter and I will be presenting at AGILE in June.
- Lastly I'd like to thank Pascal Neis for creating the data but of course my respondents who provided otherwise unrecorded information.