Anderson, Jennings

Sarkar, Dipto

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Proceedings of the Academic Track, State of the Map 2021 July 9-11, 2021 | Online ConferenceCommunity interactions in OSM editingDipto Sarkar1,* and Jennings Anderson21 Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada;diptosarkar@cunet.carleton.ca2 YetiGeoLabs, Montana, USA; jennings.anderson@gmail.com* Author to whom correspondence should be addressed.This abstract was accepted to the Academic Track of the State of the Map 2021 Conference afterpeer-review.OpenStreetMap (OSM) data is produced by a vibrant community of mappers. Thesegeospatial data produsers (a portmanteau of “producer” and “user” commonly used whendescribing members of peer-production communities) represent a plethora of individualswith different motivations, methods of contribution, and usage [1, 2]. Thus, the 1.6M OSMcontributors have been aptly described as a community of communities [3]. In recent years,corporate editing teams have introduced a new dynamic to the discussion of communities inOSM; editing teams hired by corporations, such as, Apple, Facebook, Microsoft, Uber, arecapable of contributing thousands of changesets each day, outpacing the average volunteercontributor [4, 5]. Additionally, corporate editors (CEs) tend to focus their editing onparticular types of map features. These two attributes of corporate editing can lead to CEsbreaking off into a siloed group of their own with little or no interaction with the rest of theeditors on the map.Previous research on the OSM community using network analysis methods showedthere was limited collaboration between editors with most objects being edited only a fewtimes [6]. Senior editors in particular perform a majority of the mapping work on their own,but do indeed interact with others through co-editing—where subsequent mappers edit thesame objects [7]. Since these studies were performed, the OSM community has grownsignificantly and the community dynamics have significantly evolved with more individualand organized participation, such as corporate editing.Here, we use a data driven approach to characterize the interactions between the CEsand the rest of the OSM community. We define interactions through editing patterns,constructing a network of interactions where each node represents an editor, and two nodesare connected by an edge if the two mappers have edited the same map object. If themapper represented by node A edits an object last edited by the mapper represented bynode B, then the edge connecting these nodes is directed from A to B. Thus, the node’sdegree is the number of co-editors of the node with the in-degree representing the mapperswho edited objects after the user and the out-degree representing the number of mappersediting objects before the user. The network can have multiple disconnected components asnot everyone co-edits with everyone else in the network. We utilized the OSM-Interactionstilesets to construct these networks [8]. OSM-Interactions vector tiles contain the editinghistory of all highway and building objects at zoom level 14. They include minor changes toSarkar, D. & Anderson, J. (2021). Community interactions in OSM editingIn: Minghini, M., Ludwing, C., Anderson, J., Mooney, P., Grinberger, A.Y. (Eds.). Proceedings of the Academic Track at the State ofthe Map 2021 Online Conference, July 09-11 2021, 6-8. Available at https://zenodo.org/communities/sotm-2021DOI: 10.5281/zenodo.5112211© 2021 by the authors. Available under the terms of the Creative Commons Attribution (CC BY 4.0) license. 6Proceedings of the Academic Track, State of the Map 2021 July 9-11, 2021 | Online Conferencethe geometry of objects in which only nodes are moved, but the parent way is left untouched.In this way, we are capturing the complete history of map objects in OSM, as opposed to justchanges to the basic OSM elements (primarily nodes or ways).In keeping with the objects which are primarily edited by CEs, our analysis focusesonly on highway and building objects for construction of the network. The nodes in thenetwork are further annotated with a binary category representing whether they are a CE ornot. We classify a mapper as being a CE or not by comparing usernames in the network tothe disclosed lists of usernames on a corporation’s OSM wiki or Github page.We focus on 4 locations: Egypt, Jamaica, Thailand, and Singapore. We createnetworks for each of these locations at 3 timepoints, 2015, 2017, and 2019 to characterizethe changes between over time. Thus, we constructed and analyzed 12 networks. Thelocations were chosen as they all have different groups of CEs active.Across all networks, the Largest Connected Component (LC) accounted for 94% of allnodes highlighting significant interactions amongst all mappers. The LC is the largest groupof connected nodes, meaning there exists a path between any two users via other co-editors.The more prolifit a mapper edits, the more likely they are to be a part of the LC. Within the LC,the rate of growth of CE nodes exceeds the rate of growth of non-CE nodes at a rate of 11:1between 2015 and 2019. However, both types of editors (CE and non-CE) have a comparablenumber of in and out degrees in most networks, indicating that they edit other people’s workand have their work edited at a similar rate. Figure 1 shows specific characteristics of eachnetwork:Figure 1. Number of edges in each network each year. Edges are colored by the type of source andtarget nodes. Orange represents corporate editors co-editing each other, green represents corporateeditors editing the work of non-corporate editors, blue represents the work of non-corporate editorsediting corporate editors, and purple represents volunteer co-editing activity.7Proceedings of the Academic Track, State of the Map 2021 July 9-11, 2021 | Online ConferenceIn terms of who edits whose work, CE’s edit other CE’s work most often, butinteraction between CEs and non-CEs have also grown through time, keeping the networkconnected (Figure 1). The massive growth in the orange edges in Figure 1 in 2019 representsmany CEs editing each other. The increase in the green bars shows that corporate editorsare also editing (at a much lesser rate) the existing work on the map—however, the blue areashows that volunteers are also editing corporate-sponsored work at a similarly increasingrate.With regards to age of the mappers (calculated in terms of their enrollment date inOSM) and the volume of edits they perform, younger mappers in both groups tend to editothers' work at a higher rate than senior mappers, but there is more variation in thesestatistics for non-CE mappers. This is a finding contrary to previous research on editinginteraction patterns mentioned above. Additionally, characterizing the time between editsshows that edits made by CE’s persist for a slightly shorter duration than edits made bynon-CE, primarily due to other CEs editing the same object soon after.In conclusion, the editing networks highlight the vibrancy of data co-production. Thevolunteer editor and CEs are interacting with each other's edits to produce the map. Theper-group interaction is nuanced and shows unique editing patterns which warrant furtherinvestigation. During the timespan of this study, the rate of growth of the CE community wasfaster than the non-CE community, but whether the pattern will hold over time and whetherother locations exhibit the same pattern will require further research.References[1] Budhathoki, N. R., & Haythornthwaite, C. (2013). Motivation for Open Collaboration: Crowd andCommunity Models and the Case of OpenStreetMap. American Behavioral Scientist, 57(5), 548-575.[2] Coleman, D. D. J., Georgiadou, Y., & Labonte, J. (2009). Volunteered geographic information: Thenature and motivation of produsers. International Journal of Spatial Data Infrastructures Research,4(1), 332-358.[3] Solís, P., Anderson, J., and Rajagopalan, S. (2020). Open Geospatial Tools for Humanitarian DataCreation, Analysis, and Learning through the Global Lens of YouthMappers. Journal of GeographicalSystems.[4] Anderson, J., Sarkar, D., & Palen, L. (2019). Corporate editors in the evolving landscape ofOpenStreetMap. ISPRS International Journal of Geo-Information, 8(5), 232.[5] Anderson, J., & Sarkar, D. (2020). Curious Cases of Corporations in OpenStreetMap. In: Minghini,M., Coetzee, S., Juhász, L., Yeboah, G., Mooney, P., & Grinberger, A. Y. (Eds.). Proceedings of theAcademic Track at the State of the Map 2020 Online Conference, 13-14.[6] Mooney, P., & Corcoran, P. (2012). How social is OpenStreetMap? In: Proceedings of the 15th AGILEInternational Conference on Geographic Information Science, 282-287.[7] Mooney, P., & Corcoran, P. (2014). Analysis of interaction and co-editing patterns amongstopenstreetmap contributors. Transactions in GIS, 18(5), 633-659.[8] Anderson, J (2020). Analyzing OpenStreetMap Contributions at Scale: Introducing osm-interactionsTilesets. In: Abramowicz, W., & Klein, G. (Eds.) Business Information Systems Workshops, BIS 2020International Workshops Lecture Notes in Business Information Processing, 394, 267-271.8

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