The Life Cycle of Volunteered Geographic Information (VGI) Contributors: the OpenStreetMap Example
Bibliographic record
Abstract
GIScience 2016 Short Paper Proceedings The Life Cycle of Volunteered Geographic Information (VGI) Contributors: the OpenStreetMap Example D. Begin 1 , R. Devillers 1,2 , S. Roche 2 Department of Geography, Memorial University, St. John’s (NL), Canada Email:{d.begin; rdeville}@mun.ca Centre de recherche en geomatique, Universite Laval, Quebec (QC), Canada Email: stephane.roche@scg.ulaval.ca 1. Introduction The Web 2.0 changed the way Internet users interact with knowledge (Gore 1998; Goodchild 2007) by allowing knowledge sharing through various online systems (e.g. Wikipedia). In GIScience, Volunteered Geographic Information (VGI) has attracted the attention of scholars due to its ability to crowdsource geographic information potentially useful in many contexts (Haklay 2014; Arsanjani et al. 2015). Classifications of VGI contributors have been proposed, based on users’ motivation (Coleman et al. 2009) or on the volume of their contributions (Panciera et al. 2010; Neis and Zipf 2012). Existing studies show that the nature of the contributions broadens with the time spent in a project (Kim 2000; Panciera et al. 2009) but none clearly linked them to the timespans of the different stages in the life cycle of contributors. This paper presents the first detailed analysis of the time over which contributors participate to a VGI project by using OpenStreetMap (OSM) data, identifying sets of contributors that share similar temporal patterns of contributions, and discussing the potential impacts on contributions. 2. Contributors’ Timespan Distribution While OSM data can be accessed by anyone, only registered users can edit the database. Once registered, no mechanism identifies users that stop contributing to the project. We define a ‘registered user’ as someone that created an OSM account, while a ‘contributor’ is a registered user that started at least one editing session (i.e. a changeset). ‘Contributors' timespan’ refers to the timespan between a contributor’s first and last edit. All the transactions made in OSM until September 1, 2014, were extracted and loaded into a PostgreSQL 9.3 database. Statistical analyses and visualizations were performed using the R 3.2.1 software. A first analysis compared cumulative OSM registered users with actual contributors, creating daily Contributors/Registered Users ratios (Figure 1). Ratios reveal wide variations over time, ranging from 6% to 47%, for an average of 30.9%. Results support Neis and Zipf (2012) findings that only a third of registered users eventually become contributors. A complementary cumulative distribution function (CCDF) of contributors’ timespan was also generated (Figure 2). It represents the proportion of contributors who edited the database for a similar period of time or longer. Five pivotal points were identified based on this figure and on additional analyses. A first pivotal point is found at about one hour of contributions, where 15% of participants stopped contributing in a matter of seconds. This abrupt break in the curve represents new contributors that made only a few edits, or even none, before the OSM API automatically closes their one and only editing session left idle for an hour. The proportion of OSM users who contributed data keeps decreasing rapidly for about an hour then it slows down until it reaches our second pivotal point after 24 hours (one day). Analyses show that 60% of contributors did not edit data beyond this point, a proportion
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".