The Life Cycle of Volunteered Geographic Information (VGI) Contributors: the OpenStreetMap Example
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».