Temporal Analysis on Contribution Inequality in OpenStreetMap: A Comparative Study for Four Countries

Abstract

Contribution inequality widely exists in OpenStreetMap (OSM), which means that most data come from a minority of the contributors, while the majority only accounts for a small percentage of data. This phenomenon is of great importance to understanding from where the data come and how the project evolves. The investigation in this paper is dedicated to answering the following questions: How does contribution inequality change over time in OSM? Which group of contributors plays a more important role in influencing trends in contribution inequality: the "vocal minority" or the "silent majority"? To answer the first question, we provide overall measurements for contribution inequality using the Lorenz curve and the Gini coefficient. To answer the second question, we use quantile-based classifying strategy to analyze structural changes in the community, and use the Mann-Whitney-Wilcoxon test to analyze productivity changes. Our case study shows that in countries without significant imports, contributions become more unequal over time. This trend is consistent with the rapid expansion of the silent majority, even though other classes of contributors also grow at a slower pace. On the other hand, contribution inequality fluctuates a lot in countries with huge imports, and agrees well with the productivity changes in the vocal minority.

The State of Mapillary: An Exploratory Analysis

“…Since Mapillary images include information about time and geographic coordinates, social and physical behavior of humans can be studied on a worldwide scale through an examination of user contribution patterns [3]. Similar to other VGI data, user contribution patterns and the data accumulation process are the fundamental differences between Mapillary and traditional street view data platforms [4], and these analyses can provide a basis for answering questions about the quantity, quality, and type of Mapillary data [5,6]. Walden-Schreiner et al [7] used Flickr photos to assess seasonal patterns of visitor use in protected mountain areas, providing a basis for the effective management of protected areas.…”

“…In addition, knowing about the user's contribution patterns (walking, cycling, and driving) and the equipment used for contribution can give us a more targeted understanding of the data composition of Mapillary. Contribution inequality has substantial and complicated impacts on data quality and on the developments of the project [4]. We expect the emergence of a small set of users who do most of the work in the Mapillary community.…”

Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information

“…Many comparative studies have shown that crowdsourced geographic information can be as good, if not better, than data from authoritative sources [58,59]. A comprehensive literature overview of the latest developments in crowdsourced geographic information research is presented in Reference [60], with a focus on trends related to OpenStreetMap while many others have discussed the quality of this volunteer data source [61][62][63]. Of the topics selected by the authors for future research, they emphasize the areas of: Intrinsic data quality assessment, conflation methods which combine crowdsourced geographic information and other data sources, and the development of credibility, reputation, and trust methodologies for crowdsourced geographic information.…”

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

“…Open-source software, as a method for data collection and gaining knowledge, is increasingly becoming important within the field of natural hazards (Eckle et al, 2016;Klonner et al, 2016). For instance, OpenStreetMap (OSM) and other voluntary geographic information services help to create comprehensive databases of up-to-date geospatial data which also can be used for natural risk assessment (Schelhorn et al, 2014;Vaz and Arsanjani, 2015;Yang et al, 2016).…”