Building Social Networks in Volunteered Geographic Information Communities: What Contributor Behaviours Reveal About Crowdsourced Data Quality

2017 ◽  
pp. 125-131 ◽  
Author(s):  
Quy Thy Truong ◽  
Guillaume Touya ◽  
Cyril de Runz
Keyword(s):  
Social Networks ◽  
Data Quality ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Crowdsourced Data

scholarly journals Corporate Editors in the Evolving Landscape of OpenStreetMap

2019 ◽  
Vol 8 (5) ◽  
pp. 232 ◽  
Author(s):  
Jennings Anderson ◽  
Dipto Sarkar ◽  
Leysia Palen
Keyword(s):  
Data Quality ◽  
Road Networks ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Points Of Interest ◽  
The World ◽  
History Of ◽  
Multiple Uses ◽  
Map Data ◽  
Information Project

OpenStreetMap (OSM), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the OSM community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the OSM database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot OSM-QA-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the OSM community—and researchers—might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses.

scholarly journals The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

2020 ◽  
Vol 9 (9) ◽  
pp. 497
Author(s):  
Haydn Lawrence ◽  
Colin Robertson ◽  
Rob Feick ◽  
Trisalyn Nelson
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Spatial Scales ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Metric ◽  
Fine Grained ◽  
Multi Scale ◽  
Feasible Study

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.

scholarly journals Crowdsourced Data

2021 ◽  
pp. 63-67
Author(s):  
Karel Charvát ◽  
Michal Kepka
Keyword(s):  
Data Storage ◽  
Mobile Applications ◽  
User Authentication ◽  
Volunteered Geographic Information ◽  
Scientific Work ◽  
Geographic Information ◽  
Geospatial Data ◽  
Sensor Data ◽  
Crowdsourced Data ◽  
Server System

AbstractCrowdsourcing together with Volunteered Geographic Information (VGI) are currently part of  a broader concept – Citizens Science. The methods provide information on existing geospatial data or is a part of data collection from geolocated devices. They enable opening parts of scientific work to the general public. DataBio Crowdsourcing Solution is a combination of the SensLog server platform and HSLayers web and mobile applications. SensLog is a server system for managing sensor data, volunteered geographic information and other geospatial data. Web and mobile applications are used to collect and visualize SensLog data. SensLog data model builds on the Observations & Measurements conceptual model from ISO 19156 and includes additional sections, e.g., for user authentication or volunteered geographic information (VGI) collection. It uses PostgreSQL database with PostGIS for data storage and several API endpoints.

scholarly journals QualiOSM: An Architecture to Improve Data Completeness on OpenStreetMap

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Gabriel F. B. de Medeiros ◽  
Lívia C. Degrossi ◽  
Maristela Holanda
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Data Quality ◽  
Spatial Database ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Technical Knowledge ◽  
Urban Centers ◽  
Data Completeness

  OpenStreetMap (OSM) is a large spatial database in which geographic information is voluntarily contributed by thousands of users. In Geographic Information Systems (GIS), and more specifically, in Volunteered Geographic Information (VGI), as in the case of OSM, the issue of data completeness is a constant concern, since users without technical knowledge actively participate in the processes of including, editing and excluding data. Also in thecase of OSM, users can add information to the objects assigning special labels for them. These labels are popularly called tags, and the process of assigning them to objects contributes to improving the attribute completeness, an important metric of data quality. In this context, this article proposes the QualiOSM architecture, which generates an automatic tag adder with the purpose of improving the completeness of address information for OSM objects in Brazil, using the reverse geocoding tools Nominatim, CEP Aberto and the database from Correios. The QualiOSM architecture showed good results for improving the completeness of city, neighborhood and street information in OSM objects, especially in scenarios of large urban centers, where the level of mapping is usually better compared to scenarios in rural or peripheral environments.

scholarly journals CARTOGRAPHIC REPRESENTATION OF SPATIAL DATA QUALITY IN VGI FOR USERS WITH DIFFERENT SEMANTICS

2015 ◽  
Vol XL-3/W3 ◽  
pp. 81-84
Author(s):  
R. Esmaeili ◽  
F. Karimipour
Keyword(s):  
Data Quality ◽  
Spatial Data ◽  
Volunteered Geographic Information ◽  
Quality Parameters ◽  
Geographic Information ◽  
Formal Representation ◽  
Spatial Data Quality ◽  
Metadata Standards

Volunteered geographic information is constantly being added, edited or removed by users. Most of VGI users are not experts, thus formal representation of spatial data quality parameters through metadata standards does not efficiently communicate, as it may be interpreted differently by different users with different semantics. In addition, a user may not be able to decide on the relevant dataset for their in-hand application. In this paper, we propose providing VGI users with the spatial data quality parameters through simple cartographic representations, which is independent of users’ semantics. The problem is described and its implementation results for a simple case study are represented.

Volunteered geographic information

Geography ◽  
2014 ◽  
Vol 99 (3) ◽  
pp. 157-160
Author(s):  
Doreen S. Boyd ◽  
Giles M. Foody
Keyword(s):  
Volunteered Geographic Information ◽  
Geographic Information
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