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 Volunteered Geographic Information: Interpretation, Visualisation and Social Computing (VGIscience)

2018 ◽  
Vol 1 ◽  
pp. 1-5 ◽  
Author(s):  
Dirk Burghardt ◽  
Wolfgang Nejdl ◽  
Jochen Schiewe ◽  
Monika Sester
Keyword(s):  
Visual Analysis ◽  
Spatial Information ◽  
Volunteered Geographic Information ◽  
Scientific Basis ◽  
Geographic Information ◽  
Sensor Data ◽  
Short Paper ◽  
German Research Foundation ◽  
Knowledge Presentation ◽  
Large Heterogeneity

In the past years Volunteered Geographic Information (VGI) has emerged as a novel form of user-generated content, which involves active generation of geo-data for example in citizen science projects or during crisis mapping as well as the passive collection of data via the user’s location-enabled mobile devices. In addition there are more and more sensors available that detect our environment with ever greater detail and dynamics. These data can be used for a variety of applications, not only for the solution of societal tasks such as in environment, health or transport fields, but also for the development of commercial products and services. The interpretation, visualisation and usage of such multi-source data is challenging because of the large heterogeneity, the differences in quality, the high update frequencies, the varying spatial-temporal resolution, subjective characteristics and low semantic structuring.<br> Therefore the German Research Foundation has launched a priority programme for the next 3&amp;ndash;6 years which will support interdisciplinary research projects. This priority programme aims to provide a scientific basis for raising the potential of VGI- and sensor data. Research questions described more in detail in this short paper span from the extraction of spatial information, to the visual analysis and knowledge presentation, taking into account the social context while collecting and using VGI.

A Generic Spatial OLAP Model for Evaluating Natural Hazards in a Volunteered Geographic Information Context

2016 ◽  
pp. 485-501
Author(s):  
Sandro Bimonte ◽  
Omar Boucelma ◽  
Olivier Machabert ◽  
Sana Sellami
Keyword(s):  
Natural Hazards ◽  
Spatial Data ◽  
Evaluation Model ◽  
Data Gathering ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Sensor Data ◽  
Application Development ◽  
Online Analysis ◽  
Spatial Data Warehouses

Spatial data warehouses (SDW) and spatial OLAP (SOLAP) systems are well-known business intelligence technologies that aim to support a multidimensional and online analysis for a large volume of geo-referenced datasets. SOLAP systems are already used in the context of natural hazards for analyzing sensor data and experts' measurements. Recently, new data gathering tools coined as volunteered geographic information systems (VGI) have been adopted especially by non-expert users. Hence, (spatial) application development is facing a new challenge, which is the integration of expert-oriented data with citizen-provided data. In this paper, we propose a new generic spatio-multidimensional model based on the question/answer risk evaluation model that allows the integration of VGI data with classical SDW and SOLAP systems for the online analysis of natural hazards monitored by volunteers.

A Generic Spatial OLAP Model for Evaluating Natural Hazards in a Volunteered Geographic Information Context

2014 ◽  
Vol 5 (4) ◽  
pp. 40-55
Author(s):  
Sandro Bimonte ◽  
Omar Boucelma ◽  
Olivier Machabert ◽  
Sana Sellami
Keyword(s):  
Natural Hazards ◽  
Spatial Data ◽  
Evaluation Model ◽  
Data Gathering ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Sensor Data ◽  
Application Development ◽  
Online Analysis ◽  
Spatial Data Warehouses

Spatial data warehouses (SDW) and spatial OLAP (SOLAP) systems are well-known business intelligence technologies that aim to support a multidimensional and online analysis for a large volume of geo-referenced datasets. SOLAP systems are already used in the context of natural hazards for analyzing sensor data and experts' measurements. Recently, new data gathering tools coined as volunteered geographic information systems (VGI) have been adopted especially by non-expert users. Hence, (spatial) application development is facing a new challenge, which is the integration of expert-oriented data with citizen-provided data. In this paper, we propose a new generic spatio-multidimensional model based on the question/answer risk evaluation model that allows the integration of VGI data with classical SDW and SOLAP systems for the online analysis of natural hazards monitored by volunteers.

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

Assessing The Quality of Water Depth Derived From Volunteered Geographic Information (VGI) For Flood Monitoring. A Case Study of Hurricane Harvey In Harris County, Texas

2021 ◽  
Author(s):  
Abdullatif Alyaqout ◽  
T. Edwin Chow ◽  
Alexander Savelyev
Keyword(s):  
Water Depth ◽  
Temporal Distribution ◽  
Volunteered Geographic Information ◽  
Temporal Variations ◽  
Geographic Information ◽  
Text Messages ◽  
Quality Of Water ◽  
Significant Difference ◽  
Flood Monitoring

Abstract The primary objectives of this study are to 1) assess the quality of each volunteered geographic information (VGI) data modality (text, pictures, and videos), and 2) evaluate the quality of multiple VGI data sources, especially the multimedia that include pictures and videos, against synthesized water depth (WD) derived from remote sensing (RS) and authoritative data (e.g. stream gauges and depth grids). The availability of VGI, such as social media and crowdsourced data, empowered the researchers to monitor and model floods in near-real-time by integrating multi-sourced data available. Nevertheless, the quality of VGI sources and its reliability for flood monitoring (e.g. WD) is not well understood and validated by empirical data. Moreover, existing literature focuses mostly on text messages but not the multimedia nature of VGI. Therefore, this study measures the differences in synthesized WD from VGI modalities in terms of (1) spatial and (2) temporal variations, (3) against WD derived from RS, and (4) against authoritative data including (a) stream gauges and (b) depth grids. The results of the study show that there are significant differences in terms of spatial and temporal distribution of VGI modalities. Regarding VGI and RS comparison, the results show that there is a significant difference in WD between VGI and RS. In terms of VGI and authoritative data comparison, the analysis revealed that there is no significant difference in WD between VGI and stream gauges, while there is a significant difference between the depth grids and VGI.

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