scholarly journals Quality Verification of Volunteered Geographic Information Using OSM Notes Data in a Global Context

2020 ◽  
Vol 9 (6) ◽  
pp. 372
Author(s):  
Toshikazu Seto ◽  
Hiroshi Kanasugi ◽  
Yuichiro Nishimura
Keyword(s):  
Quality Management ◽  
Geographical Distribution ◽  
Regional Differences ◽  
User Interaction ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Positional Accuracy ◽  
New Approach ◽  
Global Context

Although the data obtained from volunteered geographic information (VGI) are inherently different from public surveys, the quantity of the data are vast and the quality of the data are often poor. To improve the quality of VGI data, the positional accuracy and diversity and interaction of the number of users involved in the regional generation of the data are important. This research proposes a new approach for the accumulation of OpenStreetMap (OSM) data by using OSM Notes and attempts to analyze the geographical distribution and the characteristics of the contents of the contributions, quantitatively and qualitatively. Subsequently, the results demonstrated regional differences in OSM Notes, but it provided users with an understanding of the new features of quality management in OSM, even in regions where OSM activities are not necessarily active. In addition, it was also possible to discover new factors such as the time transition required for the correction and contribution of anonymous users. These results are expected to serve as a tool for users to communicate with each other to resolve data bugs that exist in OSM and provide future researchers with examples of user interaction in global OSM activities.

scholarly journals ASSESSMENT OF COMPLETENESS AND POSITIONAL ACCURACY OF LINEAR FEATURES IN VOLUNTEERED GEOGRAPHIC INFORMATION (VGI)

2015 ◽  
Vol XL-1-W5 ◽  
pp. 169-174
Author(s):  
M. Eshghi ◽  
A. A. Alesheikh
Keyword(s):  
Spatial Data ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Positional Accuracy ◽  
Linear Feature ◽  
Almost All ◽  
Information Collecting ◽  
Correct Data ◽  
Official Data

Recent advances in spatial data collection technologies and online services dramatically increase the contribution of ordinary people to produce, share, and use geographic information. Collecting spatial data as well as disseminating them on the internet by citizens has led to a huge source of spatial data termed as Volunteered Geographic Information (VGI) by Mike Goodchild. Although, VGI has produced previously unavailable data assets, and enriched existing ones. But its quality can be highly variable and challengeable. This presents several challenges to potential end users who are concerned about the validation and the quality assurance of the data which are collected. Almost, all the existing researches are based on how to find accurate VGI data from existing VGI data which consist of a) comparing the VGI data with the accurate official data, or b) in cases that there is no access to correct data; therefore, looking for an alternative way to determine the quality of VGI data is essential, and so forth. In this paper it has been attempt to develop a useful method to reach this goal. In this process, the positional accuracy of linear feature of Iran, Tehran OSM data have been analyzed.

Quality Evaluation of Volunteered Geographic Information

Crowdsourcing ◽  
2019 ◽  
pp. 1173-1201
Author(s):  
Hongyu Zhang ◽  
Jacek Malczewski
Keyword(s):  
Quality Evaluation ◽  
Evaluation Process ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Positional Accuracy ◽  
Product Selection ◽  
Geographic Space ◽  
Large Size

A large amount of crowd-sourced geospatial data have been created in recent years due to the interactivity of Web 2.0 and the availability of Global Positioning System (GPS). This geo-information is typically referred to as volunteered geographic information (VGI). OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of quality of geo-information generated by OSM has become a trending research topic because of the large size of the dataset and the inapplicability of Linus' Law in a geospatial context. This chapter systematically reviews the quality evaluation process of OSM, and demonstrates a case study of London, Canada for the assessment of completeness, positional accuracy and attribute accuracy. The findings of the quality evaluation can potentially serve as a guide of cartographic product selection and provide a better understanding of the development of OSM quality over geographic space and time.

Quality Evaluation of Volunteered Geographic Information

2017 ◽  
pp. 19-46 ◽  
Author(s):  
Hongyu Zhang ◽  
Jacek Malczewski
Keyword(s):  
Quality Evaluation ◽  
Evaluation Process ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Positional Accuracy ◽  
Product Selection ◽  
Geographic Space ◽  
Large Size

A large amount of crowd-sourced geospatial data have been created in recent years due to the interactivity of Web 2.0 and the availability of Global Positioning System (GPS). This geo-information is typically referred to as volunteered geographic information (VGI). OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of quality of geo-information generated by OSM has become a trending research topic because of the large size of the dataset and the inapplicability of Linus' Law in a geospatial context. This chapter systematically reviews the quality evaluation process of OSM, and demonstrates a case study of London, Canada for the assessment of completeness, positional accuracy and attribute accuracy. The findings of the quality evaluation can potentially serve as a guide of cartographic product selection and provide a better understanding of the development of OSM quality over geographic space and time.

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.

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.

An Integrated Method for Assessing the Text Content Quality of Volunteered Geographic Information in Disaster Management

2017 ◽  
Vol 9 (2) ◽  
pp. 1-17
Author(s):  
Kuo-Chih Hung ◽  
Mohsen Kalantari ◽  
Abbas Rajabifard
Keyword(s):  
Disaster Management ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Content Quality ◽  
Integrated Method ◽  
Needed Information ◽  
Text Content ◽  
Flooding Events

Volunteered geographic information (VGI) has the potential to provide much-needed information for emergency management stakeholders. However, stakeholders often lack scalability to identify useful and high-quality text content from the often-overwhelming amount of information. To solve this problem, most studies have concentrated on using text-related features in supervised learning models to classify text contents. This article proposes an assumption that the geographic attributes of VGI can be integrated into the model as features for enhancing the model's performance. To evaluate this assumption, the authors developed a case study based on VGI collected from two flooding events in Brisbane. They validated the accuracy of associated geographic coordinates and defined the geographic features relevant to the flood phenomenon. From their experiments, model based on this integrated method can have better performance in comparison with the model trained from the text-related features. The results suggest great potential for using the integrated method to harvest useful VGI for the needs of disaster management.

scholarly journals MAPPING THE WORLD – A NEW APPROACH FOR VOLUNTEERED GEOGRAPHIC INFORMATION IN THE CLOUD

2015 ◽  
Vol XL-6/W1 ◽  
pp. 9-10 ◽  
Author(s):  
M. S. Moeller ◽  
S. Furhmann
Keyword(s):  
Volunteered Geographic Information ◽  
Developed Countries ◽  
Geographic Information ◽  
Red Cross ◽  
Less Developed Countries ◽  
American Red Cross ◽  
Short Introduction ◽  
New Approach ◽  
The World ◽  
Entire World

The OSM project provides a geodata basis for the entire world under the CC-SA licence agreement. But some parts of the world are mapped more densely compared to other regions. However, many less developed countries show a lack of valid geo-information. Africa for example is a sparsely mapped continent. During a huge Ebola outbreak in 2014 the lack of data became apparent. Help organization like the American Red Cross and the Humanitarian Openstreetmap Team organized mappings campaign to fill the gaps with valid OSM geodata. This paper gives a short introduction into this mapping activity.

scholarly journals Towards Development of a Real-Time Point Feature Quality Assessment Method for Volunteered Geographic Information Using the Internet of Things

2021 ◽  
Vol 10 (3) ◽  
pp. 151
Author(s):  
Sepehr Honarparvar ◽  
Mohammad Reza Malek ◽  
Sara Saeedi ◽  
Steve Liang
Keyword(s):  
Internet Of Things ◽  
Quality Assessment ◽  
Real Time ◽  
Volunteered Geographic Information ◽  
Assessment Method ◽  
Geographic Information ◽  
Positional Error ◽  
Matched Data ◽  
Quality Map

One of the most important challenges of volunteered geographic information (VGI) is the quality assessment. Existing methods of VGI quality assessment, either assess the quality by comparing a reference map with the VGI map or deriving the quality from the metadata. The first approach does not work for a real-time scenario and the latter delivers approximate values of the quality. Internet of Things (IoT) networks provide real-time observations for environment monitoring. Moreover, they publish more precise information than VGI. This paper introduces a method to assess the quality of VGI in real-time using IoT observations. The proposed method filters sensor observation outliers in the first step. Then it matches sensors and volunteers’ relationships in terms of location, time, and measurement type similarity using a hypergraph model. Then the quality of matched data is assessed by calculating positional and attribute accuracy. To evaluate the method, VGI data of the water level and quality in Tarashk–Bakhtegan–Maharlou water basin is studied. A VGI quality map of the data is assessed by a referenced authoritative map. The output of this step is a VGI quality map, which was used as a reference to check the proposed method quality. Then this reference VGI quality map and the proposed method VGI quality map are compared to assess positional and attribute accuracy. Results demonstrated that 76% of the method results have less than 20 m positional error (i.e., difference with the reference VGI quality map). Additionally, more than 92% of the proposed method VGI data have higher than 90% attribute accuracy in terms of similarity with the reference VGI quality map. These findings support the notion that the proposed method can be used to assess VGI quality in real-time.

Sign in / Sign up

Export Citation Format

Share Document