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.

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 A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information

2020 ◽  
Vol 9 (6) ◽  
pp. 385 ◽  
Author(s):  
Xiaoyi Yuan ◽  
Andrew Crooks ◽  
Andreas Züfle
Keyword(s):  
New York ◽  
Low Income ◽  
Topic Modeling ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Network Approach ◽  
Similarity Network ◽  
Textural Data ◽  
Restaurant Reviews

The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network approach enhances previous research involving place clustering using geo-textual information, which often simplifies relationships between places to be either in-cluster or out-of-cluster. To demonstrate our approach, we use as a case study in Manhattan (New York) that compares networks constructed from three different geo-textural data sources—TripAdvisor attraction reviews, TripAdvisor restaurant reviews, and Twitter data. The results showcase how the thematic similarity network approach enables us to conduct clustering analysis as well as node-to-node and node-to-cluster analysis, which is fruitful for understanding how places are connected through individuals’ experiences. Furthermore, by enriching the networks with geodemographic information as node attributes, we discovered that some low-income communities in Manhattan have distinctive restaurant cultures. Even though geolocated tweets are not always related to place they are posted from, our case study demonstrates that topic modeling is an efficient method to filter out the place-irrelevant tweets and therefore refining how of places can be studied.

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 Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests

Land ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 174
Author(s):  
Desheng Wang ◽  
A-Xing Zhu
Keyword(s):  
Random Forests ◽  
Cross Validation ◽  
Soil Mapping ◽  
High Quality ◽  
Integrated Method ◽  
Average Accuracy ◽  
Soil Information ◽  
Fold Cross Validation

Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM. However, the accuracy of the similarity-based approach is limited, and the performance of random forests is affected by the quality of the feature set. The objective of this study was to present a method for soil mapping by integrating the similarity-based approach and the random forests method. The Heshan area (Heilongjiang province, China) was selected as the case study for mapping soil subgroups. The results of the regular validation samples showed that the overall accuracy of the integrated method (71.79%) is higher than that of a similarity-based approach (58.97%) and random forests (66.67%). The results of the 5-fold cross-validation showed that the overall accuracy of the integrated method, similarity-based approach, and random forests range from 55% to 72.73%, 43.48% to 69.57%, and 54.17% to 70.83%, with an average accuracy of 66.61%, 57.39%, and 59.62%, respectively. These results suggest that the proposed method can produce a high-quality covariate set and achieve a better performance than either the random forests or similarity-based approach alone.

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