Using textual volunteered geographic information to model nature-based activities: A case study from Aotearoa New Zealand

Journal of Spatial Information Science ◽

10.5311/josis.2021.23.157 ◽

2021 ◽

pp. 25-63

Author(s):

Ekaterina Egorova

Keyword(s):

Volunteered Geographic Information ◽

Online Reviews ◽

Temporal Variations ◽

Geographic Information ◽

Training Dataset ◽

Environment Interaction ◽

Human Environment ◽

Urban Settings ◽

Fine Grained ◽

Spatio Temporal

A boom in volunteered geographic information has led to extensive data-driven exploration and modeling of places. While many studies have used such data to explore human-environment interaction in urban settings, few have investigated natural, non-urban settings. To address this gap, this study systematically explores the content of online reviews of nature-based recreation activities, and develops a fine-grained hierarchical model that includes 28 aspects grouped into three main domains: activity, settings, and emotions/cognition. It further demonstrates how the model can be used to explore the variation in recreation experiences across activities, setting the stage for the analysis of the spatio-temporal variations in recreation experiences in the future. Importantly, the study provides an annotated corpus that can be used as a training dataset for developing methods to automatically capture aspects of recreation experiences in texts.

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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

10.21203/rs.3.rs-644550/v1 ◽

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090497 ◽

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.

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Environmental Noise Sensing Approach Based on Volunteered Geographic Information and Spatio-Temporal Analysis with Machine Learning

Computational Science and Its Applications -- ICCSA 2016 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-42089-9_7 ◽

2016 ◽

pp. 95-110 ◽

Cited By ~ 1

Author(s):

Miguel Torres-Ruiz ◽

Juan H. Juárez-Hipólito ◽

Miltiadis Demetrios Lytras ◽

Marco Moreno-Ibarra

Keyword(s):

Machine Learning ◽

Volunteered Geographic Information ◽

Temporal Analysis ◽

Environmental Noise ◽

Geographic Information ◽

Spatio Temporal

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Dimensions of the Use of Volunteered Geographic Information in Mass Crisis Events

10.33915/etd.5348 ◽

2015 ◽

Author(s):

◽

Xiannian Chen ◽

Keyword(s):

Rural Communities ◽

Real Time ◽

Disaster Management ◽

Volunteered Geographic Information ◽

Temporal Analysis ◽

Geographic Information ◽

Web Gis ◽

Kent State University ◽

Spatio Temporal ◽

Mass Evacuation

Recent studies have suggested that catastrophic events that trigger mass evacuation require surrounding communities to be well-prepared to act as ingress or pass-through areas for potential evacuees; however surrounding rural communities may have insufficient disaster-related logistical resources. In the response phase of disaster management, officials must be able to deploy resources to demand locations in types and quantities based on real-time requirements. Effective cross-jurisdictional disaster management needs real-time information, which is usually unavailable from official, authoritative sources. Conversely, VGI (volunteered geographic information) has the capability to provide real-time and local information in disaster management. This study investigates the possibility of utilizing real-time or near real-time VGI in mass evacuation scenarios. The study identifies a potential VGI data source, Tweets from Twitter and how to search for, discover and select relevant Tweets. The dissertation proposes research methods for harvesting, managing live Tweets and saving them to a distributed geodatabase for further spatio-temporal analysis and dissemination to users, such as responders and evacuees.;The study implements a Web GIS application, which includes a tweets discovery component, a geo-tagged tweets mapping component, and an online geo-tagged tweets operation component. The major research goals include designing an application programing interface (API) to harvest relevant Tweets and implement a distributed geodatabase system for storage, analysis, and display of the harvested Tweets so that vital information can be distributed in near real-time. Two case studies, based on Super Storm Sandy in 2012 and a shooting at Kent State University in 2014, were used to evaluate the pros and cons of Tweets from Twitter for response in emergency management and offered prototypes for the development of the final on-line Web GIS.

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Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8010029 ◽

2019 ◽

Vol 8 (1) ◽

pp. 29 ◽

Cited By ~ 10

Author(s):

Tengfei Yang ◽

Jibo Xie ◽

Guoqing Li ◽

Naixia Mou ◽

Zhenyu Li ◽

...

Keyword(s):

Data Mining ◽

Social Media ◽

Big Data ◽

Temporal Analysis ◽

Geographic Information ◽

Disaster Mitigation ◽

Emotional Information ◽

Fine Grained ◽

Big Data Mining ◽

Spatio Temporal

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.

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