Utilizing fuzzy set theory to assure the quality of volunteered geographic information

Yan, Yingwei; Feng, Chen‐Chieh; Wang, Yichen

doi:10.1007/s10708-016-9699-x

Cited by 16 publications

(14 citation statements)

References 40 publications

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“…More robust VGI quality assurance approaches are therefore needed to satisfy the diverse scenarios of VGI-based IPM. Fortunately, we have preliminarily developed an expert system based on fuzzy set theory to assess the quality of volunteered pest monitoring data (quantitative) [54]; and we have developed image recognition techniques to assist users to correctly identify pest species through taking photos of observed species (https://cosmic.nus.edu.sg/). Future research will extend the work to the quality assurance of qualitative and quantitative-qualitative combined VGI.…”

Section: Discussionmentioning

confidence: 99%

“…This case study was considered as an initial step of this line of research, facilitated-VGI approach appeared to be pragmatic because it was relatively more controllable in terms of data quality. Note that although we have previously developed a fuzzy expert system to assess the quality of volunteered pest data [54], it is good at informing us the trustworthiness rather than the binary true or false of the data. Therefore, a facilitated-VGI approach was adopted in this case study to assure the correctness of famer-generated pest infestation reports.…”

Section: Study Setupmentioning

confidence: 99%

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Towards Enhancing Integrated Pest Management Based on Volunteered Geographic Information

Yan

Feng

Chang

2017

IJGI

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Integrated pest management (IPM) involves integrating multiple pest control methods based on site information obtained through inspection, monitoring, and reports. IPM has been deployed to achieve the judicious use of pesticides and has become one of the most important methods of securing agricultural productivity. Despite the efforts made to strengthen IPM during the past decades, overuse as well as indiscriminate use of pesticides is still common. This problem is particularly serious in underserved farming communities which suffer from ineffectiveness with respect to pest management information collection and dissemination. The recent development of volunteered geographic information (VGI) offers an opportunity to the general public to create and receive ubiquitous, cost-effective, and timely geospatial information. Therefore, this study proposes to enhance IPM through establishing a VGI-based IPM. As a starting point of this line of research, this study explored how such geospatial information can contribute to IPM enhancement. Based on this, a conceptual framework of VGI interaction was built to guide the establishment of VGI-based IPM. To implement VGI-based IPM, a mobile phone platform was developed. In addition, a case study was conducted in the town of Shuibian in Jiangxi province of China to demonstrate the effectiveness of the proposed approach. In the case study, by analyzing infestation incidents of an overwintering outbreak of striped rice stem borers voluntarily reported by farmers through mobile phones, spatiotemporal infestation patterns of the borers throughout the study area were revealed and disseminated to the farmers. These patterns include the dates and degree-days the pest infestations intensified, and the orientation or spatial structural variations of the clustering of the infestations. This case study showcased the unique merit of VGI in enhancing IPM, namely the acquisition of previously unrecorded spatial data in a cost-effective and real-time manner for discovering and disseminating previously unknown pest management knowledge.

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Section: Discussionmentioning

confidence: 99%

Section: Study Setupmentioning

confidence: 99%

Towards Enhancing Integrated Pest Management Based on Volunteered Geographic Information

Yan

Feng

Chang

2017

IJGI

Self Cite

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“…• Quantitative measures : Kappa index method [114,218,219], completeness and correctness [208], and confusion matrix [114,208].…”

Section: Quality Indicator Methods Usedmentioning

confidence: 99%

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Sehra

Singh

2017

IJGI

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OpenStreetMap (OSM), based on collaborative mapping, has become a subject of great interest to the academic community, resulting in a considerable body of literature produced by many researchers. In this paper, we use Latent Semantic Analysis (LSA) to help identify the emerging research trends in OSM. An extensive corpus of 485 academic abstracts of papers published during the period 2007-2016 was used. Five core research areas and fifty research trends were identified in this study. In addition, potential future research directions have been provided to aid geospatial information scientists, technologists and researchers in undertaking future OSM research.

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“…Figure 2 shows the distributions of the locations in the three datasets. Crisp rules [7,16,26], fuzzy rules [47][48][49], machine learning and deep learning approaches [22,50], and hybrid approaches [51] are four major approaches for calculating the final score based on the various input contextual features. The sum rule belongs to the crisp rules, it is a simple and conventional fusion method.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

Liu

Zhang

Liu

et al. 2020

IJGI

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With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and social influence, geographical influence has also been widely researched in location recommendation. Kernel density estimation (KDE) is a key method in modeling geographical influence. However, most current studies based on KDE do not consider the problems of influence range and outliers on users’ check-in behaviors. In this paper, we propose a method to exploit geographical and synthetic social influences (GeSSo) on location recommendation. GeSSo uses a kernel estimation approach with a quartic kernel function to model geographical influences, and two kinds of weighted distance are adopted to calculate bandwidth. Furthermore, we consider the social closeness and connections between friends, and a synthetic friend-based recommendation method is introduced to model social influences. Finally, we adopt a sum framework which combines user’s preferences on a location with geographical and social influences. Extensive experiments are conducted on three real-life datasets. The results show that our method achieves superior performance compared to other advanced geo-social recommendation techniques.

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Utilizing fuzzy set theory to assure the quality of volunteered geographic information

Cited by 16 publications

References 40 publications

Towards Enhancing Integrated Pest Management Based on Volunteered Geographic Information

Towards Enhancing Integrated Pest Management Based on Volunteered Geographic Information

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

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