Using Kappa methodology to consider volunteered geographic information in official land administration systems in developing countries

Moreri, Kealeboga Kaizer

doi:10.1007/s41324-019-00289-7

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…The quality of VGI hinders its utilization for official purposes such as in land administration. To minimize the ground-truthing of VGI to assess its quality, a Kappa methodology-based solution has been proposed in [38] by offering a proxy quality of VGI through agreement of consensus. Similarly, other proxy measures of VGI quality using trust and reputation modeling have been proposed by [39].…”

Section: Sdi and Vgimentioning

confidence: 99%

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Munir

Khayal

Tahir

2022

IJGI

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Spatial data infrastructures (SDIs) have been implemented for the last four decades in most countries. One of the key objectives of SDIs is to ensure the quick availability and accessibility of spatial data. The success of SDI depends on the underlying spatial datasets. Many developing countries such as Pakistan are facing problems in implementing SDI because of the unavailability of spatial data. Volunteered Geographic Information (VGI) is an alternate source for obtaining spatial data. Therefore, the question is what factors hamper the adoption of VGI for making it part of SDI in Pakistan. The intention behind this paper is to explore such factors as the key research question. To do so, we make use of the Technology–Organization–Environment (TOE) framework along with the partial least square structural equation model (PLS-SEM) to empirically analyze the factors impeding VGI from becoming part of SDI in the country. The study concludes that many technical, organizational, and environmental factors affect the adoption of VGI to be part of SDI in Pakistan.

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Section: Sdi and Vgimentioning

confidence: 99%

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Munir

Khayal

Tahir

2022

IJGI

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“…An approach to solving this could be comparing VGI and data from other sources, such as public registers. However, it may be problematic to deal with contradictions between public records and VGI as both sources may be incorrect [7], [17], [18], [21], [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Infrastructure Model Volunteered Geographic Infor-mation Social Assistance Program To Support eGovernment Case Study of Central Aceh District

Syahputra¹,

Alfitra²

2022

Int. J. Eng. Scie. and Inform. Technology.

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The government's role in providing social assistance to reduce poverty has been going very well. However, in practice, there are obstacles to collecting and verifying data on prospective beneficiaries, so fast, precise, and accurate information is needed through the participation of the Community or Community to provide information regarding potential beneficiaries—social assistance. This study's goal is to create and develop a model of the Volunteered Geographic Information (VGI) system involving the participation of the Community to determine the effectiveness of the distribution of social assistance by the government and to support the role of a transparent government or good government. This research method is descriptive research with quantitative and qualitative descriptive approaches. This system also refers to the design, development, and stages of the VGI system process suitable for a crowd or community-based approach. The expected result is a VGI-based geographic information system: First: Collect and observe data on social assistance recipients in Aceh Tengah district, Aceh Province. Second: Designing and implementing a VGI-based geographic information system. Third: System testing through two stages: system functional and non-functional testing. The final results of this research are a web-based VGI Social Assistance system model and, for volunteers, the Android-based VGI Social Assistance application.

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“…Quantitative measures are concerned with the ability of a product to satisfy implied and stated needs (ISO, 2013). These include completeness, consistency, positional accuracy (Jacobs, Satkin, Roman, Speyer, & Pless, 2007), thematic accuracy (Moreri, 2020), and temporal accuracy (Girres & Touya, 2010). In contrast, qualitative quality indicators are mainly used to express the quality overview of the data and these include purpose, usage (Mendoza, Poblete, & Castillo, 2010), and lineage (Van Oort & Bregt, 2005).…”

Section: Quality Assurance In Crowdsourcing Projectsmentioning

confidence: 99%

Volunteer reputation determination in crowdsourcing projects using latent class analysis

Moreri

2020

Transactions in GIS

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Geospatial information from crowdsourcing projects is produced by multiple, often unorganized, individuals, which makes it a challenge to establish the credibility of volunteers based on the content they produce. The vast amount of knowledge that the public possess establishes them as viable sources of up-to-date and detailed information on their local community (Zhu et al., 2015). Nonetheless, assessment of how, based on the reputation of contributors, the reliability and credibility of this crowdsourced information can be resolved remains a challenge. Moreover, the lack of data quality measures, common in traditional systems, can lead to the production of

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Using Kappa methodology to consider volunteered geographic information in official land administration systems in developing countries

Cited by 5 publications

References 21 publications

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Infrastructure Model Volunteered Geographic Infor-mation Social Assistance Program To Support eGovernment Case Study of Central Aceh District

Volunteer reputation determination in crowdsourcing projects using latent class analysis

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