A provenance approach to assess the quality of geospatial data

Malaverri, Joana E. Gonzales; Medeiros, Cláudia Bauzer; Lamparelli, Rubens Camargo

doi:10.1145/2245276.2232116

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Ouma et al [40] downsized the quality measures from global to national perspective and proposed an alternative set of quality dimensions encompassing both quantifiable and subjective ones. Malaverri and Medeiros [41] developed a meta-study surveying the use of quality measures/dimensions in agricultural applications as well as the context of use. Their observations revealed that the most often mentioned (and probably used) dimensions are accuracy, timeliness, and completeness, followed by consistency and relevancy.…”

Section: Data Quality and Uncertaintymentioning

confidence: 99%

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

et al. 2020

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Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.

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Section: Data Quality and Uncertaintymentioning

confidence: 99%

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

et al. 2020

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“…There are a number of studies, that have used provenance in the geospatial domain [10], [11], [12], [13]. For example, Wang et al [10] propose a provenance-aware architecture to record the lineage of spatial data in Geographic Information Systems (GIS).…”

Section: Related Workmentioning

confidence: 99%

A Model of Provenance Applied to Biodiversity Datasets

Amanqui

Nies

Dimou

et al. 2016

2016 IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)

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Abstract-Nowadays, the Web has become one of the main sources of biodiversity information. An increasing number of biodiversity research institutions add new specimens and their related information to their biological collections and make this information available on the Web. However, mechanisms which are currently available provide insufficient provenance of biodiversity information. In this paper, we propose a new biodiversity provenance model extending the W3C PROV Data Model. Biodiversity data is mapped to terms from relevant ontologies, such as Dublin Core and GeoSPARQL, stored in triple stores and queried using SPARQL endpoints. Additionally, we provide a use case using our provenance model to enrich collection data.

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“…Previous applications of trust models for spatial datasets (Malaverri et al, 2012) and crowdsourced geographic features (Bishr & Mantelas , 2008;Celino, 2013) have produced a scalar value as a proxy for trustworthiness. This can then be employed in much the same way as metadata or provenance information to give an indication of data quality.…”

Section: Direction Of Researchmentioning

confidence: 99%

Trusting Crowdsourced Geospatial Semantics

Goodhue

McNair

Reitsma

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The degree of trust one can place in information is one of the foremost limitations of crowdsourced geospatial information. As with the development of web technologies, the increased prevalence of semantics associated with geospatial information has increased accessibility and functionality. Semantics also provides an opportunity to extend indicators of trust for crowdsourced geospatial information that have largely focused on spatio-temporal and social aspects of that information. Comparing a feature's intrinsic and extrinsic properties to associated ontologies provides a means of semantically assessing the trustworthiness of crowdsourced geospatial information. The application of this approach to unconstrained semantic submissions then allows for a detailed assessment of the trust of these features whilst maintaining the descriptive thoroughness this mode of information submission affords. The resulting trust rating then becomes an attribute of the feature, providing not only an indication as to the trustworthiness of a specific feature but is able to be aggregated across multiple features to illustrate the overall trustworthiness of a dataset.

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A provenance approach to assess the quality of geospatial data

Cited by 6 publications

References 5 publications

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

A Model of Provenance Applied to Biodiversity Datasets

Trusting Crowdsourced Geospatial Semantics

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