Thirty Years of Research on Spatial Data Quality: Achievements, Failures, and Opportunities

Devillers, Rodolphe; Stein, Alfred; Bédard, Yvan; Chrisman, Nicholas; Fisher, Peter; Shi, Wenzhong

doi:10.1111/j.1467-9671.2010.01212.x

Cited by 102 publications

(84 citation statements)

References 36 publications

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“…The disjunction of the uncertainty research with user requirement and practical application has now become one of the major problems which demands great emphasis (Devillers et al, 2010). The phenomena is partly generated by the deficiency of uncertainty research on the whole process from data acquisition and preparation, spatial analysis operation and results display from the perspective of certain application.…”

Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

“…The early computer mapping exercise done at the Harvard Laboratory for Computer Graphics and Spatial Analysis in the late 1970s revealed that imperfection is inherent to spatial data and can directly influence the reliability of spatial analysis output (Chrisman, 2006). Works on the uncertainty of spatial data increased significantly with the arrival of GIS in the early 1980s with their capability to integrate spatial and non-spatial data, including the evaluation of specific data quality elements of vector, raster, and Digital Elevation Model (DEM) data, also remote sensing images (Devillers et al, 2010). Wu (2002) established a frame of GIS uncertainty and summarized the methods towards GIS data uncertainty.…”

Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

“…The comprehension of the nature of the problem should get done before any best possible solutions are promoted. (Devillers et al, 2010).…”

Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

“…(Goodchild and Gopal, 1989;Beard et al, 1991;Buttenfield, 1991;Zhang and Goodchild, 2002;Xiao et al, 2007). Therefore, data users should be aware that "all spatial data are wrong, but some are useful (outside of some exceptions where the data becomes legally the reality)" (Devillers et al, 2010). The early computer mapping exercise done at the Harvard Laboratory for Computer Graphics and Spatial Analysis in the late 1970s revealed that imperfection is inherent to spatial data and can directly influence the reliability of spatial analysis output (Chrisman, 2006).…”

Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

See 3 more Smart Citations

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

Liu

Zhan

2017

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

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Commission VI, WG VI/4 KEY WORDS: Uncertainty, Land suitability assessment, GIS, Planning ABSTRACT:The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS) based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the "Nature Breaks" method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

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Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

“…The comprehension of the nature of the problem should get done before any best possible solutions are promoted. (Devillers et al, 2010).…”

Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

Section: The Research On Spatial Data and Spatial Analysis Uncertaintymentioning

confidence: 99%

See 2 more Smart Citations

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

Liu

Zhan

2017

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

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“…They usually assume data is safe and not risky regardless of the intended usage context (Jones et al 2013). However, perceptions about the fitness-for-use of the data may diverge from untrained users, geo-IT experts and application domain experts (Grira et al, 2009;Devillers et al 2010). Accordingly, the assumption of safety usage of the data has led to a number of accidents and other adverse consequences that remind the need to protect users against the risks of data misuses (Larrivée et al, 2011).…”

Section: Risks Of Geospatial Data Missusementioning

confidence: 99%

Towards a Collaborative Knowledge Discovery System for Enriching Semantic Information About Risks of Geospatial Data

Grira

Bédard

Roche

et al. 2013

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

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ABSTRACT:The aim of this research is to design and implement a knowledge discovery system that facilitates, using a web 2.0 collaborative approach, the identification of new risks of geospatial data misuse based on a contributed knowledge repository fed by application domain experts. [Context/Motivation] This research is motivated by the irregularity of risk analysis efforts and the poor semantic of the collected information about risks. In the context of risk analysis during geospatial database design, the knowledge about risks of geospatial data misuse is typically held by domain application experts. The collection and record of that knowledge are usually considered as optional activities. It is usually performed through face-to-face risk assessment meetings and reports. Such techniques end up by restricting the scope of risk analysis to a set of obvious risks usually already identified. Besides, little consideration is devoted to the storage of risk information in an appropriate format for automatic reasoning and new risk information discovery. As a consequence, many foreseeable risky aspects inherent to the data remain overlooked leading to ill-defined specification and faulty decisions. [Principal ideas/results] In this paper, we present a contributed knowledge discovery system that aims at enriching the semantic information about risks of geospatial data misuse in order to identify foreseeable risks. The proposed web-based system relies on a systematic and more active involvement of users in risk analysis. The approach consists of 1) providing an overview of the related work in the domains of risk analysis within the context of geospatial database design, 2) presenting an ontology-based knowledge discovery system that helps experts in risks identification based on an upper-level risk ontology and on a structured representation of the domain-specific knowledge and, 3) presenting the components of the proposed system architecture and how it may be implemented and used in practice, and finally 4) we conclude by discussing the approach. [Contribution] A major outcome is that the proposed platform can help discovering implicit domain knowledge, and facilitating the identification of foreseeable risks of geospatial data misuse in a way to preventively improve the resulting fitness-for-use.

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Imperfection of Geographic Information: Concepts and Terminologies

Devillers¹,

Desjardin

Runz

2019

Geographic Data Imperfection 1

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Thirty Years of Research on Spatial Data Quality: Achievements, Failures, and Opportunities

Cited by 102 publications

References 36 publications

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

Towards a Collaborative Knowledge Discovery System for Enriching Semantic Information About Risks of Geospatial Data

Imperfection of Geographic Information: Concepts and Terminologies

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