Communicating Thematic Data Quality with Web Map Services
<p>Sample visualisations of sea surface temperature and its uncertainty from the Sea Surface Temperature dataset from the European Space Agency’s Climate Change Initiative (CCI-SST) dataset [<a href="#B19-ijgi-04-01965" class="html-bibr">19</a>], generated by the ncWMS-Q software (see <a href="#sec6dot1-ijgi-04-01965" class="html-sec">Section 6.1</a>) using the proposed extensions to the Symbology Encoding (SE) specification (see <a href="#sec5dot8-ijgi-04-01965" class="html-sec">Section 5.8</a>). From top left: (<b>a</b>) temperature encoded as lightness of colour, with overlain contours of its uncertainty (variance); (<b>b</b>) uncertainty represented through successive levels of stippling, with denser stippling representing high uncertainty; (<b>c</b>) uncertainty represented as black shading; (<b>d</b>) use of a bivariate colour map, with temperature encoded as brightness and uncertainty encoded as colour saturation.</p> "> Figure 1 Cont.
<p>Sample visualisations of sea surface temperature and its uncertainty from the Sea Surface Temperature dataset from the European Space Agency’s Climate Change Initiative (CCI-SST) dataset [<a href="#B19-ijgi-04-01965" class="html-bibr">19</a>], generated by the ncWMS-Q software (see <a href="#sec6dot1-ijgi-04-01965" class="html-sec">Section 6.1</a>) using the proposed extensions to the Symbology Encoding (SE) specification (see <a href="#sec5dot8-ijgi-04-01965" class="html-sec">Section 5.8</a>). From top left: (<b>a</b>) temperature encoded as lightness of colour, with overlain contours of its uncertainty (variance); (<b>b</b>) uncertainty represented through successive levels of stippling, with denser stippling representing high uncertainty; (<b>c</b>) uncertainty represented as black shading; (<b>d</b>) use of a bivariate colour map, with temperature encoded as brightness and uncertainty encoded as colour saturation.</p> "> Figure 2
<p>Schematic representation of the structure of Layers in a particular “quality-enabled” profile of Web Map Service (WMS-Q) service instance, illustrating the Service-Dataset-Variable-Component hierarchy. Each box is a Layer in the tree: blue boxes represent non-displayable Layers, whereas orange boxes represent displayable Layers. The derivation of this hierarchy is given in <a href="#sec5dot5-ijgi-04-01965" class="html-sec">Section 5.5</a>.</p> ">
Abstract
:1. Introduction
2. What is “Data Quality”?
3. Terminology Used in This Paper
4. Encodings of Data Quality
4.1. The ISO Suite of Standards
4.2. Building on the ISO Model
4.3. Vocabularies for Describing Data Quality
- As individual realisations of an uncertain variable. For example, the uncertainty of a temperature field could be expressed by running a simulation ten times under different conditions, and recording the results of each simulation in a data file as a separate field.
- As summary statistics. Instead of recording each realisation individually, the data provider may choose to calculate statistics describing the spread of values at each measurement location. For example, the spread may be expressed as a mean field and a variance field.
- As probability distribution functions (PDFs). This is the most powerful representation of uncertainty and gives a functional form for the probability of a variable having a given value at a given location. In a data file, a PDF may be represented by specifying the functional form of the PDF as metadata (e.g., Gaussian or log-normal) and giving the values of each parameter of the distribution as a separate field.
5. Design of WMS-Q
5.1. Overview of WMS
5.2. Design Goals for WMS-Q
- To maintain compatibility with version 1.3.0 of the WMS standard. In other words, WMS-Q aims to be a profile of WMS, not an extension. Therefore, instead of adding new functionalities, it provides a set of rules on how to use the existing mechanisms permitted by the WMS standard to convey dataset, sample and variable level quality. In this way, standard WMS clients will be able to read information from a WMS-Q.
- To re-use existing general methods for expressing data quality, where appropriate (e.g., UncertML, see Section 4.3 above), but to avoid methods that are highly specific to particular communities.
- To be independent of any particular format or convention for data or metadata storage.
- To focus on conveying the thematic accuracy of both categorical and continuous raster data (including its uncertainty), but allow techniques to be more widely applicable in future (e.g., to vector data).
5.3. Identification of Conformance to WMS-Q
5.4. Dataset-Level Quality
5.5. Variable-Level Quality
5.6. Sample-Level Quality
5.7. Behaviour of GetFeatureInfo
5.8. Extensions to the Symbology Encoding Standard
- Portraying the “best estimate” as a colour-mapped image, overlain with contours showing a measure of data uncertainty (top left of Figure 1).
- As above, with uncertainty represented using black shading, the opacity of which increases with data uncertainty (bottom left of Figure 1).
- Using glyphs (i.e., small icons), the shape, size or colour of which can be mapped to different components of an uncertain variable. A special case of this is the use of “confidence triangles” (e.g., [36]), which visualize the estimated spread of data by dividing the image into squares, each of which is divided into two triangles. The lower triangle is assigned a colour representing the lower bound of the variable, and the upper triangle is coloured according to the upper bound of the variable. The contrast in colours between the two triangles gives a visual estimate of the uncertainty.
5.9. Mixing “Quality-Enabled” Data with “Non-Quality-Enabled” Data
6. Implementations
6.1. Server Implementations
6.2. Client Implementations
7. Integration of WMS-Q in a Quality Enabled Spatial Data Infrastructure
8. Discussion and Future Work
- Communication of data quality at the level of datasets, variables and individual samples
- Re-use of concepts from related standards and vocabularies, including UncertML and QualityML, using the WMS Keyword tag to communicate the quality and uncertainty of a measured variable.
- Full compatibility with version 1.3.0 of the WMS standard.
- Extensions to the SE specification to give greater control over visualizations of uncertain components.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Blower, J.D.; Masó, J.; Díaz, D.; Roberts, C.J.; Griffiths, G.H.; Lewis, J.P.; Yang, X.; Pons, X. Communicating Thematic Data Quality with Web Map Services. ISPRS Int. J. Geo-Inf. 2015, 4, 1965-1981. https://doi.org/10.3390/ijgi4041965
Blower JD, Masó J, Díaz D, Roberts CJ, Griffiths GH, Lewis JP, Yang X, Pons X. Communicating Thematic Data Quality with Web Map Services. ISPRS International Journal of Geo-Information. 2015; 4(4):1965-1981. https://doi.org/10.3390/ijgi4041965
Chicago/Turabian StyleBlower, Jon D., Joan Masó, Daniel Díaz, Charles J. Roberts, Guy H. Griffiths, Jane P. Lewis, Xiaoyu Yang, and Xavier Pons. 2015. "Communicating Thematic Data Quality with Web Map Services" ISPRS International Journal of Geo-Information 4, no. 4: 1965-1981. https://doi.org/10.3390/ijgi4041965