Quality Aspects in Spatial Data Mining

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International Symposium on Spatial Data Quality 2017 - ISPRS Geospatial Week 2017

The results show that, among the generic indicators, the most represented are positional accuracy and completeness, having a quite similar importance After that, we find completeness commission Unfortunately, the link to the file was missing at the time this study was performed. Therefore, there are: i 3.

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Through this element, producers can describe the usages for which the dataset is created, or describe other applications that users have reported back to them. However, in practice, there is no satisfactory mechanism for users to record their feedback. There are 1. To summarize, these results show that quality indicators and lineage are far from complete in the GEOSS, but the current status is enough to start developing tools for exposing and exploiting the quality data that already exist. Even if some quality indicators are used, the name and description of the measure used to quantify the indicator are rarely well described, revealing the need for a good list of these measures.

Finally, the paucity of usage records is a clear indication that this mechanism is not the right solution for user feedback or is unknown by most of the users. Section 5 extends the ISO model to better address consumer quality aspects. Nothing prevents the information in the models being combined in a single system if appropriate for a given application. The models are under continuous development under careful version control , and the current versions of both models at the time of writing are available as UML diagrams see electronic supplementary material.

The diagrams are too large and complex to be included as figures in this paper. The main novel features of the model are discussed in the following sections.

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The model will be described in greater detail in future specialist publications. The primary requirement was for a means to link datasets with relevant citations in the scholarly literature, but a desire was also expressed for less formal feedback mechanisms such as user comments.


Data providers have also expressed their desire for such a system, as user feedback is a key driver for them to improve their data products. Additionally, a means is provided to characterize datasets against a reference standard, as recommended by the QA4EO guidelines. A particular issue with the existing ISO and superseding ISO standards is that the quality measures contained within the proposed standard do not, in themselves, provide a complete and self-consistent statistical definition of quantitative uncertainty.

This description is fairly rich in information, provided that it is used by a reader who can understand the semantics. However, this encoding is deficient in the statistical information we can obtain about the uncertainty itself. Was the spread of error at the sampled locations genuinely Gaussian?

Quality aspects in spatial data mining

Was there any bias in the error? How many samples were taken in order to calculate this value? The questions above are particularly important for propagating this uncertainty through a modelling process and adequately handling its impact. We propose the use of the UncertML v. UncertML is based on concretely defined elements, which represent a number of distributions, summary statistics, realizations and samples.

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This brings us to the important question: should uncertainty be considered as metadata, or is it more realistic to recognize the inherent uncertainty of all data and to treat the uncertain values whether it be a distribution, an interval or a sample as the data themselves? However, this causes complications owing to the design of UncertML, which separates concerns and ensures that UncertML does not represent phenomena outside the remit for which it should be complete: the probabilistic representation of uncertainty.

The example below takes this approach to encode exactly the same information as in the first example in this section, assuming that the errors have a mean of 0 i. This time, there is an explicit recognition that the errors were considered to acceptably fit a normal distribution with mean 1.

Examination of typical spatial data holdings reveals a technical requirement in addition to the user requirements discussed earlier. Many datasets are very large and hierarchical in nature. Quality information may apply at different levels in the hierarchy: for example, in an EO scenario, quality information can be given for a whole platform series e.

Storing all metadata at all levels is impractical and would lead to redundancy, inconsistency or incompleteness of metadata. We propose an inheritance mechanism to store each metadata item and quality information at the optimum hierarchical level and to allow an easy and efficient documentation of metadata. This applies not only to quality metadata but to many other metadata types as well, and is applicable to many kinds of geographical data from topographic maps to multi-band satellite imagery.

Metadata items are documented only for the highest applicable level and then are automatically inherited by all the dependent elements. ISO suggests that metadata aggregated levels can be defined so that a generic metadata record for an aggregation is inherited by a child dataset but can be overwritten using specific values. Some metadata elements make sense on all the hierarchy levels and are never inherited; for example, fileIdentifier, hierarchyLevel, dateStamp. Metadata elements with multiple cardinality allowing general values to be inherited from upper levels as well as defining particular values at lower levels that are added to the previous ones, e.

Metadata elements that if defined on upper levels must be adopted or specified by lower levels but cannot be left undefined, e. Typically for free-text metadata elements, a concatenation method has been designed allowing the metadata value at a specific hierarchy level to be defined combining the values of the same metadata element at upper levels following a pattern and allowing some additional information also to be defined, e.

Instead of including the quality indicators for each individual dataset in the series, some generic ones can be selected e. The metadata following the hierarchical model avoid repetition and increase metadata coherence among all individual datasets in the series. Once implemented, we checked that repeated queries performed by users returned complete metadata reports for each dataset that integrate the metadata quality coming from different levels.

By including information about the accuracy of individual pixels in a DEM, a flood risk map could be derived. The hierarchical metadata model is readily applicable to the new quality model proposed in this paper. For example, the overall positional accuracy of a satellite product can be defined for nearly all satellite data-processing levels on a scene because they are derived from the same geometric correction process done early in the processing chain.

On the other hand, the overall thematic accuracy of a specific derived product can be defined for the whole set of scenes because they are obtained in the same way e. For a specific product and scene, a specific value for either overall thematic or positional accuracy can be defined. Furthermore, these quality parameters can be even provided for each pixel e. Other levels can add a citation of a specific publication focused on a scene of a product.

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The innovations in informatics described in this paper are in the process of being implemented in prototype software within GeoViQua and related projects. The GEOSSBack portal can allow metadata about a particular topic to be searched and a collection of related datasets identified. The user can pick a particular dataset and read the catalogued metadata from the producer and also the previous user comments. Additionally, the user can enter new comments or update their own. All of this is stored and becomes immediately available to other users.

More work will be required to evaluate and evolve this system. These reports include a great deal of precise information about data quality in a human-readable format, and provide a means for users to provide feedback on the efficacy of the quality model without the need to understand the technical details of the information model. A sample report is provided in the electronic supplementary material.

The GeoViQua project is actively researching these methods, which will be the subject of future publications. The modified information model described in this paper will be a key component of this, allowing consistent recording of quality information. Strongly related to data quality is the complete capture of origins of quality information meta-quality, as defined in ISO Particular aspects require further investigation in the quality model to support meta-quality, including:.

The provision of meta-quality will work to further engender trust in users of the data, because it is essential that users can also trust the quality information itself. Clearly, there is the potential for an infinite hierarchy of meta-meta-quality; however, we believe that meta-quality would be most usefully provided in a form that describes as completely as possible how the quality indicators were arrived at, so that they could potentially be reproduced.

In this study, we addressed the problem of EO data quality from a number of different angles, including i clarifying concepts and terminology, ii reviewing existing relevant international standards, iii establishing unfulfilled user needs, and iv surveying the current state of the GEOSS. We proposed an expanded model for representation of EO data quality and described how it can be applied to large data holdings through a metadata inheritance mechanism.

Although specifically aimed at the EO community, this study is relevant to many other areas of e-Science, particularly where data need to be provided to a diverse user community. Many of the concepts e. UncertML are entirely general and can be much more widely applied. Conversely, the EO informatics community will benefit from innovations occurring in other communities, particularly concerning the linking of information from different sources.

Quality Aspects in Spatial Data Mining Quality Aspects in Spatial Data Mining
Quality Aspects in Spatial Data Mining Quality Aspects in Spatial Data Mining
Quality Aspects in Spatial Data Mining Quality Aspects in Spatial Data Mining
Quality Aspects in Spatial Data Mining Quality Aspects in Spatial Data Mining
Quality Aspects in Spatial Data Mining Quality Aspects in Spatial Data Mining
Quality Aspects in Spatial Data Mining Quality Aspects in Spatial Data Mining

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