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While moving, integrating, and transforming data, it's also important to ensure that the data quality is of a suitable standard.
FME has a number of tools and transformers that can be used for data validation. Some - such as the GeometryValidator and AttributeValidator transformers - are specifically designed for data validation. Others - as this tutorial will show - are not specific to data validation, but can be used for that purpose.
When assessing data quality there are three techniques that can apply:
每一个examples in this tutorial will include information on how to identify, quantify, and fix the problem that is being discussed.
This tutorial will cover several distinct areas of data validation and QA.
NB:If the following bullet points do not yet have a link, they are still a work in progress.
These are geometries that are bad in themselves, rather than being part of a substandard network or coverage. This includes:
These are features that have some form of duplication. This includes:
Often very small features are indicative of poor geometry and/or topology. This includes:
These are issues related to a continuous polygon coverage. This includes:
These are issues relating to a linear network. This includes:
These are geographic features containing some form of logical issue, for example a road represented by a polygon or a bicycle path that runs through a lake. This includes:
These are attributes that have some form of logical error. This includes:
Data QA: Identifying Self-Intersections with FME
Data QA: Identifying Non-Consecutive Duplicate Vertices with FME
Data QA: Identifying Spikes and Outliers with FME
Data QA Identifying Sliver Overlaps and Gaps in Polygon Coverage
Data QA: Identifying Bad Topology in Linear Networks
Data QA: Identifying Duplicate Features with FME
Data QA: Identifying Consecutive Duplicate Vertices with FME
Data QA: Identifying Features Closer than a Minimum Distance
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