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Outliers
Description of Error: Data can have legit outliers, but sometimes it's more nefarious. Check yourself before you wreck yourself.
Things to look for: Sort or order all applicable fields. Double-check the veracity of any super-high or super-low numbers. Call to confirm with your data source if needed. Related: are there any negative values? This can indicate an import or calculation error. Calculate average and standard deviation of fields and inspect any values that aren't within two or three standard deviations.
Examples:
GeoCoding Fails
Description of Error :
Latitude and longitude are reversed, resulting in the points being in completely the wrong place.
Things to look for :
pick something not near the equator to test. Ideally, you should have an idea of what its lat/long should be
Examples:
Selection Errors
Description of Error :
Things to look for :
Examples :
Scaling: Admin level + Population Density comparisons
Description of Error :
Things to look for :
Examples :
You're Not My Type
A general page to cover issues with implicit type coercion
Centroid data smells
Description of Error:
Things to look for:
Examples:
Ecological Fallacies/Boundaries
Description of Error :
Things to look for :
Examples :
The Bed of Procrustes
This is the classic case where there might not be something wrong within the data but the number of rows suggests very strongly that the data was truncated by Excel and is thus incomplete.
Scale Comparisons
Description of Error :
Things to look for :
Examples :
Nulls/blanks
Description of Error: Is your data shooting blanks? This can be an import issue.
Things to look for: Search for null or blank values in numeric fields. Pay close attention to fields that are entirely null or blank; this can indicate an import error.
Examples:
Projection errors and smells
Description of Error :
Things to look for :
Examples :
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