GST 101 Introduction to Geospatial Technology��Unit 2 – Understanding Spatial Data �Module 2.3 - Quality of Spatial Data���� � �

Author: Ann Johnson

Associate Director

ann@baremt.com

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Based upon work supported by the National Science Foundation under Grants DUE 1304591, DUE 164409, DUE 1700496, DUE 1937177, Due 1938717 DUE 1937237, 2030206 and 2015927. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

https://www.fgdc.gov/organization

Spatial Data Quality�

This Module will briefly review the importance of data quality. It will be covered in more depth in other courses and in resources mentioned in this unit. The important point is:

When creating or acquiring and using spatial data both its

positional and attribute quality should be evaluated based

on how it will be used for a given task or project.

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Data can be highly accurate and still not be useful for all tasks or projects.

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• For example: A global data set may include high quality data, but not be

appropriate for use by a city creating detailed land use planning projects

because of the scale of the data.

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A Look at Accuracy, Precision, and Types of Errors��

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• Understanding error inherent in GIS data is critical to ensuring that any spatial analysis performed using those datasets meets a minimum threshold for accuracy

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• According to GIS Data: A Look at Accuracy, Precision and Types of Errors in a GIS Lounge article

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“There is no such thing as perfect GIS data.”

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• These two articles from the GIS Lounge take a deeper look at Accuracy, Precision and Types of Errors:
• http://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/
• http://www.gislounge.com/digitizing-errors-in-gis/

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Accuracy and Precision�

Accuracy can be defined as the degree or closeness to which the information matches the values in the real world

• For GIS data accuracy referrers to both

positional accuracy and attribute accuracy

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Precision refers to how consistently repeated measurements under unchanged conditions show the same result.

• Precise data may be inaccurate because of a systematic error made as it is gathered.

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Error involves assessing both the imprecision of data and its inaccuracies.

Precise and accurate

Precise but not accurate

Imprecise and accurate

Not precise, not accurate

Adapted from image by Caitlin Dempsey, GIS Lounge

Data Quality Improvement Techniques

• For data that is free and downloaded choose relevant data from a known or recognized authority for that type of data and include its Metadata
• Metadata is the data about the data – who created it, when was it updated, what extent (scale), does it include its projection, datum, units, etc.
• Review its Metadata - Look at its data table and attributes
• Is there documentation on definition of its attributes, are attributes those needed for your project or will you have to create new attributes

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• For data you create:
• Check your data quality as you capture the data (location, attributes)
• Document your data – create Metadata for your data
• Use automated software tools for spatial and non-spatial data validation

Data Quality Checklist

• Source of Data:
• It is from a know, reliable source with good metadata? Or did you collect the data and ensure its accuracy?
• If you or someone else created the data did you check for digitizing or attribute errors?
• Age of Data:
• Generally, current or recently created data is most up to date and useful, but older data may be used to evaluate change.
• How are you going to deal with data from different dates?
• Scale of Data:
• Was the data created at the right scale for use in your project?
• How much detail will your task or project require?
• Small Scale Maps: Cover a large area but require less detail – a city is a point on the map
• Large Scale Maps: Cover a small area but require more detail – a light pole may be a point

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Checklist Continued

• Completeness:
• Are data coverage the same across your project study area? If not, how will you rectify this uneven coverage in your analysis?
• Processing and formatting:
• Will the data need to be processed or reformatted to be useful?
• Reprojected data, method of import/export, transformation from raster to vector, etc. are examples of possible sources of formatting errors that you will have to correct or make allowances for.
• Attribute errors:
• Missing or mislabeling feature attributes, lack of consistent naming conventions or use of accepted terms (land use terms, soils, etc.). These attribute issues will need to be corrected in order to effectively use the attributes in analysis.
• Right to use data:
• Can you legally or ethically use the data or is its use restricted or have copyright?
• Can you contact and get permissions – if not, do you use the data?

National Spatial Data Infrastructure (NSDI)

• Updated and plan approved for 2020-2024
• The goals of this Infrastructure is to:
• Make geographic data more findable, accessible, interoperable and reusable
• Increase the benefits of using and sharing available data
• Enable and promote collaborations and partnerships

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http://www.fgdc.gov/nsdi-plan/index_html

Federal Geographic Data Committee (FGDC)

• Develop and Promote collaboration and partnerships
• Develop Geospatial Shared Services
• Manage Federal Geospatial Resources
• Standards Development
• Geospatial Metadata
• Data Protal – Geoplatform.gov

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https://www.fgdc.gov/

https://www.fgdc.gov/organization

See GeoTech Center website (https://geotechcenter.org) �for additional Model Courses and other curriculum resources. ���������Note: some content is a derivative of other authors���

Author: Ann Johnson

Associate Director

ann@baremt.com

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2-15-2021 V6