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Dr. Melinda Laituri
Secondary Cities Principal Investigator
State of the Map, Asia
23 - 24 September 2017
Kathmandu, Nepal
Data Integrity: Quality Assurance/Quality
Control (QA/QC) and Metadata
Procedures
What is data integrity?
Maintenance and assurance of accuracy and consistency of
data over its entire life cycle
Three key parts to data integrity:
1) metadata;
2) quality assurance/quality control (QA/QC process);
3) ethics
What is metadata?
contents of the data (the attibutes), the structure of the data
(data format), and source of data
What are Quality Assurance/Quality Control procedures?
process to verify the completeness of data throughout all
stages of the data development cycle
Integrity and correctness of data
Review Correct Verif
y
Validate: field-truthing
Checklist:
____ Metadata: spatial reference, key words (tags)
____ File structure/organization -- schema
____ Naming conventions
____ Attribute definitions
____ Cartographic templates
____ Documentation of analysis (flowchart)
____ Data disclaimer!
Quality Control and Quality Assurance
(QA/QC)
• Quality Assurance Plan:
Review of objectives, procedures, and output
which may include additional steps based on user input
• Quality Control Process:
Iterative methods to prepare data, output, and results
• Conduct pilot or demonstration project
Test design and methods of project and data
development
• Dynamic process
2C Data Sharing Structure
2C Geonode
http://secondarycities.geonode.state.gov/
GeoNode:
All data must be posted
to
the 2C Geonode.
Data cannot be shared until it has been
quality checked and metadata is
complete!
Data without metadata is garbage!
What is Metadata?
• Data about data
• Unique to each GIS project
• Summary information about data
• (essential for) Data management
• (necessary for) Data discovery
• (facilitates) Data sharing
Metadata includes:
-Description
-Quality
-Type
-Creation
-Spatial information
Content Format
-Text file
-XML
-Database record
Standards
-FGDC
-ISO
Metadata Protocol
QA/QC Protocols
• Existing data from project partners
– National, local government
– Non-governmental organizations
• Data generated from workshops through field
data collection
– University and local government partners
QA/QC:
Existing data from project partners
• Assess metadata of existing data from project
partners
– Determine if there is an existing metadata protocol
• If data have incomplete metadata, assess data for
inclusion in project based on:
– Is spatial reference data discernable?
– Can attributes be understood?
– Can the data be used without complete metadata?
– Identify limitations in metadata
QA/QC:
Data generated from workshops
through field data collection
• Identify who will conduct QA/QC other than
person who collected the data
• Examine data content:
– Completeness of attribute data
– Identify errors and process to correct data
– Review metadata for completeness
• Examine spatial data
– Check spatial reference data
Prof. Melinda Laituri, Colorado State University | Map Data Integrity | SotM Asia 2017

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Prof. Melinda Laituri, Colorado State University | Map Data Integrity | SotM Asia 2017

  • 1. Dr. Melinda Laituri Secondary Cities Principal Investigator State of the Map, Asia 23 - 24 September 2017 Kathmandu, Nepal Data Integrity: Quality Assurance/Quality Control (QA/QC) and Metadata Procedures
  • 2. What is data integrity? Maintenance and assurance of accuracy and consistency of data over its entire life cycle Three key parts to data integrity: 1) metadata; 2) quality assurance/quality control (QA/QC process); 3) ethics What is metadata? contents of the data (the attibutes), the structure of the data (data format), and source of data What are Quality Assurance/Quality Control procedures? process to verify the completeness of data throughout all stages of the data development cycle
  • 3. Integrity and correctness of data Review Correct Verif y Validate: field-truthing Checklist: ____ Metadata: spatial reference, key words (tags) ____ File structure/organization -- schema ____ Naming conventions ____ Attribute definitions ____ Cartographic templates ____ Documentation of analysis (flowchart) ____ Data disclaimer!
  • 4. Quality Control and Quality Assurance (QA/QC) • Quality Assurance Plan: Review of objectives, procedures, and output which may include additional steps based on user input • Quality Control Process: Iterative methods to prepare data, output, and results • Conduct pilot or demonstration project Test design and methods of project and data development • Dynamic process
  • 5. 2C Data Sharing Structure
  • 7. Data cannot be shared until it has been quality checked and metadata is complete! Data without metadata is garbage!
  • 8. What is Metadata? • Data about data • Unique to each GIS project • Summary information about data • (essential for) Data management • (necessary for) Data discovery • (facilitates) Data sharing
  • 9. Metadata includes: -Description -Quality -Type -Creation -Spatial information Content Format -Text file -XML -Database record Standards -FGDC -ISO
  • 11. QA/QC Protocols • Existing data from project partners – National, local government – Non-governmental organizations • Data generated from workshops through field data collection – University and local government partners
  • 12. QA/QC: Existing data from project partners • Assess metadata of existing data from project partners – Determine if there is an existing metadata protocol • If data have incomplete metadata, assess data for inclusion in project based on: – Is spatial reference data discernable? – Can attributes be understood? – Can the data be used without complete metadata? – Identify limitations in metadata
  • 13. QA/QC: Data generated from workshops through field data collection • Identify who will conduct QA/QC other than person who collected the data • Examine data content: – Completeness of attribute data – Identify errors and process to correct data – Review metadata for completeness • Examine spatial data – Check spatial reference data