Table of Contents
- Developing a Quality Assurance Plan
- Skill Drill: Setting up your Workspace
- The Elements of Geospatial Data Quality
- Creating a quality assurance checklist
- Implementing your Quality Assurance Plan
- Implementing Quality Control with Tabular Data
- Skill Drill: Implementing Quality Control with Tabular Data
The Elements of Geospatial Data Quality
According to the ISO 19157:2013 Geographic Information Data Quality standards, there are six primary elements of geospatial data quality:
- Thematic Accuracy
- Logical Consistency
- Temporal quality
- Positional accuracy
Completeness relates to the presence and absence of features, their attributes, and relationships. For example, street centerline data may be missing speed limits. This error can cause problems or inaccuracies when planning routes.
Thematic accuracy relates to the correctness of the classification of features and their attributes. For example, land parcels may be coded incorrectly. A commercial land parcel may be coded as residential only. This may cause delays in the granting of building permits and construction — a potentially costly mistake.
Logical consistency relates to the adherence to the logical rules of data structure, attribution, and relationships. For example, one GIS technician may digitize existing roadways as linear features while another GIS technician digitizes roads as polygon features. This would cause problems when trying to merge datasets or connect routes.
Temporal quality relates to the time the data was collected or created and how that impacts present-day use of that information. For example, an out-of-date aeronautical chart may show a permanently closed runway as available for landing. The results could be disastrous.
Positional accuracy relates to the correctness of the location of features within a spatial reference system. For example, inaccurate or incorrectly defined geographic coordinates for fire hydrants may cause problems for emergency responders.
Usability relates to the data adhering to the user requirements for its intended use. For example, a project requiring detailed coastal data (large-scale) would not be able to use coastal data generalized for global visualization (small-scale).
This tutorial focuses on developing a quality assurance plan for the acquisition of data. In this scenario, you are working as a GIS manager who oversees two GIS technicians. You have just begun a new project related to the recent files near Santa Rosa, California, and are in the process of collecting any and all relevant data for your study area (Santa Rosa and the surrounding region). You have assigned Technician A the duty of collecting as many related datasets as possible. Technician B is in charge of reviewing the data collected for quality assurance and quality control.
At this time, you are not sure what type of datasets or how many Technician A will collect related to your study area. Technician B needs guidance on what to look for as it relates to quality assurance and quality control. Your job is to come up with a series of standardized questions that Technician B can use as a checklist and quality assurance documentation for each dataset.