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18-Geom-A7 Geospatial Information Systems · December 2018

Question 10 of 15: Criteria of geospatial data quality

Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)

Notes on this paper

Paper format. National Exams, December 2018 — 04-Geom-A7, Geospatial Information Systems. Closed book (one approved Casio or Sharp calculator permitted); duration 3 hours. Fifteen (15) questions are provided and any ten (10) constitute a complete paper; each question is of equal value (10 marks), so a complete paper totals 100 marks. Most answers are essay-format; clarity and organization count. All fifteen questions are solved below for completeness.

Reference texts (subject)

Longley, Goodchild, Maguire & Rhind, Geographic Information Systems and Science (4th ed.); P. Bolstad, GIS Fundamentals (5th ed.); Worboys & Duckham, GIS: A Computing Perspective (2nd ed.); Burrough, McDonnell & Lloyd, Principles of Geographical Information Systems (3rd ed.); de Smith, Goodchild & Longley, Geospatial Analysis; OGC Simple Feature Access (ISO 19125); ISO 19157 Geographic information — Data quality; ISO 19115 Metadata.

Question 10: Criteria of geospatial data quality (10 marks — 5 × 2)

Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.

Geospatial data quality is described by a standard set of elements (formalized in ISO 19157). At least five commonly-used criteria are:

  1. Positional (spatial) accuracy. How close feature coordinates are to their true ground positions, reported separately for horizontal and vertical components (e.g., horizontal RMSE, error ellipse, vertical RMSE). It governs whether locations, distances and areas are trustworthy.
  2. Attribute (thematic) accuracy. How correct the descriptive values are — for categorical attributes measured by classification accuracy and a confusion (error) matrix (% correctly classified, producer/user accuracy, kappa); for continuous attributes by measurement error. It tells whether the labels/values are right.
  3. Completeness. The presence or absence of features and attributes relative to the real world — errors of omission (features missing) and commission (extra features that should not be there). It answers whether anything is missing or spurious.
  4. Logical consistency. The degree to which the data obey their structural, topological and domain rules — no gaps or slivers between polygons, no self-intersections, valid connectivity, attribute values within permitted domains. It measures internal integrity.
  5. Temporal accuracy / currency. How up-to-date the data are and the correctness of any time attributes — the validity/last-update date and temporal consistency. Stale data may be positionally perfect yet wrong for today.
  6. Lineage (provenance). The source material, capture methods and full processing history of the dataset, allowing users to trace and judge fitness-for-use.

(The first five satisfy the question; lineage is added as a widely-cited sixth element. Resolution/scale is another commonly quoted descriptor.)