Data Quality

Estimated time: 45 minutes

Overview

Questions

• What does “quality” mean for geospatial data?
• What kinds of errors are common in vector data?
• How can I check and improve the quality of a dataset?

Objectives

• Describe the elements of spatial data quality
• Recognize invalid geometries and topology errors
• Plan a quality assurance and quality control workflow
• Record quality checks so others can judge fitness for use

Quality means “fit for use”

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No dataset is perfect. A road layer accurate to 10 meters is excellent for a regional access study and useless for engineering a new intersection. Data quality is therefore about fitness for use: is the data good enough for this purpose, and do we know its limitations?

The elements of spatial data quality

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International standard ISO 19157 (endorsed by the U.S. Federal Geographic Data Committee) describes data quality through a set of elements. In plain terms:

Element	Question	Grocery store example
Completeness	Is anything missing (omission) or extra (commission)?	A new store is missing; a closed store is still listed
Logical consistency	Does the data follow its own rules: domains, formats, topology?	store_type contains “gas station”, which is not in the domain
Positional accuracy	How close are the coordinates to the true location?	A point is 40 m from the store, in the parking lot
Thematic (attribute) accuracy	Are the attribute values correct? Are the classes right?	A store marked as accepting SNAP does not
Temporal quality	Is the data current, and are dates correct and consistent?	Hours were recorded three years ago
Usability	Overall, can it be used for the intended purpose?	Good enough for a city-wide access study?

A further, essential piece of information is lineage: where the data came from and what was done to it. Without lineage, nobody can judge the other elements. (Lineage is recorded in metadata; see the Data Curation module.)

Geometry errors

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Vector geometry has rules. A polygon’s boundary must close and must not cross itself; neighboring polygons in a layer such as census tracts should not overlap or leave gaps. Data that breaks these rules can make tools fail or, worse, quietly give wrong answers.

Common topology and geometry errors.

Error	What it is	Typical cause	Why it matters
Invalid geometry (for example a self-intersection)	A polygon whose edges cross, or a ring that does not close	Careless digitizing, format conversion	Overlay, clip and area tools fail or return nonsense
Gaps and slivers	Thin empty spaces between polygons that should touch	Digitizing neighbors separately; mixing data of different scales	Points in the gap belong to no polygon; totals do not add up
Overlaps	Two polygons covering the same area	The same causes	Areas and counts are double-counted
Undershoots and overshoots (dangles)	Lines that stop short of, or cross past, the line they should meet	Digitizing without snapping	Network routes break or go the wrong way
Duplicate features	The same feature entered twice	Repeated imports; several data collectors	Counts and access measures are inflated
Null or empty geometry	A record with no shape	Failed edits or imports	The feature silently disappears from maps and analyses

Finding and fixing them in QGIS

QGIS has tools in the Processing Toolbox for each kind of problem:

• Check validity (Vector geometry group) lists invalid geometries and explains each error
• Fix geometries creates a repaired copy of a layer (always keep the original)
• The Check geometry group includes tools such as Small gaps, Overlaps, Self-intersections, Dangle-end lines and Duplicated geometries
• Delete duplicate geometries and Delete duplicates by attribute remove exact copies
• Snapping options (Project > Snapping Options) prevent undershoots and gaps while you digitize

Automatic fixes are a starting point, not an answer. Look at what was changed before trusting it.

Attribute errors

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Problems in the attribute table are at least as common as geometry problems:

• values outside the domain (misspellings, inconsistent capitalization, old categories)
• impossible values (a negative population, a store opening at 25:00, a percentage over 100)
• missing values, and missing values disguised as 0 or -9999
• IDs that are not unique, or that lost their leading zeros
• units mixed within a column (miles and kilometers, dollars and thousands of dollars)

Simple checks catch most of them: count the distinct values in each categorical field, look at the minimum and maximum of each numeric field, and count nulls. In QGIS, Vector > Analysis Tools > Basic Statistics for Fields and the Statistics panel do this quickly.

Quality assurance and quality control

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• Quality assurance (QA) prevents errors: a careful design, domains and dropdowns, required fields, snapping, training, and a test run before collecting.
• Quality control (QC) finds errors afterwards: validity and topology checks, attribute summaries, comparisons with another source, and field checks of a sample.

A practical QC routine for each new or updated dataset:

1. Confirm the CRS and that the layer lines up with a trusted reference layer.
2. Count features, and compare with what you expect.
3. Run Check validity and the relevant Check geometry tools.
4. Summarize each attribute: distinct values, ranges and nulls.
5. Look for duplicates by ID, by name and by location.
6. Check a random sample against a reference (imagery, a website, a field visit).
7. Record what you checked, what you found and what you changed.

Callout

Report quality honestly

Every dataset has limitations. State them: “Store locations were collected with phone GPS (typically 3 to 10 m accuracy); 12 of 64 stores were verified in the field in March 2026.” This kind of statement does not weaken your work. It lets others use your data correctly, and it is part of good metadata.

Challenge

Name the quality element

Which element of data quality does each problem affect?

1. A census tract layer is from 2010, but the analysis is about 2024.
2. Three stores were recorded twice by different volunteers.
3. A store’s point is on the wrong side of a river.
4. store_type contains the values “Supermarket”, “supermarket” and “Grocery”.
5. Two new supermarkets that opened last year are missing.

Show me the solution

1. Temporal quality (tract boundaries were redrawn for 2020)
2. Completeness (commission: extra records), and logical consistency if IDs are duplicated
3. Positional accuracy
4. Logical consistency (values outside the domain), and thematic accuracy if “Grocery” is a different category
5. Completeness (omission)

Challenge

Why did the total change?

A researcher sums the population of all tracts that intersect a city and gets a number 4% higher than the city’s official population. Give two possible data quality explanations.

Show me the solution

• Overlaps: if some tract polygons overlap, people are counted twice.
• Mismatched geometry: tracts that only touch or slightly cross the city boundary (because of slivers or boundaries digitized at different scales) are included in full, adding people who live outside the city.
• Temporal mismatch: the tract populations and the official city figure may describe different years.

Checking overlaps, using tract centroids (or proper areal weighting) instead of “intersects”, and matching years would resolve it.

Key Points

• Data quality means fitness for a particular use, and every dataset has limitations
• The elements of quality are completeness, logical consistency, positional accuracy, thematic accuracy and temporal quality, with lineage recorded alongside
• Invalid geometries, gaps, overlaps, dangles and duplicates are common vector errors; QGIS has tools to find each
• Prevent errors with good design (QA) and find them with systematic checks (QC)
• Record what you checked and report limitations honestly