📚 CTEVT | GIS Application – Chapter 4: Data Quality (Full Notes + Practice Questions + Complete PDF)
Data Quality is one of the most important but often overlooked components of GIS.
Even the most powerful analysis or beautifully designed maps are meaningless if the data is inaccurate, inconsistent, or outdated.
Chapter 4 of GIS Application (EG 3103 GE) explains how to measure, evaluate, and ensure spatial, attribute, temporal, and logical accuracy of GIS datasets.
🧩 Chapter Overview: Data Quality
Data Quality = The degree to which GIS data correctly represents real-world conditions.
High-quality data ensures:
✔️ Correct analysis
✔️ Reliable decision-making
✔️ Accurate mapping
✔️ Reduced errors
✔️ Professional outputs for survey offices, municipalities & planning projects
Poor data quality leads to:
❌ Wrong analysis
❌ Misaligned layers
❌ Wrong parcel ownership
❌ Incorrect route planning
❌ Misleading maps
📥 Download the Full Chapter 4 Notes (FREE PDF)
🎥 Watch the Chapter 4 Explanation Video on YouTube
🎥 Watch the Full Chapter 4 Video
👉 [Click Here to Watch on YouTube]
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🔍 4.1 Data Quality
📍 Definition
Data Quality refers to the accuracy, completeness, consistency, and reliability of GIS datasets.
It determines whether the data is:
✔️ Correct
✔️ Precise
✔️ Up-to-date
✔️ Free from errors
✔️ Suitable for analysis
Data quality ensures that the GIS output closely matches reality.
📍 Major Components of Data Quality
GIS data quality is evaluated using the following components:
⭐ A. Spatial Accuracy
Spatial accuracy measures how close the location of a feature on the map is compared to its true position on Earth.
Example:
If a road is misaligned by 5 meters, spatial accuracy is poor.
Spatial accuracy depends on:
- Survey method
- Projection/datum used
- Digitizing quality
- Sensor accuracy (GPS, satellite imagery)
⭐ B. Attribute Accuracy
Attribute accuracy refers to the correctness of non-spatial information linked to features.
Example:
- Wrong parcel owner name
- Incorrect land-use code
- Mistyped road type
- Incorrect population value
Attribute accuracy is checked using:
✔️ Field verification
✔️ Cross-checking with official records
✔️ Table consistency checks
⭐ C. Temporal Accuracy (Time Accuracy)
Temporal accuracy measures:
✔️ How current the data is
✔️ Whether timestamps are correct
✔️ Whether the data represents the correct time period
Example:
- Land-use map from 2010 is outdated
- Flood hazard zones updated every year
- Population census updated every 10 years
Outdated data = Wrong planning decisions.
⭐ D. Logical Consistency
Logical consistency measures whether:
✔️ Data follows rules
✔️ Relationships are correct
✔️ Topology is error-free
Logical errors include:
- Gaps between polygons
- Overlapping boundaries
- Dangling lines
- Duplicate nodes
- Polygons not closed
This is especially important in:
- Cadastral mapping
- Road networks
- Administrative boundaries
⭐ E. Lineage
Lineage describes:
✔️ Where the data came from
✔️ How it was created
✔️ What processes were applied
✔️ Who created/edited it
Lineage helps track:
- Data authenticity
- Modifications over time
- Source reliability
Example:
DEM created from 1:25,000 topo map (1984 Survey Dept).
This metadata must be stored for reference.
🔍 4.2 Accuracy Assessment
After understanding data quality components, the next step is accuracy assessment.
Accuracy assessment ensures that GIS datasets meet required quality standards.
⭐ A. Accuracy vs Precision
✔️ Accuracy
How close the data is to reality.
✔️ Precision
Level of detail or consistency in the measurements.
Example:
- A GPS point recorded at a wrong location but consistent → precise but inaccurate
- A point placed correctly but not consistently → accurate but imprecise
Both accuracy AND precision are required for high-quality GIS data.
⭐ B. Error Propagation
Error propagation = When one error leads to more errors during analysis.
For example:
❗ Incorrect projection → misaligned layers
❗ Misaligned layers → wrong overlay result
❗ Wrong overlay → faulty suitability map
Errors accumulate and multiply if not corrected early.
⭐ C. RMSE (Root Mean Square Error)
RMSE is the most important measure of positional accuracy.
It shows the difference between:
✔️ Actual (field) coordinates
vs
✔️ Predicted (map/GIS) coordinates
💡 Formula:
Lower RMSE = higher accuracy.
Used in:
- Georeferencing
- GPS accuracy checks
- DEM validation
- Cadastral map alignment
⭐ D. User’s Accuracy & Producer’s Accuracy
Used mainly in classification accuracy assessments (remote sensing, land use mapping).
✔️ Producer’s Accuracy
Correctness from the mapper’s perspective.
✔️ User’s Accuracy
Reliability from the user’s perspective.
🧪 Practical Example (QGIS/ArcGIS)
📌 Case: Accuracy Assessment of a Georeferenced Topographic Map
Input:
- Scanned topo map
- Ground control points (GCPs)
- GPS survey points
Steps:
1️⃣ Georeference the map
2️⃣ Compare GPS points with map coordinates
3️⃣ Calculate error for each point
4️⃣ Compute RMSE
5️⃣ Evaluate if RMSE ≤ acceptable limit (e.g., < 3m)
Output:
Map validation report with RMSE and accuracy class.
📝 Short Questions
- Define data quality.
- What is spatial accuracy?
- What is attribute accuracy?
- What do you mean by temporal accuracy?
- Define logical consistency.
- What is lineage?
- What is RMSE?
- Differentiate between accuracy and precision.
- What is error propagation?
- What is producer’s accuracy?
- What is user’s accuracy?
- Mention two sources of attribute errors.
📝 Long / Analytical Questions
- Explain data quality and its major components.
- Discuss spatial, attribute, and temporal accuracy with examples.
- What is logical consistency? Explain common logical errors in GIS.
- Define lineage and explain its importance in GIS metadata.
- Explain RMSE with formula and an example.
- Differentiate between accuracy and precision with diagrams.
- Discuss error propagation and its impacts in GIS analysis.
- Write a detailed note on data accuracy assessment.
- Explain user’s and producer’s accuracy with examples.
- Describe a case where poor data quality affected GIS results.
🎯 Key Takeaways
✔️ Data quality determines the reliability and correctness of GIS results.
✔️ Five major components: spatial, attribute, temporal, logical consistency, lineage.
✔️ RMSE is the key indicator of positional accuracy.
✔️ Good data quality prevents error propagation and improves analysis.
✔️ Essential for cadastral work, surveying, planning, hazard mapping & municipal GIS.
📂 Download Free PDF Notes – Chapter 4: Data Quality
🔽 [Download Notes – Chapter 4 (PDF)]
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