CTEVT || GIS Application || Unit 1 || Data Integration || EG3103GE || Diploma in Geomatics Engineering

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📚 CTEVT | GIS Application – Chapter 1: Data Integration (Full Notes + Practice Questions + Complete PDF )




GIS Applications (EG 3103 GE) begins with Unit 1: Data Integration, the foundation of all GIS workflows.
Before spatial analysis, visualization, or project development, GIS users must integrate multiple datasets into a single coordinate framework.

This chapter explains how different types of data—maps, coordinates, images, tables, and cadastral layers—are combined to form a consistent GIS database.

🧩 Chapter Overview: Data Integration

Data Integration means combining multiple data sources so that they work together seamlessly in a GIS environment.
It ensures all datasets:

✔️ Line up correctly
✔️ Use the same coordinate system
✔️ Share common attributes
✔️ Are compatible for spatial analysis

Real-world projects—land-use planning, cadastral mapping, road network analysis, hazard mapping—depend heavily on data integration.


📥 Download the Full Chapter 1 Notes (Free PDF)
🎥 Watch the Chapter 1 Video on YouTube

🎥 Watch the Full Chapter 1 Video

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🔍 1.1 Concept of Data Integration

Data Integration = The process of combining spatial data + attribute data from different sources into a unified GIS database.

🔹 Sources may include:

  • Topographic maps
  • Cadastral maps
  • Satellite images
  • Survey data (TS / GPS)
  • Administrative boundaries
  • Tables (Excel/CSV)
  • DEM, contours, LiDAR data

✨ Why integration is needed:

Each dataset is created at different times, scales, formats, and coordinate systems.

GIS brings them into a common environment so they can support analysis and planning.

⭐ 1.2 Importance of Spatial Data Integration

Data integration is essential because:

✔️ Ensures data compatibility

Misaligned layers = wrong results
(e.g., parcel map not matching orthophoto)

✔️ Supports accurate decision-making

Integrated data gives a complete picture for:

  • Urban planning
  • Disaster risk mapping
  • Transportation planning
  • Resource management

✔️ Enables advanced analysis

Overlay, network, 3D, suitability analysis require aligned & coordinated data.

✔️ Helps maintain a unified GIS database

Survey offices, municipalities, and planning units need standardized datasets.

🛠️ 1.3 Process of Data Integration

Step-by-step Process:

1️⃣ Data Collection

Gather maps, images, survey points, tables, DEM, shapefiles.

2️⃣ Data Preprocessing

  • Clean errors
  • Remove duplicate features
  • Topology correction
  • Attribute editing

3️⃣ Convert into GIS formats

  • CAD → Shapefile
  • Raster → GeoTIFF
  • Tables → CSV to attribute tables

4️⃣ Projection & Coordinate Transformation

All datasets must be in same projection + datum
(e.g., MUTM 45N, WGS84)

5️⃣ Join and Relate Data

Link spatial features with attribute tables.

6️⃣ Integration & Layer Alignment

Overlay, snap, merge, append, mosaic datasets.

7️⃣ Quality Checking

Check:

  • Alignment
  • Accuracy
  • Attribute completeness
  • Topology consistency

🔗 1.4 Join and Relate Spatial & Attribute Data

GIS integrates attribute data with spatial features using common fields.

✔️ JOIN (One-to-One / Many-to-One)

Attach a table to a spatial layer using a common key.

Example:
Join a parcel shapefile (Parcel_ID) with owner information table (Parcel_ID).

Result:
Each parcel polygon now displays landowner details.

✔️ RELATE (One-to-Many)

Connect tables without merging.

Example:
A parcel may have multiple ownership records or multiple tax records.

Relate maintains the relationship without combining tables.

🌐 1.5 Projection & Transformation of Spatial Data

Different datasets come in different coordinate systems.

✔️ Projection

Mathematical method to display earth’s curved surface on a flat map.

✔️ Transformation

Converts coordinates from one datum/projection to another.

🔹 Common Systems in Nepal:

  • MUTM Zone 45N / 46N
  • Everest 1830 datum
  • WGS 84 geographic (lat/long)
  • Local grids in municipalities

❗ Importance:

If layers use different projections, they won’t align, producing wrong analysis.

Example in QGIS:

Raster → WGS84
Vector → MUTM 45N
Solution: Reproject raster to MUTM 45N using:
Raster > Projections > Warp (Reproject)

🗺️ 1.6 Integration of Cadastral Data

Cadastral data includes:

  • Parcel boundaries
  • Plot numbers
  • Ownership tables
  • Land-use info
  • Field books

Integrating cadastral data is crucial for:

  • Land administration
  • Urban planning
  • Road expansion
  • Tax mapping

✔️ Steps in Cadastral Integration:

1️⃣ Convert cadastral maps (DXF) into GIS format

AutoCAD → DXF → QGIS/ArcGIS

2️⃣ Georeference the map

Using control points (benchmarks)

3️⃣ Digitize parcels

Polygon creation, snapping, topology rules

4️⃣ Enter ownership data

Join parcel polygons with landowner tables

5️⃣ Align with orthophoto

Shift/warp using rubbersheeting if needed

6️⃣ Final Integration

All layers aligned with national reference system (MUTM)

🧪 Practical Example (QGIS/ArcGIS)

Case: Integrating Cadastral Map with Satellite Image

Input:

  • Parcel Map: DXF
  • Satellite Image: GeoTIFF (WGS84)
  • Owner Data: Excel table

Steps:

  1. Import DXF
  2. Clean topology
  3. Georeference using MUTM control points
  4. Reproject satellite image to MUTM
  5. Digitize parcels
  6. Join with owner Excel table
  7. Overlay with administrative boundaries
  8. Final integrated dataset ready for analysis

🔍 Diagram Descriptions (Text-Based)

Diagram 1: Data Integration Workflow

Data Collection → Data Cleaning → Projection → Joining → Integration → Quality Check

Diagram 2: Join & Relate

Parcel Layer ----JOIN----> Ownership Table

Diagram 3: Projection Transformation

WGS84 (Lat/Long) ----→ MUTM 45N (Meters)

📝 Short Questions 

  1. Define data integration.
  2. Why is data integration important in GIS?
  3. What is spatial data integration?
  4. List any four sources of GIS data.
  5. What is the difference between join and relate?
  6. Define projection and transformation.
  7. What is MUTM?
  8. Why is projection standardization important?
  9. What types of errors arise without data integration?
  10. What is cadastral data?
  11. Mention steps in integrating cadastral maps.
  12. What is a coordinate system?

📝 Long / Analytical Questions 

  1. Explain the concept and importance of data integration in GIS applications.
  2. Describe the step-by-step process of spatial data integration with examples.
  3. Discuss join and relate functions with suitable diagrams.
  4. Explain projection and transformation and their significance in GIS.
  5. Describe how cadastral data is integrated into GIS.
  6. Compare different sources of spatial data used in GIS integration.
  7. Write in detail about errors caused by improper data integration.
  8. Explain the workflow of combining raster and vector datasets for spatial planning.
  9. How do coordinate systems affect spatial data alignment?
  10. Describe a real-world example of integrating multi-source GIS data for a municipal project.

🎯 Key Takeaways

  • Data integration is the first and most important step in GIS.
  • Ensures datasets match in projection, scale, and attributes.
  • Basis for spatial analysis, network modeling, 3D analysis, and GIS project development.
  • Essential in Nepal’s survey, land administration, and urban planning sectors.


📂 Download Free PDF Notes – Chapter 1: Data Integration

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