📘 Unit 3: GIS Data Model

[Total: 14 Hours]

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🔹 3.1 Raster Model and Structure

✅ What is a Raster Model?

A raster model represents the Earth’s surface using a grid of cells (pixels). Each cell contains a value representing information, such as:

• Elevation

• Temperature

• Land cover

• Satellite image reflectance

✅ Structure:

• Organized into rows and columns

• Each cell has a fixed size and resolution

• More suitable for continuous data (e.g., terrain, rainfall)

✅ Characteristics:

• Easy to perform mathematical operations

• Large file size for high-resolution rasters

• Not ideal for representing discrete objects like roads or buildings

🔹 3.2 Vector Representation

✅ What is a Vector Model?

The vector model represents geographical features using geometric shapes:

• Points: e.g., wells, street lights

• Lines: e.g., roads, rivers

• Polygons: e.g., lakes, land parcels

Each shape is associated with attribute data in a table.

✅ Characteristics:

• Best for discrete features with clear boundaries

• Offers higher precision in spatial representation

• Easier to manage in attribute-rich datasets

• Smaller file sizes for detailed mapping

🔹 3.3 Surface Representation in Raster Model

In the raster model, surfaces (like elevation or temperature) are represented as:

• Grid-based cells, where each pixel contains a numeric value indicating the magnitude of the surface property at that location.

✅ Example Applications:

• Digital Elevation Models (DEMs)

• Land cover classification

• Satellite imagery

✅ Advantages:

• Suitable for complex surface modeling

• Simple structure for spatial analysis and overlays

🔹 3.4 Surface Representation in Vector Model

In the vector model, surfaces are represented using:

• Contour lines: Lines connecting points of equal value (e.g., elevation)

• TINs (Triangulated Irregular Networks): Surface constructed from triangles based on sample points

✅ Example Applications:

• Topographic maps

• Terrain modeling for engineering purposes

✅ Advantages:

• More precise representation of features

• Efficient for data querying and attribute management

🧪 Practical Works Using QGIS

Here are practical exercises using QGIS to apply what you've learned:

🔸 1. Analyzing Raster Data

• Load raster data (e.g., DEM, satellite image)

• Use tools like:

  • Raster Calculator

  • Hillshade, Slope, and Aspect analysis

• Perform classification of raster data (e.g., land use)

🔸 2. Combining Raster and Vector Data

• Overlay vector layers (e.g., roads, administrative boundaries) on raster maps

• Clip raster data using a vector boundary

• Extract raster values for vector features using Sampling tools

🔸 3. Leveraging Spatial Databases

• Connect to a PostGIS database

• Store large vector and raster datasets

• Perform SQL-based spatial queries

🔸 4. Advanced Vector Styling

• Apply categorized or graduated color schemes

• Use rule-based symbology

• Set data-defined properties for dynamic visualization

🔸 5. Labeling

• Add labels to vector features using attributes

• Customize:

  • Font size and color

  • Placement (e.g., above or beside features)

  • Conditional labeling (e.g., label only cities with population > 10,000)

🔸 6. Designing Print Maps

• Use Layout Manager to create printable maps

• Add:

  • Title

  • Legend

  • North arrow

  • Scale bar

• Export to PDF, PNG, or print directly

🔸 7. Presenting Maps Online

• Use QGIS plugins (e.g., qgis2web) to export web maps

• Publish vector/raster layers as interactive maps

• Upload to platforms like:

  • GitHub Pages

  • Web servers

  • QGIS Cloud

📝 Summary

Concept	Raster Model	Vector Model
Data Structure	Grid of cells (pixels)	Geometries (points, lines, polygons)
Best For	Continuous data (e.g., elevation)	Discrete data (e.g., buildings)
File Size	Large (high resolution)	Smaller
Precision	Depends on cell size	High
Common Uses	Remote sensing, terrain analysis	Urban planning, cadastral mapping