📘 Unit 2: Reading, Analyzing and Interpreting Maps

[Total: 12 Hours]

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🔹 2.1 Space Categorization on a Map

✅ What is Space Categorization?

Space categorization means dividing the map into different regions or zones based on:

• Land use (residential, industrial, agricultural)

• Natural features (mountains, rivers, forests)

• Administrative boundaries (wards, municipalities)

This helps us to analyze data more effectively and focus on specific areas.

✅ Why is it important?

• Helps in planning and development

• Supports resource allocation

• Aids in thematic mapping (e.g., rainfall zones, population density)

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🔹 2.2 Levels of Measurement

There are four main levels of data measurement in GIS:

Level	Description	Example
Nominal	Categories with no order	Land use type (forest, urban)
Ordinal	Ordered categories	Soil quality (good, average, poor)
Interval	Numeric, no true zero	Temperature (°C, °F)
Ratio	Numeric with a true zero	Population, rainfall, distance

Understanding the data type helps in:

• Choosing the right analysis method

• Applying correct symbology (color/symbol styles)

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🔹 2.3 Symbology and Data Measurement Relationship

✅ What is Symbology?

Symbology refers to the visual representation of data on a map:

• Colors

• Shapes

• Sizes

• Line styles

• Symbols

✅ Why it matters:

Different types of data require different styles. For example:

• Nominal: Different colors for land use types

• Ordinal: Gradual color shades (light to dark)

• Interval/Ratio: Continuous color ramps or proportional symbols

Using correct symbology improves map readability and helps convey the right message.

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🔹 2.4 Pattern Recognition

✅ Definition:

Pattern recognition is the ability to identify spatial arrangements of features on a map.

✅ Types of Patterns:

1. Random – Features are scattered with no clear order
   (e.g., lightning strikes)

2. Clustered – Features are grouped together
   (e.g., shops in a market area)

3. Uniform – Features are evenly spaced
   (e.g., trees in a plantation)

Recognizing these patterns helps in understanding natural or human behavior.

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🔹 2.5 Pattern Analysis and Quantification

✅ What is Pattern Analysis?

It involves examining spatial patterns to find relationships or trends.

✅ Quantification means:

• Measuring how clustered or dispersed data is

• Using tools like spatial statistics, hotspot analysis, or density mapping

This helps in:

• Detecting areas with high or low activity

• Making data-driven decisions

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🔹 2.6 Result Interpretation and Decision Making

Once analysis is complete, we interpret the results to:

• Understand what the data means

• Make practical decisions (e.g., where to build a health center)

• Communicate findings using maps, charts, and reports

In GIS, maps are not just visual tools but analytical instruments for real-world decision-making.

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🧪 Practical Work (Using QGIS)

Use QGIS software to apply theoretical concepts with hands-on experience.

🔸 1. Loading Vector Data from Files

• Go to: Layer > Add Layer > Add Vector Layer

• Load files like shapefiles (.shp), GeoJSON, KML, etc.

🔸 2. Loading Raster Files

• Go to: Layer > Add Layer > Add Raster Layer

• Load .tif, .jpg, .png maps

🔸 3. Styling Raster Layers

• Use symbology tab to:

  • Change color scales

  • Adjust brightness/contrast

  • Apply color ramps (e.g., for elevation or temperature)

🔸 4. Styling Vector Layers

• Change line styles, colors, symbols

• Use categorized or graduated styles based on data

🔸 5. Creating New Vector Layers

• Go to: Layer > Create Layer > New Shapefile Layer

• Choose geometry type (Point, Line, Polygon)

• Add attribute fields

🔸 6. Editing Vector Geometries

• Enable Edit Mode on layer toolbar

• Add/move points, cut polygons, split lines, etc.

🔸 7. Editing Attributes

• Open Attribute Table

• Add, edit, or delete values

• Use Field Calculator for bulk editing or calculations