In Geographic Information Systems (GIS), data is categorized based on its nature (discrete or continuous) and its measurement scale (nominal, ordinal, interval, or ratio). These distinctions influence how the data is collected, analyzed, and visualized. Let's break down these categories with concepts, terminologies, and examples:
1. Discrete Data
Discrete data is obtained by counting distinct items or entities. Values are finite and cannot be infinitely subdivided.
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Characteristics:
- Represent distinct objects or occurrences.
- Commonly represented as vector data (points, lines, polygons).
- Values within a range are whole numbers or categories.
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Examples:
- Number of People: Counting individuals on a train or in a hospital.
- Building Types: Categorizing buildings as residential, commercial, or industrial.
- Tree Count: Number of trees in a specific area.
2. Continuous Data
Continuous data is obtained by measuring phenomena that can take any value within a range.
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Characteristics:
- Represent gradual changes across space or time.
- Commonly represented as raster data (grid format).
- Values can have decimal precision.
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Examples:
- Height: Measuring elevation across a terrain (Digital Elevation Model).
- Temperature: Mapping temperature variations across a region.
- Precipitation: Rainfall measured continuously across time and space.
3. Measurement Scales
a. Nominal Scale
Data is categorized without any implied order or numerical significance.
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Characteristics:
- Used for identification or classification.
- No inherent order or hierarchy.
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Examples:
- Unique Identifiers: Social Security Numbers (SSN).
- Land Cover Types: Forest, water, urban, etc.
- Administrative Boundaries: States, countries.
b. Ordinal Scale
Data is categorized and ordered, but the intervals between categories are not meaningful.
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Characteristics:
- Represents ranked or hierarchical data.
- The differences between categories are not uniform or measurable.
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Examples:
- Education Level: Primary, secondary, tertiary.
- Income Level: Low, medium, high.
- Slope Classes: Gentle, moderate, steep.
c. Interval Scale
Numerical data with meaningful differences between values but no true zero point.
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Characteristics:
- Arbitrary zero point; ratios are not meaningful.
- Allows for addition and subtraction but not multiplication or division.
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Examples:
- Temperature: Celsius or Fahrenheit scales (e.g., 20°C is not twice as warm as 10°C).
- Credit Scores: A range indicating financial risk.
- pH Value: Measurement of acidity or alkalinity.
d. Ratio Scale
Numerical data with meaningful differences and a true zero point.
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Characteristics:
- Zero indicates the absence of the phenomenon.
- Allows for all mathematical operations (addition, subtraction, multiplication, division).
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Examples:
- Flow Rate: Water flow in a river (e.g., 0 m³/s indicates no flow).
- Pulse Rate: Heartbeats per minute.
- Weight: Measured in kilograms or pounds.
4. Discrete vs. Continuous Phenomena
Whether data is considered discrete or continuous can depend on scale (spatial or temporal) and perspective.
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Spatial Scale:
- Discrete: Mapping individual trees at a local scale.
- Continuous: Representing forest density at a regional scale.
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Temporal Scale:
- Discrete: Daily rainfall totals for a week.
- Continuous: Hourly rainfall trends over time.
Summary Table
| Category | Definition | Examples |
|---|---|---|
| Discrete | Countable entities with finite values. | Number of buildings, road types, tree count. |
| Continuous | Measurable phenomena with infinite values. | Elevation, temperature, precipitation. |
| Nominal | Categorized data without order. | Land use types, unique IDs, administrative zones. |
| Ordinal | Ordered categories without uniform intervals. | Income levels, slope classes, risk levels. |
| Interval | Numerical data with arbitrary zero. | Temperature (°C), credit scores, pH values. |
| Ratio | Numerical data with true zero. | Weight, length, flow rate, pulse rate. |
Fyugp note
GIS second semester
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