GIS data continuous discrete ordinal interval ratio

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.

Characteristics:
- Represent distinct objects or occurrences.
- Commonly represented as vector data (points, lines, polygons).
- Values within a range are whole numbers or categories.
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.

Characteristics:
- Represent gradual changes across space or time.
- Commonly represented as raster data (grid format).
- Values can have decimal precision.
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.

Characteristics:
- Used for identification or classification.
- No inherent order or hierarchy.
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.

Characteristics:
- Represents ranked or hierarchical data.
- The differences between categories are not uniform or measurable.
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.

Characteristics:
- Arbitrary zero point; ratios are not meaningful.
- Allows for addition and subtraction but not multiplication or division.
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.

Characteristics:
- Zero indicates the absence of the phenomenon.
- Allows for all mathematical operations (addition, subtraction, multiplication, division).
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.

Spatial Scale:
- Discrete: Mapping individual trees at a local scale.
- Continuous: Representing forest density at a regional scale.
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

Comments

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification: - You have prior knowledge about the c...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Scope of Disaster Management

Disaster management refers to the systematic approach to managing and mitigating the impacts of disasters, encompassing both natural hazards (e.g., earthquakes, floods, hurricanes) and man-made disasters (e.g., industrial accidents, terrorism, nuclear accidents). Its primary objectives are to minimize potential losses, provide timely assistance to those affected, and facilitate swift and effective recovery. The scope of disaster management is multifaceted, encompassing a series of interconnected activities: preparedness, response, recovery, and mitigation. These activities must be strategically implemented before, during, and after a disaster. Key Concepts, Terminologies, and Examples 1. Awareness: Concept: Fostering public understanding of potential hazards and appropriate responses before, during, and after disasters. This involves disseminating information about risks, safety measures, and recommended actions. Terminologies: Hazard Awareness: Recognizing the types of natural...

Role of Geography in Disaster Management

Geography plays a pivotal role in disaster management by facilitating an understanding of the impact of natural disasters, guiding preparedness efforts, and supporting effective response and recovery. By analyzing geographical features, environmental conditions, and historical data, geography empowers disaster management professionals to identify risks, plan for hazards, respond to emergencies, assess damage, and monitor recovery. Geographic Information Systems (GIS) serve as crucial tools, providing critical spatial data for informed decision-making throughout the disaster management cycle. Key Concepts, Terminologies, and Examples 1. Identifying Risk: Concept: Risk identification involves analyzing geographical areas to understand their susceptibility to specific natural disasters. By studying historical events, topography, climate patterns, and environmental factors, disaster management experts can predict which regions are most vulnerable. Terminologies: Hazard Risk: The pr...

Vineesh V, Geography

Search This Blog