Skip to main content

gis data continuous and discrete

  • Discrete GIS data refers to geographic data that only exists in specific locations, rather than being continuous across an entire area.


  • Discrete data is characterized by having well-defined boundaries, particularly for polygon data. This means that the data is constrained within certain limits and does not extend indefinitely.


  • Examples of discrete GIS data include point and line data, such as the location of trees, rivers, and streets. These data types are inherently discrete because they occur at specific locations and are not continuous across the landscape.


  • Discrete GIS data can be contrasted with continuous GIS data, which is data that varies smoothly across space without any well-defined boundaries. An example of continuous data might be temperature or elevation measurements.


  • Discrete GIS data is particularly useful for mapping specific features, such as infrastructure or natural resources, that are present in limited, specific locations. By contrast, continuous data is more useful for identifying patterns and trends across a larger area.


  • Discrete GIS data can be represented in various ways, including as points, lines, and polygons, depending on the nature of the data and the purpose of the mapping. For example, roads might be represented as lines, while individual trees might be represented as points.

  • Continuous GIS data is geographic data that varies smoothly across space without any well-defined boundaries, in contrast to discrete data which is constrained to specific locations.


  • Examples of continuous GIS data include elevation, slope, temperature, precipitation, and other environmental or climatic measurements that vary continuously across the landscape.


  • Every point on a map made with continuous GIS data will contain a value, indicating the value of the measured variable at that location.


  • Unlike discrete data, which has well-defined boundaries, continuous data is characterized by a lack of clear limits or borders between different values. Instead, the values vary smoothly across space, with no abrupt changes or discontinuities.


  • Continuous GIS data is particularly useful for identifying patterns and trends across a larger area, such as mapping the distribution of rainfall or temperature across a region.


  • Continuous data can be contrasted with discrete GIS data, which is data that only exists in specific locations and is characterized by well-defined boundaries.


  • Continuous GIS data can be represented in various ways, including as contour lines, heat maps, and color-coded surfaces, depending on the nature of the data and the purpose of the mapping.


  • GIS analysts use various tools and methods to process, analyze, and visualize continuous GIS data, including statistical methods, interpolation, and spatial analysis techniques.

  • Most ArcGIS applications use discrete geographic information, which is characterized by well-defined boundaries and specific locations. Examples include landownership, soils classification, zoning, and land use.


  • Discrete data is typically represented by nominal, ordinal, interval, and ratio values, depending on the nature of the data and the level of measurement.


  • Nominal data is data that cannot be ranked or ordered, such as landownership or soil type. Ordinal data is data that can be ranked, but the differences between the values are not necessarily equal, such as zoning categories.


  • Interval data is data where the differences between values are meaningful and can be measured, but there is no true zero point, such as temperature measurements. Ratio data is data where there is a true zero point, such as weight or height.


  • Surfaces, on the other hand, are continuous data that vary smoothly across space without any well-defined boundaries. Examples of surfaces include elevation, rainfall, pollution concentration, and water tables.


  • Continuous data can be represented in various ways, such as contour lines, heat maps, and color-coded surfaces, depending on the nature of the data and the purpose of the mapping.


  • GIS analysts use various tools and methods to process, analyze, and visualize both discrete and continuous GIS data, including statistical methods, interpolation, and spatial analysis techniques. 

Comments

Popular posts from this blog

Photogrammetry – Types of Photographs

In photogrammetry, aerial photographs are categorized based on camera orientation , coverage , and spectral sensitivity . Below is a breakdown of the major types: 1️⃣ Based on Camera Axis Orientation Type Description Key Feature Vertical Photo Taken with the camera axis pointing directly downward (within 3° of vertical). Used for maps and measurements Oblique Photo Taken with the camera axis tilted away from vertical. Covers more area but with distortions Low Oblique: Horizon not visible High Oblique: Horizon visible 2️⃣ Based on Number of Photos Taken Type Description Single Photo One image taken of an area Stereoscopic Pair Two overlapping photos for 3D viewing and depth analysis Strip or Mosaic Series of overlapping photos covering a long area, useful in mapping large regions 3️⃣ Based on Spectral Sensitivity Type Description Application Panchromatic Captures images in black and white General mapping Infrared (IR) Sensitive to infrared radiation Veget...

Photogrammetry – Geometry of a Vertical Photograph

Photogrammetry is the science of making measurements from photographs, especially for mapping and surveying. When the camera axis is perpendicular (vertical) to the ground, the photo is called a vertical photograph , and its geometry is central to accurate mapping.  Elements of Vertical Photo Geometry In a vertical aerial photograph , the geometry is governed by the central projection principle. Here's how it works: 1. Principal Point (P) The point on the photo where the optical axis of the camera intersects the photo plane. It's the geometric center of the photo. 2. Nadir Point (N) The point on the ground directly below the camera at the time of exposure. Ideally, in a perfect vertical photo, the nadir and principal point coincide. 3. Photo Center (C) Usually coincides with the principal point in a vertical photo. 4. Ground Coordinates (X, Y, Z) Real-world (map) coordinates of objects photographed. 5. Flying Height (H) He...

Raster Data Structure

Raster Data Raster data is like a digital photo made up of small squares called cells or pixels . Each cell shows something about that spot — like how high it is (elevation), how hot it is (temperature), or what kind of land it is (forest, water, etc.). Think of it like a graph paper where each box is colored to show what's there. Key Points What's in the cell? Each cell stores information — for example, "water" or "forest." Where is the cell? The cell's location comes from its place in the grid (like row 3, column 5). We don't need to store its exact coordinates. How Do We Decide a Cell's Value? Sometimes, one cell covers more than one thing (like part forest and part water). To choose one value , we can: Center Point: Use whatever feature is in the middle. Most Area: Use the feature that takes up the most space in the cell. Most Important: Use the most important feature (like a road or well), even if it...

Photogrammetry

Photogrammetry is the science of taking measurements from photographs —especially to create maps, models, or 3D images of objects, land, or buildings. Imagine you take two pictures of a mountain from slightly different angles. Photogrammetry uses those photos to figure out the shape, size, and position of the mountain—just like our eyes do when we see in 3D! Concepts and Terminologies 1. Photograph A picture captured by a camera , either from the ground (terrestrial) or from above (aerial or drone). 2. Stereo Pair Two overlapping photos taken from different angles. When seen together, they help create a 3D effect —just like how two human eyes work. 3. Overlap To get a 3D model, photos must overlap each other: Forward overlap : Between two photos in a flight line (usually 60–70%) Side overlap : Between adjacent flight lines (usually 30–40%) 4. Scale The ratio of the photo size to real-world size. Example: A 1:10,000 scale photo means 1 cm on the photo...

Logical Data Model in GIS

In GIS, a logical data model defines how data is structured and interrelated—independent of how it is physically stored or implemented. It serves as a blueprint for designing databases, focusing on the organization of entities, their attributes, and relationships, without tying them to a specific database technology. Key Features Abstraction : The logical model operates at an abstract level, emphasizing the conceptual structure of data rather than the technical details of storage or implementation. Entity-Attribute Relationships : It identifies key entities (objects or concepts) and their attributes (properties), as well as the logical relationships between them. Business Rules : Business logic is embedded in the model to enforce rules, constraints, and conditions that ensure data consistency and accuracy. Technology Independence : The logical model is platform-agnostic—it is not tied to any specific database system or storage format. Visual Representat...