Geospatial Data Models, Vector And Raster Data Model
- 1. Geospatial Data Model (Vector And Raster Data Model) IRS -502 Session 2024-25 (4th batch) Institute of Remote Sensing and GIS Jahangirnagar University 1
- 2. Vector (discrete) and Raster (continuous) Models GIS works with three fundamentally different types of geographic data models: Vector (discrete), Raster (continuous) and triangulated irregular network (TIN)
- 4. 4 Data models are at times interchangeable in that many phenomena may be represented with either the vector or raster approach. Pm 2.5 Source: Paul Bolstad (2016)
- 5. 5 Vector data model and Raster data model can represent same phenomena E.g. Elevation represented as surface (continuous field) using raster grid or as lines representing contours of equal elevation (discrete objects), or as points of height (Z values). Data can be converted from one conceptual view to another E.g. raster data layer can be derived from contour lines, point cloud Selection of raster or vector model depends on the application or type of operations to be performed E.g. Elevation represented as surface (continuous field) in raster - to easily determine slope, or as discrete contours if printed maps of topography Data Model Concepts
- 6. In the vector model, information about points, lines and polygons is encoded and stored as a collection of x,y coordinates The vector model is extremely useful for describing discrete features, but less useful for describing continuously varying features such as soil type The raster model has evolved to model such continuous features Modern GISs are able to handle all three models All three models (vector, raster & TIN) have unique advantages and disadvantages Data Model Concepts
- 7. 7 Data model is the objects in a spatial database plus the relationships among them Coordinates are used to define the spatial location and extent of geographic objects Attribute/non-spatial data are linked with coordinate data to define each spatial object in the spatial database Most conceptualizations or models view the world as set of layers Each layer organizes the spatial and attribute data for a given set of cartographic/spatial objects E.g. Lake, river, road, etc. Data Model Concepts Coordinates define spatial location and shape. Attributes record the important non-spatial characteristics of features in a vector data model Source: Paul Bolstad (2016)
- 8. 8 Vector Data Model (Point) There are three basic types of vector objects: points, lines and polygons Vector data model uses sets of coordinates and associated attribute data to define discrete objects Point objects in spatial database represent location of entities considered to have no dimension Simplest type of spatial objects E.g. wells, sampling points, poles, telephone towers, and accident location.
- 9. • Line objects are used to represent linear features using ordered set of coordinate pairs.A line is one-dimensional and has the property of length, in addition to location. E.g. infrastructure networks (transport networks: highways, railroads, etc.); utility networks: (gas, electric, telephone, water, etc. natural networks such as river channels • Typically have a starting point, an ending point, and intermediate points to represent the shape of the linear entity. • Starting points and ending points for a line are referred to as nodes, while intermediate points in a line are referred to as vertices 9 Source: Paul Bolstad (2016) Vector Data Model (Line)
- 10. 10 Vector Data Model (polygon) Polygon objects in spatial database represent entities which covers an area. A polygon is two-dimensional and has the properties of area (size) and perimeter, in addition to location. E.g. lakes, Buildings, parcels, etc. Area entities are most often represented by closed polygons.These polygons are formed by a set of connected lines, either one line with an ending point that connects back to the starting point, or as a set of lines connected start-to-end.
- 15. Sl. No. Characteristic Vector Structure Raster Structure 1. Data structure Complex Simple 2. Ease of learning Difficult – software is complex Easy - functions tend to be more intuitive than in vector 3. Positional precision Can be very precise and thus accurate Precision increased with increased processing time and data storage needs accuracy. Limited by pixel size 4. Attribute precision Good for polygon, point and line data; not good for continuous data unless connected to TIN or similar technology Good for continuous data; limited by size of pixels in representing attribute distribution in real world 5. Comprehensive ness of analysis capability Good for spatial query and relatively simple data, analysis-limited to Intersections Not good for spatial query but very good for spatial analysis filtering, and modeling
- 16. Sl. No. Characteristic Vector Structure Raster Structure 6. Overlay ability Limited, but overlaying many layers can cause many splinters, etc. in the result which are difficult to eliminate Because all pixels line up, overlay procedures do not create problems 7. Storage requirements Relatively small but complex Relatively large and simple but may be complex 8. Ability to work with image data Poor - data must be vectorized first Good - uses same kind of data structure 9. Conversion to other map projections Usually included in package and relatively simple to do Difficult and quite often creates warped images which do not fill the raster, causing problems with neighborhood functions 10. Ability to work with network data structures Good - because system can handle lines Poor - raster structure not amenable to network 11. Cost Expensive Inexpensive 12. Output map quality Very good - looks like a map Poor - doesn't look like a map to lay people
- 17. The measure of how closely pixels can be resolved in an image is called spatial resolution, and it depends on properties of the system creating the image. For practical purposes the clarity of the image is decided by its spatial resolution. Spatial resolution
- 19. 19 TIN Data Model Triangulated Irregular Network (TIN) is data model commonly used to represent terrain heights x, y, and z locations, used as measured points inTIN Result in TIN composed of nodes, lines and triangulated faces TIN used for digital elevation models (DEM) or digital terrain models (DTM) Very efficient way of representing topography