Components of Geographical Referenced Data: Vector and Raster Data Model (Especially for GATE-Geospatial 2022)

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The ability of GIS to handle and process geographically referenced data, distinguish GIS from other Information systems.

Spatial Data

Spatial data describes location of spatial features that can be either discrete or continuous.

Discrete Features are individually different features that do not exist between observation like number of people, population, pin code etc. It includes Point, Line and Areas (Polygon) .

Continuous Features are features that exsit spatially between obsevations. Examples of continuous are height, temperature, time and rain.

Image Shows Discrete Feature Example

For example, if we can count there are 10 drops, we can say that the data is discrete but in case of a flowing tap, it is nearly impossible to count the drops and so we say data is continuous.

GIS helps to represent these spatial features on the Earth՚s surface as Map features on a plain surface and there are two major transformations involved:

  • Spatial reference system
  • Data model

Location of the spatial features on the Earth՚s surface are based on a geographic coordinate system with latitude and longitude values, whereas the location of map features are based on the plain coordinate system with X, Y coordinates.

Projection is the process the can transform the Earth՚s spherical surface to a plane surface and bridge the two spatial reference system. The Data Model defines how spatial features are represented in a GIS.

Vector Data Model

Uses Points and their X and Y coordinates to constructs spatial features of Point, Line and Area (Polygon) . These data are geometrically and mathematically associated and stored as coordinates. For example, point can be denoted as (x1, y1) while line can be denoted as (x1, y1) to (x2, y2) .

Vector Data Model Shows X and Y Coordinates

Here (x1, y1) denotes a point while (x1, y1) to (x2, y2) denotes a line. Vector formats map data by storing the spatial coordinates, which define individual entities, and such models can be geo-relational or object based.

Raster Data Model

Uses grid and grid cells (commonly called as pixels) to represent the spatial variation of features. Here each location has a pixel and the attributes are explained as color, elevation or ID number. Raster data formats map data by dividing space into equal sized grids and then coding each cell with appropriate value. It uses simple data structure with rows and columns and fixed cell location.

Figure Shows Examples of Vector Data and Raster Data View

Understanding Raster and Vector View of the Same Spatial Entity

Image Shows X and Y Coordinates to Constructs Spatial Featur …

Below diagram explains the raster and vector view of the sample spatial entity on earth. For this purpose 5 entities are taken into account:

  • Point
  • Line
  • Area (Polygon)
  • Network
  • Surface (Elevation)
Image Shows Understanding Raster and Vector View

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