Raster Data

Raster data models incorporate the use of a grid cell data structure where the geographic are is in to cells identified by rows and column, This data structure is commonly called raster, while the term raster implies a regularly spaced grid other tessellated data structures do exist in grid based GIS system. A grid consist of rows, columns and cells. The origin of rows and columns is at the upper left corner of the grid. Rows function as y-coordinate and column as x-coordinate in a two-dimensional coordinate system. A cell is defined by its location in terms of row and column.

The most popular cell structure is the regularly spaced matrix or raster structure. This data structure involves a division of spatial data into regularly spaced cell is of the same shape and size.

A raster data matrix is in fact a matrix where any coordinate can be quickly calculated if the origin point is known and the size of the grid cells is known.

ELEMENTS OF THE RASTER DATA MODEL

There are three elements in a raster data model. Point, Line and Area.

1. Raster data represents points by single cells,.
   
   
2. Raster data represents lines by sequence of neighboring cells.
   
   
3. Raster data represents areas by collection of contiguous cell. Each cell in a grid carries a value either an integer or a floating value. Integer cell values typically represent categorical data for Ex- A land cover model may use 1 for forest, 2 for water body, 3 for built-up area and so on. Floating point cell value represents continuous data. For ex- A precipitation model may have precipitation values of 2.5, 30.6 and so on. A floating-point grid requires more computer memory than an integer grid.

TYPES OF RASTER DATA

Raster data can be categorized in following types: -

1. Satellite Imagery: - Remotely sensed satellite data are recorded in raster format. The pixel value in a satellite image represents light energy reflected or emitted from earth's surface. By analyzing the pixel values, an image processing system can extract a variety of themes from satellite images, such as land use and land cover, hydrography, water quality and other areas. Satellite images can be displayed in black and white or in color. Satellite images can also simulate color photographs if they have pixel values from the red green and blue spectral bands. The image looks like a color photograph if bands 3,2 and 1 are assigned to red , green and blue respectively, and a color infrared photograph if bands 4,3 and 2 are assigned to red, green and blue respectively.
   
   
2. Binary Scanned Files: - In this type of raster data a scanned image contains value of 0 or 1.
   
   
3. Graphics Files: - In this type of raster data we can include maps, photographs and images which can be stored as digital graphic files. Major popular graphic files in raster format are GIF (Graphic Interchange Format), TIFF (Tagged Image File Format), JPEG ( Joint Photographic Experts Group).
   
   
4. Digital Elevation Models: -A digital elevation model (DEM) consist of an array of uniformly spaced elevation data. A DEM is point-based, but it can easily be converted to raster data by placing each elevation point at the center of a cell.
   
   
5. Digital Orthophotos: - A digital orthophoto quad (DOQ) is a digitized image prepared from an aerial photographs or other remotely sensed data, in which the displacement caused by camera tilt and terrain relief has been removed. A digital orthophoto is geo-referenced and can be registered with topographic and other maps.

COMPACT METHODS FOR STORING RASTER DATA

When each cell has a unique value it takes a total of n rows X m columns X3 values ( X, Y coordinates and attribute value) to encode each overlay. If sets of cells within a polygon or a mapping unit all have the same value, however, it is possible to effect considerable savings in the date storage requirements for the raster data, providing of course that the date structures are properly designed. There are four main ways in which compact storage can be achieved.

1. Chain Code: - Chain code provide a very compact way of storing a region representation and they allow certain operation such as estimation of areas and perimeters or detection of sharp turns and concavities to be carried out easily. On the other hand overlay operations such as union and intersection are difficult to perform without returning to a full grid representation. Another disadvantage is the redundancy introduced because all boundaries between the regions will be stored twice.
   
   
2. Run-Length Codes:- Run-length codes allow the point in each mapping unit to be stired per row in terms, from left to right, of a begin cell and an end cell. These are useful in reducing the volume of the data that need to be input to a simple raster database.
   
   
3. Block Codes:- The data of run- length codes can be extended to two dimensions by using square block to tile the area to be mapped . The data structure consist of just three numbers, the origin (the centre or bottom left) and radius of each square. Both run-length and block codes are clearly most efficient for large simple shapes and so for small-complicated areas that are only a few times larger than the basic cell. For some operations data stored in block run-length codes must be converted to simple raster format.
   
   
4. Quadtree:- The fourth method for more compact representation is based on successive division of the 2n X 2n array into quadrants are wholly content with the region. The lowest limit of division is the single pixel. The block structure can be described by a tree of degree 4, known as the quadtree.

lumes of graphic data.

The use of large cells to reduce data volumes means that phenomenological recognizable structures can be lost and there can be a serious loss of information.

Crude raster maps are considerably less beautiful than maps drawn with fine lines.

Network linkages are difficult to establish.

Projection transformations are time consuming unless spatial algorithms or hardware are used.