Raster Data In Gis Computer Science

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A Geographical Information System is a method of spatially storing, analysing, manipulating, managing and displaying geographical data. GIS data represents real objects such as roads, rivers, urban areas, place names, railway, places of interest, town names etc. with digital data determining the mix. A geodatabase is a database that is in some way referenced to locations on earth. Traditionally, there are two broad methods used to store data in a GIS; raster images and vector. Ordnance Survey Ireland (OSI) data is supplied in both Vector and Raster format. In both cases the data is geo-referenced.

Vector data is split into three types; polygon, line (or arc) and point data. Vector is a method for storing spatial data involving assigning coordinates for each entity; an X,Y, Z for a point, a pair of such points for a line and a series of such lines for a polygon. This method is very useful for modeling discrete physical features.

Different geographical features are expressed by different types of geometry:

Points

A point is a zero-dimensional abstraction of an object represented by a single X, Y co-ordinate. It is normally used to represent a geographic feature too small to be displayed as a line or an area (e.g. location of a building on a small scale map or, for example, cities on a map of the world might be represented by points not polygons). No measurements are possible with point features.

Figure 1- Vector representation

Source: http://www.geom.unimelb.edu.au/gisweb/GISModule/GIST_Vector.html

Lines or polylines

A set of co-ordinates that represent the shape of geographic features that are too narrow to be displayed as an area, such as, county boundary lines or contours. At small scales geographic features may have no area, e.g. streams or streets and may be represented as linear features rather than as a polygon. Line features can measure distance.

Polygons

Polygons are used to represent areas. Such as lakes, park boundaries or land uses etc. Polygons convey the most amount of information of the file types and can measure perimeter and area.

Rigaux et al. (2002:p.38) states, 'A point is represented by its pair of coordinates, whereas more complex linear and surfacic objects are represented by structures (lists, sets, arrays) on the point representation.' These geometries can be linked to a row in a database that describes their attributes. For example, a database that describes lakes may contain a lake's depth, water quality, pollution level. Different geometries can also be compared and the GIS could be used, for example, to identify all wells (point geometry) that are within one kilometre of a lake (polygon geometry) that has a high level of pollution. Vector data can be displayed at any scale and individual layers (e.g. roads, buildings, etc) can be displayed or omitted (see Appendix A).

Raster

Ellis states 'that raster is a method for the storage, processing and display of spatial data.' There are three types of raster datasets; thematic data, spectral data and pictures. Raster data consists of rows and columns of cells, with each cell storing a single value. Raster data can be images containing individual dots with colour values, called cells (or pixels), arranged in a rectangular evenly spaced array.

'Each cell must be rectangular in shape, but not necessarily square' (Ellis 2001). Each cell within this matrix contains location co-ordinates as well as an attribute value. The spatial location of each cell is implicitly contained within the ordering of the matrix, unlike a vector structure which stores topology explicitly. Areas containing the same attribute value are recognised as such, however, raster structures cannot identify the boundaries of areas such as polygons.

Raster data is an abstraction of the real world where spatial data is expressed as a matrix of cells or pixels with spatial position implicit in the ordering of the pixels. With the raster data model, spatial data is not continuous but divided into discrete units. Ellis states that 'this makes raster data particularly suitable for certain types of spatial operation, for example overlays or area calculations.'

Raster structures may lead to increased storage in certain situations, since they store each cell in the matrix regardless of whether it is a feature or simply 'empty' space. Additional values recorded for each cell may be a discrete value, such as land use, a continuous value, such as temperature, or a null value if no data is available. While a raster cell stores a single value, it can be extended by using raster bands to represent RGB (red, green, blue) colours, colour maps (a mapping between a thematic code and RGB value), or an extended attribute table with one row for each unique cell value. The resolution of the raster data set is its cell width in ground units.

Anyone who is familiar with digital photography will recognize the Raster graphics pixel as the smallest individual grid unit building block of an image, usually not readily identified as an artifact shape until an image is produced on a very large scale (see Appendix B). A combination of the pixels making up an image colour formation scheme will compose details of an image, as is distinct from the commonly used points, lines, and polygon area location symbols of vector graphics. Aerial photographs and satellite images are examples of raster images used in mapping.

Figure 2 -

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