Spatial data are what drive a GIS. Spatial features or entities and their attributes are stored in computers using a number of spatial data models. It is vital to understand the characteristics of them since the data model employed has considerable influence on the functionality of the GIS. Spatial data can represent real world features with discrete boundaries (such as roads, buildings, lakes, rivers, administrative boundaries) as well as real world phenomena with non-discrete boundaries (such as precipitation and nutrient levels, terrain). The basic approaches are: raster data model and vector data model. Depending on the type of problem that needs to be solved, the type of maps that need to be made, and the data source, either raster or vector, or a combination of the two can be used. Each data model has strengths and weaknesses in terms of functionality and representation.
| Raster Data Model
| Vector Data Model
| The raster data model is the simpler model and is based on the division of reality into a regular grid of identically shaped cells. Raster data represent the landscape as a rectangular matrix of square cells.In raster data model, attributes are limited to the numeric values of the cells themselves, and while it is possible to link additional attributes to the groups of cells having same values, which is rarely done in practice because of the low utilizing value and cumbersome data management.
| In vector data model, an object’s shape is represented by dots which are located where the shape of the object changes. The dots which are known as vertices are joined by straight lines. Vector data represent features as discrete points, lines, and polygons.In vector model, as a point of difference, vector objects are additionally described by one or more characteristics, in GIS called attributes. Vector files attributes are stored in tables which consists of records (rows) representing individual features, fields (columns) representing a particular theme describing the feature, and attributes that refers to an intersection between a record and a field.
| * The geographical location of each cell is implied by its position in the cell matrix. Accordingly, no geographical coordinates are stored other than an origin point. * Due to the nature of the data storage technique data analysis is usually easy to program and quick to perform. * The inherent nature of raster maps that is one attribute maps, is ideally suited for mathematical modeling and quantitative analysis. * Grid-cell systems are very compatible with raster-based output devices. * As reconnaissance satellites and aerial surveys use raster-based scanners, the information can be directly incorporated into GIS.
| * Data can be represented at its original resolution and form without generalization. * Graphic output is usually more aesthetically pleasing (traditional cartographic representation). * Allows precise representation of points, boundaries, and linear features. * Accurate geographical location of data is maintained. * Since most data, e.g. hard copy maps, is in vector form no data conversion is required. * Allows for efficient encoding of topology and operations that require topological information.
| * The cell size determines the resolution at which the data is represented. * It is especially difficult to adequately represent linear features depending on the cell resolution. Accordingly, network linkages are difficult to establish. * Processing of associated attribute data may be cumbersome if large amounts of data exist. Raster maps inherently reflect only one attribute or characteristic for an area. * Since most input data is in vector form, data must undergo vector-to-raster conversion. Besides increased processing requirements this may introduce data integrity concerns due to generalization and choice of inappropriate cell size. * Most output maps from grid-cell...
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