Image compression is a well known and very pretty efficient process and almost every coding library has built in classes to do this work. In practice, most images don't have 100% unique pixels can be compressed into smaller data packets, and many vector files contain excess detail that is not needed at many low detail zoom levels. Image Compression vs Structure Compression Vector drawings (and maps) can scale with a higher degree of fidelity than pixels because vector data contains coordinate patterns (points, polygons, lines etc) that can rendered relative to each other at different resolutions using simple formulas, while pixel resizing typically uses a smoothing algorithm that results in image artifacts. It sounds like you (and probably most readers) already know the most obvious difference between raster fixed pixels and vector (coordinate maps). For example, a shape file detailing zoning boundaries of an entire city (potentially millions of Raster tiles) area might only have 65,000 Vector shapes. When I think Vector, I think polygons and lines. A single tile in a web map (typically a variant of Mercator loosely referred to as " Spherical Mercator" or " Web Mercator" and supported by Google, Bing, Yahoo, OSM and ESRI)typically has 256 x 256 = 65,536 pixels, and each zoom level has (2^zoom * 2^zoom) tiles. Almost every pixel in a detailed satellite image of a urban area could contain unique information. When I think Raster maps, my first thought is satellite imagery. Most output maps from grid-cell systems do not conform to high-quality cartographic needs.
Besides increased processing requirements this may introduce data integrity concerns due to generalization and choice of inappropriate cell size. Since most input data is in vector form, data must undergo vector-to-raster conversion. Raster maps inherently reflect only one attribute or characteristic for an area. Processing of associated attribute data may be cumbersome if large amounts of data exists. Accordingly, network linkages are difficult to establish. It is especially difficult to adequately represent linear features depending on the cell resolution. The cell size determines the resolution at which the data is represented. electrostatic plotters, graphic terminals. Grid-cell systems are very compatible with raster-based output devices, e.g. elevation data, and facilitates the integrating of the two data types. forestry stands, is accommodated equally well as continuous data, e.g. one attribute maps, is ideally suited for mathematical modeling and quantitative analysis.ĭiscrete data, e.g. bottom left corner, no geographic coordinates are stored.ĭue to the nature of the data storage technique data analysis is usually easy to program and quick to perform. Accordingly, other than an origin point, e.g. The geographic location of each cell is implied by its position in the cell matrix. Spatial analysis and filtering within polygons is impossible Usually substantial data generalization or interpolation is required for these data layers. a large number of features.Ĭontinuous data, such as elevation data, is not effectively represented in vector form. Often, this inherently limits the functionality for large data sets, e.g. As well, topology is static, and any updating or editing of the vector data requires re-building of the topology.Īlgorithms for manipulative and analysis functions are complex and may be processing intensive. This is often processing intensive and usually requires extensive data cleaning. The location of each vertex needs to be stored explicitly.įor effective analysis, vector data must be converted into a topological structure. hard copy maps, is in vector form no data conversion is required.Īccurate geographic location of data is maintained.Īllows for efficient encoding of topology, and as a result more efficient operations that require topological information, e.g. Graphic output is usually more aesthetically pleasing (traditional cartographic representation) Data can be represented at its original resolution and form without generalization.
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