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) Note that this same process can be used with geotiffs and many other raster formats.Data can be represented at its original resolution and form without generalization. tol - the tolerance value - a large number will remove more vertices, make the data small AND yield a “blockier” looking object.You probably don’t want to do this if you are performing any sort of quantitative analysis on the data but you definitely want to do this if you are creating online maps and other visual products from your data. A circle is simply a line with lots of vertices - the more vertices it has, the more ‘round’ the line appears.Īs you use this function keep in mind that you are modifying your data. Remember that a line is composed of vertices. The simplify function removes vertices from complex lines. The gSimplify function is a part of the rgeos package.
There are many details in the boundaries as rendered that we may want if we zoom in but may not need to produce a global map.
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