giftmasters.blogg.se - Raster versus vector data

#RASTER VERSUS VECTOR DATA SERIES#

The majority of output maps from grid-cell systems do not conform to high-quality cartographic needs.Besides increased processing requirements this may introduce controversy over data 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.

In general, there are four steps to georeference your data: Add the raster dataset that you want to align with your projected data. The georeferencing tools on the Georeference tab allows you to georeference any raster dataset. electrostatic plotters, graphic terminals. Georeferencing raster data allows it to be viewed, queried, and analyzed with your other geographic data.

Grid-cell systems are very compatible with raster-based output devices, e.g.

elevation data, and facilitates the integration of both 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.

The inherent nature of raster maps, e.g.

Storage techniques allow data analysis to be easily programmed, and quickly performed.

bottom left corner, no geographic coordinates are stored. Accordingly, other than an origin point, e.g.

The geographic location of each cell is implied by its position in the cell.

Spatial analysis and filtering within polygons is impossible.

Usually substantial data generalization or interpolation is required for these data layers.

Continuous data, such as elevation data, is not effectively represented in vector form.Often, this inherently limits the functionality for large data sets, e.g. Algorithms for manipulative and analysis functions are complex and may be processing intensive.Furthermore, topology is static, and any editing of the vector data requires re-building of the topology. This is often processing intensive and usually requires extensive data cleaning. For effective analysis, vector data must be converted into a topological structure.location of each vertex needs to be stored explicitly.Allows for efficient encoding of topology, and as a result more efficient operations that require topological information, e.g.hard copy maps, is in vector form no data conversion is required. Graphic output is usually more aesthetically pleasing (traditional cartographic representation) Since most data, e.g.Data is represented at original resolution and form without generalization.12.2 Inverse Distance Weighted interpolation.11.3 Aggregation of spatio-temporal rasters.10.7.5 Extracting to polygons: multi-band.10.7.4 Extracting to polygons: single-band.10.7.3 Extracting to points: multi-band.10.7.2 Extracting to points: single-band.8 Geometric operations with vector layers.6.6 Generalizing raster algebra with st_apply.

6.4.3 True color and false color images.

6.4.1 Arithmetic and logical operations on layers.

6.2.1 Selecting rows, column and layers.

5.3.7 Visualization with plot, mapview and cubeview.

4.4.3 Example: the rainfall.csv dataset.

3.3.2 Function definition vs. function call.

#RASTER VERSUS VECTOR DATA SERIES#

3.1.1 Times and time series classes in R.2.3.6 Consecutive and repetitive vectors.2.3.3 Vector subsetting (individual elements).0.3.9 osmdata: Access to OpenStreetMap data.0.3.8 spatstat: Spatial point pattern analysis.0.3.7 spdep: Spatial dependence modelling.0.3.5 geosphere: Geometric calculations on longitude/latitude.