For example, you can process data through a geoprocessing model to create a raster dataset that maps suitability for a specific activity. Thematic maps can also result from geoprocessing operations that combine data from various sources, such as vector, raster, and terrain data.

Basically, this activity groups the values of multispectral data into classes (such as vegetation type) and assigns a categorical value. A common analysis application is classifying a satellite image into land cover categories. Rasters representing thematic data can be derived from analyzing other data. The raster below displays elevation-using green to show lower elevation and red, pink, and white cells to show higher elevations. Elevation values measured from the earth's surface are the most common application of surface maps, but other values, such as rainfall, temperature, concentration, and population density, can also define surfaces that can be spatially analyzed. They also provide a regularly spaced representation of surfaces. They provide an effective method of storing the continuity as a surface. Rasters are well suited for representing data that changes continuously across a landscape (surface). Below is an orthoimage as a basemap for road data. Three main sources of image basemaps are orthoimages from aircraft, satellite imagery, and scanned maps. For example, orthoimages displayed underneath other GIS layers provide the map user with confidence that map layers are spatially aligned and represent real objects, as well as additional information. Within a GIS, the uses of raster data fall under four main categories:Ī common use of image data in a GIS is as a background display for other feature layers. While the structure of raster data is simple, it is exceptionally useful for a wide range of applications. Picture rasters are often used as attributes in tables-they can be displayed with your geographic data and are used to convey additional information about map features.

Pictures include scanned maps or drawings and building photographs.Ĭontinuous and thematic rasters may be displayed as data layers along with other geographic data on your map but are often used as the source data for spatial analysis with the ArcGIS Spatial Analyst or ArcGIS Image Analyst extension.

Thematic data (also known as discrete) represents features such as land use or soils data.

Continuous data represents phenomena such as elevation and spectral image data collected from satellites and airborne platforms such as drones.

Rasters are digital aerial photographs, imagery from satellites, digital pictures, or even scanned maps.ĭata stored in a raster format represents real-world phenomena: In this case, the value of each pixel corresponds to a category ( 1 = forest, 2 = water, etc.).In its simplest form, a raster consists of a matrix of cells (or pixels) organized into rows and columns (or a grid) where each cell contains a value representing information, such as temperature.

They can also be used to represent land cover data. Rasters can be used to represent data related to elevation or temperature. In this type of raster each pixel has a value corresponding to the elevation. Topographic data is an example of a raster data type. This defines the size of the pixels and therefore the precision of the data. A raster consists of a matrix of cells, or pixels, organized in rows and columns (grid) in which each cell has a value representing real world information.Īs in photography, a key characteristic of rasters in GIS is the resolution. To put it simply, the raster data represents the real world through an image. We do not proceed to the same analysis according to the type of data. Why distinguish between the two? Depending on the data that you want to display / search online, I will focus on one or the other type of geographic data. These digital representation modes, thanks to their own characteristics, allow different treatments in terms of spatial analysis and representation. Geographic data have two representation modes: the vector mode and the raster mode. This toolbox was developed by the teams of CartONG 8.4 Remote sensing and satellite images.6.1.1 The Mobile Data Collection Toolkit.

3.3.5 Coordinates, coordinate systems and projections.

Raster and vector data model in gis