Local functions are always performed with one specific raster cell but in the entire grid. Map algebra functions follow some rules (spatial resolution, the same coordinate system, mathematical operators) to combine all of its components. Depending on whether the functions work with only one raster cell or more, they are divided into local, focal, zonal, and global. Tomlin ( 1994) classifies all GIS transformations of rasters into four basic classes, and it is used in several raster-centric GISs as the basis for their analysis languages. In addition to arithmetic operators, there are Boolean operators (true, false), relational (greater than, smaller than or equal to), statistical (minimum, maximum, average and median), trigonometric (sine, cosine, tangent, arcsine), exponential and logarithmic. It is possible to add, subtract, multiply, divide, or perform the same single layer operations. The basic type of operators are arithmetic operators (+, −, ∗, /). Operators perform mathematical calculations with one or more raster layers. Higher spatial resolution leads to higher detail but also increases needed storage capacity and computational time.Īs part of the map algebra, operators and functions of mathematical language are used for data processing. The spatial resolution is a key characteristic that influences the quality and detail of any raster analysis.
3.10, cell size can vary from centimetres (some aerial images) to kilometres (satellite images). The most significant characteristics of the raster is its spatial resolution that can be expressed as the length of a cell side as measured on the ground. All geographic variation is then expressed by assigning properties or attributes to these pixels (Longley et al. In a raster representation space is divided into an array of rectangular (usually square) cells (pixels). 1, vector and raster data models are two main ways for geographic data representation. modelling of renewable energy potential, land suitability modelling, cost-distance analysis and many others). climatic change, weather forecasting, flood modelling) but there are also some focused on economic aspects (e.g. Most of the application fields cover environmental issues (e.g. There exist many raster analysis options in GIS like hydrologic analysis, multi-criteria analysis, terrain analysis, surface modelling, surface interpolation, suitability modelling, statistical analysis, and image classification (processing of remote sensing data). Map algebra (mathematical operations with rasters) is used to processing this data. Raster analysis (as part of spatial analysis) refers to the analytical operations with raster data. “The WHERE clause is used to extract only those records that fulfill a specified condition” ( 2018) according to the feature’s properties stored in the attribute table of a feature class (Fig. This method is like selecting datasets in a database using a so-called WHERE-clause of the very common Structured Query Language (SQL). Depending on the GIS used, these two different methods can be applied successively or in one process. The selection of the desired objects can be performed based on the attributes of the features or incorporating their spatial characteristics. In many cases, not all features of a feature class are supposed to take part in an analysis. To automate workflows all operators that are involved in an analysis can be combined with a geoprocessing model. The process of manipulating geodata is called geoprocessing. Nevertheless, depending on the data involved and the workflow incorporating these methods, they can deliver highly valuable output. This sub-chapter gives an overview of fundamental GIS methods for performing basic spatial analysis with feature data.
To understand why and how these toolsets are utilised, how they are parametrized and which other things are important to make proper use of all the different possibilities these toolsets are providing, this chapter sums up the analyses in reasoned groups and illustrates the many different approaches of spatial analyses through proper examples and depictions. From basic tools like buffering certain vector geometries or merging operations of two different datasets to interpolating area wide raster datasets out of point data there is a huge variety of different toolsets that can be applied when using geodata. This chapter introduces the most common analyses that are conducted using a GIS. For processing geodata there are many different approaches of which all of them require their own specific input data and parameters to generate an outcome that suits the respective case of application.
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