GIS Data Types and Methods of Analysis

 In general, data can be divided into two basic types: spatial data and non-spatial data. Spatial data determine the geographical location (coordinates) of the phenomenon or spatial feature under study on the Earth's surface. Non-spatial data is any data that is not related to a geographical location but is related to the same spatial feature. Some refer to non-spatial data as metadata (Attribute Data), but this term may not be accurate or comprehensive in describing the quality and characteristics of this type of data. When we talk about a specific school, for example, the geographical coordinates that express the location of this school on the earth's surface are its spatial data, whereas the non-spatial data for these teachers include their names, numbers, classes, classes, and students.... etc.

Data Analysis in the Framework of Geographic Information Systems

There are two types of data analysis within the GIS framework. While spatial data is referred to as "spatial data analysis," statistical analysis also includes the interpretation of non-spatial numerical data. A statistical examination of the number of classes and pupils in this institution can be used to determine the average intensity of one classroom. On the other hand, the spatial analysis of the schools in one of the city’s neighborhoods will provide us with a description of the nature of the distribution of these schools according to the urban area and will answer the question of whether this distribution is regular and homogeneous in this neighborhood or does it make a difference to the locations of these areas.  It is also possible that the spatial analysis will identify the best geographical locations for the establishment of these new schools.

To build a spatial model of spatial phenomena, the spatial analysis assumes that each phenomenon has a space or spatial scope and a certain spread and distribution (i.e., a pattern of distribution).

Spatial analysis can be performed at different levels: two-dimensional, three-dimensional, and four-dimensional.  Only horizontal locations (longitude and latitude, for example) that express the geographical location of the elements of the phenomenon under study are analyzed in two-dimensional analysis (or 2D). If the values of the third dimension (height) for the phenomenon's elements are available, it is possible to analyze the data in three dimensions, or what is known as Surface Analysis, for example, it is possible to represent the model of the study area and show the difference in its topography between its different Sections and the creation of vertical sections in the area. Also, it is possible to perform spatio-temporal analysis (4D) if the same data is available for several time periods.

Types of Spatial Data

Data in GIS is represented by two models: (1) linear or directional data (Vector Data) and (2) network or cellular data (Raster Data). A realistic representation of the world can be achieved by combining raster and vector data types.

Vector Data

The vector data type stores data as points, lines, and polygons. In comparison to the raster format, less computer memory is used, and position accuracy is improved. Vector data can be used to store data with discrete spatial boundaries, such as country borders, land parcels, and streets. The vector data type records and displays object coordinates with complete measurement accuracy in comparison to ground measurements. In comparison to the raster data type, the vector data type contains less information for the same area. Furthermore, alphanumeric attributes can be easily applied to the defined schemes that express physical objects with points, lines, or polygons. The calculation of vector positions and intersections adheres to analytic geometry principles.

Raster Data

Data in the raster data type are represented by pixels with values, forming a grid, allowing certain operations that were not possible with the vector data type. Map Algebra is used to create index maps using the raster data type and multiple data layers. Raster data is useful for storing data that changes continuously, such as aerial photographs, satellite images, chemical concentration surfaces, and/or elevation surfaces. Raster data is made up of a two-dimensional (2D) grid of cells (pixels). The grid is distinguished by its georeferenced origin, georeferenced orientation, and raster cell size (pixel size). Raster data can be arranged in three dimensions as well. In this case, the three-dimensional (3D) cell is transformed into a cube (a voxel). The pixel resolution limits the geometric accuracy of raster data. Aside from geometric correction procedures, radiometric transformations can be applied to raster data. Furthermore, Boolean algebra operators can be used to combine data from different raster layers.