Spatial Data warehouse

It is estimated that about 80% of the data stored in databases has a spatial or location component. Therefore, the location dimension has been widely used in data warehouse and OLAP systems. However, this dimension is usually represented in an alphanumeric, nonspatial manner (i.e., using solely the place name), since these systems are not able to manipulate spatial data. Nevertheless,it is well known that including spatial data in the analysis process can help to reveal patterns that are difficult to discover otherwise.

Spatial Databases: General Concepts

Spatial databases have been used for several decades for storing and manipulating spatial data. They allow us to describe the spatial properties of real-world phenomena.

There are two complementary ways of modeling spatial data in database applications. In the object-based approach, space is decomposed into identifiable objects and their shapes are described. This allows us, for example, to represent a road as a line or a state as a surface. The field-based approach is used to represent phenomena that vary over space, associating with each point in a relevant extent of space a value that characterizes a feature at that point. Typical examples are temperature, altitude, soil cover, and pollution level.

Spatial Objects

A spatial object corresponds to a real-world entity for which an application needs to store spatial characteristics. Spatial objects consist of a descriptive (or conventional) component and a spatial component. The descriptive component is represented using conventional data types, such as integer, string, and date; it contains general characteristics of the spatial object. For example, a state object may be described by its name, population, area, and major activity. The spatial component includes the geometry, which can be of various spatial data types, such as point, line, or surface,