Introduction - Data Warehouse Systems: Design and Implementation - page 7

are being explored for coping with the massive amount of information that
must be processed in today's decision-support systems. We comment on these
issues in the next sections.
1.2 Spatial and Spatiotemporal Data Warehouses
Over the years, spatial data has been increasingly used in various areas, like
public administration, transportation networks, environmental systems, and
public health, among others. Spatial data can represent either objects located
on the Earth's surface, such as mountains, cities, and rivers, or geographic
phenomena , such as temperature, precipitation, and altitude. Spatial data
can also represent nongeographic data, that is, data located in other spatial
frames such as a human body, a house, or an engine. The amount of spatial
data available is growing considerably due to technological advances in areas
such as remote sensing and global navigation satellite systems (GNSS),
namely, the Global Positioning System (GPS) and the Galileo system.
Management of spatial data is carried out by spatial databases or
geographic information systems (GISs). Since the latter are used for
storing and manipulating geographic objects and phenomena, we shall use
the more general term spatial databases in the following. Spatial databases
are used to store spatial data located in a two- or three-dimensional space.
These systems provide a set of functions and operators for querying and
manipulating spatial data. Queries may refer to spatial characteristics of
individual objects, such as their area or perimeter, or may require complex
operations on two or more spatial objects. Topological relationships
between spatial objects, such as intersection, touches, and crosses, are
essential in spatial applications. For example, two roads may intersect, two
countries may touch because they have a common border, or a road may
cross a dessert. An important characteristic of topological relationships is
that they do not change when the underlying space is distorted through
rotation, scaling, and similar operations.
Spatial databases offer sophisticated capabilities for the management of
spatial data, including spatial index structures, storage management, and
dynamic query formulation. However, similarly to conventional operational
databases, they are typically targeted toward daily operations. Therefore,
spatial databases are not well suited to support the decision-making process.
As a consequence, a new field, called spatial data warehouses ,emergedas
a combination of the spatial database and data warehouse technologies.
Spatial data warehouses provide improved data analysis, visualization,
and manipulation. This kind of analysis is called spatial OLAP (SOLAP),
conveying a reference to the ability of exploring spatial data through map
navigation and aggregation, as it is performed in OLAP with tables and
charts. We study spatial data warehouses in Chap. 11 .