of a SOLAP system is composed of a multidimensionally structured spatio-temporal
database, a SOLAP server and a SOLAP client . The
spatio-temporal database stores the geometry associated with spatial dimension
members and spatial measures. The SOLAP server handles the spatio-temporal multidimensional
database and the numerical and spatial calculations necessary to compute
the measure values associated with possible combinations of dimension
members. Currently, no such server is available on the market; it must
then be implemented using a custom combination of technologies.
system must provide capabilities to adequatly handle spatial
data, such as the capabilities described in the following sections.
of this page is from Rivest, S. et al.  and Proulx,
M.-J. & Bédard, Y. 
Support for a spatial datacube structure
SOLAP Tool must include a spatial datacube structure to manage and configure
the spatial elements. This structure must contain spatial dimensions
organized in a hierarchy of spatial members. For example, the structure
must support the abstraction layers composed by world, countries and
provinces elements. This
is different than managing map layers where spatial elements are organized
by level and no hierarchical relationship exists between the levels.
Support for several spatial data sources
Within an organization, spatial
data are usually available in various formats. In a data integration
context like spatial datacubes, it should not be necessary to standardize or translate
these data formats. A SOLAP tool should then be able to access the most common vector
and raster formats of the GIS industry (ex. ESRI Shapefiles, Mapinfo
Tab, SVG) in a native way.
many spatial dimensions in a spatial datacube
a SOLAP application, it can be necessary to map data on several
spatial dimensions. The system must then support more than one spatial
dimension per cube. For example, measures can be assigned to power
lines (lines) and regional districts (polygons).
Support for all simple and complex
spatial primitives (ISO standard)
elements can be grouped into three subclasses: base geometries
(ex. points, lines and polygons), complex geometries (ex. line
networks), and multiple geometries (ex. polygonal and linear networks).
describes the shape of objects with coordinates
and mathematical functions.
International Organization for Standardization
(ISO) produced the 19107-Spatial
Schema standard that defines a set of spatial data
types and operations for geometric and topologic spaces. The
geomatics community uses these rules when developing
is an example of aggregated polygons where all islands compose
the complex geometry of the global element.
Support for historical spatial data
multidimensional systems requires gathering data from heterogeneous
sources. Then, data is integrated in multidimensional
structures organized around several analysis axes, or dimensions.
These analysis structures are likely to vary over time and the
existing multidimensional models do not (or only partially) take these
evolutions into account. Hence, a dilemma appears for the designer
of data warehouses: either keeping trace of evolutions therefore limiting
the capability of comparison for analysts, or mapping all data in
a given version of the structure that entails alteration (or even
loss) of data . The following example shows two
different spatial dimensions resulting of the Soviet Union's division
in many smaller countries.
1991 when Soviet Union was unified.
1994 after the Soviet Union's division, 15 new countries were
created (ex. Federation of Russia, Armenia, Ukraine).
studies have been proposed to handle some of these issues:
updating models focus on mapping
data into the most recent version of the structure;
tracking history models keep
trace of evolutions of the system. Some choose to represent data
in the temporally consistent mode of presentation. Therefore these
models are not able to draw comparisons across time.
unchanged structure models
are aware of the users' needs of both accurately tracking history and
comparing data, and provide a way of mapping data in an “unchanged structure”,
chosen by the user.
combined approaches, like the one proposed by Body et al ,,
lay down bases for handling evolutions in multidimensional
structures with the exploitation of the knowledge on evolutions
in order to map data in a given representation.
Support for automatic cartographic generalization
or multiple cartographic representation of elements
When one needs to have
a more global cartographic view of a phenomenon, it is often not possible to simply aggregate spatial data since the map or display becomes overcrowded
and unreadable. One must rather use map generalization processes. According to Weibel and Dutton , “Map generalization is responsible for
reducing complexity in a map in a scale reduction process, emphasizing the essential while suppressing the unimportant, maintaining logical and
unambiguous relations between map objects, and preserving aesthetic quality”. Every map, including the map made from source data, uses some level
of generalization. By definition, a map is a model of only a subset of the reality where unnecessary details are eliminated and useful data emphasized while
maintaining the map's readability. Going from a large map scale to a smaller map scale worsens the situation. Categories of objects as well as individual objects
are eliminated, others are replaced by a symbol of larger or smaller size, some are displaced, their shape is simplified, topological relationships may change,
groups such as “building blocks” replace individual buildings where the density is too high, and so on. In other words, the content of every map may lie,
the measurements made on every map may lie and a topological relationship on every map may lie. The following example shows the spatial aggregation-generalization
mismatch where aggregated data provide true data but unreadable map (on the left) while generalized data produce readable map but inexact data (on the right).
The user of a SOLAP tool must be aware of that fact when zooming or drilling on a map.
|Example of spatial
aggregation-generalization mismatch. 
certain occasions, the representation of objects at smaller scales can be purely
symbolic (i.e. generalized to the highest degree). For example, in an origin-destination
analysis context, the origin and destination points can be linked by straight
lines instead of the actual roads, like in the figure below. This concept can
be called symbolic generalization.
|Example of symbolic
vectors representing origin-destination data. 
the context of interactive web applications such as SOLAP applications, response
times must be very fast. Because most cartographic generalization processes
are complex and still require human intervention, they can not be conducted
on-the-fly. A solution is to store the precomputed generalizations in what is
called a multiple representations database.
A multiple representations database can be described as a spatial database,
which can be used to store the same real world phenomenon at different levels
(that are linked) of thematic and geometric detail.
Body, M., M. Miquel, Y. Bédard & A. Tchounikine, 2003. Handling Evolutions
in Multidimensional Structures, IEEE 19th Int. Conf. on Data Engineering
(ICDE), March 5-8th, Bangalore, India.
Body, M., M. Miquel, Y. Bédard & A. Tchounikine, 2002. A Multidimensional
and Multiversion Structure for OLAP Applications, Proceedings of the ACM Fifth International
Workshop on Data Warehousing and OLAP, pp. 1-6, http://portal.acm.org/citation.cfm?doid=583890.583891.
S., Y. Bédard, M.-J. Proulx, M. Nadeau, F. Hubert & J. Pastor, 2005. SOLAP:
Merging Business Intelligence with Geospatial Technology for Interactive
Spatio-Temporal Exploration and Analysis of Data, Journal of International
Society for Photogrammetry and Remote Sensing (ISPRS) "Advances in spatio-temporal
analysis and representation", 60 (1), pp. 17-33.
 Weibel, R. & G. Dutton, 1999.
Generalizing Spatial Data and Dealing with Multiple Representations. In P. Longley, M. Goodchild, D. Maguire & D. Rhind (Eds.), Introduction.
Geographical Information Systems: Principles and Technical Issues, pp. 125-155.
 Bédard, Y., S. Rivest, & M.-J. Proulx,
2007, Spatial On-Line Analytical Processing (SOLAP): Concepts, Architectures and Solutions from a Geomatics Engineering Perspective, In: Robert Wrembel
& Christian Koncilia (Eds.), Data Warehouses and OLAP: Concepts, Architectures and Solutions, Chap. 13, IRM Press (Idea Group), London, UK, pp. 298-319.
 Proulx, M.-J., Bédard, Y., 2008, Fundamental Characteristics of Spatial OLAP Technologies as Selection Criteria, Location Intelligence 2008, April 29, Santa Clara, CA, USA.