Spatial OLAP Concepts
In order to support automated drill operations between the spatial levels of a spatial
dimension, a particular configuration must be used to link the multidimensional
data and the spatial data. By linking the spatial members of a datacube to the geometries of objects stored in a spatial database, these objects
can be viewed and manipulated on maps. Such a structure results from the complete integration of the cartographic and the multidimensional components of a SOLAP solution and allows for interactive multidimensional exploration of phenomena.
various types of spatial dimensions and measures supported by Spatial OLAP
tools and they are presented below.
major part of the information of this page is from Rivest, S. et al.
system supports three types of spatial dimensions :
the non-geometric spatial dimensions, the
geometric spatial dimensions and
the mixed spatial dimensions. The
non-geometric spatial dimensions use nominal spatial reference,
i.e. only the name of places or objects such as Canada, Province of Quebec, Quebec City and St.John Street. This type of spatial dimension is the only one supported
by conventional (non-spatial) OLAP tools. When used with SOLAP tools,
the non-geometric spatial dimension is treated like the other descriptive
dimensions and the geometric data allowing for the representation of
the dimension members on maps is not used. In this case, the spatio-temporal
analysis can be incomplete and certain spatial relations or correlations
between the phenomena under study can be missed by the analyst. The
two other types of spatial dimensions aim at minimizing this potential
problem. To do so, the geometric spatial dimensions
comprise, for all dimension members, at all levels of details, geometric
shapes (ex. polygons to represent country boundaries) that are spatially
referenced to allow their dimension members (ex. Canada) to be visualized
and queried cartographically. The mixed spatial
dimensions comprise geometric shapes for a subset of the
members or the levels of details.
1 presents an example of the three types of spatial dimensions .
of a spatial dimension that has geometric shapes associated to its
dimension members can support spatial drill operations (see Navigation
operators: spatial drill) on cartographic features, thus increasing
the number of degrees of freedom for interactive spatio-temporal exploration
a SOLAP tool, maps are used to display the members of the geometric
or mixed spatial dimensions, using visual variables that relate to the
values of the different measures contained in the datacube being analyzed.
A SOLAP system also supports two types of spatial measures as well as
type of spatial measure is the set of all the
geometries representing the spatial objects corresponding
to a particular combination of dimension members (it is possible to
have many geometric spatial dimensions in a datacube). It consists of a set of coordinates,
which requires a geometric operation such as a spatial union, a spatial
merge or a spatial intersection, to be computed. To implement this type
of spatial measure, it may be necessary to use pointers (stored within
the multidimensional data structure) to the geometric shapes stored
in another structure or software.
type of spatial measure results from the computation
of spatial metric or topological operators. Examples of this
type of spatial measure could be “surface” and “distance” 
as well as “number of neighbours”. Similarly, spatial dimensions can
also be used to present the results of spatial analysis operations in a hierarchical manner
(ex. adjacent–>adjacent by points–>adjacent by only one point) and can
be used to find the facts that correspond to the selected spatial operator
members of a spatial operators dimension .
values are calculated by the OLAP or the SOLAP server. The server aggregates and
physically stores the aggregations according to the possible combinations of dimension
members. In the case of SOLAP servers, however, it is almost impossible
to materialize all the possible geometric aggregations of spatial measures (or views)
as this can result in an explosion of the necessary storage space. Algorithms
have been defined in order to optimally select the spatial aggregations
to be materialized.
Bédard, Y., Merrett, T., Han, J., 2001. Fundamentals of spatial data warehousing
for geographic knowledge discovery. In: Miller, H., Han, J. (Eds.), Geographic
Data Mining and Knowledge Discovery. Taylor and Francis, London, pp. 53–73.
Marchand, P., Brisebois, A., Bédard, Y., Edwards, G., 2004. Implementation
and evaluation of a hypercube-based method for spatio-temporal exploration
and analysis. Journal of the International Society of Photogrammetry and
Remote Sensing (ISPRS) 59 (1-2), pp. 6–20.
S., Bédard, Y., Marchand, P., 2001. Towards better support for spatial
decision-making: defining the characteristics of Spatial On-Line Analytical
Processing (SOLAP). Geomatica, the Journal of the Canadian Institute of
Geomatics 55 (4), pp. 539–555.
S., Bédard, Y., Proulx, M.-J., Nadeau, M., 2003. SOLAP: a new type of user
interface to support spatio-temporal multidimensional data exploration
and analysis. Proceedings of the ISPRS Joint Workshop on Spatial, Temporal
and Multi-Dimensional Data Modelling and Analysis, Quebec, Canada, October 2-3.
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 the International
Society for Photogrammetry and Remote Sensing (ISPRS) "Advances in spatio-temporal
analysis and representation" 60 (1), pp. 17-33.