Interactive Exploration Functions

The user interface of a Spatial OLAP tool provides unique capabilities to explore spatial data in an intuitive and interactive way. The capability of linking or synchronizing several views of the same information in a context of interactive exploration of data brings new possibilities to benefit from current research in geovisualization.

This section presents some exploration functions of an ideal SOLAP technology with regards to the manipulation of the geometric components of spatial data and to the database navigation operators.

The major part of the information of this page is from Rivest, S. et al. [5] and Proulx, M.-J. & Bédard, Y. [6].

Exploration functions available in the map displays (spatial drilling)

In a SOLAP client interface, variants of the OLAP operators are used in order to take advantage of the spatial multidimensional data structure and of the different levels of details of the spatial data. The operators are drill-down, roll-up (or drill-up), drill-across, swap (or pivot) and slice and dice. When defined to manipulate the data contained in the geometric or mixed spatial dimensions, the drill operators can be named spatial drill-down, spatial roll-up and spatial drill-across. They are executed directly by clicking on the elements (dimension members) shown on the maps or on a selection of many elements (at the same or at different levels of details of a dimension)[2].

From the initial state (ex. the continents level) a spatial drill-down is executed by clicking on Africa.
A spatial drill-down on members allows for the navigation from one geometric level of details (ex. continents) to another within a spatial dimension (ex. countries), while keeping the same thematic and temporal granularities. The result isolates the countries that compose the Africa continent on the map. The same operator can be applied on a selection of many members (at the same or at different levels of details of a dimension).
A spatial drill-down on level applies the operator on all the geometric elements of the level (ex. continents) of the dimension. The result shows a complete level of details of a dimension (ex. countries).

An open spatial drill-down drills on the selected member while keeping the context of the other dimension members around it. The result shows the countries of Africa on a continents map.

Pivoting dimensions on maps

A useful operation on multidimensional data is the pivot. Applied on a map, the pivot can allow to change the orientation of the displayed dimensions to produce a different type of map. However, precise rules are necessary in order to produce the pivoted map that corresponds to the active dimension selections. For example, a temporal multimap based on three years of interest can be pivoted as a map with superimposed diagrams based on years.

Supporting Intelligent automatic mapping

To improve the knowledge discovery process and help maintain the user’s train of thoughts, the automation of the display creation processes is required. The system must instantaneously generate coherent maps by using predefined display rules in accordance to the user's selections. Symbologies, classifications and display types can be defined in advance for the user. When a system requires a manual map creation, the map creation knowledge must be owned by the user and this type of process can be time-consuming. In an automatic map creation process, the system owns the knowledge and the user has more time for the discovery of data.

Analysis of the temporal dimension using a drillable timeline

The ideal SOLAP tool must include a drillable interactive timeline. This timeline allows the user to control the display of the time dimensions and supports drill-down and drill-up operations (directly on the time cursor). This timeline allows for the display of animated maps and hence, facilitates the visualisation of the evolution of phenomena. The animated maps can depict a trend or a pattern that would not be apparent if the maps were viewed individually [1],[3].

Grouping of spatial members

The ideal SOLAP tool must allow for the grouping of spatial members when it is neccessary to create new spatial objects based on smaller ones. The measures associated with these spatial members would be aggregated to produce the measure values for the new spatial member (ex. the sum of the values must be calculated).


Spatial and metric analysis on spatial members

The ideal SOLAP tool must include spatial analysis operators that allow for the calculation of aggregated measures based on spatial operators and for the creation of the resulting spatial members.

For example, applying a 1 kilometer buffer along roads would aggregate the measure values of the objects included inside the buffer and produce an aggregated spatial member and the corresponding measure value.


[1] Kraak, M.-J., Edsall, R.M., MacEachren, A.M., 1996. Cartographic animation and legends for temporal maps: exploration and/or interaction. Proceedings of the 7th International Conference on Spatial Data Handling, The Netherlands, August 12–16th, pp. 17–28.

[2] Pastor, J., 2004. Conception d'une légende interactive et forable pour le SOLAP. Unpublished M.Sc. Thesis, Geomatics Sciences Department, Laval University. Accessed October 12, 2005.

[3] Peterson, M.P., 1999. Active legends for interactive cartographic animation. International Journal of Geographic Information Science 13 (4), pp. 375–383.

[4] Rivest, 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.

[5] Rivest, 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.

[6] Proulx, M.-J., Y. Bédard, 2008, Fundamental Characteristics of Spatial OLAP Technologies as Selection Criteria, Location Intelligence 2008, April 29, Santa Clara, CA, USA


Université Laval - Canada
Updated: November 2009