G E O M A T I C A

TOWARD BETTER SUPPORT FOR SPATIAL DECISION MAKING: DEFINING THE CHARACTERISTICS OF SPATIAL ON-LINE ANALYTICAL PROCESSING (SOLAP)

Sonia Rivest, Yvan Bédard and Pierre Marchand Centre for Research in Geomatics, Laval University, Québec

To exploit the full potential of the spatial and temporal dimensions of a data warehouse, new tools are needed. It has been shown that OLAP possesses a certain potential to support spatio-temporal analysis. However, without a spatial interface for viewing and manipulating the geometric component of the spatial data, the analysis may be incomplete. A new category of OLAP tools, SOLAP (Spatial OLAP) tools, is pre- sented. SOLAP tools are defined and the associated concepts are presented. A series of essential features, as well as desirable characteristics, are then described. Finally, application prototypes are described and an example of spatio-temporal analysis is presented.

Afin de tirer plein profit des dimensions spatiales et temporelles d'un entrepôt de données, de nouveaux outits sont necessaires. Il a été démonréi que OLAP possède un certain potentiel pour supporter l'analyse spatio-temporelle. Cependant, sans un volet spatial permettant de visualiser et de manipuler la composante géométrique des données spatiales, l'analyse peut demeurée incomplète. Une nouvelle catégorie d'outils OLAP, les outils SOLAP (Spatial OLAP), est introduite. Les outils SOLAP sont définis et les concepts associés sont présentés. Une série de caracteristiques essentielles, ainsi que des caractéristiques souhaitables, de cette catigorie d'outils sont ensuite énumérées. Finalement, des prototypes d'application SOLAP sont décrits et un exemple d'analyse spatio-temporelle est présenté.

1. Introduction Organizations collect increasingly significant

volumes of data. Once stored in data warehouses, they form the basis for data analysis processes and guide the organization's strategic decisions. However, data are not always used to their full potential and part of their richness is simply left out, that is, their spatial component. "Hidden in most data is a geographical component that can be tied to a place: an address, postal code, global positioning system location, (…) region or country" [ESRI 2000]. Indeed, it has been estimated that about eighty percent of all data stored in corporate databases has a spatial component [Franklin 1992] that can be characterized by position, shape, orientation or size.

Time may also be a central component of data: "...without a record of the time of the observation, the useful content of the information may be mini- mal" [Sinton 1978]. The importance of the temporal dimension for decision making is crucial and it is reflected in the data warehouse definition: "...a

subject-oriented, integrated, time-variant, non- volatile collection of data in support of manage- ment's decision making process" [Inmon 1996]. But time is only one of the two analytical components, in addition to the descriptive components containing the attributes of the data, needed to take full advantage of a data warehouse; space, as previously mentioned, is the other [Gonzales 1999]. Spatial dimensions, just like temporal ones, should then be considered standard for any data warehouse implementation. In fact, to date, the spatial dimension has been widely integrated in data warehouses, but usually in a nominal, non-cartographic manner (i.e. using solely place names). In rare occasions, one may use coordinates (as is, or reorganized such as with HHCodes) inside a data warehouse for map display and drilling, but it is rarely used to its full potential for data exploration.

To gain better advantage of the spatial and temporal dimensions in decision making, the

GEOMATICA Vol. 55, No. 4,2001, pp. 539 to 555

G E O M A T I C A

appropriate tools must be used. Geographic Information Systems (GISS) are the obvious poten- tial candidates for such tasks. While having some spatio-temporal analytical capabilities, it is recog- nized that existing GISs per se are not adequate for decision-support applications when used alone and that alternative solutions must be used [Bédard et al. 2001]. Among the possible solutions, the coupling of spatial and non-spatial technologies, GIS and OnLine Analytical Processing (OLAP) for instance, may be an interesting option. OLAP is a category of decision-support tools often used to provide access in an efficient and intuitive manner to a data warehouse. Some of the best known commercial examples include Cognos Powerplay, Business Objects and Oracle Express. The efficiency of OLAP to conduct data analysis easily and rapidly has been recognized [Caron 1998]. Such easiness and rapidity are two essential conditions for an analyst (decision maker) to maintain a train of thought when exploring or validating hypothesis [Caron 1998]. Accordingly, the coupling of OLAP and GIS functionalities paves the way for the emergence of a new category of decision-support tools that are better adapted for spatio-temporal exploration and analysis of data. We termed this category Spatial OLAP systems, or SOLAP [Bédard et al. 1997].

This paper first discusses the need to develop spatio-temporal analysis tools. It then defines SOLAP and characterizes some of its basic concepts. The third section presents some results obtained in GEOIDE project DEC#2 (Designing the technological foundations of geospatial decision-making with the World Wide Web), i.e. a series of what we have defined as the essential core features of SOLAP tools, as well as their desirable characteristics. Finally, the last section presents technological considerations with an example taken from a prototype application developed in a project for the Quebec Department of Transportation.

The efficiency of OLAP to conduct data analysis easily and rapidly has been recognized.

2. From OLAP to SOLAP This section briefly reviews the basic concepts

of OLAP, its definition and specific vocabulary. The potential of OLAP for spatio-temporal analysis is later characterized, with its advantages and limitations. These will lead to the introduction of the SOLA,P concept.

2.1 Brief Review of OLAP Concepts OLAP has first been defined as "...the name

given to the dynamic enterprise analysis required to create, manipulate, animate and synthesize inf-

ormation from exegetical, contemplative and formulaic data analysis models. This includes the ability to discern new or unanticipated relationships between variables, the ability to identify the parameters necessary to handle large amounts of data, to create an unlimited number of dimensions, and to specify cross-dimensional conditions and expressions" [Codd et al. 1993]. The exegetical, contemplative and formulaic data analysis models necessitate a different degree of user involvement. The reader is referred to [Codd et al. 1993] for a detailed description of each model. Other OLAP definitions have since been proposed, including "A software category intended for the rapid exploration and analysis of data based on a multidimensional approach with several aggregation levels" [Caron 1998].

The multidimensional approach is based on dimensions and measures. Dimensions represent the analysis axes, while measures are the numerical attributes being analyzed against the different dimensions. For example, in a sales analysis by product line, store and time, the number of sales represents a measure while the product line, the store and the time represent dimensions. In other words, for a given product line at a given store and a given time (the dimensions), X items have been sold (the measure, i.e. the value found at the inter- section of the three dimensions). A measure can then be considered as the dependent variable while dimensions are the independent variables.

A dimension contains members that are organ- ized hierarchically into levels, each level having a different granularity going from coarse at the most aggregated level to fine at the most detailed level. The members of one level can be aggregated to form the members of the next higher level. For example, in a time dimension comprising three lev- els (days, months and years), days aggregate into months, and months aggregate into years. The measures at the finest level of granularity can be aggregated or summarized following this hierarchy and, according to the aggregation rules or algo- rithms, provide information at the higher levels (e.g. sales by mouth or by year).

A set of measures aggregated according to a set of dimensions forms what is often called a data cube or a hypercube [Thomsen et al. 1999]. Inside a data cube, possible aggregations of measures on all the possible combinations of dimension mem- bers are pre-computed. This greatly increases query performance in comparison to the conventional transaction-oriented data structures found in rela- tional and object-relational database management systems (DBMS).

The common OLAP architecture can be divided into three parts: the multidimensionally structured 540

G E O M A T I C A

database, the OLAP server that manages the data- base and carries out the different calculations, and finally the OLAP client that accesses the database via the OLAP server. This access allows the end user to explore and analyze the data using different visualization methods and adapted operators [Bédard et al. 1997] such as drill-down (show details), roll-up (show a more global picture, also called drill-up), drill-across (show another theme at the same level of detail) and swap (change a dimension for another one).

Based on the technology used to implement the OLAP database, it is current practice to distinguish different OLAP approaches: relational OLAP (ROLAP), multidimensional OLAP (MOLAP) or hybrid OLAP (HOLAP) [Pendse 2000]. In a ROLAP and an HOLAP implementation, a multidimensional structure can be simulated in a relational database using what are known as star, snowflake, mixed or constellation schemas or structures [Gill and Rao 1996; Archer Decision Sciences 1995]

Finally, it is commonly found in the literature, and our-prototypes have also shown, that the multidimensional approach of analysis is more in agreement with the end user's mental model of the data. Based on this approach, the interface of a tool exploiting the multidimensional paradigm, such as OLAP is usually very intuitive (like Web hyper- links) and the user can perform analyses ranging from simple to complex, mostly by clicking on the data being organized in a meaningful way [Yougworth 1995] This adds to the fact that a mul- tidimensional data structure is optimized for rapid ad hoc information retrieval [OLAP Council 1995], which greatly facilitates the data exploration and analysis process.

1997], which deals with spatio-temporal data. It combines two complementary processes, explo- ration and analysis, in order to identify hypotheses (e.g. potential patterns, associations and unusual occurrences) and determine their prevalence, their strength and predictability [Marchand et al. 2001]. Exploration and analysis participate symbiotically to knowledge discovery in a circular process, which is often called heuristic analysis or iterative analysis [Marchand et al. 2001]. For the geographic context, this process has been studied by a number of researchers [Cleveland and McGill 1988; Openshaw et al. 1990; MacDougall 1992; Cook et al. 1995; Wise et al. 1998].

OLAP is geared towards decision-support as it is designed from the start to be easy and rapid. First, the ease comes from the ability to conduct analysis without having to master a query language or understand the underlying structure of the database [Marchand et al. 2001], which may be very complex in the particular case of spatio-temporal databases [Bédard and van Chestein 1995]. In fact, the analyst interacts directly with the data and focusses on the results of the analysis rather than on the procedure required by the tool to perform the analysis process. Second, it is rapid because data are pre-aggregated, computation time is then reduced [OLAP Council 1995; Pendse 2000b] and very fast answers to complex queries are possible. This allows the user to maintain his train of thought, his attention not being distracted by slow response times.

Caron [1998] demonstrated the practical potential of traditional, non-spatial OLAP, to sup- port spatio-temporal analysis with the successful execution of a number of typical spatio-temporal analyses related to intensive agriculture. The OLAP application helped answer questions such as: "What is the possible cause of high nitrate rates observed on the territory for the last two years?" and "Why is there an increase of the agricultural insurance claims at Ste-Jeanne, in 1994?". However, only nominal data, place names for instance, could have been used in these analyses because OLAP tools (Cognos PowerPlay in this particular project), at that time, only supported tabular data.

Figure 1 shows another example of spatio-temporal analysis conducted with a commercial OLAP tool. The example is based on road network data from Transport Quebec. A cube for the analysis of road conditions inside a particular district has been developed including the following dimensions: time, road network, road conditions, territorial subdivisions, environment, functional classification, slopes and curves. The spatial dimension (road network) has five levels ranging from roads to segments. This cube also contains the following measures: annual

OLAP is geared

towards decision-

support as it is designed

from the start to be

easy and rapid.

2.2 The Potential of OLAP to Support Spatio-Temporal Exploration and Analysis

As OLAP exploits multidimensionally struc- tured data and as this structure reflects the analyst's cognitive model of the data, the deduction process is less fastidious and analysis is conducted more easily. The OLAP approach supports the iterative nature of the analysis process because it allows the user to explore and navigate across the different dimensions at different levels of detail. This per- mits access to all the possible views or combina- tions of the data, facilitating the emergence of new hypotheses and encouraging knowledge discovery. Spatio-temporal exploration and analysis is a sub- set of Exploratory Data Analysis [Glymour et al. 541

G E O M A T I C A

average daily traffic, estimated annual average daily traffic, average condition value, estimated average condition value, intervention threshold, intervention cost and road section length. This cube allowed easy and rapid analysis such as finding the effect of the annual average daily trafic variation on the average road condition according to road functional classes, determining if the average road condition reaches the intervention thresholds according to road functional classes, or calculating the intervention costs according to

Having seen the example in Figure 1, it is possible to conclude that traditional OLAP offers good

2.3 A Need for SOLAP

Figure lc: Results of a drill-across operation on the 2002 member of the time dimension. The display now shows the estimated intervention thresholds for 2002, for the segments of road 88841, if no intervention (resurfacing, reconstruction) is made on the road network (translated from the Quebec Transportation project).

Figure 1b: Results of a spatial drill-down operation on road 88841 to the segments level. The display now shows the intervention thresholds for 2000, for the segments of road 88841 (translated from the Quebec Transportation project).

Figure 1a: Visualization of the intervention thresholds for 2000, for the roads in the analysis territory. We can see that the road 88841 has an intervention threshold of 2 (translated from the Quebec Transportation project).

Figure ld: Results of a drill-across operation on the intervention costs measure. The display now shows the potential intervention costs for 2002, for the segments of road 88841 (translated from the Quebec Transportation project).

the intervention thresholds, the functional classes and the type of environment. In this example, the ProClarity Analytic Platform 4.0 Desktop Client software [ProClatity Corporation 2001] was used to help conduct the analysis.

542

G E O M A T I C A

Figure 2: Position of GIS and OLAP with regards to three axes of requirements for spatial decision-support.

3. The SOLAP Concept

2000], offer a more or less elaborated subset of the desirable functionalities of this new category of OLAP tools that we coined spatial OLAP or SOLAP. Today's commercial systems present many limitations and the ideal SOLAP tool must present a flexibility, not currently offered, to meet multidimensional spatio-temporal analysis needs. In particular, the need to visualize, manipulate and navigate through the cartographic component of the spatio-temporal data cube as easily and rapidly as with the tabular and diagram displays is fundamental to the SOLAP concept. Developing solutions to reach this goal is part of our research program in Spatial Databases Analysis and Design and of our participation in three GEOIDE projects.

support for simultaneous usage of temporal and spatial dimensions in a multidimensional analysis process. However, as the spatial dimension is treated as any other descriptive dimension, without consideration for the cartographic component of the data, OLAP tools present serious limitations in support of spatio-temporal analysis (no spatial visualization, practically no spatial analysis, no map-based exploration of data, etc.).

Data visualization facilitates the extraction of insight from the complexity of the spatio-temporal phenomena and processes being analyzed, as well as it offers a better understanding of the structure and relationships contained within the dataset. In the context of information exploration, maps and graphics do more than make data visible; they are active instruments in the end user's thinking process [MacEachren and Kraak 2001]. Without a cartographic display, OLAP tools lack an essential feature, which could help the completion of spatio- temporal exploration and analysis processes. In order to fully exploit the spatio-temporal analysis capabilities of a conventional OLAP tool, it is essential to add a spatial display offering, as a min- imum, a cartographic visualization of the geometric spatial elements of the dataset.

Commercial systems, coupling OLAP and spa- tial display functionalities, recently appeared on the market [Business Objects 1998; Cognos 2000; Knosys 2000]. These systems, some OLAP-domi- nant and others GIS-dominant [LGS Group Inc.

SOLAP can be defined as a visual platform built especially to support rapid and easy spatio-temporal analysis and exploration of data following a multidimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays [Bédard 1997]. SOLAP are meant to be client applications sitting on top of a multi-scale spatial data warehouse [Bédard et al. 2001]. However, the non-expert can also see them as a new type of user interface for multi-scale GIS applications and Web mapping. Figure 2 illus- trates the domain of typical SOLAP applications compared to the domain of typical GIS applications.

543

While designing a SOLAP application and architecture, one has to determine the spatial dimensions needed by the user, the desired geometry for the dimension members, the spatial measures to be included, and the spatial operators to be available in order to perform spatial analysis.

Three types of spatial dimensions are recog- nized: the non-geometric spatial dimensions, the geometric spatial dimensions, and the mixed spatial dimensions. In the first type of spatial dimension, the spatial reference uses nominal data only (e.g. place names) as no geometry or cartographic repre- sentation is associated with the dimension members. This type of spatial dimension is the one currently used for conventional OLAP tools. The two other types of spatial dimensions include geometric shapes spatially referenced on a map to allow its dimension members to be visualized and queried graphically. These geometries exist for all the levels in the case of geometric spatial dimensions, and for some of the levels in the case of mixed spatial dimensions. The choice between these three types of spatial dimensions has to be made according to user needs. For example, one user might wish to implement a spatial dimension based on the following hierarchy: Country/ Provinces/ Administrative regions/ Municipalities. Another might wish to implement a different hierarchy for the spatial dimension such as: Country/ Group of provinces/ Socio-economic regions/Municipalities, while a third one might wish to implement both of the described dimensions.

In the spatial multidimensional context, not only the dimensions can possess a geometric shape, but also the measures. Two types of measures then exist, a conventional measure and a spatial measure. A SOLAP system should be able to manipulate both: the numerical measures as used in conventional OLAP systems and the spatial measures [Han et al. 1998; Bédard et al. 2001].

We propose three types of spatial measures. The first consists of a geometrical shape or set of shapes being obtained by the combination of multiple geometric spatial dimensions (or mixed spatial dimensions when using the levels in which the members have a corresponding geometric shape). It consists of a set of coordinates, which requires a geometric operation, such as spatial merge or spatial intersection, to be computed. It is important to note that a geometric measure is not the same as the geometric shape corresponding to a dimension member. It is a new shape, or set of shapes, computed using an operator involving the

3.1 Spatial Dimensions, Measures and Operators within SOLAP

... it is essential to provide topological and metric operators to end-users.

G E O M A T I C A

544

geometry of members from many dimensions. An example of a geometric spatial measure would be the set of intersected polygons resulting from the combination of the polygonal members of two fully geometric spatial dimensions such as political boundaries and watersheds.

A second type of spatial measure results from the computation of spatial metric or topological operators, as described in the next section. The results of this computation are stored in the data cube cells. Examples of this type of spatial measure are "surface" and "distance".

A last type of spatial measure is the set of pointers (stored within the data cube cells) to the geometric shapes stored in another structure or software. Today's commercial technologies only allow this third type of spatial measure, and research is needed in order to determine if the two first types are relevant and possible. It is important to note that not every spatial measure produced by the combination of spatial dimensions is relevant. One must always validate the adequacy of such combinations and determine whether or not they are required.

In order to take advantage of the spatial nature of data involved in the geometric and mixed spatial dimensions, it is essential to provide topological and metric operators to end-users. As in conven- tional GIS systems, such operators permit selection of database subsets based on topological and/or geometrical constraints. Marchand et al. [2001] have proposed a method that implements a Spatio- Temporal Topological Operators Dimension (ST20D) in multidimensional databases. The hier- archy of this dimension is structured according to multiple levels of granularities of topological rela- tionships, and to behavior models of spatial analysis in order to improve usability. Used in conjunction with other dimensions, the ST2OD produces cross- dimensional views that satisfy specific spatial and temporal topological constraints. End users can navigate within the ST2OD hierarchy from highly aggregated spatio-temporal topological operators (e.g. "same time, same place") to finer granularities (e.g. "started by, interior intersection") down to the finest granularities (e.g. "1-00-102; -1-0102" based on the ISO/TC211 c-Relate operator). We believe that the same approach can be replicated with metric operators (e.g. distance, area). A hierarchy of metric operators could well be implemented within another new dimension or, combined with the ST2OD, as a subset of a generic dimension of spatio-temporal operators. As these approaches gain support from the community it is likely that other operators will be added (e.g. visibility and slope operators). While it is possible to pre-process

G E O M A T I C A

Visualization of Context Data A SOLAP tool should make it possible to display background maps, also called spatial context data, in addition to the geometric data of the spatial dimensions members, for the sole purpose of help- ing the user to locate the displayed information. This includes maps for the regions adjacent to the territory being analyzed to facilitate the understanding of phenomena near the borders. For example, a high concentration of accidents on a road segment

545

Representation of One or More Measures Simultaneously

It should be possible to represent more than one measure at the same time in the different displays. For example, in the diagram display, the representation of several measures could be carried out using a different diagram for each measure or a different visual variable (colour, value, size, orientation, shape and texture) for each measure within a same diagrarn. In a similar way, in the spatial display, the representation of several measures could be carried out by simultaneously displaying one map for each measure, by using a different visual variable for each measure on the same map applying graphical semiology rules, or by superimposing statistical diagrams to the map elements. Figure 3 presents different ways of representing two measures at the same time in the diagram and spatial displays of a SOLAP client interface.

statistical charts display of the analysis results in the form of different types of 2-D or 3-D diagrams (pie charts, bar charts, histograms, etc.), and the spatial display of the analysis results in the form of different types of maps.

Flexible Display Management The tabular, diagram and spatial displays must be accessible separately or simultaneously. When used simultaneously, the user should be able to perform his analysis with all displays using a synchronized granularity (e.g. City map with City table) or an independent granularity (e.g. City map with Region table). Several displays of the same type must also be offered (e.g. three map displays at the same level of granularity for different epochs). In addition, a SOLAP tool should allow the user to specify his preferred type of representation for the data being visualized in the different displays. The type of representation specified, for example a pie chart in the diagram display, should be modifiable anytime during the multidimensional analysis as different types of representations may highlight different types of information.

4. Defining the Features of a SOLAP

This section presents fundamental and desirable characteristics of SOLAP systems, as defined during our GEOIDE project (DEC#2). Additional characteristics may be defined in the future as more SOLAP systems are implemented. Our results are based on both theoretical research and detailed analysis of practical projects achieved over the last five years dealing with forestry, environmental health, road network maintenance, marine transportation, and public security. These projects have involved diverse specialists (engineers, foresters, marine pilots, crime specialists, geographers, geomatics engineers, computer scientists and epidemiologists) as well as diverse organizations (government agencies, private companies) and diverse technologies (OLAP, GIS, software components, native pro- gramming, relational DBMS, universal servers, visualization engines) from different software providers (Intergraph, Maplnfo, Oracle, Cognos, AVS, Softinap, N4icrosoft, ProClarity).

all the aggregations, it is likely that a combination of pre-computed aggregations and algorithms per- forming on-the-fly calculations within the multidi- mensional database can offer an interesting trade off. Potential architectures and approaches offering different distribution of pre-computed aggregations go beyond the scope of this paper, we refer the readers to [Han et al. 1998; Rivest 2000] and literature about on-the-fly map generalization and multiple representations.

... a SOLAP tool should

allow the user to spec-

ify his pre- ferred type of

represen- tation for the

data being visualized ..

4.1 Visualization of Data Cartographic and Non-Cartographic Displays A SOLAP client is an OLAP client that permits the representation of cross-dimensional analysis implying one or more spatial dimensions through a cartographic display seamlessly integrated with the application. This cartographic display should permit adequate exploitation of the geometric component of the spatial (or spatio-temporal) data being analyzed. Other conventional types of display include a tabular display and statistical charts or diagram display (this includes pie charts, histograms, etc.). All the display types mentioned above should be integrated and accessible from a common user interface. The end user can then begin his analysis via one of the displays, the tabular display of the analysis results in the form of different styles of tables or spreadsheets, the

on the boundary of the analyzed territory could be explained by the presence of an intersection located in the adjacent territory. Figure 4 illustrates this example.

Modification of the Graphical Semiology The user of a SOLAP tool should be able to modify

the graphical semiology according to its specific needs, in order to highlight relevant information. For example, a forest engineer who always uses the same semiological rules for the representation of different tree species should be able to use the same rules in his SOLAP tool in order to quickly locate his information. Such possibilities would also allow greater coherence for reporting.

Interactive Legend The graphic semiology presented in the legend of a SOLAP tool would, by default, apply to all the different displays, whether they are descriptive (tabular display and statistical charts display) or spatial. This characteristic aims at facilitating the exploration of data among different representations (same semiology in every display). The legend should be modifiable and changes should apply to all types of displays. This would facilitate reclassifying for instance, or highlighting categories. The user should also be offered the possibility to navigate through the legend (e.g. drilling-down in a category depicted on the legend to see more details in the numerical data while keeping the same level of spatial granularity, i.e. a thematic drill as opposed to a spatial drill).

4.2 Exploration of Data

Availability of the Data Exploration Operations in all Display Types

The drill-down and roll-up operations should be directly available in all display types. Such operations should be executed, for example, on one or more elements selected by the user in the displayed tables, diagrams or maps. They could also, with the drill-across and swap operations, be available from buttons or menu items and apply simultaneously to all displays. For example, in the spatial display of an administrative divisions dimension, spatially drilling down on a region polygon should display a new map with only the measure value for all the city polygons that compose the selected region polygon (Figure 5 illustrates this example). Conversely, the roll-up operation on one city results in a map showing its region with the related regional data aggregated from the cities of this region. Several hierarchy levels must be supported. Also, as previously explained, it should be possible to carry out drill-down and

G E O M A T I C A

Figure 3a. Vizualization of several measures simultaneously using amap for each measure (from the GEOIDE SOC #1 project).

Figure 3b. Vizualization of several measures simultaneously usingdifferent visual variables (from the GEOIDE SOC #1 project).

Figure 3c. Vizualization of several measures simultaneously bysuperimposing statistical diagrams to the map elements (from theGEOIDE SOC #1 project). 546

Availability of Spatio-Temporal Analysis Functions (Metric and Topological)

Within a SOLAP client, the addition of metric and topological spatio-temporal analysis functions is of prime necessity to assure exploration and analysis flexibility. Spatial analysis functions could include, for example, buffering, visibility or network analysis functions, in order to refine the multidimensional spatio-temporal analysis according to particular needs. These operations should apply to the various levels of aggregation. For example, in forestry, to analyze wood volumes produced according to various dimensions, including a tree species dimension and a restricted zone dimension, using corridors along lakes and rivers may be necessary. In order to optimize metric and topological analyses, some metric calculations must be performed beforehand and topological relations between spatial elements stored

Manipulation of the Temporal Dimension with a Timeline

In order to easily exploit the temporal dimen- sion, a specific interface element, the temporal scroll bar could be included in a SOLAP tool. This scroll bar would facilitate temporal navigation, making it possible for the user to position the analysis at the time or at the period of interest by simply sliding a cursor on a timeline covering the temporal extent of the database. An example of a temporal scroll bar is described in Caron [1998]. Manually or automaticaly moving the cursor along the scroll bar generates "films" of the evolution of the depicted measure. When comparing periods, additional cursors could be placed on this temporal scroll bar. When analyzing evolution using regular intervals for a given period, the cursor could stretch longer or shorter. Some commercial OLAP client solutions already offer a temporal bar which is similar to the concept described above but lacks the ability to scroll between members of a temporal dimension.

directly inside the multidimensional database, in a spatio-temporal operators dimension as developed by Marchand et al. [2001] for topological operators.

roll-up operations from the legend directly to exe- cute thematic operations, that is, drill-down or roll- up operations in the numerical data while keeping the same level of spatial granularity. Figure 6 pres- ents an example of a drill-down operation conducted via an interactive legend.

G E O M A T I C A

Figure 4: Visualization of road network data in a territory adjacent to the analysis territory (in light green) (translated from the Quebec Transportation project).

547

Figure 5a: Region selected for a drill-down operation (from the GEOIDE SOC#1 project). I

G E O M A T I C A

ber of accidents compared to the previous period. The cartographic display of the first measure could use the colour, while that of the calculated measure could be superimposed on the first display using texture (Figure 7). Several OLAP clients available on the market now feature the ability to define new measures from the client.

Filtering on the Dimension Members A SOLAP toot should include filtering

functions to allow the end-user to select the members of a dimension during a particular analysis, in order to restrict the data being visualized to the data of interest. For example, in an accidents dimension, it could be desirable to visualize only certain accident levels, such as accidents that resulted in serious injuries and fatal accidents. A filter could thus be applied to the accidents dimension members.

Display of the Significant Aggregations SOLAP tools should allow the visualization of

only significant aggregates, in order to avoid confusion with data that may be irrelevant in a particular case. This function would make it possible to exclude the empty cells of the cube in use. Usually these cells correspond to irrelevant combinations of members from the different dimensions. To illustrate this matter, let's suppose a sales cube containing a time, a store and a product dimension. If a certain type of store only sells sports items, then the combination of the corresponding member with a food member from the product dimension would produce an empty cell. It should be possible to filter out such cells so the user can visualize only the data that are of interest.

Addition of Calculated Measures It should be possible, inside a SOLAP tool, to define new measures calculated from existing measures that are stored in the cube. This could be done via the interactive legend where the graphical semiology associated with the new measure would be defined by using various visual variables, thus making it possible to refine displayed information. For example, if a cube has a measure showing the number of accidents, a new calculated measure could consist of a percentage increase in the num-

4.3 Structure of data Support for Many Geometric and Mixed Spatial Dimensions

SOLAP tools should have the capacity to handle several geometric and mixed spatial dimensions contained in a single data cube. For this purpose, the tool must provide functions for the pre-computation of the geometric spatial measure values (e.g. merge polygons). This is essential to study spatial correlations between phenomena belonging to different spatial dimensions, such as forest stands and watersheds.

Support for a Complete set of Geometric Primitives For mapping, spatial exploration and spatial

analysis of data, SOLAP tools should support all types of geometric primitives defined by ISO [ISO/TC 211 2001]. Of particular importance is the

Figure 5b: The results of the drill-down operation on the Region polygon select- ed in figure 5a, showing the city polygons (from the GEOIDE SOC#1 project).

Support for the Storage of Historical Geometric Data

A SOLAP tool should include functions for the storage and management of historical geometric data in order to permit easy and fast correspondence with their descriptive counterpart. As with different scales, multiple temporal representations offer a promising route.

Figure 6a: Bar chart showing the number of accidents for 3 roads. A drill- shaped cursor is positioned for a drill-down operation on the All accidents member of the Accidents dimension shown in the legend (example using ProClarity 4).

combinations of tools to support the database, the server and the client. For example, the SOLAP database can be implemented using the multidi- mensional database supplied with an OLAP server, with a relational DBMS or with an object-relational DBMS. A GIS can also be used to store the geometric data when this storage is not otherwise supported. In addition, the SOLAP server may be based on a

support of the geometric aggregates and geometric complexes made up of several geometric primi- tives, such as a set of disjoint surfaces that can be aggregated to form an occurrence of archipelago. Support for Automatic Generalization and Multiple Representations

A SOLAP server should handle and permit various representations of the analyzed data. For example, different geometric views could have different levels of map generalization according to scale and displayed data. A scale reduction (zoom out) could require automatic map generalization that has an impact on the information displayed. Research has shown that automatic cartographic generalization, when possible, requires heavy processing that is rarely satisfactory in terms of response times in a SOLAP context [Martel 1999]. In response to this limitation, the use of multiple representations is desirable [Bédard et al. 2000]. SOLAP tools should therefore allow the storage, possibly in a hierarchical structure, of several representations, or geometric versions, for a member of a geometric (or mixed) spatial dimension or a spatial measure. SOLAP tools should themselves handle different views and permit their representation. For example, a building could be represented as a point on a small scale map and as a polygon on a larger scale map. The concept of Vuel developed by Bédard et al. [2000] is a generic model which handles such multiple views.

From a system architecture point of view, SOLAP applications can be developed with different

Support for Different Data Sources A SOLAP tool should be able to exploit data stored and managed on an OLAP server. It should also be able to exploit data stored and managed in a relational DBMS simulating the operation of an OLAP server using queries on a relational database structured according to a star, snowflake, mixed or constellation schema. In some cases, OLAP servers offer a user a transparent bridge to DBMS to store either complete or partial hypercubes (in the case of ROLAP and HOLAP architectures).

5. Technological Considerations

Figure 6b: The results of the drill-down operation illustrated in figure 6a, showing the number of the five different types of accidents, for three roads (example using ProClarity 4).

G E O M A T I C A

549

G E O M A T I C A

550

Figure 7: Example of a superimposed calculated measure. The measure, a stan- dardized rate, is displayed using colour. The calculated measure, a significance indicator, is displayed using a pattern.

previous section. It was developed for the Quebec Department of Transportation to support their deci- sions in allocating the road maintenance budget considering different scenarios as well as their impact on road quality. Examples of analysis realized with this application are given. The SOLAP prototype allowed easy and fast analysis and predictions of the road conditions. The high level of satisfaction with this new system, especially when compared to existing solutions, has led the Department into completing and implementing the prototype. The technologies used are presented in Table 1.

This prototype is based on a MOLAP architec- ture, and two different structures are used to imple- ment the SOLAP database: the multidimensional database of Microsoft Analysis Services to store the descriptive data and the Geomedia Access warehouse database to store the geometric data. The interface of the prototype is shown in Figure 8. The left side of the interface presents the tabular and diagram displays. It contains tools to manipulate the dimensions and measures. The right side contains the cartographic display with its cartographic navigation and SOLAP functions.

Using the data cube described in Section 2.2, an example of the estimated costs of road reconstruction in 2002 can be realized. After the application is started, a few OLAP operations allow the display of intervention thresholds for the year 2000, for the roads in the analyzed territory (Figure 8). We can easily determine that road 88841 has an intervention threshold of 2 (in yellow). In this example, an intervention threshold of I is displayed in green,

non-spatial OLAP server, on a database management system (relational or object-relational), and on a GIS. Finally, the implementation of the SOLAP client may be based on an OLAP client, a GIS, a viewer, a visu- alization software, or a combination of these wrapped within a single user interface developed with third or fourth generation programming languages. Other possibilities exist with CAD, spreadsheets, statistical packages, Web mapping and software components. Today, in order to have access to most of the SOLAP characteristics described in Section 4, it is necessary to mix tools using, for example, application programming interfaces (APIs). Two very different architectures are summarized hereafter.

The first one is a high-end SOLAP prototype that includes several of the features described in the

Table 1. Technologies used for the development of the Transportation SOLAP prototype.

SOLAP database The multidimensional database of Microsoft Analysis Services 2000 for the descriptive data

Technology

SOLAP server

Component of the SOLAP system

SOLAP client

Programming language of the GUI Visual Basic

Intergraph Geomedia 4 for the geometric data

ProClarity 2 (from ProClarity Corporation, formerly Knosys) for the descriptive data

Intergraph Geomedia 4 for the geometric data

Microsoft Analysis Services 2000 for the descriptive data

Intergraph Geomedia 4 (Access warehouse) for the geometric data

Figure 8: Intervention thresholds for 2000, for the roads in the analyzed territory (translated from the Quebec Transportation project).

551

G E O M A T I C A

tain measures. This allows us, using a drill-across operation on the 2002 member of the time dimen- sion (again by pointing with the mouse on the desired member), to check the predicted interven- tion threshold of each segment of road 88841 in 2002 if no budget is allocated to this road in 2000 and 2001 (Figure 10). It then becomes possible to see that if no budget is allocated, the road will degrade rapidly and seven segments will have an intervention threshold of 3.

A drill-across operation on the intervention costs measure permits the representation of potential intervention costs for each segment, if interventions are made in 2002 (Figure I 1).

This example, if compared with the same analysis conducted using a conventional OLAP tool (as presented in Figure 1) shows that a SOLAP tool allows us to visualize and manipulate the spatial information in a much more convenient way. Compared to the existing solutions used to conduct the same kind of analysis, a SOLAP tool is easier to use because no query needs to be formulated. The data can be manipulated using the mouse only. Also, because the response times are very short in

meaning that the road element is in good condition. An intervention threshold of 2 is displayed in yel- low and means that the road element necessitates a new coating. An intervention threshold of 3 is dis- played in red and means that the road element must be reconstructed.

To obtain more details about road 88841, for example to check the intervention threshold of each segment composing the road, a spatial drill-down operation can be used. A spatial drill-down on road 88841 (executed by simply pointing with the mouse on this road) shows the intervention threshold of each of its road segments (Figure 9). It is possible to see that two segments of that road have an intervention threshold of 3 (reconstruction) and also that the two segments are relatively close to each other. This last spatial information cannot be easily and rapidly obtained when using a conventional OLAP tool allowing only tabular and statistical charts displays, as shown in Figure lb.

Models to predict the road conditions degrada- tion (as defined by the Quebec Department of Transportation) have been incorporated into the prototype as formulas to calculate the value of cer-

This application is designed to improve decision making and interventions, to improve accessibility to statistics and other existing information, and to support the discovery of new knowledge. The system uses ROLAP architecture based on the database engine of MS-Access for the storage and management of descriptive data, on SoftMap Pro Toolkit for the storage and management of geometric data, and on VB for the GUI. The use of a ROLAP architecture is possible because the volume of data is relatively low. All the measure values are calculated by statisticians and then integrated in the database. For example, for cancer data, five measures are integrated: the number of cases, the standardized rate, the standardized rate ratio (calculated using the provincial standardized rate), the correlation coefficient and the statistical significance.

Figures 5a and 5b illustrate the display of the standardized rate ratio of breathing disorders for two geographic levels. In these displays, yellow means a ratio of around 1, red means a ratio higher than I and blue means a ratio lower than 1. The use of this prototype is very easy and rapid (the data are

G E O M A T I C A

Figure 9: Results of a spatial drill-down operation on road 88841, to the segments level, showing the intervention thresholds for 2000 (translated from the Quebec Transportation project).

the case of SOLAP, the user can test many different scenarios in a short time.

The second example is a light, low-end SOLAP prototype that aims at satisfying user needs at a lower cost. It was developed in GEOIDE project SOC#1 as a new tool to help epidemiologists and other specialists to reduce health risks caused by environmental sources, such as released industrial pollutants. It provides quick and easy access to environmental and health data having temporal coverage of five to fifteen years and geographic coverage at the local (CLSC), regional (RSS), and provincial levels. Currently, the prototype contains data about:

− Cancer (incidence, hospitalization and death) − Breathing disorders (hospitalization and

death) − Intoxications − Air quality − National pollutant release inventory − Greenhouse gas − Pesticide sales − Waste management − Activities of environmental health teams 552

Figure 10: Results of a drill-across operation on the member 2002 of the time dimension. The display shows the estimated intervention thresholds for 2002, for the segments of road 88841, if no intervention (resurfacing, recon- struction) is made on the road network (translated from the Quebec Transportation project).

This paper presented a new category of OLAP tools that are adapted for effective spatio-temporal exploration and analysis: SOLAP tools. SOLAP has been defined as "a visual platform built espe- cially to support rapid and easy spatio-temporal analysis and exploration of data following a multi- dimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays". Its inherent con- cepts have then been presented. The essential fea- tures as well as desirable characteristics of this new type of tool, grouped into data visualization, data exploration and data structure categories, have been described. Only limited functionality of the

6. Discussion and Conclusions

manipulated using mouse clicks). The result was also very well received by our partners in the project, that is, the Quebec Public Health Centre, and discussions are underway with the Quebec Department of Health and SoftMap to implement the system for public health specialists throughout the province.

This research has been carried out with the financial support of the GEOIDE Network of Centres of Excellence DEC#2 project (Designing the technological foundations of geospatial decision making with the World Wide Web) and the Natural Sciences and Engineering Research Council of Canada individual research grant program. The authors also acknowledge the collaboration of two applied research projects, that is a Transportation Quebec project (Multidimensional analysis and geographic information system applied to road network data analysis) and GEOIDE SOC#1 project (Cartographic interface for the multidimensional exploration of environmental health indicators).

7. Acknowledgements

three general characteristics presented are currently available in commercial OLAP packages. However, results from this DEC#2 GEOIDE project, combined with development in other projects (including GEOIDE projects SOC#1 in environmental health and DEC#9 in generalization), pave the way to more complete SOLAP solutions.

G E O M A T I C A

8. References

Figure 11: Results of a drill-across operation on the Intervention costs measure. The display shows the potential intervention costs for 2002, for the segments of road 88841 (translated from the Quebec Transportation project).

Business Objects. 1998. Business Objects and ESRI

Announce Strategic Partnership, Business Objects Press Releases. http://wwwbusinessobjects.com/news/.

Caron, P.-Y 1998. Étude du potential de OLAP pour supporter l'analyse spatio-temporelle. Mémoire de M.Sc., Département des sciences géomatiques, Faculté de foresterie et géomatique, Université Laval, 132 p.

Cleveland, W. S. and M. E. McGill. 1988. Dynamic graphics for statistics. Belmont, California, USA, Wadsworth & Brookes/Cole.

Codd, E. F., S. B. Codd and C. T. Salley. 1993. Providing OLAP (On-Line Analytical Processing) to User- Analysts: An IT Mandate. Hyperion white papers, 20 p.

Cognos. 2000. Cognos Visualizer Product Information. http://www.cognos.com/products/visualizer.

Cook, D., A. Buja, J. Cabrera and C. Hurley. 1995. Grand tour and projection pursuit. Computational and Graphical Statistics, 4(3), pp. 155-172.

ESRI. 2000. ArcView GIs Brochure. http://www.esri.com/library/whitepapers/avlit.html.

Franklin, C. 1992. An Introduction to Geographic Information Systems: Linking Maps to Databases. Database, April, pp. 13-21.

G E O M A T I C A

Archer Decision Sciences. 1995. Star Schema 101: A pictorial Introduction to Dimensional Modeling. http://www.archer-decision.com/knowpre.htm.

Bédard, Y. 1997. Spatial OLAP, Vidéoconférence. 2ème Forum annuel sur la R-D, Gématique VI: Un monde accessible, 13-14 novembre, Montréal.

Bédard, Y, R. Devillers, E. Bernier and B. Moulin. 2000. Automatic Generalization or Multiple Representations: Towards a combination of both approaches. GEOIDE DEC#9 Workshop on feature extraction and cartographic generalization, November 17, Québec.

Bédard, Y., S. Larrivée, M.-J. Proulx, P.-Y. Caron and F. Létourneau. 1997. Geospatial Data Warehousing: Positionnement technologique et stratégique. Rapport pour le Centre de recherche pour la défense de Valcartier (CRDV), 79 p.

Bédard, Y., T. Merrett and J. Han. 200 1. Fundamentals of spatial data warehousing for geographic knowledge discovery. In: Geographic Data Mining and Knowledge Discovery. Fdited by H. Miller and J. Han, Taylor & Francis.

Bédard, Y. and Y. van Chestein. 1995. La gestion du temps avec les systèmes de gestion de données localisées : état actuel et avenues futures. Actes du Colloque Géomatique V - La route de l'innovation, ACSG, Montr6al.

Sonia Rivest holds an M.Sc. in Geomatics Sciences from Laval University. She now works at the Centre for Research in Geomatics of Laval University as a research professional within the GIS and spatial databases team. Her professional interests include multidimensional databases, spatial data warehouses and SOLAP.

a case study. First international advanced study symposium on topological data structures for geo- graphic information systems, Harvard papers on Geographic Information Systems, 7.

Thomsen, E., G. Spofford and D. Chase. 1999. Microsoft OLAP Solutions, John Wiley & Sons, 495 p.

Wise, S., R. Haining and R Signoretta. 1998. The role of visualization for exploratory spatial data analysis of are-based data. Fourth International Conference on Geocomputation (GeoComputation'98), Bristol, UK.

Yougworth, P. 1995. OLAP Spells Success For Users and Developers. Data BasedAdvisor, December, pp. 38-49.

G E O M A T I C A

Gill, H. S. and P. C. Rao. 1996. The official Guide to Data Warehousing, QUE Corporation, 382 p.

Gonzales, M. L. 1999. Spatial OLAP: Conquering Geography, DB2 Magazine, Spring, http:llwww.db2mag.com/db-area/archives/1999/ql/ 99sp-gonz.shtn-A.

Glymour, C., D. Madigan, D. Pregibon and P. Smyth. 1997. Statistical themes and lesson for data mining. Data mining and knowledge discovery, 1: pp. 11 -28.

Han, I., N. Stefanovic and K. Koperski. 1998. Selective materialization: An Efficient Method for Spatial Data Cube Construction. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'98), Melbourne, Australia.

Inmon, W. H. 1996. Building the Data Warehouse, John Wiley & Sons, 401 p.

ISO/TC 211. 2001. Draft international standard ISO/DIS 19107, Geographic Information - Spatial Schema. 184 p.

Knosys. 2000. Geo Spatial Mapping: Maplnfo MapX Plug-in for Knosys ProClarity 3.0. http:/Iwww.knosysine.conVproductextenti.htm].

LGS Group Inc. 2000. Analysis of Health Surveillance Business Intelligence Tools and Applications, Final Draft, III P.

MacDougall, E. B. 1992. Exploratory analysis, dynamic statistical visualization and geographic information systems. Cartography and geographical informa- tion systems, 19(4), pp. 237-246.

MacEachren, A. M. and M.-J. Kraak. 2001. Research challenges in geovisualization. Cartography and Geographic Information Science. 28(l).

Marchand, P., Y. Bédard and G. Edwards. 2001. A hyper cube-based method for spatio-temporal exploration and analysis. Geolnfomatica, Submitted May 2001.

Martel, C. 1999. Développement d'un cadre théorique pour la gestion des representations multiples dans les bases de données spatiales. Mémoire de M.Sc., Département des sciences géomatiques, Faculté de foresterie et géomatique, Université Laval, 128 p.

OLAP Council. 1995. OLAP and OLAP Server Definitions http:/Iwww.olapcouncil.org/research/ glossarjfy.htm.

Openshaw, S., A. Cross and M. Charlton. 1990. Building a prototype geographical correlates machine. International journal of geographical information systems, 4(4), pp. 297-312.

Pendse, N. 2000. Glossary, The OLAP report. http://www.olapreport.com/glossary.htin.

Pendse, N. 2000b. What is OLAP? The OLAP report. http://www.olapreport.com/fasmi.htin.

ProClarity Corporation. 2001. ProClarity 4.0 Desktop Client Data Sheet.

Rivest, S. 2000. Investigation des modes d'intégration physique entre un serveur de base de donn6es mul- tidimensionnelle et un SIG. Essai de M. Sc., Département des sciences géomatiques, Faculté de foresterie et g6omatique, Université Laval, 61 p.

Sinton, D. F. 1978. The inherent structure of information as a constraint to analysis: mapped thematic data as

Yvan Bédard

Pierre Marchand

Sonia Rivest

Authors

Pierre Marchand is a PhD student at the Centre for Research in Geomatics at Laval University. He holds an engineering degree in rural engineering from the Faculty of Agronomical Sciences of Gembloux, Belgium. Prior to his return to academic life, Mr. Marchand had been involved with several international projects dealing with GIS, remote sensing and water management. His research interests include multidimensional databases, spatio-temporal exploration, as well as water management and creativity.

Dr Yvan Bédard has been professor of GIS and Spatial Databases at Laval University Department of Geomatics Sciences since 1986. He is a member of the Centre for Research in Geomatics, where he acted as Founding-Director, and is involved in the GEOIDE Network of Centers of Excellence. He is also associate researcher at the Centre Hospitalier Universitaire de Québec Research Centre (CHUQ) an associate professor to the Dept. of Geodesy and Geomatics Engineering at the University of New Brunswick and Chair of the ISPRS WG-IV/1 on Spatial and Temporal Data Modeling and Analysis. Dr. Bédard has collaborated with major government agencies and private industries in Canada and abroad, especially for the design of commercial products, strategic planning and project evaluations. He has presented about 300 papers and conferences worldwide.