SUPPORTING GEOGRAPHICAL MEASURES THROUGH A

NEW VISUALIZATION METAPHOR IN SPATIAL OLAP

Sandro Bimonte, Anne Tchounikine

Laboratoire d'InfoRmatique en Images et Systèmes d'information UMR CNRS 5205

INSA, 7 avenue Capelle 69621 Villeurbanne Cedex, France

Sergio Di Martino, Filomena Ferrucci

Dipartimento di Matematica e Informatica

Università di Salerno, Via Ponte don Melillo 84084 Fisciano (SA) – Italy

Keywords: Spatial OLAP, Data Visualization, Spatial Decision Support Systems, Human Computer Interaction.

Abstract: Pivot tables are the de-facto standard paradigm for the visualization of data in the context of

multidimensional OLAP analysis. However it is recognized that they are not suited, in their original

definition, to support spatio-temporal data analysis (and in particular geographical measures). In this paper,

we propose the GeOlaPivot Table, a visual metaphor intended as an extension of the pivot tables

specifically conceived to assist decision makers in analyzing geographical measures in spatial data

warehouses. In order to show the analysis capabilities of our metaphor we describe it using an example

concerning the supervision of infectious diseases in Italy. This approach represents a first effort in adapting

advanced geovisualization techniques to SOLAP ones, in order to create a specific visual paradigm for

Spatial OLAP able to effectively support and fully exploit spatial multidimensional analysis process.

Moreover, we present an architecture for a web-based environment able to support geographical measures in

SOLAP analyses exploiting the GeOlaPivot Table visual metaphor.

1 INTRODUCTION

Nowadays organizations are collecting in Data

Warehouses even more and more heterogeneous

information, often containing precious but hidden

information. OLAP (OnLine Analytical Processing)

systems support the Decision Makers in discovering

this concealed information, by allowing him/her to

interactively explore the multidimensional database

through a visual interactive user interface.

Indeed, the main strength of these solutions is

the possibility to gain an insight into the data,

allowing the user to discover unknown phenomena,

patterns and data relationships without requiring

him/her to master neither the underlying

multidimensional structure of the database, nor

complex multidimensional query languages (Stolte

et al. 2002). For these reasons, OLAP solutions are

widely and successfully adopted in many business

contexts.

(Franklin, 1992) has shown that about 80% of

the data stored in databases integrates some kind of

spatial information. It is clear that a fully

exploitation of spatial data into decisional process

could add a lot of significance to the analytical

process. On the other hand, it is also clear that if the

spatial dimension is treated as any other descriptive

dimension, without consideration for the

cartographical component of the data, the resulting

analysis capabilities will be highly compromised.

However, current OLAP tools present serious

limitations when dealing with spatio-temporal

analysis: they lack of visual interactive maps, that,

revealing spatial trends or relationships and

stimulating user’s thinking process, represent the

main instruments to support a real and effective

spatio-temporal analysis process (MacEachren,

2001).

To overcome these limitations, previous

researches on integration of spatial information into

multidimensional models leaded to the definition of

the Spatial OLAP (or SOLAP) concept (Bedard,

Bimonte S., Tchounikine A., Di Martino S. and Ferrucci F. (2007).

SUPPORTING GEOGRAPHICAL MEASURES THROUGH A NEW VISUALIZATION METAPHOR IN SPATIAL OLAP.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - HCI, pages 19-26

DOI: 10.5220/0002371100190026

 SciTePress

1997). SOLAP solutions usually lie on coupling

OLAP functionalities, used to provide

multidimensionality, and Geographic Information

Systems (GIS) functionalities, used to store and

visualize the spatial information (Rivest et al, 2005).

These solutions, thanks to a cartographic

representation of the multidimensional data, permit

to visualize measures on map and to discover

geographical correlations between facts and

members. It is worth pointing out that common GIS

techniques, such as Overlay or MultiMaps, are not

appropriate. This because the Overlay, even if

reveals spatial relations, hides the precious

information that a measure could belong to different

layers (a spatial measure could be associated to

different combination of level members), while

MultiMaps are conceived to emphasize thematic

relations rather than spatial ones.

Even if the numerous ongoing works

(Malinowski, E., and Zimányi 2005), (Bimonte et al

2006), (Kouba et al 2000) confirm the importance

and the innovating character of SOLAP, a detailed

analysis of capabilities of current SOLAP tools

reveals that many further enhancements are required

in their user interfaces. In particular, at best of our

knowledge, currently there are no SOLAP tools

offering the possibility to visually compare

geographical measures (for instance to compare

areas of two regions affected by some kind of

phenomenon). This is a crucial task in the analytical

process, since it can take advantage of human

abilities to perceive visual patterns and to interpret

them.

To address this issue, in this paper we propose a

new visualization metaphor, named GeOlaPivot

Table, especially suited to deal with spatial measures

in SOLAP environments. It exploits the concept of

Pivot Table, adding to it a 3

dimension by using the

Space-Time Cube (Hägerstrand, 1970) 3D

representation. In this way, it is possible to overlap,

into a single and coherent visual representation,

different layers of information, thus permitting to

evaluate the relationships among the geographical

measures. To better explain the usefulness of the

proposal, we provide an example of application to a

real Spatial Data Warehouse concerning the

supervision of infectious diseases in Italy.

Moreover, we also propose a user interface and

an architecture for a web based SOLAP solution,

supporting the GeOlaPivot Table metaphor, starting

from existing (S)OLAP frameworks.

The remainder of the paper is structured as

follows. Section 2 describes the main OLAP and

SOLAP concepts and provides a panorama of

existing SOLAP tools. In Section 3 we describe our

GeOlaPivot Table visualization metaphor and the

case study. In Section 4 we introduce the

architecture and the user interface of a solap tool

supporting the GeOlaPivot Table methaphor

together with the main technical issues we deal with.

Some final remarks and future work conclude the

paper.

2 OLAP AND SOLAP

In Data Warehouse systems, data is usually

modelled conforming to the multidimensional model

(Immon, 1992): analysis axes are dimensions, that

can be organized into hierarchies, and the subject of

the analysis is a fact, described by several numerical

measures. This approach permits the Decision

Maker to explore the Data Warehouse at different

levels of details, from aggregated to detailed

measures. Typical OLAP operators are Slice

(selection of a part of the dataset), Dice (eliminate a

dimension), RollUp (moves up into a dimension

hierarchy) and DrillDown (reverse of RollUp).

An example of OLAP multidimensional analysis

carrying on a fact “sales” of a stores chain can be

realized defining as measures “quantity” and

“amount” of sold products, and as dimensions

“Time” (Month<Year), “location”, and “product”

(Item<Type). Figure 1 represents the

multidimensional model for this application and its

measure values (the cells of the cube). Thus, an

OLAP query in our example can be: “What are the

volume and the amount for all the sold products Alc

54 in 1999 in the store Carebim?

Figure 1: “Sales” OLAP application: multidimensional

model and cube values.

OLAP decision making process is interactive

and iterative, so OLAP tools have to support the user

into this kind of particular decisional process by a

simple interaction with the user interface, translating

his/her actions into OLAP operators.

Integration of spatial data in OLAP systems

brings to two new concepts: spatial dimension

(Bedard et al, 2001) and geographical measure

(Bimonte et al 2005). The former is a hierarchy of

members with alphanumeric and spatial attributes,

while the latter extends the classical concept of

ICEIS 2007 - International Conference on Enterprise Information Systems

measure, being not only a numeric value but also a

set of alphanumeric and spatial inter-dependent

attributes having a meaning as whole, or in other

terms a geographical object.

2.1 (S)OLAP Visualization Issues

In order to effectively support OLAP analysis of

multidimensional databases, some specific

interaction metaphors are required to present and

browse data.

The Pivot Table is the most used visualization

paradigm for OLAP tools, due to its intrinsic

characteristics of incorporating the multidimensional

structure of the Data Warehouse and the visual

support for an easily measures’ comparison.

Basically, a Pivot Table is a 2D spreadsheet with

associated subtotals and totals that supports viewing

more complex data by nesting several dimensions on

the x- or y-axis and displaying data on multiple

pages. Pivot tables generally permit to interactively

select a subset of the data warehouse and change the

displayed level of detail.

As a result, Pivot Tables are a very powerful

interaction metaphor, to support the browsing of

alphanumerical Data Warehouses, and it is

implemented in almost all OLAP tools (Pende,

2005), (Thomsen and Pedersen, 2005). However the

Pivot Table, in its original formulation has the

inherent constraint to be limited to deal with

alphanumeric information, thus turning out to be

inadequate with spatial data.

It is worth pointing out that some OLAP

solutions (e.g. (Stolte et al., 2002)) have extended

the pivot table concept with graphic representations

and visual variables (shape, size, etc…). Following

this approach, cells of the pivot table are graphic

canvas which collapse using visual variables

members of a same hierarchy levels into a unique

visual description. The main advantage of this

approach is to effectively support the user in data

comparison.

2.2 SOLAP Tools

The introduction of cartographic data in the

(S)OLAP process implies a reformulation of

classical visualization paradigms, to support a

multidimensional analysis based on maps, tabular

and graphical data representation, that have a

different expressive power. Indeed a SOLAP user

interface has to support multidimensional analysis

using maps, tabular and graphical data

representation in a concerted, interactive and

synchronized way. Synchronization means that an

action (i.e. drill-down or roll-up) on one interface

widget (i.e. map) has to be replied on the other ones

(i.e. pivot table and graphic displays). Finally ad-hoc

semiology rules, or particular geovisualization

techniques as for example the choremes (Laurini et

al., 2006) for visualization of alphanumeric

measures on maps could improve the expressiveness

power of SOLAP tools.

In the following we will describe how these

problems have been tacked in the literature. Several

SOLAP tools have been developed until now. In the

following we focus on their visualization features.

(Rivest et al, 2005) describe a SOLAP tool which

supports pivot table and several different types of

diagrams and maps, composed by visual variables

and maps superimposed with graphical diagrams, for

supporting spatial dimensions, numeric measures

and spatial measure (result of topological and metric

operators on spatial dimension members. In (Scotch

and Parmanto, 2005) authors describe SOVAT, a

multidimensional spatial-numerical decision support

system. This tool permits to navigate into

multidimensional databases, and to analyze spatio-

temporal data using graphical displays, maps, and

tabular representation. SOVAT provides some

multidimensional and spatial data mining operators,

but allows user to analyse only numerical measures.

In (Voss et al, 2005) is presented CommonGIS, a

powerful tool for interactive visual geo-analytics,

extended to process hierarchical multidimensional

data from OLAP warehouses. This approach is very

different from the previous ones, since no tabular

representation of data is provided for the

multidimensional navigation, which is permitted by

means of some parametric geovisualization

techniques. Finally a commercial SOLAP tool (ESRI

2006) has been developed by ESRI and SAS,

providing a solution for spatial multidimensional

application using spatial dimensions

All these works try to introduce a link between

the cartographic representation of a dimension and

the corresponding tabular representation of the

multidimensional database, to achieve a

synchronized multidimensional navigation. Visual

representation of numeric measures on maps (spatial

dimensions) is sometime supported by

geovisualization techniques (i.e. multimaps). But

how geovisualization tools and visualization-based

techniques for exploratory analysis of spatio-

temporal data (Andrienko and Andrienko, 2005) can

be coupled for a more coherent analysis of

geographical measures and spatial dimensions rest

an open issue.

SUPPORTING GEOGRAPHICAL MEASURES THROUGH A NEW VISUALIZATION METAPHOR IN SPATIAL

OLAP

3 THE GEOLAPIVOT TABLE

As for numeric data, to answer the strong needing

for a visual technique suited to compare

geographical measures, according to different

members of the same hierarchy level, and to

effectively understand spatial/thematic relations

between measures, we propose the metaphor of

GeOlaPivot Table, intended as a 3-Dimensional

extension of the OLAP Pivot Table.

Our main idea is to exploit the 3

dimension to

provide insight on how a spatial phenomenon

evolved in function of another factor (such as time,

or incidence), by overlapping data onto a map. To

this aim, we have combined the concept of Space-

Time Cube (Hägerstrand, 1970) (Gatalsky et al.,

2004) and Pivot Table giving rise to the notion of

GeOlaPivot Table. Indeed, cells of the Pivot Table

related to spatial data are cubes, representing into a

single, visual description measures associated to

different members of a same hierarchy level, like

previously described OLAP tools. A cube can be

rotated to obtain the best point of view, avoiding

screen and information cluttering. So, user can freely

rotate the cube on 3 axes, to analyze the dataset. The

base of the cube is associated to a spatial dimension

(if it exists) and its 3

dimension to another

alphanumeric dimension. Spatial measures

associated to the same fact are depicted by the same

color. All the data that do not match the query

parameters, set by the user, are removed from the

visualization cube. An example of GeOlaPivot Table

is reported in Figure 2, showing the district of the

Northern Italy affected by some infectious diseases

during the period 2001-2003.

+ Northern Italy>6.0

Time.All Time -DeptLocationIncidence

+ Northern Italy>6.0

Time.All Time -DeptLocationIncidence

Figure 2: Example of a GeOlaPivot Table.

In GeOlaPivot Table, geographical measures can

be associated to a spatial context that permits to

localize them in the space. In other terms a spatial

dimension can be present in a SOLAP application

with geographical measures. The cartographic

members of the spatial dimension will be the base of

the cube. Moreover, thematic attributes of

geographical data are necessary for an effective

decision-making process. For example, what

characterize a particular area can help decision-

maker to understand the causes of a particular

localization of a phenomenon.

As a result, the main characteristics a SOLAP

client tool based on GeOlaPivot Table are:

1. Visualization of spatial geometric dimension

and spatial measure at same time.

2. Adoption of a visualisation technique to

compare spatial and thematic relations between

measures associated to different members of a

same hierarchy level.

3. Explicit visualization of spatial relations

between measures and dimensions members.

4. Visual encapsulation of the structure of

multidimensional application.

5. Visual representation of OLAP operators

6. Display of thematic attributes of measures.

The main advantage of this approach is to

effectively support the user in data comparison. Our

proposal is an improvement of SOLAP solutions,

because it permits to coherently merge, in a single

visual environment, the key concepts of pivot tables

and Space-Time Cube. This allows us to represent

and effectively analyze geographical measures

according to spatial and alphanumeric dimensions.

3.1 A Case Study

In order to illustrate the fundamental characteristics

of the designed visualization tool, we describe a

scenario of use, exploiting data on infectious

diseases (as for example AIDS, tetanus, etc…) in

Italy. The complete dataset is freely available on the

web site of the Italian Health Institution “Istituto

Superiore di Sanità” (www.iss.it).

The multidimensional application presents as

dimension:

– Location: (Region, Nation) Spatial geometric

dimension, i.e. Lombardia, Italy.

– Time: (Year, 3 Years), i.e. 1991, 1990-1992

– Infectious Disease: (Disease, Class) A

classification of diseases according to the

International Classification of Disease, i.e. AIDS

– Incidence: (Rate) Rate of incidence of deaths by

population per 100000 inhabitants, i.e. 2,5-3,0

Districts are measures. A district is characterized

by some attributes, as the Name, the Geometry, the

Number of Hospitals and the Areaclass. The latter

one is a social-economic classification of the district,

ICEIS 2007 - International Conference on Enterprise Information Systems

such as “cities and services”. Aggregation functions

are spatial union for geometry, sum for the number

of hospitals and a ratio function for the areaclass. An

example of the fact table is presented in Table 1.

Table 1: Fact table of the Infectious Diseases Spatial Data

Warehouse.

Disease Time Incidence Location District

AIDS 2001 >6.0 Lombardia Piacenza

AIDS 2002 >6.0 Lombardia Brescia,

Piacenza

AIDS 2003 >6.0 Lombardia Lecco,

Piacenza

AIDS

2002 >6.0 Emilia-

Romagna

Ravenna

…

… … … …

In our case study we considered the years 2001,

2002 and 2003. If user wants to know what zone of

Italy has an incidence rate superior to 6.0 for AIDS

in the time period 2001-2003, the UI configuration

shown in figure 4 permits to answer to this question.

We notice the AIDS value in the Filter component of

the Cube Navigator and 3 Year in the Cube Axis

component. So, now the GeOlaPivot Table will

contain only one cell showing a cube which has as

base the cartographic representation of Italy, as

vertical axis the 2001-2003 time period, and a

geographical zone as internal value. It shows that the

highest incidence rate for AIDS from 2001 to 2003

regards at most north Italy districts. What

characterize this area? Answers could be found in

thematic attributes. Showing thematic attributes for

the new aggregated measure it can be noticed that

the areaclass for this area is classified as “cities and

services”.

Let us suppose that the user wishes to have a

more detailed insight of the application, and in

particular she/he wants to know what districts have

an incidence rate superior to 6.0 for AIDS for each

year.

He/she can apply the Drill-Down on the

Location dimension, by clicking on the ‘+‘ operator

at the left of Italy in the GeOlaPivot Table.

Then using the Cube Navigator he/she choices to

no more visualize measures for Italy, and through

the Cube Axis component he/she applies a drill

down operator in the Time dimension too. This

action changes the axis of the cube in the

GeOlaPivot Table, which now shows the districts

for each year. The UI configuration shown in figure

5 permits to answer to this question.

The GeOlaPivot Table permits to see that for the

region “Lombardia”, three districts are present and

in particular the district Piacenza is always present

from 2001 to 2003. Moreover these districts are all

neighboring. Changing year does not imply

changing of geographical area.

4 VIS

OLAP: A WEB BASED

SOLAP TOOL

We are currently developing a web-based system,

named VIS

OLAP, meant to provide SOLAP

features and to support geographical measures by

exploiting the GeOlaPivot Table. In this section we

present the software architecture of the system and

then we detail the mock-up of its user interface.

4.1 System Architecture

VIS

OLAP is based on a three tier architecture, as

shown in Figure 3. It consists of a DBMS able to

support spatial data, an OLAP Server, and a web

client providing the GeOlaPivot Table metaphor.

More in detail, Oracle 10g will be used to

implement the Data Warehouse tier, due both to its

native support of spatial data and to its Object-

Relational capabilities. Indeed, user-defined

aggregation functions and user defined types, which

are necessary when dealing with spatial data, can be

easily implemented over an Object-Relational

DBMS. Moreover Oracle’s native support for spatial

data ensures scalability and security to spatial

multidimensional applications.

For the OLAP tiers, two widely-adopted, free

tools for OLAP applications will be employed:

Mondrian (Mondrian 2006) and JPivot (JPivot,

2006).

The former is a software package designed to

provide OLAP functionalities in an open and

extensible framework, on the top of a relational

database. This is achieved by means of a set of

JAVA APIs, that can be used for writing

applications, such as a graphical interface, for

browsing the multidimensional database. These

APIs can also be invoked by JSP/Servlets, within a

web environment. Mondrian includes a Calculation

layer, that validates and executes MDX

(Multidimensional Expressions) queries, and an

Aggregation layer that controls data in memory and

request data that is not cached. MDX is a standard

language to query multidimensional databases, just

like SQL for the relational ones. In order to

guarantee the greatest flexibility, to interface the

relational data, an XML description of the

multidimensional application has to be written.

JPivot is a software package designed for

providing a web-based, graphical presentation layer

on top of Mondrian. It provides specific JSP tags for

SUPPORTING GEOGRAPHICAL MEASURES THROUGH A NEW VISUALIZATION METAPHOR IN SPATIAL

OLAP

easily building powerful graphical interfaces, suited

to explore the data warehouse.

In order to implement the visual metaphor of the

GeOlaPivot Table in this system, JPivot’s APIs

should be deeply modified, to generate on-demand,

within the cells, the 3D cube. Details about data

presentation are provided in the next section.

Spatial

Data

Warehouse

Calculation

Aggregation

Presentation

Mondrian

Oracle

Spatial

Modified

JPivot +

Ajax

Figure 3: The System Architecture.

4.2 The User Interface

In an environment like the one described in the

previous section, a Knowledge Engineer can query

the Spatial Data Warehouse by defining the analysis

dimensions and the level of detail to use, to get

insight on spatial relationships occurring among

data. In particular, a direct manipulation of both

attributes and depicted values is allowed. To this

aim, the interface should propose some widgets to

carefully select the information to deal with, which

will be rendered in 3D using the GeOlaPivot Table.

To clarify the main aspects of the GeOlaPivot

Table, we have developed a mock-up of the User

Interface (UI) of the visualization tool meant to

support the proposed metaphor (see figure 4 and 5).

This UI is aimed at providing an interactive

environment which graphically encapsulates the

structure of the multidimensional application and

translates interactions with the visual interface into

operators.

The UI is an extension of the standard one

provided by JPivot, and is composed by two main

blocks: the OLAP tool bar, and the GeOlaPivot

Table. The former set of controls provides

functionalities to navigate in the cube, affecting the

pivot table in several way: drill-down replace, drill-

down position, expand-all, drill-through.

The drill-down replace enables drilling from one

pointed member to its child members in the

dimension hierarchy, hiding the parents. drill-down

position enables drilling from one pointed member

to its child members, still showing the members of

the initial higher level in the table. The expand-all

operator enables drilling from all visible members in

the table to child members. Moreover the OLAP tool

bar permits through the Cube Navigator tool to

select dimensions, levels and members to display.

This browser is used by the user in order to map the

hierarchies on table axes and to express selection of

members to slice or dice the cube. Finally the Cube

Navigator permits to select the level’s members to

be used as vertical axes of our cube metaphor by the

icon:

The GeOlaPivot Table is used to show in 3D, at

an arbitrary detail level, the geographical data. It

allows for 6 Degree of Freedom, achieved through

some specific buttons placed at the bottom of the

cube.

Figure 4: Districts in GeOlaPivot Table.

ICEIS 2007 - International Conference on Enterprise Information Systems

Figure 5: An example of User Interface with Aggregation of Districts in GeOlaPivot Table.

In our opinion, one of the most critical

implementation aspects could be the generation of

the 3D cubes with the underlying map, since it

requires (I) to rasterize the vectorial geographical

information provided by Oracle Spatial (this could

be achieved by many APIs), and (II) to project this

bitmap on the base of the cube, with the

corresponding perspective adjustments

5 CONCLUSIONS AND FUTURE

WORK

The growing amount of spatial information in Data

Warehouses leads to the formulation of the SOLAP

concept. Indeed, it is a key technology to take full

advantage of the knowledge concealed within

enterprise datasets, and many efforts are being

devoted in this field to provide Decision Makers

with powerful analysis tools. However, when

introducing geographical measures in the SOLAP

domain, many problems arise, mainly because there

isn’t a widely accepted interaction metaphor to

support comparisons of spatial measures.

In this paper, starting from an analysis of

existing SOLAP tools, revealing a lack in the

support of geographical measures, we have firstly

proposed the metaphor of GeOlaPivot Table, which

coherently merges, in a single visual environment,

the key concepts of Pivot Tables and Space-Time

Cube. This approach represents a first effort in

adapting advanced geovisualization techniques to

SOLAP ones, in order to create a specific visual

paradigm for Spatial OLAP able to effectively

support and fully exploit spatial multidimensional

analysis process.

A preliminary case study has been described,

presenting a possible application of our metaphor to

a spatial Data Warehouse concerning the supervision

of infectious diseases in Italy.

Then, we have illustrated the main issues about

the development of a web-based environment

exploiting the GeOlaPivot Table visual metaphor to

support geographical measures in SOLAP analyses

and a three tier architecture has been described.

Currently we are working on the implementation of

the proposed architecture. In particular, for the

development of the Graphical User Interface able to

present the 3D cube of the GeOlaPivot Table, we are

currently experimenting the use of AJAX

technology, in order to significantly reduce the

amount of network traffic, and consequently the

latency of the user interface. It is worth to remark

that the projection of raster maps within the cubes

could require significant programming efforts. Thus,

we plan to develop an API supporting this kind of

task.

About future work, we would like to extend the

GeOlaPivot Table with adequate graphic semiology

rules to visualize thematic attributes of spatial

dimensions and measures and by introducing

chorems in order to visualize spatial trends.

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