GlobeOLAP

Improving the Geospatial Realism in Multidimensional Analysis Environment

Vinícius Ramos Toledo Ferraz and Marilde Terezinha Prado Santos

Computer Department, Federal University of São Carlos, Rod. Washington Luiz, km 235, São Carlos, SP, Brazil

Keywords: Thematic Mapping, Geovisualization, SOLAP, Virtual Globes, KML.

Abstract: The combination of Geographic Data Warehouses (GDW) and Spatial OLAP (SOLAP) provide efficient

browsing and storing of very large spatio-temporal databases. However, some advanced techniques of

geovisualization are not well supported in many of these tools. Three-dimensional Thematic Mapping

through Virtual Globes is one of them, and it can provide a friendly but powerful mechanism for summarize

visually huge amounts of geospatial-analytical data and their changes over time. This paper shows the

GlobeOLAP, a prototype of a Web-based SOLAP tool that allows the customized generation of many types

of Thematic Maps to be visualized three-dimensionally in a Virtual Globe (Google Earth), together with the

traditional tabular view. This tool can improve management decisions by allowing managers to identify

patterns and build knowledge in a spatial realistic environment.

1 INTRODUCTION

Storing and browsing efficiently very large

geospatial databases for multidimensional analysis

and decision making can be a challenging task.

Many authors point Geographic Data Warehouses

(GDW) and Spatial On-Line Analytical Processing

(SOLAP) as a good approach (Bédard, 1997; Rivest

et al., 2003; McHugh, Roche, Bédard, 2008; Tao and

Papadias, 2009) to such task. By combining this

mature technology of Business Intelligence with

very useful functionalities from Geographic

Information Systems (GIS), it is possible to provide

a complete and homogeneous platform for decision

support.

The inherent complexity of geospatial data

(Wieczorek and Delmerico, 2009) and its related

decision problems (Andrienko et al., 2007), makes

evident the need of interactive visualization

mechanisms that not only displays data and allows

its browsing but also do it intuitively, making

substantially easier for human beings the pattern

recognition and to build knowledge that will help to

solve real problems (Dykes, MacEachren, Kraak,

2005).

Within a typical GDW-SOLAP approach,

geospatial data have several levels of aggregation

and can be retrieved under many perspectives

through multidimensional analysis. In this case, it is

necessary some particular techniques for

summarizing and presenting, in an organized way,

the results of complex, multidimensional and

geospatial queries.

This work shows the GlobeOLAP, a prototype of

a Web-based SOLAP tool, which is capable of

generating several types of KML-based Thematic

Maps with a high level of customization to be

visualized within a three-dimensional environment

provided by a Virtual Globe. Consequently, decision

makers can experience the advantages of a plausible,

flexible and friendly geospatial analysis

environment.

The remainder of this paper consists of three

main sections. First, in section 2, we discuss some

key concepts together with related works. Next, in

section 3, we present the prototype architecture and

its functionalities through a study case in the domain

of a national education system planning analysis.

Finally, we make some conclusions and discuss

future works.

2 RELATED CONCEPTS

AND WORKS

Essentially, in this section, we try to present some

answers to the following question: “Once geospatial

data are inherently complex and its related problems

Ramos Toledo Ferraz V. and Terezinha Prado Santos M. (2010).

GlobeOLAP - Improving the Geospatial Realism in Multidimensional Analysis Environment.

In Proceedings of the 12th International Conference on Enterprise Information Systems - Human-Computer Interaction, pages 99-107

DOI: 10.5220/0002977100990107

 SciTePress

are sometimes difficult to solve through an

automated approach, how can we build

computational tools capable of retrieve and

interactively explore very large geospatial databases,

providing an intuitive, realistic and analytical

environment for efficient decision making?”

2.1 Geographic Data Warehouses and

Spatial On-line Analytical

Processing

The combination of Data Warehouses (DW) and On-

Line Analytical Processing (OLAP) technologies

(Chaudhuri, Dayal, 1997; Inmon, 2002; Kimball and

Ross, 2002; Song, 2009; Abelló and Romero, 2009)

is widely used to establish environments for decision

support that prioritizes efficient querying of huge

amounts of historical, clean and consolidated data.

Frequently, a geospatial perspective is needed

for strategic analysis. A DW that contains geospatial

data is called Geographic Data Warehouse (GDW).

A GDW is generally modeled using one of the

flavors of star schema. This kind of

multidimensional modeling is comprised by a fact

table with one or more measures that can be numeric

or geospatial. The calculation of these measures is

made regarding a set of dimensions of interest.

A GDW can be spatially queried and processed

by a Spatial OLAP (SOLAP) tool that allows easy

and fast exploration of very large geospatial data

repositories and offers a set of visualization

techniques such as maps, tables and diagrams

(Bédard, 1997; Bédard, 2005; Rivest et al., 2005)

Additionally to OLAP operators (e.g. drill-down,

roll-up, slice and dice, pivot and drill across),

different authors have proposed a number of SOLAP

operators. Ruiz and Times (2009) reviewed these

proposals and organized them into a taxonomy.

Siqueira et al. (2009) propose to add one or more

geospatial dimensions to avoid redundancy. Further

reading on GDW modeling strategies can be seen in

Damiani and Spaccapietra (2006), as well as index

structures can be found in Tao and Papadias (2009).

2.2 Virtual Globes as Geobrowsers

The entry point of a SOLAP tool is its graphical

interface. Many authors have been integrating

Geobrowsers to their SOLAP solutions in order to

bring suitable visualization techniques for geospatial

data.

Geobrowser is defined by Craglia et al. (2008) as

a software that offers interactive navigation in a set

of information organized over a geographic space. It

can be seen as the visualization module of a GIS.

Between the free 2D Geobrowser that provide

API for mashup on web applications, stand out

Google Maps (Google, 2010a), Bing Maps

(Microsoft, 2010), Yahoo! Local Maps (Yahoo,

2010) and OpenLayers (OpenLayers, 2010). These

API offer pan, zoom, layer control and exhibition of

detailed information about geospatial objects, plus a

set of services such as geocoding, route calculation

and local search. Most of these API also provide

access to some base layers whose set of tiles has two

main types: street/boundaries maps or remote

sensing images.

However, it is quite known that 2D Geobrowsers

are deficient in portraying dynamic and three-

dimensional phenomena, causing a gap of realism

between the bi-dimensional model and the real

world. Some works have pointed that Virtual Reality

(VR) techniques can help to mitigate this issue,

especially regarding to knowledge construction for

decision making (MacEachren et al., 1999;

MacEachren et al., 2004; Hodza, 2009). VR

provides 3D environments capable of giving to the

users a feeling of "being there", enhancing their

cognitive experience.

Fortunately, there are 3D Geobrowsers such as

Google Earth (Google, 2009b) and NASA World

Wind (NASA, 2010) that uses a three-dimensional

model of the Earth surface, known as Virtual Globe

(Butler, 2006). To overlay this model, the same base

layers of maps and remote sensing images are used,

but now with a new projection which includes an

orientation angle for each tile. Additionally to the

same other functionalities present in 2D

Geobrowsers, we got the camera control to travel

through the space in any desired inclination and to

rotate the virtual globe with ease.

The most popular 3D Geobrowser between

general users is Google Earth because of its ease to

use (Goodchild, 2008). Moreover, there are desirable

functionalities in Google Earth such as access to

user-generated content, high rendering performance,

the resolution of the satellite images and orthophotos

reaching to 1m/pixel in hundreds of places,

visualization of 3D models and orthorectified

volumetrically images.

For developers in particular who want to use

maps in their decision support tools, mashing up

Google Earth API (Google, 2009c) with a web-

based application makes possible to generate

datasets and add them in a georeferenced virtual

reality environment as "custom overlays" of vector

and raster geospatial elements. However, for users in

particular who want to use this realistic environment

for analyzing summarized thematic data and make

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decisions, all mentioned technologies could bring

better results if we add among them a set of

geovisualization techniques.

2.3 Thematic Mapping

The main type of map for analysis of summarized

and contextualized (both geographically and

thematically) data are Thematic Maps (TM).

MacEachren (1979) sees Thematic Cartography as a

representation of the spatial distribution of

phenomena at specific points in time. Therefore, he

states Thematic Cartography as a primary

mechanism of visual communication suggesting that

TM have played substantial influence in knowledge

construction and dissemination.

Slocum et al. (2005) brings an overview of all

aspects of TM, including the set of existing

techniques, its corresponding symbolization

principles and when to use them. All techniques and

symbols supported in this work are discussed in

section 3.

TM are widely used in today’s GIS, but we point

some arguments that suggest TM as powerful

visualization artifacts for GDW/SOLAP

environment. First, any GDW is intrinsically

thematic, since every dimension can be seen as a

theme (e.g. one can say that the transformation of a

query from "sales quantity by product" to "sales

quantity by client" is a theme change). Following,

data can be aggregated in many ways by a number

of (S)OLAP operators which go toward a theme

refining. Finally, the resultsets from these queries

can contain geospatial data (e.g. client's "region"

attribute), thus some TM could be efficiently drawn

in order to allow geospatial-enabled analysis and

decision making.

2.4 Advantages and Issues of the Third

Dimension

The use of Virtual Globes for thematic mapping

brings some technical advantages. Most of 2D

Geobrowsers uses Mercator projection which is a

good choice for mosaics of remote sensing imagery,

but causes area distortions which is bad for thematic

mapping. This issue is partly overcome in Virtual

Globes because of its Perspective Orthographic

projection (Sandvik, 2008). A second point is the

whole new thematic mapping techniques using

volume symbols (Slocum et al., 2005) that a three-

dimensional environment allows, such as Prism

maps.

Combining three-dimensional thematic mapping

and Virtual Globes for analytical purposes are an

emergent approach, and some authors raise potential

issues to be addressed. Goodchild (2008) warns that

overlaying Virtual Globes with survey and census

statistics may produce confusing hybrids of the

visual and symbolic, which is also pointed by

Monmonier (1996) when he claims that any map

must omit all non-essential features. Shepherd

(2008) remembers that this is an old discussion

among researchers and cites the varying of scale

caused by perspective visualization, which

prejudices the volume estimation by humans and,

consequently, the comparison among volume

symbols.

On the other hand, Shepherd raises advantages of

the three-dimensional mapping, such as: (1) a richer

environment where symbols are more detailed and

there is more space for information on the same

display; (2) possibility of extruding areas to

emphasize and to detail a traditional visual variable

(see the second example in section 3.2) or to

introduce a new one; (3) possibility of resolving the

problem of symbol occlusion more efficiently than

in 2D maps (e.g. by adding transparency and

spinning the globe); and finally, (4) providing a

familiar environment for complex analysis, by

taking advantage of the user's intimacy with the

Earth shape, then providing a convenient and

intuitive cognitive experience. Tiede and Lang

(2010) also point such familiarity and conducted

case studies with three-dimensional symbols for

analytical-geospatial data, concluding that

unfamiliar scientific content can be grasped more

directly by simple embedding it in a seamless

geospatial context.

This work was conducted considering such

discussion and partial solutions to these problems, as

well as its advantages can be seen at sections 3.2 and

section 4. Furthermore, three-dimensional thematic

mapping through Virtual Globes is not a natural

evolution or the state-of-art of digital mapping, but

actually one additional and useful functionality in

mapping tools, including SOLAP solutions, which

existing implementations we discuss briefly in the

next section.

2.5 Related Works on Geovisualization

in SOLAP Tools

There are several proposals, prototypes and full

SOLAP tools, including both free and commercial

final products that are already established in

Business Intelligence market. Each one of these

tools has its own innovations, strengths and

weakness. We will discuss briefly these tools.

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Table 1: Comparison of existing SOLAP tools.

Authors Tool name Platform Geobrowser Geovisualization techniques

Han et al. (1997)

GeoMiner Desktop 2D

Simple

Shekhar et al. (2000) Map Cube Desktop 2D Simple, choropleth and multimap

Stolte et al. (2002)

Rivet Desktop 2D

Simple and choropleth

Fidalgo et al. (2004) GMLA WS Client Web 2D Simple

Scotch and Parmanto (2005) SOVAT Desktop 2D Simple and choropleth

Rivest et al. (2005)

JMap® Spatial

OLAP Extension

Desktop 2D

Simple, choropleth, proportional symbol,

multimap

Colonese et al. (2005) PostGeoOlap Desktop 2D Simple

Bimonte et al. (2007) GeWOlap Web 2D Simple, choropleth, bar, pie

Compieta et al. (2007) - Desktop 3D Simple, remote sensing and complex vectors

Silva et al. (2008) WebGeoOlap Web 2D Simple and remote sensing

Di Martino et al. (2009) GooLAP Web 3D Simple, remote sensing, 3D-bar, pie

Pentaho

Pentaho Google

Maps Dashboard

Web 2D

Simple, colored point

ESRI OLAP for ArcGIS Desktop 2D Simple, bars

JRubik JRubik Web 2D Simple, bars

SpatialAnalytics SOLAPLayers Web 2D Simple, choropleth

GlobeOLAP Web 3D

Simple, remote sensing, choropleth, prism,

bar and custom proportional symbols



Figure 2: An overview of GlobeOLAP architecture.

The GeoMiner tool, introduced by Han et al. (1997),

allows OLAP operations over GDW and includes an

efficient method for materialization of geospatial

datacubes. Map Cube, a data operator proposed by

Shekhar et al. (2000), generates galleries of maps as

visualization method for a given set of aggregated

geospatial data. Stolte et al. (2002) developed the

Rivet tool which focuses in multiscale visualization

for generating abstract overviews of data in a GDW.

Scotch and Parmanto (2005) presented SOVAT, an

effort to implement new SOLAP operators so that

both geospatial and numeric aggregations can be

performed. Compieta et al. (2007) investigates VR

and SOLAP integration for building simulation

environments of climate phenomena with complex

vectors.

Recently some works focused in Web-based and

distributed SOLAP tools. Fidalgo et al. (2004)

proposed OLAP and GIS bridges through GMLA

Web Services. Bimonte et al. (2006) developed

GeWOlap which are also based on open source

tools. Silva et al. (2008) made a Web mashup with

Google Maps in their WebGeoOlap tool which

kernel are based on PosGeoOlap tool from Colonese

et al. (2005). Di Martino et al. (2009) presented a

Web-based prototype of SOLAP tool that makes use

of existing well-established modules such as

Mondrian OLAP Server, JPivot and Google Earth as

geobrowser.

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There are also final products which aim to bring

some SOLAP functionalities to users of DW/OLAP

software packages. Among commercial solutions,

Rivest et al. (2005) presented JMap® Spatial OLAP

Extension, one of the most complete existing

SOLAP tools with regard to the set of SOLAP

operators and geovisualization techniques provided.

Pentaho Google Maps Dashboard (Pentaho, 2010a)

is part of an open-source and commercial package

for Business Intelligence which allows to plot

colored point symbols reflecting localizations of

objects of interest and a numerical measure

regarding them. Yet another commercial tool is

OLAP for ArcGIS (ESRI, 2010). Among free and

open-source solutions, SOLAPLayers

(Spatialanalytics, 2010) is a web mapping client for

exploring geospatial data cubes and create

choropleth maps. Finally, JRubik (JRubik, 2010) has

a SOLAP module that allows creating bar chart

maps.

An overview of geovisualization capabilities of

all cited works are summarized in Table 1. Besides

the obvious transition for Web-based SOLAP tools,

it is possible to see that the use of Virtual Globes in

SOLAP tools is quite rare. Furthermore, the use of

three-dimensional thematic maps for

multidimensional exploration was practically

unexplored in literature. This motivated us to

investigate the potential of combining such

technologies. The main result is a SOLAP tool

prototype called GlobeOLAP.

3 THE GlobeOLAP PROTOTYPE

This section is dedicated to the GlobeOLAP system

prototype. We will discuss its architecture;

enumerate its functionalities of analytic

geovisualization and how they can improve decision

support solutions. Some issues are also discussed

along this section.

3.1 Architecture

The chosen architecture preserves the possibility of

changing the Geobrowser or even the OLAP server,

with low additional refactoring cost. A three-tier

approach was applied. Each tier comprises a set of

modules based on open standards. Figure 2 shows an

overview of the architecture, where all modules are

presented following SADT notation for better

comprehension of the information flow.

3.1.1 Presentation Tier

The main role of this tier is to provide some of the

interactive visualization techniques considered in

this work. In order to achieve this goal, we designed

the following modules:

a. Pivot Table: Shows resultsets in a tabular

view and allow dynamic modification of the query

through interactive OLAP operations and an ad hoc

Multi-Dimensional eXpressions (MDX) query

editor. In our prototype we used JPivot (JPivot,

2010).

b. 3D Geobrowser: A Virtual Globe capable of

rendering three-dimensional and animated thematic

maps. We choose Google Earth API (Google,

2010c) which consists of a Web browser plug-in and

a set of JavaScript libraries for custom overlaying of

the globe with thematic data.

c. Thematic Map Designer: Allows user to set

their thematic mapping preferences (e.g. color scale,

classification method, TM type, among other

Thematic Cartography project decisions).

3.1.2 Processing Tier

The Processing Tier is responsible for processing

requests from Presentation Tier and generation of

resultsets and thematic maps. The modules are

following:

a. OLAP Server: This module has both

Processing Tier and Data Tier components since

existing OLAP Servers such as Mondrian (Pentaho,

2009b) perform internally the MDX query parsing

and also handle with database connection drivers.

Subsequently, the OLAP Server receives a response

from DBMS which include analytical and geospatial

data, so that a resultset can be formatted. Finally,

this module sends it simultaneously to the Pivot

Table and to Geovisualization Manager.

b. Geovisualization Manager: Behaves as an

interface between 3D Geobrowser, Thematic Map

Designer and OLAP Server. As soon as it receives a

new resultset from OLAP Server, this module

merges user preferences with the resultset into a data

store which is then encapsulated within a REST

(Fielding and Taylor, 2002) request for the Thematic

Map Service. Once TM is generated, it is sent to

Thematic Map Designer.

c. Thematic Map Service (TMS): This module is

a standardized geospatial Web service, more

precisely a Web Processing Service (WPS) (OGC,

2007). TMS has a mandatory Execute() operation

which receives as parameter the request containing

the encapsulated data store. After extracting this data

store, TMS has all data needed (user preferences,

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103

summarized data and geospatial data) for generating

a Thematic Map.

The kernel of the modules TMS and Thematic Map

Designer is a generation engine of KML-based

Thematic Maps, adapted from Sandvik (2008).

3.1.3 Data Tier

Data Tier is comprised of some of the modules from

the OLAP Server that derivate SQL queries from

MDX and XML mappings between dimensional

model and physical model of the GDW. The other

module in this tier is the GDW itself and its DBMS

hosting.

3.2 Study Case

In order to set an evaluation for our proposal, we

implemented an instance of GlobeOLAP

architecture for the domain of a national education

system planning. This study case was conducted

under the context of a larger project called

WebPIDE, which main goal is integrating several

heterogeneous databases containing real data from a

set of national evaluations applied by the Brazil’s

Ministry of Education.

We choose a test called SAEB (Evaluation

System for Basic Education) which is applied every

two years for 4

and 8

grades for basic education

and 3

grade for high school students. We built a

GDW which included cleanup analytical data

extracted from the original dataset. Figure 3 shows

its schema. GDW was modelled using a star schema

and a geospatial dimension (geometry attributes

were omitted).

Figure 3: The GDW proposed for the SAEB tests.

Writing an XML schema and loading it on

Mondrian, it is allowed the tabular view of the data

through JPivot and the execution of OLAP operators

for multidimensional exploration.

By slicing ‘Test Year=2003’, choosing

Localization as column and Avg Proficiency as a

measure, we can answer the following question:

“Which are proficiency average the students have in

each state in 2003?” By the end of these OLAP

operations, a choropleth Thematic Map is rendered

in the geobrowser, as can be seen in Figure 4. The

map shows evidence of the better proficiency of

students from the southern and south states of the

country.

The user is allowed to choose, at any time,

different colour scales, classifications methods,

thematic mapping techniques, types of labels, among

other settings. For instance, let us choose Prism

map, setting a max height of ‘600000’. In this case,

the polygons of are proportionally raised up

according to the chosen measure, as we can see in

Figure 5.

Height and colour are used as visual variables for

the same measure to emphasize it and to allow the

appearance of some detail such as height difference

between polygons classified with the same colour.

When such difference becomes too high, despite the

"emphasizing factor" works even better, a potential

issue with prism maps shows up: there is possibility

of visual occlusion of lower areas by taller prisms.

Setting suitable max height is important to avoid it.

It is also important to choose a suitable

transparency for the map, so that it can be more

opaque when no other prism or volume symbol is

hidden, less opaque in the contrary, or substantially

transparent when other features or lower layers are

useful in the analysis.

Moreover, the presence of labels, legend and

descriptions in the map help users with some

difficulty with estimation of volumes and with the

lack of a “zero point” while spinning the globe (and

consequently, rotating polygons and prisms).

4 CONCLUSIONS AND FUTURE

WORK

It is possible to use three-dimensional Thematic

Maps and Virtual Globes for geospatial-enable

visualization of multidimensional queries. It can

make easer for managers to summarize and analyze

huge amounts of data and make faster decisions.

Some issues still need to be addressed such as the

world mapping become prejudiced in a Virtual

Globe, since only half of the world is visible at once.

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Figure 4: GlobeOLAP showing results in a choropleth thematic map.

Figure 5: GlobeOLAP showing results in a prism thematic map.

In future work we will to use two side-by-side

Virtual Globes to overcome this situation. Pie chart

thematic maps will also take place on GlobeOLAP.

The GlobeOLAP prototype demonstrated to be very

customizable, bringing a high level of

personalization for most of the common Thematic

Cartographic Project decision.

The main idea of this work was tested against real

data and users reports will help to define further

future improvements.

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105

ACKNOWLEDGEMENTS

We thank CAPES and INEP for providing funds to

the WebPIDE project under the Education

Observatory grant, as well as data for the case study.

We also thank FAPESP and CNPq for supporting

correlated projects which converged to this work.

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