A NEW VISUAL DATA MINING TOOL FOR GVSIG GIS

Romel Vázquez-Rodríguez, Carlos Pérez-Risquet, Inti Y. Gonzalez-Herrera, Alexis Fajardo-Moya

Center of Studies on Informatic, Central University of Las Villas, Santa Clara, Cuba

Juan Carlos Torres-Cantero

Departament of Languages and Informatics Systems, Granada University, Granada, Spain

Keywords: Visual data mining, Scientific visualization, Information visualization, GIS.

Abstract: The integration of scientific visualization (ScVis) techniques into geographic information systems (GIS) is

an innovative alternative for the visual analysis of scientific data. Providing GIS with such tools improves

the analysis and understanding of datasets with very low spatial density and allows to find correlations

between variables in time and space. This paper presents a new visual data mining tool for the GIS gvSIG.

This tool is implemented as a gvSIG module and contains several ScVis techniques for multiparameter data

with a wide range of possibilities for interaction with the data. The developed module is a powerful visual

data mining and data visualization tool to obtain knowledge from multiple datasets in time and space. A real

case study with meteorological data from Villa Clara province (Cuba) is presented, where the implemented

visualization techniques were used to analyze the available datasets. Although it is tested with

meteorological data, the developed module is general and can be used in multiple application fields.

1 INTRODUCTION

The fast development of Geographic Information

Systems (GIS) caused in part by the revolution of

the new information and communication

technologies, has become an essential tool for

analyzing and solving several problems in business,

industry, as well as social and governmental

institutions.

Scientific Visualization (ScVis) has become a

research area of increasing interest in recent years.

This is mainly motivated by the constant increase of

the data volumes generated in multiple application

fields and by the continuous increase in the power of

modern graphics interfaces, which allows to

generate sophisticated images.

In the past two decades, significant progress has

been made in setting up the visualization as a data

exploration tool flexible and easy to use. Visual data

analysis is a new approach that integrates both

human perception and automatic computational

methods, enabling a better understanding and

analysis of large and complex datasets.

One special data class is composed of

multiparameter data (multidimensional data;

multivariate data). Multiparameter data consists of

m-dimensional data of scalar type distributed over

points in the observation space. Many visualization

techniques have been developed for multiparameter

data. The most relevant of them are referenced in

section 2. The flexibility and possibilities of

interaction of these techniques can replace the

shortcomings of the interpolation methods used by

current GIS when working with very low spatial

resolution data.

The integration of ScVis techniques into GIS is

an innovative idea that combines the advantages and

strengths for data analysis of both approaches.

Section 3 discusses the alternatives for the

integration of ScVis techniques into GIS.

Section 4 presents the module developed for the

integration of scientific visualization techniques for

multiparameter data in the gvSIG GIS. This module

was used with meteorological data from the province

of Villa Clara, Cuba; thus showing the effectiveness

of this type of analysis when the spatial resolution of

the data is poor and the time information is wide.

These meteorological data are weather variables

measured from 1977 to 2007 in Villa Clara’s

weather stations. Although this time series is fairly

complete, it only contains information from four

weather stations. By using the multiparameter

visualization techniques developed in this work it is

428

Vázquez-Rodríguez R., Pérez-Risquet C., Gonzalez-Herrera I., Fajardo-Moya A. and Torres-Cantero J..

A NEW VISUAL DATA MINING TOOL FOR GVSIG GIS.

DOI: 10.5220/0003089904280431

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2010), pages 428-431

ISBN: 978-989-8425-28-7

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

possible to make an analysis of these data, obtaining

some conclusions about their performance. The

conclusions are presented in the last section of this

paper.

2 MULTIPARAMETER

VISUALIZATION

There is a large number of problems where each data

point contains more than one attribute. These

attributes can be dates, locations or descriptive

values, and may have or not a spatial reference. This

type of data is called multiparameter, and it is

usually found in applications of data mining,

statistics and artificial intelligence (Keim, 2002).

The main goal of multiparameter data

visualization methods is to make representations or

reveal correlations between attributes. There is a

wide range of ScVis techniques, for which several

improvements have also been created. The

techniques can be classified in geometric, icon-based

and pixel-based (Keim, 2000).

3 SCVIS – GIS INTEGRATION

The integration of ScVis techniques into GIS is an

innovative alternative for the visualization of

multiparameter data. Providing GIS with such tools

improves the analysis and understanding of datasets

with very low spatial density and allows to find

correlations between variables in time and space.

The integration of ScVis and GIS, according to

(Rhyne, 1997), is based on 4 methods:

 Rudimentary: Minimal data sharing between

GIS and visualization systems.

 Operational: Consistency of geospatial data.

 Functional: Transparent communication

between GIS and visualization systems.

 Merged: Comprehensive toolkit. Tools that

widely involve the 2 disciplines.

The rudimentary method uses minimal data

integration and exchange between both technologies.

The operational method is intended to provide data

consistency, while deleting redundancies between

the two technologies. The functional method

provides transparent communication between the

software environments. The merged approach is

concerned with the development of systems where

the concepts of cartography, GIS and ScVis are

merged in a comprehensive toolkit.

Some early attempts of merged integration

approach are materialized through tools like

GeoVista Studio and Snap-Together Visualization.

Both tools are based on networks of components that

allow the construction of complex workflows

linking components for managing geographic data

formats and different ScVis techniques.

4 GVSIG SCVIS MODULE

The main objective of this work was to build a

multiparameter visualization data module that helps

to obtain knowledge form data measured in the same

time interval with a large amount of records, but

spatially scarce.

The solution consisted of integrating some

multiparameter visualization techniques into a GIS,

in such a way that data could be analyzed by having

a geographical perception of their origin.

An integration by adding a scientific-information

visualization module to a GIS was implemented

allowing the user to prepare projects to be used in a

coordinated visualization. A coordinated

visualization means visualizing, by using scientific

visualization techniques, multiple datasets

corresponding to multiple data points. It is

coordinated because all visualizations are

normalized according to the global extreme values

of every variable of all data points.

These projects can be customized to be

visualized over a vector map of points (point shape

file given by the user) or over a vector map of area

(area shape file used as background of the

visualization); in the latter, the graphics are

displayed over the centroid of the corresponding

area. The techniques that can not be visualized as a

graphic over the map due to space restrictions are

visualized in independent panels, where the

geographical perception is still present.

The GIS selected to be extended with the

scientific visualization module was gvSIG, a

powerful open source GIS from Generalitat

Valenciana. It is a java-based GIS which is very

easy to extend and have very good documentation

for developers. gvSIG have a well-structured class

hierarchy and supports the reading of several

geographical and non geographical formats like

tables.

In order to visualize using a coordinated

visualization in gvSIG, 2 kinds of data are

necessary: geographical and multiparameter data.

The geographical data can include a background

map and a point map, or only a background map,

both as ESRI shape files.

Each data point should match a multiparameter

data file. This extension allows to read

multiparameter data in the table format dbf and arff

A NEW VISUAL DATA MINING TOOL FOR GVSIG GIS

429

file format, that specify metadata like names and

types for every variable of the table. The data in

each data point have to match the number of

variables and records. Missing values are allowed.

The meteorological data collected by the

Meteorological Institute of Villa Clara represent a

time series of each of the variables collected from

1977 to the present. The meteorological variables

are collected over a ten-day period (dekadal), the

variables are: dekadal average mean, maximum and

minimum temperature, dekadal average of mean,

minimum and maximum relative humidity,

cloudiness (dekadal average in 1/8 cover sky),

dekadal mean wind speed, dekadal total rainfall,

dekadal insolation (average daylight hours), dekadal

water vapor pressure and dekadal average

atmospheric pressure. From each of the four weather

stations in the province, there is a time series of 13

variables.

Users can prepare their own multiparameter

dataset for each data point and use a wizard that

helps to customize datasets to be visualized. It

creates a configuration file that is used by the

coordinated visualization module.

There are several ways to visualize data with

multiparameter ScVis techniques. Some of them

allow visualizations over maps, where the

association of the data with the geographical area is

perceived. In particular, ScVis techniques for

multiparameter data do not necessary have to be

associated with a map. However, these techniques

can be applied to all variables in each data point to

obtain correlations between certain variables of

several points. It is possible to use for example,

Parallel coordinates to display the 13x4 (52)

variables corresponding to the 4 meteorological

stations.

Another implementation method is to display

data from each point separately, and show a map

where it is evident to which region each graphic is

related to. This is achieved with an arrow that

connects the region in the map with the visualization

panel and naming each panel with the same name of

the data point file visualized on the map.

Some ScVis techniques for multiparameter data

can be displayed directly over the map. Circle

segments, Recursive pattern, and icon-based

techniques are examples of such techniques (see Fig

1). With icon-based techniques is possible to show

an icon for each data source (a weather station in our

case), which represents the set of variables for an

observation at a given time. Some widgets (sliders,

scroll bars) are used to scroll over the time, changing

the icon on the map accordingly. This permits the

user to study the evolution of the data over time. The

same approach can be used to scroll the data with

respect to other variables.

All these methods were implemented in the

developed module for gvSIG. For example, for non

coordinated visualizations, that is, when the data do

not necessarily have to be associated with a map, the

system allows the user to load a data file to be

analyzed using any of the following techniques:

Parallel coordinates, Andrews graphics, Starfield,

Shape coding, Profile Glyphs, Circle segments,

Recursive patterns. Coordinated visualizations with

maps include the following techniques: Parallel

coordinates (in independent panels), Andrews

graphics (in independent panels), Starfield (a record

at a time), Shape Coding (a record at a time), Profile

Glyphs (a record at a time), Circle segments (all

selected records), Recursive patterns (all selected

records)

In our case study we obtained the best results

with the Recursive patterns technique. It seems to be

a very good technique to carry out spatio-temporal

analysis in GIS.

The developed module allows some

functionalities that are available for all the

techniques: Selecting attributes (only the selected

attributes are shown in the graphic), Selecting a

percentage of the records, Selecting according to an

attribute a range of values (makes a subsample of all

data in a range of values selected for a given

attribute), Showing legends (shows the global color

range for each variable, it takes minimum and

maximum values of all datasets, nominal values are

assigned a different color for each value, some

techniques like Profile glyphs show in the legend a

different color for each attribute), Reorganizing

attributes (a new order of attributes is given)

The ordering by functionality orders all datasets

according to a given attribute. Pixel-based

techniques and icon-based techniques use this

functionality to order all the values using this

attribute. By using the time as the attribute the user

can analyze data over time.

All developed pixel-based techniques also allow

to modify the length of the graphics. These

techniques can be visualized in independent panels

as well as over the map.

The developed geometric techniques were

designed to be visualized in independent panels, one

for each data point. They are also coordinated by the

main configuration panel. The coloring of the

records is showed with a color that is defined by a

given variable.

The ScVis module for gvSIG allows the user to

obtain visual information about the variables. In this

case study, meteorological data from the Villa

KDIR 2010 - International Conference on Knowledge Discovery and Information Retrieval

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Clara’s four weather stations is analyzed.

The main advantages of using ScVis techniques

with multiparameter data in GIS are the possibilities

of making comparisons, identifying patterns &

correlations, detecting anomalies, variability, and

extracting the different patterns that may occur in

the variables, in such a way that space and time

information can be obtained. Once detected, a

graphical relationship or a hypothesis between

variables or a pattern can be proved by mathematical

methods.

Another advantage provided by the use of such

techniques is the possibility offered by the

developed module of interacting with the data.

The pixel-based technique Recursive pattern

have a special way of interacting with data. This

technique seems to be the most useful for

meteorologist, due to the large amounts of data that

can be shown for each meteorological station and

because data is time-dependent.

Figure 1: Coordinated visualization with Recursive pattern

over the map, 1080 values for each attribute of the four

weather stations of Villa Clara.

Figure 1 shows the coordinated visualization using

Recursive Pattern. Each station displays its owns

data of the 17 attributes, that is, time attributes like

year, month, dekadal, year-dekadal, and the 13

weather variables. The recursive pattern used in the

picture is (36,1)(1,30) which show a year per row

from left to right (the 36 dekadal average of a year),

and 30 years from top to bottom, 1080 values are

shown for each attribute (small square) and for each

station. It could be clearly seen the behaviour of the

medium, maximum and minimum temperatures of

each station in the fourth, fifth, and sixth square

from left to right and from the top to the bottom. As

expected, lower values can be identified at the

beginning and end of the square from left to right

(blue color), and higher values in the middle (orange

color) which represent the hotter months. Missing

values are represented in black color, that is, the

moments when weather stations did not take

measures. This kind of anomalies can be detected

using this module.

Some regularities stand out after using the

visualization module with the meteorological data.

The 1996-1999 period was analyzed in order to

identify the effects of El Niño in Cuba. The

Recursive Pattern technique was used with the

pattern (36,1) (1,4). This pattern shows the four-year

period. It was concluded that in Santo Domingo: the

maximum and mean temperature are higher in 1997

and 1998. The maximum and mean temperature in

1997 were higher early in the year. In 1998

temperatures were higher during the warmer months.

In Sagua weather station: the maximum and

minimum temperatures are higher in 1997 and 1998.

Temperatures in 1997 were higher early in the year

The minimum temperature in the first three

months of the year is usually higher in Caibarién

than in all other stations. This is because this place is

a coastal area.

5 CONCLUSIONS

In this paper the basic ideas about the ScVis-GIS

integration are discussed.

A real case study with meteorological data from

Villa Clara was presented, where several

multiparameter visualization techniques were

implemented, and some implementation alternatives

were analyzed. A ScVis module was developed and

incorporated into a GIS (gvSIG) by implementing an

approach of integration between these disciplines.

The developed module includes several visualization

techniques, and offers a wide range of possibilities

for interaction with the data.

It should be noted that, although this study was

carried out to solve a real problem of visual analysis

of meteorological data in Villa Clara province, the

developed tools are general.

Future work will focus on extending the

developed module so as to allow the reading of data

in other formats. Also, new multiparameter data

visualization techniques will be added to the

module.

REFERENCES

Keim, D. A. 2000. Designing Pixel-Oriented Visualization

Techniques: Theory and Applications. IEEE Transac-

tions On Visualization and Computer Graphics, 6.

Keim, D. A. 2002. Information Visualization and Visual

Data Mining. IEEE Transactions On Visualization and

Computer Graphics, 7, no. 1.

Rhyne, T. M. 1997. Going Virtual with Geographic Infor-

mation and Scientific Visualization.

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