COLLABORATIVE EDUCATIONAL GEOANALYTICS

APPLIED TO LARGE STATISTICS TEMPORAL DATA

Mikael Jern

Department of Science and Technology, National Center for Visual Analitics, Linkoping University, Norrköping, Sweden

Keywords: Information and Geovisualization, Geovisual Analytics, Collaborative Time Animation, Storytelling,

Statistical Data, MediaWiki, Blogs, Collaborative Work, Learning.

Abstract: Recent advances in Web 2.0 graphics technologies have the potential to make a dramatic impact on

developing collaborative geovisual analytics that analyse, visualize, communicate and present official

statistics. In this paper, we introduce novel “storytelling” means for the experts to first explore large,

temporal and multidimensional statistical data, then collaborate with colleagues and finally embed dynamic

visualization into Web documents e.g. HTML, Blogs or MediaWiki to communicate essential gained insight

and knowledge. The aim is to let the analyst (author) explore data and simultaneously save important

discoveries and thus enable sharing of gained insights over the Internet. Through the story mechanism

facilitating descriptive metatext, textual annotations hyperlinked through the snapshot mechanism and

integrated with interactive visualization, the author can let the reader follow the analyst’s way of logical

reasoning. This emerging technology could in many ways change the terms and structures for learning.

1 INTRODUCTION

The major tenets of Web 2.0 are creating a

revolution in the way in which information is

produced and shared among different interest groups

and individuals. Concepts like “collective

intelligence” has become undisputed linked with

developments such as blogs, wikis, social

networking and collaborative software development.

Web 2.0 can make dramatic impact on developing

interactive and collaborative geovisual analytics

tools for the Internet. Tools are needed that advances

humans ability to exchange gained knowledge and

develop a shared understanding with other people.

Stimulate brainstorming and problem-solving

through creative and incremental discovery and

develop a contextual collaborative understanding -

commonly referred to as geospatial “analytics

reasoning” are important tasks to solve.

While the benefits of geovisual analytics tools

(Andrienko 2003 and 2005) are many, it remains a

challenge to adapt these tools to the Internet and

reach a broader user community. In this context, we

introduce a web-enabled tool Geovisual Analytics

Visualization “GAV Flash" for analysing and

communicating knowledge explored in large

volumes of statistical data. This tool and associate

applications OECD Factbook (OECD 2009b) and

Regional eXplorer (OECD 2009a) facilitate a broad

collection of dynamic visualization methods

integrated with the Adobe

Flash

and Flex

development platform. The eXplorer platform

focuses on the analytics reasoning aspects enabling

statisticians to explore spatial, temporal and

multivariate data from multiple perspectives

simultaneously using dynamically linked views,

views (Brodbeck 2003) discover interesting

relationships, share their incremental discoveries

with colleagues and finally communicate selected

relevant knowledge to the public.

In this paper, we introduce novel “storytelling”

means for the analyst to 1) select any spatial-

temporal and multidimensional national or sub-

national statistical data, 2) explore and discern

trends and patterns, 3) then orchestrate and describe

metadata, 4) collaborate with colleagues to confirm

and 5) finally publish essential gained insight and

knowledge embedded as dynamic visualization

“Vislet” in blogs or wikis with associate metadata.

The main features can be summarized:

• Interactions, dynamic time animation and state-

changes in an analytical reasoning process, such

as visual inquiries or highlighted outliers but

also linked, can be saved during an explorative

process through “memorized interactive views”.

233

Jern M. (2010).

COLLABORATIVE EDUCATIONAL GEOANALYTICS APPLIED TO LARGE STATISTICS TEMPORAL DATA.

In Proceedings of the 2nd International Conference on Computer Supported Education, pages 233-238

DOI: 10.5220/0002780302330238

 SciTePress

Figure 1: The analyst (author) uses eXplorer to1) import any national or sub-national statistical data, 2) explore and make

discoveries through trends and patterns and derive insight. Gained knowledge is the foundation for 3) creating a story that

can be 4) shared with colleagues and reach consensus and trust. The visual discoveries are captured into snapshots together

with descriptive metadata and hyperlinks in relation to the analytics reasoning. The author gets feedback from colleagues,

adopts the story and 5) finally publishes “tell-a-story” to the community using a “Vislet” that is embedded in blogs or wikis.

• A Story mechanism records the status of an

explorative discovery including tasks, events,

conditioning, dynamic linked views, region

highlights, colour legend scale, results from

filter or cluster operations and time animation;

• Combination of a descriptive and conceptual

metatext with an interactive and guided

discovery process could improve not only the

educational aspect but also the credibility of the

sharable understanding of analytical results;

• An eXplorer Story can be published on a HTML

page, blog or wikis as a Vislet with integrated

dynamic visualization and metatext;

A geovisual analytics framework and class library

for web developers with integrated service for

communicating and collaborating analytical

assessments to remote team members and

public; 2 related work.

2 RELATED WORK

The importance of providing explorative sessions in

geovisualization and incorporated features to capture

and reuse interactions and integrate them into

electronic documents was early demonstrated by

MacEachren (MacEachren and Brewer 2001) and

Jern (Jern 2001, 2008). CCMaps presents a

conditioned choropleth mapping tool that allows

users to save snapshots and reuse them for

presentation purpose. More recent efforts were made

by Visual Inquiry Toolkit (Guo et al. 2006)

that allows users to place pertinent clusters into a

“pattern-basket” to be reused in the visualization

process. Robinson describes a method they call “Re-

Visualization” and a related tool ReVise that

captures and re-uses analysis sessions (Robinson

2006); Keel describes a visual analytics system of

computational agents that support the exchange of

task-relevant information and incremental

discoveries of relationships and knowledge among

team members commonly referred to as sense-

making (Keel 2006). Wohlfart describes a

storytelling approach combined with interactive

volume visualization and an annotated animation

(Wohlfart and Hauser 2007).

Many capture and reuse approaches are limited to be

used within the same application environment that

may well require a license and are not always easily

accessible to team members without installing

external software. Increased computer security

practices tend to limit this possibility. In this

context, we introduce a web compliant layered

component toolkit facilitating a snapshot mechanism

that captures, re-uses and shares active properties for

individual functional components. We demonstrate

that such an implementation can provide a more

open and collaborative geovisual analytics

framework for public use (OECD 2009a).

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Figure 2: eXplorer is assembled from a collection of GAV Flash visualisation and administrative components.

3 SYSTEM IMPLEMENTATION

GeoAnalytics Visualisation GAV Flash is a toolkit

and framework adapted for the Web 2.0 using

Adobe’s Flash basic graphics and Flex for user

interfaces. Programmed in Adobe’s object-oriented

language ActionScript, GAV Flash facilitates 100%

deployment to the Internet through Adobe Flash

Player V10. GAV Flash includes a collection of

common geovisualization and information

visualization components, data analysis algorithms,

tools that connect the components to each other and

data providers that can load data from various

sources (figure 2). Interactive features that support a

spatial and temporal analytical reasoning process are

exposed such as tooltips, brushing, highlight, data

zoom, visual inquiry, conditioned statistics filter

mechanisms that can discover outliers and methods

supporting dynamically linked multiple views. A

space-time-attribute data cube provides the

interactive performance required for eXplorer’s

handling of massive statistical data.

As GAV Flash is built upon Adobe Flex, a

developer has access to all Flex user interface

functionalities. By combining buttons, panels and

sliders with GAV data providers, managers and

visual representations, applications can easily be

customized. The open architecture, allows new or

existing tools to be incorporated with the already

existing components, e.g. statistical analysis tools or

visual representations. By separating the data

structure from the visual representations,

applications are created that work regardless of

input.

Figure 3: A story called “Fertility rate and women

employment rate”. Two countries Italy and Mexico are

highlighted in two coordinated views. A decline in fertility

rates may accompany increases in female employment.

Economic theory suggests that fewer births per woman are

the opportunity cost of having greater numbers of women

working. Here we see Italy during 1970-1995 moving

from almost three children to only one child per woman

and an increase in woman employment rate from 1996 to

2006. The Story (right panel) shows the metatext for

current story and a list of other associated stories.

4 COLLABORATIVE

SPACE-TIME-ATTRIBUTE

ANIMATION FOR

STORYTELLING

Complex and collaborative geovisual analytics

sense-making tasks require the external

representation and visual organization of

information. These methods could help sense-

makers compare, organize, comprehend and reflect

on what they know, to quickly access specific

information when needed, to remember relevant

thoughts and ideas, as well as to exchange

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235

Figure 4: During an eXplorer session (figure 3), the analyst first selects regions to be analysed and associate indicators.

Then a search for trends, outliers, discovers important observations, highlights regions to be compared etc. - a discovery is

made! Secondly, open the Story Panel (right view), use button Create a Story, a Story Editor panel comes up, fill in the

required information and associate reasoning text and finally press Capture , the entire current eXplorer scenario (all views

and attributes) are saved together with selected indicators. The user can now start a second Chapter (New) and create a new

scenario and repeat the process or Close and then use the button Export as, give the Story a name "my story nr 2".xml. The

Story is now saved locally on your computer and can be reused Imported or sent to a colleague for review in eXplorer.

knowledge and develop a shared understanding with

other people. Computer generated information

visualizations usually explicitly state relationships

among information items thus allowing for quick

and non-ambiguous explorations of an information

space. Human generated information arrangements

are often vague in regards to relationships thus

inviting more creative interpretations of an

information space. The GAV Flash framework

integrates tools for both collaborative interactive

visualization and sense-making. A story indicates a

successful suggestion and subsequently fosters

additional suggestions based on similar

considerations based on similar considerations. This

learning mechanism allows our storytelling system

to improve the accuracy of its suggestions as well as

to dynamically adapt to particular users, tasks and

circumstances. Colleagues can review a story

arrangement and respond with suggestions and

comments and subsequently fosters additional

suggestions based on similar considerations.

The eXplorer platform facilitates the architecture to

support means of capture, add descriptive text, save,

packaging and sharing the discovery and results of a

geovisual analytics process in a series of snapshots

“Story” (figure 4 and 5). When the button “Capture”

in the Story Editor is pressed, the state of each GAV

Flash view in OECD Factbook eXplorer (figure 3) is

saved together with user-defined metatext. Before

closing the application, the user exports the story

into a XML formatted file. Team members (figure 1)

can through descriptive text combined with

interactive visualization follow the analyst’s way of

logical reasoning by loading selected stories. At any

time a team member can access stories and apply

them in eXplorer or any other GAV Flash

application assembled from the same component. A

comprehensive story in the context of a remote

collaborative sense-making activity can thus be

created by the analyst through a set of linked

snapshots (chapters). Users will discuss relevant

issues through storytelling based on solid evidence,

thus raising awareness and increasing the common

knowledge on a certain phenomenon.

An eXplorer story hyperlink (figure 5) is a reference

in the story metatext to an external web site or

another eXplorer discovery (view). A text item in

the current story is highlighted so that when clicked,

the story automatically displays in eXplorer, for

example, a zoom to a certain region, change of

indicator etc. or shows some referenced external

content. This highlighted word(s) is known as a

hyperlink and makes a logical connection to an

eXplorer state or external reference. Hyperlinks are

the basic building block of hypertexts. For example,

some key words in a story such as Italy are

highlighted, and provide links to explanations of

those words in the same eXplorer story.

To insert a hyperlink in the metatext that links to a

chapter or an external URL is often simply called to

"link" (figure 5). Hypertext (meaning "more than

just text") is a form of text that provides a richer

functionality than simple metatext by allowing the

reader to learn about topics within the story by

clicking on key words. Typically the link anchor will

be descriptive of the target's content (e.g. Italy), for

example NCVA home page. Highlight the text to

become a Hypertext, choose New Capture or

External URL. New Chapter allows you to create a

new eXplorer snapshot (e.g. zoom, highlight

regions). When the Hypertext is initiated, eXplorer

will display the state-of-the-snapshot.

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Figure 5: The Story Editor includes metatext with

hyperlinks and describes a discovery in an exploration

scenario here called “fertility rate and women employment

rate for 1970-2006” (figure 1). The story has two chapters.

5 CONCLUSIONS AND FUTURE

DEVELOPMENT

We expect that the web-enabled and collaborative

nature of the eXplorer platform will enhance the use

and understanding of statistics, thus adding to sound,

evidence-based policy decisions and transformed

into shared knowledge. At the same time, it will

encourage the practical use of advanced,

collaborative geovisual analytics technologies

because of its easy accessibility on the Internet. It

will enable the analyst to take a more active role in

the discovery process of exploring regional

indicators, for example, to identify those regional

areas that outperform other regions of their country

or mean values. The tool will increase the interest in

and knowledge of regional structures and

development patterns among specialist as well as

non-specialist users. The patterns of development

may differ widely in urban and rural areas and

regions may lag behind even when the national

economy is performing well. Comments from our

NCVA partners who have evaluated the tool

highlights the following features:

• eXplorer is free available;

• Easy-to-use external statistical data access;

• Ability to have dynamic time-link views and see

the multi-dimensionality of regional

development;

• Possibility to capture, save and open discoveries

(snapshots) with attached analytics reasoning

metadata;

• IT expertise is not required to publish interactive

visualization embedded in blogs and wikis;

• Important tool to publish statistics news on the

Web;

• Increased expectations in terms of user

experience;

• Will encourage more educational use of official

statistics;

NCVA is an associated partner to the OECD Global

Project on “Measuring the Progress of Societies”.

This “WikiProgress” project should represent the

catalyst of initiatives existing around the world on

the measurement of progress, as well as their use for

raising awareness amongst stakeholders, informing

them through statistical indicators describing

economic, social and environmental trends and

allowing them to discuss relevant issues through

storytelling based on solid evidence. WikiProgress

represents the place where both experts and public

share their analysis practices on indicators, about the

data that underlies our knowledge and hence our

action. OECD eXplorer has demonstrated that

geovisual analytics could represent a fundamental

tool in developing knowledge, thus making better

evidence based decisions possible and will provide

answers to questions like:

• Who is developing initiatives on measuring

progress (well-being, quality of life, etc.)?

• What type of classification do these initiatives

use?

• Which indicators are being used to measure the

different dimensions of progress?

• How is my country/region/community achieving

over time and in comparison to other similar

territories?

Our latest research includes a “Vislet”. A standalone

Flash application (widget) that is assembled from the

GAV Flash class library and Flex GUI tools (figure

6) and integrates selected statistical indicators

supported by highly interactive visualization with

descriptive metadata embedded into blogs, wikis or

any HTML document.

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Figure 6: A proof-of-concept – A Story created in OECD

Factbook (figure 3) and here published in MediaWiki. A

vislet with two dynamic time-linked visualization views

choropleth map and scatter plot are embedded in a

wikiprogress article.

ACKNOWLEDGEMENTS

This applied research was carried out by NCVA

ITN, Linkoping University in close collaboration

with OECD who supplied data and comprehensive

evaluation. The research is in part supported by

funding from the “Visualization Program”

coordinated by the Swedish Knowledge Foundation.

The author thanks colleagues Tobias Åström, and

Markus Johnsson and special thanks to OECD Paris

for a dedicated contribution that made this project

possible.

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