Polopoly will be shut down December 15, 2023. Existing pages will have to be moved or will be removed at that date. Empolyees may read more at FAQ Polopoly Avveckling

 

 

 

 

 

 

 

Welcome to a "Geovisual Analytics Educational Web site"

 

Swedish Web site: http://ncva.itn.liu.se/?l=sv

 

National Center for Visual Analytics (NCVA) is a Swedish national educational and research resource for Geovisual Analytics established in 2007 with financial support from the Swedish Knowledge Foundation's Visualization Program, VINNOVA (Swedish government agency), Vetenskapsrådet (Swedish Research Council), Unilever Research, Statistics Sweden, OECD, European Commission, Eurostat, SMHI, Norrköping Kommun and others.

Learning with Geovisual Analytics and Storytelling applied to Statistics

Today, our research focuses on learning based on Geovisual Analytics Statistics applied in education. The VISE (Visual Storytelling in Education) project supports the use of learning for these focus areas:

• Implementation of a Geovisual Analytics in education;
• Study how visual analytic storytelling methods can improve the student's learning process in development of knowledge and better understanding of statistics data;
• The students’ experiences when employing Geovisual Analytics in their learning process;
• Introducing a Geovisual analytics to teachers with the aim to develop their knowledge about visual storytelling methods;
• Conducting research concerning teachers’ experiences and students learning processes when our visualization tools are used within education;

The storytelling process available in “Statistics eXplorer” will be applied to improve the students knowledge and understanding of sophisticated statistical relations. Teachers will be able to, individually and together, develop a dynamic teaching material through storytelling, through the web. Students will be able to, with help of powerful geographical statistics, explore statistical relations on their own. A better understanding of how educators and their students can elicit deeper user understanding and participation by exploiting dynamic web-enabled statistics visualization is of importance. Together with the associated science of perception in learning in relation to the use of multidimensional spatio-temporal statistical data this will contribute to the research fields of Geovisual Analytics as well as educational science.

VISE has received financial support for 2015-2017 from VINNOVA (Swedish government agency) 1 milj Skr, Vetenskapsrådet (Swedish Research Council) 4,5 milj Skr, and Norrköping Kommun 1,6 milj Skr.

More information about VISE.

Geovisual Analytics - Concept, Idea and Implementation

The large and ever-increasing amounts of multidimensional, multi-source, time-varying digital information represent a major challenge. The need to analyze and make decisions based on these information streams, often in time-critical situations, demands integrated, automatic and sophisticated interactive tools that aid the user to manage, process visualize and interact with large information spaces. This approach has been encapsulated in the idea of Geovisual Analytics - "defined as the science of analytical reasoning facilitated by interactive visual interfaces, creative visualization and storytelling".

Our overall objective is to promote within education and research Geovisual Analytics in particular but also the more general Visual Analytics. To this end we must make sure that education is not left behind in this rapid evolution and can keep up with the international development. In various applied research projects innovative Geovisual Analytics techniques have been developed and used to build prototype visualization applications that can be used to educate academia with demonstration and educational tools.

"Our mission is to educate researchers and students in Geovisual Analytics and Storytelling, discuss problems and possible approaches to solving them, and define appropriate directions for further research"

 

 

 

 

Figure: Geovisual Analyticscomprises several research topics including information visualization, geographical visualization, perceptual science and storytelling. These methods are all implemented and available in our Statistical eXplorer applications - see below.

 

 

 

 

 

 

 

 

 

 

Interactive Statistical Visualization with integrated Storytelling

---

Statistics eXplorer, developed is a collection of web compliant applications that enables the statistics visualization of socio-economic information at national or detailed territorial level providing clear insight on regional differences and performance within a country and comparison of different areas across countries. Statistics eXplorer enables users to explore simultaneously spatial, temporal and multivariate data (both numeric and categorical) from multiple perspectives, to discover interesting relationships, to share their incremental discoveries with colleagues and finally to communicate selected relevant knowledge to other users. Statistics eXplorer is composed of Geovisual Analytics Visualization components facilitating a broad collection of dynamic visualization methods developed and integrated for use with the Adobe Flash and Flex development platform. NCVA provides access to: World eXplorer, Europe eXplorer, Sverige eXplorer and the more general Mdim eXplorer.  You can load your own data (EXCEL etc.), analyse, gain insight and create your own stories to be shared - see figure below.

Figure: Three linked and coordinated views covering the following visualization methods (choropleth map representing the indicator "age group 65+" , the extended scatter plot shows four indicators "age group young 0-14" (Y-axis), "age group 65+ (Y-axis), "age group 65+" (colour) and dot size is related to the indicator "total population". The fish eye bar chart uses a focus&context method to highlight about 20 countries (bars in focus) representing countries with highest rate elderly people in 2014 while the remaining 270 countries (context) are displayed as thin bars. The bars are coloured according to the active colour legend ("age group 65+") and applied in all three views. Three conuntries (Japan, Sweden and Niger) are highlighted in each of the tree views. The Story in the right panel represents our Storytelling method and controls how eXplorer will be initiated including selected visualization methods, colour legend, selected countries, time step, etc. 

World eXplorer is loaded with national data in EXCEL format (see figure above) or access latest statistics data directly from the integrated World Databank. Use MDim eXplorer with any table data (NO geographical reference).

Read more about data formats at: http://ncva.itn.liu.se/explorer 

 

Figure: storytelling loop  Statisticians with diverse backgrounds and expertise participate in a creative discovery processes that transform statistical data into knowledge. Our Storytelling tools integrate this geovisual analytics process using collaboration. This knowledge exchange process develops a shared understanding with colleagues and, after consensus has been reached, the story can be placed in the public domain to be shared. The snapshot mechanism helps the author of a story to highlight data views of particular interest, and subsequently guide others to important visual discoveries. The final Story (xml format) can then be imported by others with interest in the selected topic. 

 

Figure: The Statistical  eXplorer visualizations facilitate information and geographical visualization methods (e.g., choropleth maps, bar charts and histograms, table lens, parallel coordinates plots “profile plots”, scatter or "bubble" plots, scatter matrices, time graphs, treemap, and pie graphs but also flow maps - all methods applied and customized for statistics data. They also show hidden data or trends. The distribution plot was developed in collaboration with OECD and presents evidence-based facts on regions allowing comparison of regions within countries. Interactive features that support a spatial analytical reasoning process are applied, such as tooltips, brushing, highlight, visual inquiry and conditioned statistics filter mechanisms that help detect outliers.

 

 

Fisheye Lens technique in bar charts enhances large statistical data

---

Here is an example of an important visual technique that has evolved from Information Visualization: the Fisheye Lens technique that magnify the center of the field of view (Focus), with a continuous fall-off in magnification toward the edges (Context). Degree-of-interest is determined by the level of detail to be displayed assigned through user interaction. The Fisheye Lens, here applied to an interactive bar chart developed by NCVA in 2008, represents an interactive data reduction visualization tool for seeing both local detail and global context of a large number of bars simultaneously. Use the interactive Vislet demonstrator below with the following world indicators.

 

 

 

 

 

 

 

 

 

 

Figure: Interactive Fisheye Bar Chart at:

 

http://ncvademo-mobile.blogspot.se/2015/05/focus-based-on-fish-eye-technique.html

More example of educational information at: http://ncva.itn.liu.se/explorer/learn-more

 

 

Parallel Coordinate Plot applied to statistical data

---

The Parallel Coordinate Plot (PCP) is another appealing visualization component introduced and customized by NCVA to the statisticians. Our PCP supports a number of appealing interactive visual analytics methods, for example, to analyze the relationships between indicators and to see a profile for a selected region. Each region (country below) is represented by a string passing through the parallel axes where each axis represents a single indicator, for example, "Population ages 0-14" etc. Differences between highlighted regions can be found by visually comparing the profiles representing them. This has been a highly appreciated method to the statistics community - see figure below highlighting three countries Niger, Qatar and Japan.

 

A special advantage of the our PCP exploration technique is the capability to dynamically make visual inquiries and filter data. Filtering data is a critical step in the process of statistical data analysis. Filter out uninteresting regions; reduce the data set to a smaller, more manageable size. Each indicator axis has a pair of range sliders which define the bottom and top range for the query area. The range of an indicator can be specified by “dragging” the handles on the top and bottom of the corresponding range slider. Regions with values for a selected indicator, that fall outside of the spec­ified range, are filtered out. A combination of range slider movements can be used to dynamically formulate a more complex visual inquiry. These visual conditions and constraints will immediately re­flect the visual contents in all linked views. More information  at: http://ncva.itn.liu.se/explorer/learn-more?l=en

txt

Distribution Plot applied to OECD Regional statistical data

 

http://rag.oecd.org/interactiveedition/population/populationstory1.html

text

Our Partners

 

Figure: Regional eXplorer "ageing population in regional TL3 Europe"   An emerging and challenging statistical application domain is geovisualization of regional (sub-national) statistics. Higher integration driven by institutional processes and economic globalisation is eroding national borders and creating competition along regional lines in the world market. Sound information at sub-national level and benchmarking of regions across borders, therefore, has increased in importance in the policy agenda of many countries. http://stats.oecd.org/OECDregionalstatistics

 

 

 

 

Figure: "The Organization for Economic Co-operation and Development (OECD) makes a lot of world indicators available (e.g. world population and birth rate). Much of it goes unnoticed, because most people just see a bunch of numbers. However, the Factbook eXplorer from the OECD, in collaboration with the National Center for Visual Analytics, is a visualization tool that helps you see and explore the data"  http://stats.oecd.org/oecdfactbook/#

 

Collaboration with Eurostat and European Commission

Another important user of NCVA/NComVA technology is the European Commission that have used Statistics eXplorer since 2009 for internal analysis of data from Eurostat.

In 2012, Eurostat also decided to license the HTML5/JavaScript technology and tools from the spin-off company NComVA. The interactive dashboard application below was a joined development by Eurostat and NComVA based on our GAV HTML5/JavaScriptvisualization technique purchased in 2012 from the spin-off NComVA.

The application analyses regional statistical data for European at both NUTS2 and NUTS3 level, grouped into different statistical domains. Using the interactive map, individual regions can be selected while different visualisation methods allow the comparison and analysis of regional data in a user-friendly analytical way. The animated timeline shows how regions have performed over time.

Figure: Regional Statistics Illustrated here with latest "Unemployment rates" 2014 contains different dashboards, which always feature a choropleth map on the left pane. On the right window, the user can select four different views:

• Distribution plot
• Scatter plot
• Bar chart
• Data table

 

 

 

 

 

 

 

 

 

 

Figure: 3000 NUTS3 regional statistics showing "Purchasing Power". This Eurostat application is available at:http://epp.eurostat.ec.europa.eu/cache/RSI/

Other Eurostat web sites were also created with NComVA/NCVA HTML5 GAV HTML5 tools - See picture and open the web site at: http://ec.europa.eu/eurostat/web/europe-2020-indicators

More about this at: http://ncva.itn.liu.se/great-statistics-visualization/eurostat-statistics-visualization

 

 

Statistics Sweden SCB

NCVA started its collaboration with SCB already in 2004. SCB provided statistical data and professional advice. The first Statistikatlas was installed at SCB in 2010. For more information see:

text http://www.scb.se/statistikatlasen/

 

 

 

 

Figure: Sweden has an ageing population. The average age has increased from 37% in 1968 to 41% in 2014. The amount elderly population 65+ has increased from 13% to 19.6% during the same period. This interactive picture is available at:

http://www.scb.se/Kartor/Statistikatlas_KN_201404/index.html#story=0

 

Figure: Interactive educational statistical material based on the 4Ws concept “What-When-Where-Why”

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure: Storytelling facilitates three complementary characteristics: explore and gain insight, tell-a-story and publish (Vislet) which finally can be embedded in a web page/blog using the HTML code. See example of a Vislet embedded into a web site below.

 

Embedding interactive visualization in web sites

 

The interactive picture below provides insight into several key statistical indicators (see list) covering world countries during 1968-2014.  We suggest that you start reading the storytelling text below the interactive picture and click on the snapshots buttons or the red text (snapshots). The geographical world countries are rather big and loading could take some time. You can reduce the map window to the left to see 100% of the scatter and bar chart. Or go to the Vislet web site:

http://ncvademo-mobile.blogspot.se/2015/12/world-databank-1960-2014-in-html5.html

http://ncvademo-mobile.blogspot.se/2015/12/world-databank-1960-2014-in-html5.html

 

Interactive Figure: World eXplorer with 3 time-linked views showing indicators related to countries of the world “ageing population in world” during 1960–2014; map, scatter plot (age 65+ vs. age 0–14) and bar chart; comparing 4 countries Niger,Germany,  Sweden and Japan. Students learn interactively that Japan maintains highest elderly rate, while Niger has the lowest. The students can  interact  and  change  indicators  (many other indicators are available in this story) to  discover  reasons  behind  this  trend  and  gain knowledge. 

More HTML5 Vislets available at http://ncvademo-mobile.blogspot.se

 

 

 

 

 

 

 

 

Figure: Try also these two interactive documents from OECD and ISTAT. 

http://noi-italia2013en.istat.it/ and http://rag.oecd.org/

 

text

Treemap implementation with bubbles and bar chart

A potential Swedish sales management data is stored in EXCEL data and can be imported direct into MDim eXplorer. The data has five categorical data columns (Sales person, Sales Manager, District, Gender and Age group; and four numerical data columns). The sales data is here represented with a treemap and fish eye bar chart. Red colour indicates negative sales results (column H). The treemap is divided into "Districts" (Column C) and the "Sales person name" (Column A) is shown. The size of the rectangles are related to "Total Sales" Column F. The combination of the treemap and bar chart provides a deep insight into this kind of abstract data. More information: http://ncva.itn.liu.se/explorer/mdim-explorer?l=en

 

This treemap visualization shows hierarchies in which the rectangular screen space is divided into several hundreds rectangles. Each rectangle represents a sales person in a hierarchy. The colourful presentations accommodates hundreds of sales management data items in a meaningfully organized display allowing patterns and exceptions to be spotted immediately. Combining a treemap visualization with, for example, fisheye bar chart makes the two linked and coordinated views powerful.

 

The picture below is a "Vislet"   produced in HTML5 analyses the Swedish sales management data above. The Vislet was created as an story example produced with MDIM eXplorer and then inserted into a blog site:

Vislet demo: http://ncvademo-mobile.blogspot.se/2015/02/sales-management.html

 

 

 

 

This colourful presentations accommodates hundreds of sales management data items in a meaningfully organized display allowing patterns and exceptions to be spotted immediately. The size of the rectangle refers to "Total Sales" while the colour attribute represents "Sales result %". 

When the colour and size dimensions are correlated in some way with the tree structure, one can often easily see patterns that would be difficult to spot in other ways, such as if a certain colour is particularly relevant.

 

 

 

 

 

 

 

 

 

Linked and Coordinated Choropleth with Treemap

This European treemap visualization (see figure below) shows hierarchies in which the rectangular screen space is divided into several hundreds European statistical NUTS2 regions. Each region belongs to a country in the hierarchy. This colourful presentations accommodates thousands of statistical data items in a meaningfully organized display allowing patterns and exceptions to be spotted immediately. The size of the rectangle refers to "Total Population" while the colour attribute represents elderly population in Europe "age group 65+ in %". 

When the colour and size dimensions are correlated in some way with the tree structure, one can often easily see patterns that would be difficult to spot in other ways, such as if a certain colour is particularly relevant. We see that Germany has a high rate of eldery people while, for example, Poland has a younger representation 

Try this Dashboard Demonstrator: http://mitweb.itn.liu.se/GAV/dashboard/#story=data/nuts-regions-ageing-population-in-europe-2010.xml&layout=[map,treemap] or this with 3 linked views incl a fish eye bar chart:

http://mitweb.itn.liu.se/GAV/dashboard/#story=data/Ageing Population in the World.xml&layout=[map,(treemap,barchart)]

A special advantage of the treemap is that, by construction, it makes efficient use of screen space by displaying thousands of data items on the screen simultaneously.

Figure: High resolution: Choropleth Map with small pie charts (age 0-14 vs. 65+) and Treemap visualization showing "Age Group 65+" in Europe for NUTS2 regions". Coloured rectangles in the Treemap represent the ratio of elderly people ("age group 65 and above"). The size of each rectangle in the Treemap represents the "Total Population". 

We know that the EU population is getting progressively older as a result of a significant and continuous increase in life expectancy at birth, combined with low fertility rates and the entry into retirement of the post-World War II baby-boom generation. The linked and coordinated treemap and choropleth map below presents information for the 270 NUTS2 regions in the EU with the highest and lowest shares of elderly persons (aged 65 and above) and their respective populations. A Storytelling panel in the interactive demonstrator gives a textual background and provides some snapshots.

To see the hierarchical structure. You must first set the treemap options as seen on this picture:

Tot Pop

Pop 65+

Group by: Country

 

GEOGRAPHICAL MAP AND TREEMAP REPRESENTING GERMAN NUTS2 REGIONS

 

 

In the treemap below - focus is restricted to only  regional Germany. A City Dusseldorf with 5,2 million people is represented by a small geographical area in the map but in the treemap - a larger rectangle represents Dusseldorf based on the size of its population and the darker red colour represents an ageing population of 22%.

 

 

 

World Data Demonstrator Dashboard using HTML5 Visualization

 

 

 

 

 

 

 

 

This World Map Dashboard with three coordinated and linked interactive views map, treemap and parallel coordinates is available at:  http://mitweb.itn.liu.se/GAV/dashboard/#story=data/Ageing Population in the World.xml&layout=[map,(treemap,pcp)]

 

 

Other NCVA Research Projects

 

 

 

 

 

 

 

Collaborative Partners

---

NCVA and from 2010 spin-off company NComVA have collaborated with statistical organisations including SCB Statistics Sweden, OECD Paris, Eurostat, Norrköping Kommun, Linkoping Kommun and Göteborg Stad. But also industrial Ericsson Research in Linkoping and financial support for two industrial PhD students was provided by Unilever Research Port Sunlight UK and Swedish SMHI  (Swedish Meteorological and Hydrological Institute) Norrköping, and several European universities. Our visualization tools has during 2006-today helped these organizations to better understand the evolution of applying information visualization and geovisual analytics to multidimensional and multivariate data.

 

 

 

 

VINNOVA: "En forskargrupp på Linköpings universitet har i samarbete med bl.a. OECD, SCB, SKL, Danmark Statistik och Göteborg Stad utvecklat revolutionerande interaktiva "berättande" visuella metoder (storytelling) som enkelt kan publiceras i media som bloggar eller integreras i Web sidor och spridas till miljoner läsare genom bl.a. en annan samarbetspartner engelska tidningen Economist. Istället för en statisk bild så publiceras interaktiva bilder tillsammans med en förklarande text. Läsaren kan också själv interagera med visualiseringen och därmed kanske bättre förstå hur författaren har tänkt. Forskningsprojektet som bl.a. finansierats genom KK-stiftelsens visualiseringsprogram har vid flera utvärderingar demonstrerat en världsledande teknologi"

text

Demonstrators using Statistics eXplorer and "Vislets":

 

SCB Statistikatlas http://www.scb.se/statistikatlasen/

Haldersson Marie (SCB), Andersson Jonas (Göteborg Stad): "The benefits of visualization - how to eXplore a municipality",http://ncva.itn.liu.se/resources/publications/1.542529/ISI2011_Thebenefitsofvisualization-howtoeXploreamunicipality.pdf

Eurostat - Collaboration with Eurostat started in 2012 and spring 2013 this web site was published:

http://epp.eurostat.ec.europa.eu/cache/RSI/

OECD Regional Statistics - Collaboration with OECD started in summer 2008 - The first version of OECD eXplorer was published om the OECD web site and created a lot of attention. This public OECD eXplorer web site has more than 50,000 visitors per month. http://www.oecd.org/gov/regional-policy/regionalstatisticsandindicators.htm

OECD Region-at-a-Glance" interaktive report describes the regional development in the OECD countries.  The interactive report is available at http://rag.oecd.org/

 

Litterature about statistics visualization:

• Jern M, Thygesen L, Brezzi M: “A web-enabled Geovisual Analytics tool applied to OECD   Regional Data”, Reviewed Proceedings in Eurographics 2009, Munchen, March 2009 - Web-enabled Geovisual Analytics tools applied to OECD regional data Eugr2009.pdf
• Jern M, Thygesen L, Brezzi M: "Storytelling - How to visualize statistics", Reviewed paper, NTTS 2011 (New Techniques and Technologies for Statistics), international scientific conference on the impact of new technologies on statistical collection, production and dissemination systems, Brussels, February 2011 NTTS 2011 Storytelling - How to visualize statistics.pdf

 

Page manager: mikael.jern@liu.se
Last updated: 2016-02-12