National Center for Visual Analytics
Welcome to the "Geovisual Analytics Educational Web site"
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 (KK-Stiftelsen) Visualization Program and VINNOVA (the Swedish government agency that administers state funding for research and development). Financial support for two industrial PhD students was also provided by Unilever Research Port Sunlight and SMHI (Swedish Meteorological and Hydrological Institute).
NCVA - Concept and Idea
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".
The overall objective of NCVA 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 educate academia with demonstrating 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 Analytics comprises several research topics including information visualization, geographical visualization, perceptual science and storytelling etc. Geovisual Analytics.png - These methods are all implemented and available in our Statistical eXplorer applications.
Interactive Statistical Visualization with integrated Storytelling
Statistics eXplorer, developed by NCVA in collaboration with its spin-off company NComVA, 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.
Use World eXplorer with our preloaded national data or access directly the integrated World Databank: http://mitweb.itn.liu.se/GAV/world/
Use MDim eXplorer with any table data (NO geographical reference) http://ncva.itn.liu.se/explorer/mdim-explorer
Read more about data formats at: http://ncva.itn.liu.se/explorer
Figure: Reiterate analytical storytelling loop - Statisticians with diverse backgrounds and expertise participate in creative discovery processes that transform statistical data into knowledge. Storytelling tools integrate this geovisual analytics process using collaboration. This knowledge exchange process develops a shared understanding with other statisticians and, after consensus has been reached, it can be placed in the public domain. The snapshot mechanism helps the author of a story to highlight data views of particular interest, and subsequently guide others to important visual discoveries.
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.
Figure: Interactive Fisheye Bar Chart at: http://ncvademo.blogspot.se/2015/03/the-world-databank-indicators.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 representing three countries Niger, Qatar and Japan.
Statistical eXplorer introduces to the statistical community the PCP method originating from Information Visualization research. PCP is a proven visualization technique for identifying trends and cluster in many scientific environments and has now also been adopted, evaluated (OECD and its many users) and appreciated by many in the statistical community. Our PCP enables visual representation of large spatial-temporal multivariate data and hence is becoming an important mechanism in Statistics Analytics.
A special advantage of the NCVA 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.
OECD Regional eXplorer - A successful collaboration with OECD since 2008
'I have always believed that the very best results in dynamic visualization - the truly breakthrough innovations - are the results of cross-disciplinary collaboration. The NCVA OECD joined project brings together 'right brain' statisticians with the 'left brain' visualization designers. The interactions are incredible!'
OECD Chief Statistician Enrico Giovannini
From an international perspective, our research builds on collaborating work with OECD since 2008. See Springer paper 2009: Collaborative Web-Enabled GeoAnalytics Applied to OECD Regional Data
Figure: Regional eXplorer "ageing population in regional TL3 Europe" http://stats.oecd.org/OECDregionalstatistics - 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.
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
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 pane, the user can select four different views:
Figure: 3000 NUTS3 regional statistics showing "Purchasing Power"
Read more about this at: http://ncva.itn.liu.se/great-statistics-visualization/eurostat-statistics-visualization
The Eurostat application is available at: http://epp.eurostat.ec.europa.eu/cache/RSI/
The OECD Flow Map eXplorer Trade Demonstrator
The data which represents a movement from an origin to a destination is defined as flow data. There are many examples of flow data including commuting, migration, trade and flows of money that have drawn much attention of policy makers, city planners, researchers and ordinary citizens as well. This kind of data is two-dimensional relationship data; thus, it is normally large.
NCVA introduces an interactive Flow Map eXplorer demonstrator based on the large OECD Trade data set that effectively can explore spatio-temporal and multivariate trade statistical flows (Export and Import) data using bidirectional flow arrows where both in-coming (import) and out-going (explort) flows can be clearly shown. This applied research task is based on a collaboration between OECD and NCVA/NComVA.
The OECD Trade Flow Map eXplorer demonstrator provides interactive trade flow visualization of the OECD countries based on two flow types (Import, Export) for more than 4000 commodities, 21 time-steps and more than 200 countries. The flow interaction data table for only one focus point represents more than 1GB. The large trade data is cut into smaller more manageable data files, providing true interactive performance , where each interaction data table represents an origin point (for example, USA below), a combination of a destination points (countries) , selected commodities and all 21 time-steps. The reason of this data management is that normally a user needs to focus on 1-5 selected origins (countries), a few selected commodities but wants to analyse the data of all flow types (compare import and export), for all time-steps (to find pattern over time) and from/to all countries (to find the biggest flows).
Figure: The OECD Flow Map eXplorer for Trade Data demonstrator with the Origin USA and all countries as Destination showing Total Trade (import, export and Net) for 1988-2008.The map shows flows for the "Top 10 Flows" and the bar chart shows Total "Export", "Import" and "Net" for all countries.
The OECD Trade demonstrator is available at: http://mitweb.itn.liu.se/GAV/flowmaptrade/
Read more about our flow Visualization technology in this research paper: Flow Map - STGIS - Paper.pdf
More about Flowmap eXplorer at: http://ncva.itn.liu.se/explorer/flowmap-explorer
NCVA started Research related to Storytelling already in 1999 called "SmartDoc"
SmartDoc was our first implementation of Storytelling and integrates interactive and collaborative visualization into reports in MS Word format. The normal contents of a report, text and images, are extended with interactive 2D/3D visualization. Data and bookmarks are integrated into the document structure, which make the SmartDoc just as easy to handle as a traditional document. The reader is given a more active role and can interact with the embedded data in order to gain a better understanding of the report’s contents. First publication:
Jern: “Smart Documents with ActiveX 3D Data Visualization Components”, reviewed article in “Digital Media: The Future”, Vince J. A., R. A. Earnshaw (Eds), Springer Verlag March 2000, ISBN 1-85233-246-8.
As social creatures, we love telling stories and sharing them in a good laugh with others. It doesn’t matter how dry or boring the subject inherently is. If someone can tell a good story, others will listen and enjoy it. Analytics visualization experts are challenged to elicit a strong engagement and wonder in our stories stimulating the readers’ curiosity making them want to learn more and convey a deeper meaning.
A picture is often worth a thousand words, an interactive visualization is worth a thousand pictures but interactive storytelling provides the true difference and that is "understanding" and "knowledge"
A storytelling mechanism assists the author to improve a reader’s visual knowledge. Authors can develop interactive educational material based on the 4Ws concept “What-When-Where-Why”. A story is composed of texts and visual representations together with snapshots, metadata, hyperlinks and references providing answers to the classical 4Ws questions (see figure below): “What” is the data? The data are shown for a given time step - “When”? Moreover time series can be viewed interactively and simultaneously in the in the linked graphs; “Where” is mostly considered in regional comparisons. “Why” is the most important aspect of the story and explained in the metadata text section together with snapshots and hyperlinks that enable users to further analyse aspects related to topics and are helpful tools for both specialised and non-specialised users.
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 covering world countries during 1968-2013. 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.
Interactive Figure: World eXplorer with 3 time-linked views showing indicators related to countries of the world “ageing population in world” during 1960–2013; 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.
Try also the Mobile HTML5 version http://ncvademo-mobile.blogspot.se/2015/02/worlddatabank1968-2012-mobile.html
NOTE: Some indicators are NOT available for the last years and you might have to move the TIME SLIDER.
More Vislets (using Flash) is available at: http://ncvademo.blogspot.se/
Figure: Try also these two interactive documents from OECD and ISTAT.
Linked and Coordinated Choropleth and Treemap
This European treemap visualization 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:
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:
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
Below is a World Map Dashboard with three coordinated and linked interactive views map, treemap and parallel coordinates
This World Map Dashboard with three linked and coordinated views is available at: http://mitweb.itn.liu.se/GAV/dashboard/#story=data/Ageing Population in the World.xml&layout=[map,(treemap,pcp)]
Example of NCVA Research Projects
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 and SMHI (Swedish Meteorological and Hydrological Institute) Norrköping, and several European universities. Our visualization tools has during 2006-2013 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"
Last updated: 2015-05-29