Welcome to a "Geovisual Analytics Educational Web site"
Nu också på Svenska! 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. Professor Mikael Jern is the director. Mikael is a frequently invited speaker at international statistical Conferences - figure shows Eurostat "Europé 2020" Conference.
Our research focuses on geovisual analytics methods and applications for spatial and spatio-temporal data which demonstrate the usefulness of these techniques, storytelling, geographic and information visualization, spatial cognition, quantitative data analysis e.g., (geo-)statistics/data science. We are particular interested in how these techniques can support knowledge construction and insight from spatial data.
Learning with Geovisual Analytics and Storytelling applied to Statistics
Today, our research focuses on learning based on Knowledge Visualization and Geovisual Analytics Statistics applied in education. The VISE (Visual Storytelling in Education) project supports the use of learning for these focus areas:
The visual 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.
Geovisual Analytics and Knowledge Visualization - 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". NCVA provides interactive geovisual analytics tools:
- Analytical reasoning facilitated by interactive visual interfaces – e.g. dynamic time-linked views;
- Exploring and analyzing large spatial-temporal and multidimensional data;
- Discern trends or patterns - derive insight and Communicate discovery and knowledge effectively through Storytelling;
- NCVA is moving Geovisual Analytics Research into Practice through tools like Statistics eXplorer and Publisher;
Knowledge visualization is regarded as an independent scientific discipline and the relevance of applying storytelling towards its scientific goals is still relatively unexplored. Therefore, the emerging popularity of visual storytelling presents several interesting challenges. Our overall objective is to promote within education and research Geovisual Analytics in particular but also its relation to knowledge visualization. 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, geovisual analytics techniques have been developed and used to build prototype visualization applications (eg. Statistics eXplorer) that can be used to educate academia with demonstration and educational tools.
"Our mission is to use knowledge visualization 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. These methods are all implemented and available in our Statistical eXplorer applications - see below.
Interactive Statistical Visualization with integrated Storytelling
Introducing New Statistics eXplorer V4.6
Statistics eXplorer embraces a collection of recognized information visualization web compliant methods applied to perform with statistical data including data manipulations and data access methods. Statistics are easily accessed through distinctive and highly interactive visualization.
Statistics visualization of socio-economic information at national or detailed territorial level can provide 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 (no map) 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.
Information Visualization methods customized for Statistical Data by NCVA
Statistics eXplorer facilitates highly interactive information and geographical visualization methods including choropleth maps, bar charts and histograms, table lens, parallel coordinates plots or “profile plots”, scatter or "bubble" plots, scatter matrices, time graphs, treemap, and pie graphs but also flow maps - all methods applied and customized to support large statistical data. They also show hidden data or trends. The distribution plot, see below) 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.
Interactive Map - New Europe eXplorer - Three European Map levels
Figure: Try new Europe eXplorer loaded with latest statistics: https://mitweb.itn.liu.se/geovis/eXplorer/euro/#story=1
Fisheye Lens technique applied 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:
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
Distribution Plot applied to statistical data
The Distribution Plot is an innovative variation of the box plot visualization method. It is similar to a vertical bar chart but it excels at showing how data items that belong to a cluster are distributed according to an indicator. Each cluster is represented as one row of dots, and these dots are a representation of each data item belonging to this cluster. The mean value is represented as a thin green line for each country. This component is based on the premise that each data item belongs to a cluster. It is a good way to visualize the differences within a cluster; showing the minimum, maximum and mean value as well as the interval of the values in the cluster. Use the menu on the left side to change which indicator to be visualized and use the green scroll bar on the top to "zoom" in on interesting areas. Try this demonstratror at: http://rag.oecd.org/interactiveedition/jobs/jobstory1.html
OECD - 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
See this entusiastic BBC production from 2009: http://news.bbc.co.uk/2/hi/8130554.stm.
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" 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.
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:
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
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:
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:
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.
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 (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:
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.
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:
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:
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
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
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"
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:
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:
Last updated: 2016-04-24