NCVA Research Projects
VISE research project
Measuring innovation in education geared towards teachers who want to exploit the latest digital society by creating interactive statistical visualizations of "Hans Rosling spirit" in relation to their teaching a well as a training based on Visual Storytelling.
The VISE interdisciplinary project "Visual Storytelling applied in Education" is managed by three partners:
- ISV (Educational Department), Campus Norrköping, Linkoping University
- Norrköping Kommun
- NCVA (National Centre for Visual Analytics) ITN Campus Norrköping, Linkoping University.
Our research focuses on learning based on Knowledge Visualization and Geovisual Analytics Statistics applied in education. The VISE project supports the use of learning for these focus areas:
Official statistics are used as a more or less important background for decisions especially in public planning and policy making. However, in education, official statistics are much less recognized and used than they could be and among the informed public they are even less used. Web-enabled Visual Storytelling implemented in Geovisual Analytics is a technique that integrated with Knowledge Visualization can help illustrating spatio-temporal statistical data which for the eye are hard to uncover or even are not possible to perceive or interpret.
The authoring process applied to visual storytelling is implemented through an innovative snapshot mechanism available in Statistics eXplorer developed by NCVA and supports:
- The capture and storage of interactive events through “memorized interactive visualization views” or “snapshots” ;
- Can be captured at any time during the explorative data analysis process in Statistics eXplorer;
- Represents an important task of an authoring analytical reasoning process;
GAV HTML5 TOOLKIT and framework developed by NCVA and Ncomva 2011-2012
Visualization components are built based on the top-down principle. Each component is broken down into smaller blocks which are so-called layers. Each layer in turn is broken down and is built based on primitive graphic elements such as lines, curves, or circles. The layers can be reused and combined in different ways to form different components. This figure below shows the layer architecture of our map based component which can have an arbitrary number of layers such as a heat map layer, a glyph layer and an online map service layer.
Insight is normally gained through interaction. To increase the performance of visual exploration and analysis processes, visualizations are enhanced with a rich set of interaction techniques and interactive features. For example, the map component is enhanced with selection, tooltips, details-on-demand, focus-and-context, pan, zoom, animation, focusing (i.e. bringing selected items/regions to the centre of the view), and drill down (e.g. when applied to a hierarchy of three NUTS levels). The drill down technique is also applied to treemaps; the focus-and-context and fisheye lens techniques are applied to bar charts and time graphs, and so on. Other interactive features supported in the framework include visual inquiry, conditioned filter mechanisms, and methods supporting multiple coordinated linked views. The framework also supports data analysis algorithms, connects the components to each other, and supports data providers that can load data from various sources.
Download AGILE 2013 paper:
The interactive tool below developed with the HTML5 GAV framework has been implemented by Eurostat to visualise Eurostat's regional statistical data. Regional Statistics Illustrated contains more than 50 indicators by EC NUTS 2 regions, grouped into 10 statistical domains.
- Choropleth map
- Distribution plot
- Scatter plot
- Bar chart
- Data table
Geovisual Analytics applied to Flooding in Lisbon using GAV Flash - NCVA and NComVA 2011-2012
Figure: Flooding based on data from Lisbon 2010 - http://mitweb.itn.liu.se/GAV/flood.mp
se also live at: http://www.youtube.com/watch?v=bhd23XKHjCM
A paper "Geovisual Analytics and Storytelling Applied to a Flood Scenario" by Quan Ho and Mikael Jern was presented at GeoViz 2013 international conference in Hamburg: Interactive Maps that Help People Think.
Visualizing two statistical data sources - A collaboration between Region Västra Götaland - NCVA/NComVA 2012-2013
Dual eXplorer is a specially customized eXplorer application based on our GAV Flash toolkit and framework for visualizing and sharing a combination of point- and regional data. Point data, for example an industry location (data instances which includes X- and Y-coordinates), is placed on an interactive map component on top of a background map and a colour scale based on regional data. Download User Guide and test the application here: http://ncomva.se/apps/dual/
This research project was a collaboration between NCVA/NComVA and Region Västra Götaland http://www.vgregion.se/sv/Vastra-Gotalandsregionen/startsida/Regionutveckling/Publikationer-statistik/Fakta-och-statistik/
This implementation allows for a typical geovisual analytics of two different statistical data sources: point data such industry location and regional statistics such as population age group or any other indicator. Dual eXplorer combines this map with multiple linked visualizations components for each separate data set, and many visual analytics tools like value-, category- and record-filtering, real-time animation, customizable colour scales and data import managers.
Figure: Dual eXplorer utilizes the GAV Flash map layer technology. In this choropleth map, three layers are used Background map (Google etc.), Regional Data layer and Point data layer
Dual eXplorer also utilize the eXplorer storytelling technique which allows collaboration of discoveries and gained knowledge to be shared with others.
To learn more about this demonstrator, you can read a user guide and test the Dual eXplorer Demonstrator:
Visual Analytics for Mobile Communication Networks - NCVA 2011-2012
This applied research project was a collaboration between NCVA, Linköping University and Swedish telecom company Ericsson Research, Linköping, facilitating a strong synergy between visualization technology and management of mobile communication networks. The project targets Visual Analytics (VA), where novel techniques has been developed and applied to smart network management prototypes for mobile communication networks.
ANROSS is a visual analytics prototype developed in close collaboration with domain experts both research groups to show how ANR works. ANR is an algorithm invented by Ericsson to automatically detect and resolve physical cell identity (PCI) conflicts; therefore it can self-configure cellular radio networks that are typically mobile phone networks. To show how ANR work it is important to visualize and analyze the network data which are output of the process in which ANR configures a network.
The network in this context mainly includes two kinds of objects: cells and cell relations. A cell is a device that covers a geographical area and serves mobile phones in its area as well as handovers phone calls to other cells when necessary. Each cell has two identifiers: a globally unique cell identifier (CGI) and a PCI. CGIs are unique and constant over time but are more difficult and time consuming to detect for mobile terminals. In contrast, PCIs are easier to detect but they are not unique since there are at most 504 different PCIs in LTE. The PCI of a cell can vary over time since it needs to be unique in a region and may need to change. A cell relation is a neighbor relation between two neighbor cells. Cell relations are used to handover phone calls from one cell (source cell) to a neighbor cell (target cell).
The network data in this context mainly includes three kinds: cell data, relation data and event data. All of them are time-varying and recorded for each time period which normally is 15 minutes or 1 hour. Cell data include cell position, cell coverage area, cell PCI, cell CGI, number of call drops, and number of out-going/in-coming handover successes/failures. Relation data include number of handover successes/failures. Event data include cell added/removed, relation added/ removed, PCI conflict detected/resolved.
To visualize all three kinds of data ANROSS includes multiple-linked and interactive views (figure):
- a time slider (top view) which displays aggregation data of each time period such as number of cells added, number of cells removed, number of relations added, number of relation removed, number of PCI conflicts detected, number of PCI conflicts resolved; through using a color map designed carefully it highlights time periods in which some indicator has extreme value;
- a cell map (middle left view) presenting cell data and cell event data in a time period such as number of call drops, cell PCIs, cell PCI conflicts, cell PCI resolutions;
- a relation map (middle right view) presenting relation data and relation event data in a time period such as number of handover failures of each relations, relations added, relations selected;
- two Parallel Coordinates Plots-PCPs (bottom view), one visualizing cell data and one visualizing relation data;
- two data tables (or data grids) (bottom view), one displaying cell data and one displaying relation data;
- a data selection panel (left) highlighting cells and relations being selected in cell map and relation map;
- a search panel (right, hidden) which allows finding low/high performance cells or relations which will be then highlighted in the cell map or relation map respectively;
Through these interactive views ANROSS allows users to see how the network evolves from different initial configurations under the control of ANR and answer various questions such as
- Why a cell changes its PCI, how a PCI conflict is detected, and what is the difference in performance of a cell (e.g. number of call drops) before and after a PCI change;
- Why a relation is added or removed, what is the performance (e.g. number of handover failures) of a relation;
In addition, through using filtering ability of PCPs and searching tool ANROSS allows users to find and supervise “problem” cells and relations such as cells/relations having a large number of call drops/handover failures, or cells having a potential PCI conflict in future.
ANROSS has been evaluated by network experts from Ericsson and has also been shown to Telia, one of the biggest network operators in Sweden. The overall feedback from both the network experts and the operator is very positive. They like the time slider, the two maps and the ability to see how the network changes over time. The ability to find ‘problem’ cells and relations were also considered as highly positive “-it looks useful and very good to me”. As a result, they would like to collaborate to develop this prototype into product.
see video at: http://mitweb.itn.liu.se/GAV/anross.wmv
Read more at http://ncva.itn.liu.se/anross-mobile
Focus & Context applied to Big Regional Data - NCVA 2008-2012
Interaction with large regional datasets (more than 10,000 regions, 50 indicators and for 20 years) is a challenging task in geovisual analytics.
We have applied the well-known information visualization method "Focus & Context" or "Overview & Detail" to reduce the amount of data for the interactive analysis. Zoom in on a smaller area to see details while still maintaining the overview gives the user an idea where to find regions-of-interest and then analyze in more detail what is important to the user. This can help us to also avoid a performance issue.
NCVA supports this feature in GAV Flash by implementing the concept of sub-datasets. A sub-dataset is a list of references to data items in a large dataset. Since a data item normally corresponds to a region in a map and vice versa, a sub-dataset corresponds to a sub-area of the whole map and vice versa. Then each component works basing on its sub-dataset input. It visualizes only items in the sub-dataset. In case we want to visualize the whole dataset, the sub-dataset will be set to the whole dataset. The figure above is based on a collaboration with ISTAT (Italy Statistics) and explores more than 8000 Italian regions using Focus&Context and fisheye techniques.
The figure below shows an example of this approach applied to 10,000 Swedish Zip Code regions with more than a million geographical coordinates and 50 indicators over time. An interactive region selection is applied to the context map (left view) to focus on an area of interest in the two right views which interacts only with a reduced regional data set for better performance.
Figure: 10,000 Swedish Zip Code regions and associate indicators over several years visualized using "Focus & Context" ("Overview & Detail")
See demonstrator at:
Statistical Data Visualization - A Collaboration between OECD and NCVA/NComVA 2008-2013
Figure: OECD eXplorer with three selected time-linked views map, scatter and fisheye bar chart for Europe TL3 regions.. 3 regions are highlighted in all views. The colour map show "population age group 65+". http://www.oecd.org/gov/regional/statisticsindicators/explorer
Since 2008, OECD has experienced a growing interest in regional development . The performance of regional economies and the effectiveness of regional policy help determine a nation’s growth and shape the measure of well-being across countries. Since 2008, the OECD has been studying regional disparities and development patterns in its member countries in order to evaluate innovative strategies and diffuse successful policies. This work generated new demand for sound statistical information at the sub-national level on the source of regional competitiveness. The OECD Regional database is a unique source of statistical information at sub-national level for all the OECD Countries. It contains yearly time-series for around 60 indicators on demography, economy and labour market opportunities, environment, social issues and innovation related activities for more than 1,700 regions of the OECD countries.
While these activities have generated knowledge among experts, the OECD has since long felt the need to make regional data much more easily available on the web in an interactive and powerful way. In particular, to make a more extensive use of maps which, more effectively than a graph, can convey the four dimensions included in the regional database: statistical indicator, time variable, country value and regional value. Moreover, many users expressed wishes to be able to select subsets of the database and display the results in maps linked to analytical tools and export views of maps and charts for use in other contexts. Target groups for such a knowledge-generating visualization tool are quite diverse. A primary target group would of course be the OECD experts themselves and the country experts involved in the policy evaluations. Other audiences include regional planners in countries, regional policy makers in a broader sense, journalists looking for interesting stories about regional differences, as well as the informed citizen. Because of the different expertise and needs of the target-groups, the tool should be flexible and adaptable, so that different versions could be presented to different audiences.
Synergy between Choropleth Map and Tree Map for regional data
This research demonstrates and reflects upon the potential synergy between the use of a squarified treemap dynamically linked to a choropleth map to facilitate visualization of social science regional data at several hierarchical levels aiming to discover statistical patterns that relate to significant characteristics of regions under study.
Figure: A squarified treemap ordered by population size and NUTS2 regions, dynamically linked to a choropleth map, both coloured by population for age group 65+. The hierarchical data structure is based on 2 levels country and NUTS2 regions.The choropleth map includes a pie chart embedded in each region showing the difference between age group "young people" ege 0-16 and elderly people age 65+
We are interested in finding answers about what methods and tasks are important when exploring demographical hierarchical data, such as general overview, trends over time, geographical patterns, indicator correlation, outliers and simultaneously mapping two dependent indicators such as age group and total population. For example, for a choropleth map screen space is always allocated depending on geographical area rather than an indicator of interest? Can the treemap compensate this weakness and are its known strengths and weaknesses applicable to the demographics data domain?
Red areas have a high ratio of ageing people and blue areas have a low ratio. Cell size in the treemap represents the total number of people in each region. Some extreme clusters of regions immediately stand out in the choropleth map. Poland is one of them, with children in some parts making up almost half the population. On the opposite end of the distribution, with ratios down to nine percent, lie former East Germany, north-eastern Spain and northern Italy.
The additional information provided by the treemap that the choropleth map lacks is all based on the introduction of a second indicator; in this case the size of each region’s population. For instance, it becomes apparent that despite that Sweden is the third largest of the included countries measured in physical area, it only has a population of nine million – less than Turkey’s Istanbul region alone. It is also possible to see that while Germany and Poland currently have almost identical total population numbers, Poland has far more children.
Download scientific paper: Treemaps and Choropleth Maps applied to regional hierarchical statistical data IV2009.pdf
Last updated: Sat Feb 13 16:31:50 CET 2016