Introduction to flow visualization with Demonstrator Flowmap eXplorer
Learn from this video how to use flowmap: flowmap demonstrator.mp4
(NOTICE: Flowmap eXplorer is only available as a demonstrator with preloaded data)
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. Data of commuting among Swedish municipalities serve, in this figure, as a good example. Sweden has 290 municipalities, so there are 290 * 289 = 83810 possible commuting flows between each two of them. The figure above shows the top ten commuting municipalties (Stockholm, Halland, Skåne, ..) with origin placed in Västra Götaland.
Reading a table of tens or hundreds of thousands records to find out valuable information is tedious and very difficult. Therefore, it calls for a tool to analyse that massive data and visualise the findings so that viewers can easily derive an insight into data.
Unfortunately, even though there has appeared a great deal of statistical data visualisations, applications for visualising flow data are still rare. Moreover, these seldom applications also suffer the following drawbacks:
- some are standalone and only for expert usages;
- some do not support interactive functionalities which are essential for visual analytics;
- some can only provide an overview of flow data;
Flow Map eXplorer addresses these three challenges as a web-enabled, highly interactive and analysis support statistical flow data visualisation application.Statistics visualization facilitating methods from the geovisual analytics research domain have gained much interest.
We introduce spatial interactions for a wide variety of realized movements of people such as commuting and migration between an origin and a destination. This type of flow data can be visually expressed by directed weighted arrows over a geographic space. For a small number of adequately distributed regions directed arrow symbols can be an attractive means of visualization. Cartographic flow maps showing official statistics related to a larger number of sub-national regions (e.g. counties and municipalities) are still problematic and often skewed and detailed which leads to cluttered flows where important details are obscured. Read more about NCVA flow Visualization technology in this research paper: Flow Map - STGIS - Paper.pdf
Directed weighted arrows are used where each arrow represents a movement from an origin to a destination and the thickness of the arrow represents the number of people. Arrow thickness can be scaled dynamically to make arrows more readable. Nevertheless, they always reflect the values they represent. To avoid overlapping, arrows are displayed as quadratic Bezier curves instead of straight lines (see figures above). The curvatures of arrows can also be adjusted dynamically and edited individually to reduce clutter and be more readable.
The commuting flow dataset and the associate Demonstrator http://mitweb.itn.liu.se/GAV/flowmapsweden/# shown in these two figures above, contain flow data for people commuting to (blue) and from (green) Stockholm with other Swedish municipalities. Using a dynamic filter to find dominant flows, we see that, not surprisingly, the top ten flow arrows goes to municipalities very close to Stockholm.
The choropleth map with overlaid weighted (size) flow arrows is below linked to an interactive "fish-eye" bar chart that gives a more detailed and ordered representation of all regional flow data for commuting to and from the origin Stockholm and other Swedish municipalities.
Figure: This Flow Map eXplorer Demonstrator http://mitweb.itn.liu.se/GAV/flowmapsweden/# is based on official commuting statistics from Statistics Sweden (SCB) for all Swedish 290 municipalities. There are 290 * 289 = 83810 possible commuting flows between each two of them. The figure shows Norrköping (our reserach centre NCVA) as the origin.
The Commuting Flow Data Demonstrator with Origin Oslo and destination Swedish counties
This applied research task was based on a collaboration with Västra Götalands Regionen with the objective to provide interactive visual tools to stakeholders helping them gain knowledge exploring possible opportunities for sustainable development in the regions along the long national borders between Sweden and Norway. The geographic area is mostly sparsely populated mountain and forest regions but in the south there are the most densely populated regions in both Sweden and in Norway which represent an important area with a tradition of cooperation and cross-border movement in both directions. The research objective aimed at evaluate, for the period 2006-2010, social systems and environments with a potential to identify growth in economic, social and environmental development but also to get a better understanding of how commuting across the national border could have a negative influence on local taxes for local regions. Norwegians pursue recreation and leisure activities in the attractive coastal and inland areas of the nearby Swedish border regions. On the other hand, the Swedes living in the border regions mostly commute to Norway to work. Our web-enabled tools are introduced to support visualization and animation aimed at measuring economic, social and environmental developments and to engage policy makers, statisticians and also the citizens.
The flow dataset in the figure below contains data for people commuting across the border between Norway and Sweden (2010) at county level. Using a dynamic filter to find dominant flows - we see that the top five flows (blue) represents commuting from Västra Götaland,Värmland, Skåne, Stockholm, Östergötland to Oslo of Norway. The two first dominating Swedish counties are along the border to Norway. The number of people commuting from Oslo to Swedish counties are almost not measurable.
Figure: Commuting flows from Oslo to Swedish counties and vice versa during the period 2006-2010. The fish-eye bar chart shows (1) the volume of migration flows to Oslo (blue bars) from Sweden, (2) the volume of migration flows from Oslo to Sweden (green bars), and (3) the difference (or net value) between out-going flows and in-coming flows (red bars) which shows that there is a very positive trend of commuting to Oslo from Swedish counties. Blue arrows (right part) show top 5 commuting flows from Swedish counties to Oslo. Green arrows show inverse flows from Oslo to Swedish counties. Polygon layer (or region layer) is colored according to commuting volumes from Swedish counties to Oslo. Below is the corresponding Interaction Data Table with the number of commuting persons between the origin and destination.
There is also the need to find the reason behind a found pattern; for example, "Why people in Sweden tend to go to work in Norway" A reason may be the GDP per capita, unemployment rate or some other regional attribute. It implies that the answer might appear if the pattern of flow data matches with the pattern of a regional data choropleth map (municipality or county). Therefore, the demonstrator supports analysing and visualising regional choropleth maps together with flow data to better understand the reason behind the movement. Regional data is visualized by colour of the polygon layer using a given colour map.
A high level of user interaction controls answering various questions about flow data such as:
- Which are the dominant flows (or the trend of movement) in a certain year?
- Which are the top municipalities in Norway to which people living in Swedish border municipalities tend to commute or migrate?
- What is the net migration i.e. the difference between out-going and in-coming flows?
- How do flows vary over time?
Below you see the the Norwegian - Swedish municipality Flow Map eXplorer Demonstrator based on the origin placed in Oslo and showing the top five flow migration to (green) and from (blue) Oslo and Swedish municipalities.
The regional eXplorer flowmap demonstrator was evaluated by our case study partners (Statistics Sweden, Region Västra Götaland, and the Norwegian county Ostfold Fylke) for communicating the essentials of official migration and commuting statistics to a broad range of users via the Västra Götalandsregionen Web site specially established for Swedish-Norwegian statistical data related to migration and commuting data, for example, this interactive regional population forcast visualization.
The eXplorer flowmap demonstrator "Origin in Oslo" and "Destinations as Swedish Municipalities" was used by statistical experts to analyze in depth the geographic structures and correlations resulting in good stories which have been presented on the web site. This has proven to be an excellent way of catching the attention of many users, including the media. Statisticians from both Swedish and Norwegian authorities summarized in their evaluation reports that gained insight and knowledge based on this interactive flow map case study identifies more efficiently commuting statistics across the border and the potential political and economic consequences compared to experience from previous used more static flow map applications.
The gained knowledge from these interactive flow time animations clearly indicates an increasing trend that significant more Swedes prefer to communicate and work in Norway then the reverse scenario. An important observation that was of particular interest related to the negative tax affect for Swedish municipalities along the border. The combination of multiple linked “heat maps” with overlaid weighted arrows and a dynamic bar chart results provide comprehensive insight to many users commuting from a large number of Swedish counties was another positive comment. Consensus was that the introduced methods and demonstrator could help advance usage of interactive flow map visualization for a better understanding of both commuting and migration between sub-national regions and across national borders.
Figure: Flowmap with corresponding interaction table data
The OECD Flow Map Trade Demonstrator
We introduce here an interactive flow map 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 was based on a collaboration between OECD and NCVA/NComVA.
The OECD Trade demonstrator 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.
Demonstrator is avalable at: http://mitweb.itn.liu.se/GAV/flowmaptrade/
Figure: OECD Flowmap with Trade data. Origin is here Germany 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. Animation available at: http://ncva.itn.liu.se/resources/animations/1.555840/WorldTrading1988-2008Demonstrator.mp4
Figure: Flow map data layers based on flow data showing trade import into Germany.The time bar glyph layer shows trade for 2006-2010.
An example of flow time animation 2004-2010 based on total export/import trading between Germany and France. We see a major increase in export from Germany.
The flow map demonstrator was used by our case study partners (Statistics Sweden, Region Västra Götaland, Norwegian county Ostfold Fylke) for communicating the essentials of official migration and commuting statistics to a broad range of users via their web site. The tool has been used by statistics experts to analyze in depth the geographic structures and correlations resulting in good stories which have been presented on this web site. This has proven to be an excellent way of catching the attention of many users, including the media.
Statisticians from both Swedish and Norwegian authorities summarized in their evaluation reports that gained insight and knowledge based on this interactive flow map case study identifies more efficiently commuting statistics across the border and the potential political and economic consequences compared to experience from previous used more static flow map applications. The gained knowledge from the interactive flow animations during 2002-2010 and attached time glyphs clearly indicates an increasing trend that significant more Swedes prefer to communicate and work in Norway then the reverse scenario. An important conclusion that was of particular importance related to negative tax affect for Swedish municipalities along the border. The combination of multiple linked “heat maps” with overlaid weighted arrows and a dynamic bar chart results in a comprehensive insight into number of people commuting from a large number of Swedish counties was another positive comment. Consensus was that the introduced methods and demonstrator could help advance usage of interactive flow map visualization for a better understanding of both commuting and migration between sub-national regions and across national borders.
Last updated: 2016-06-29