From Statistical Data Dissemination to Data sharing on the web

1. “ Data Visualization on Istat's Web site ” Giulia Mottura Vincenzo Patruno ISTAT Istat - Aula Magna –26 Aprile 2010

2. Data Visualization Communicative value of Data Visualization Latest web technologies and the expectations they create amongst the user community are changing the way Statistical organizations communicate statistics. The trends in online media have big impact on statistical communication strategy. The Web is changing : even more, the message has to be presented in an interesting way to capture the interest of a largest audiences. To make the best use of the Internet, we have to take care of the appropriate language and presentation. In this way, systems for dynamic graphics make statistics alive and easier to understand even for non-experts: data visualization is a key to extracting instant meaning from large amounts of information.

3. Data Visualization To reach large audiences, the information should be presented in a structured and well organized way, providing: • metadata to help users understand the statistics; • data in a range of formats, to communicate via new devices such as mobile phones or others; Ensuring accessibility to information for everybody, in compliance with legal requirements means extra work and extra resources. However, equal access to basic statistical information is very important principle. With growth of Web 2.0 technologies, users can interact with data and create their own visualizations . Statistical institutions provide access to their dwh, allowing users to query statistical information by themselves. This functionality is even more complemented by visualization tools that allow users to create charts or maps online. Giving this level of control to users imply some risks; but problems can be minimized by providing support and monitoring misuses .

4. Data Visualization Graphical representations, useful to better communicate statistics, may have a dual function: describing the results and allows a synthetic representation of the observed phenomena and trends. Visual approach to statistics involves a graphics component (encode information in digital images) and a perceptual component that decodes the information included in the graph. A good charts is based on right statistical contents but also on issues related to perceptual and cognitive psychology and its communicative value. The communicative effectiveness of a graphic depends on the clarity of the message conveyed. Data may contain several messages that we want to highlight using a chart. And the good effectiveness of a data visualization is the result of a collaborative effort of three expertise: communication and statistical know-how + software solution.

5. Data Visualization To provide to a complex data set its key-aspects in a more intuitive way, graphic form and functionality had to be considered together. Evolution of statistical graphics depends on the interaction of specific factors: human perception of the presentation of quantitative information and software for graphics applications. To assure the understanding of statistical information and its widest dissemination, is foundamental the development of sophisticated websites that offer data extraction with visualisation tools and on the other hand to ensure the reusability of our information. . This is possible thanks to the use of common data models. An example is SDMX, that help users to combine data by multiple sources (mashup) and to access them with visualisation tools like the ECB Inflation Dashboard. .

6. Data Visualization We are developing the next Istat's web site as an aggregator able to take contents from other systems and adapt them, creating added value information.

7. Data Visualization The aggregator provide a “public” access to communicate a standard set of information and metainformation: Economic data, population in italian municipalities, migration charts, etc A site for Everybody Search, link to DW, News, Press Releases, Events, Tools, selected Widgets with syntetic data, special Areas (Educational, Gender, Regions, ...)

8. Data Visualization … and a “ custom-made ” access for authenticated users: a “content aggregator” where user can build his own site selecting data from a list of widgets A site for Everyone Economic data, population in italian municipalities, migration charts, etc

10. Data Visualization A widget is a portable chunk of code that can be installed and executed within any separate HTML-based web page by an end user without requiring additional compilation What's a Widget and Why Does we Need it?

11. Data Visualization Wi d gets

12. Data Visualization Wi d gets Data Producer Data Consumer

13. Data Visualization A widget is an application that can be embedded into third party sites by any user on a page where they have rights of authorship (eg. a webpage, blog, or profile on a social media site) Wi d gets

14. Data Visualization Wi d gets

15. Data Visualization Wi d gets and API <script type=&quot;text/javascript&quot; src=&quot;http://www.vincenzopatruno.org/dir/bilancio.js&quot;> </script> Embed Code

16. Data Visualization Wi d gets and API <script type=&quot;text/javascript&quot; src=&quot;http://www.vincenzopatruno.org/dir/net_migration.js&quot;> </script> Embed Code

17. Data Visualization Wi d gets and API <script type=&quot;text/javascript&quot; src=&quot;http://www.vincenzopatruno.org/dir/dailylife.js&quot;> </script> Embed Code

18. Data Visualization Wi d gets Working with widgets is similar to work with LEGO stones

19. Data Visualization Wi d gets

20. Three Tier A rchitecture Data Visualization

21. Regional Statistics Web Services Data Visualization A P I

22. mo b ile.istat.it Data Visualization

23. mo b ile Future is Mobile Data Visualization

24. Web Services Data Visualization The B logosphere

25. The N ew Web User The user is not anymore a “reader” of the Web. The New Web User is now a protagonist of the Web The New Web User plays an active role to disseminate, to share, to discuss, to promote and to improve information. Data Visualization

26. Data S haring Data Visualization