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ESS Visualisation Workshop 2016 – Summary and conclusions

0

ESS Visualisation Workshop

Valencia 2016

Summary and conclusions

The information and views set out in report are those of the author(s) and do not necessarily reflect the

official opinion of the European Union. Neither the European Union institutions and bodies nor any person

acting on their behalf may be held responsible for the use which may be made of the information contained

therein.

ESS Visualisation Workshop 2016 – Summary and conclusions 1

Editor: José L. CERVERA-FERRI

Authors:

Edwin de Jonge (DevStat)

José L. CERVERA-FERRI (DevStat)

Valdone KASPERIUNIENE (DevStat)

Victor DINCULESCU (DevStat)

Paola VOTTA (DevStat)

The logo of the event is a stylised histogram. This is probably one of the simplest and most ubiquitous

visualisation of category or interval-based data. The orange colour symbolises the famous fruits of

Valencia, host city of the event. Texts are in blue, the complementary colour of orange, reminding of the

importance of good perceptual principles for the design of effective visualisations. The logo has been

designed by Leonid Cotiubinschii.


2

Table of Contents

List of figures ................................................................................................................................. 4

List of Boxes ................................................................................................................................... 4

1. Introduction............................................................................................................................... 5

2. Key conclusions and recommendations of the Workshop ........................................................ 7

2.1 Summary of conclusions ..................................................................................................... 7

2.2 Recommendations ............................................................................................................ 12

Annex 1- Description of Workshop sessions ............................................................................... 14

1.1 Opening session .................................................................................................................... 14

1.2 General Sessions: Expert lectures ......................................................................................... 16

1.2.1 Organisation of the general sessions ............................................................................. 16

1.2.2 Expert lecture: Uncertainty visualisation ....................................................................... 16

1.2.3 Expert lecture: Enhancing dissemination trough gamification ...................................... 19

1.2.4 Expert lecture: Uses and abuses of data visualization in mass media ........................... 22

1.2.5 Expert panel ................................................................................................................... 23

1.3 Parallel sessions 1A & 1B – Visualisation for data analysis ................................................... 25

1.3.1 Session objective ............................................................................................................ 25

1.3.2 Summary of presentations ............................................................................................. 25

1.4 Parallel sessions 2A & 2B – Visualisation for data dissemination ......................................... 32



1.5 Parallel session 3 – Strategy for visualisation in statistical institutes ................................... 38



1.6 Parallel session 4 – Visualisation methods and IT ................................................................. 44



1.7 Parallel session 5 – Visualisation for data literacy ................................................................ 46



1.8 Parallel Session 6 – Geospatial visualisation ......................................................................... 49




Annex 2 – Workshop programme ........................................................................................... 51

Annex 3 – Acronyms ................................................................................................................ 53


List of figures

Figure 1: Visualisations on Valencia City Hall’s Open Data Portal .............................................. 14

Figure 2: Display of uncertainty around weather forecast in the Dutch TV ............................... 17

Figure 3: Visualisation of Anscombe’s quartet............................................................................ 17

Figure 4: Display of line charts with uncertainty (based on confidence intervals) ..................... 18

Figure 5: Display of bar charts with uncertainty (based on confidence intervals) ..................... 19

Figure 6: The base of gamification: dual thinking ....................................................................... 20

Figure 7: Two displays of the same statistical information, with and without added percentages

..................................................................................................................................................... 21

Figure 8: Heat-map of the eye-tracking for two displays of the same information ................... 21

Figure 9: Examples of graphs with low, medium and high data-ink ratio................................... 23

Figure 10: John W. Tuckey and his dedicated hardware for statistical analysis and visualisation

..................................................................................................................................................... 26

Figure 11: Network visualisation of the Input-Output table of the economy of the region of

Aragon (Spain) ............................................................................................................................. 27

Figure 12: The role of data in answering questions .................................................................... 28

Figure 13: Steps in preparing a visualisation ............................................................................... 28

Figure 14: Website of CBS /Statistics Netherlands ..................................................................... 30

Figure 15: Statistical table complemented with sparkTable ....................................................... 31

Figure 16: Infographics about Luxembourg (selection) .............................................................. 34

Figure 17: Visual display of the ONS’ “Better Statistics, Better Decisions” strategy .................. 35

Figure 18: Data Design skills ........................................................................................................ 35

Figure 19: Visual display of municipal indicators by Statistics Denmark .................................... 36

Figure 20: Screenshots of Eurostat Visualisations ...................................................................... 39

Figure 21: New Eurostat visualisations ....................................................................................... 41

Figure 22: A typology of visualisations ........................................................................................ 44

Figure 23: Screenshot of ISTAT’s StatView .................................................................................. 45

Figure 24: Segmentation of users according to Statistics Finland .............................................. 46

Figure 25: Screenshot of an animation video by Statistics Finland............................................. 47

Figure 26: Screenshot of the interactive “Sweden in Figures” ................................................... 48

Figure 27: Interactive tool for geospatial analysis using CartoDB tools ..................................... 49

List of Boxes Box 1: Creativity and data visualisation ........................................................................................ 6

Box 2: Purposes of visualisation .................................................................................................. 10

Box 3: Identifying user needs and cooperation with stakeholders in the ESS Vision 2020 ........ 32

Box 4: European Code of Practice, principle 15 .......................................................................... 37

Box 5: Eurostat visualisations shared by Member States ........................................................... 39


5

1 Introduction

1. Introduction

One of the five key implementation areas of the ESS Vision 2020 is devoted to the

improvement of dissemination and communication of official statistics, including data

visualisation. Data visualisation aims to aid users in exploring, understanding, and analysing

data through iterative visual exploration. With the development of user-friendly and powerful

IT tools for data visualisation and the boom in big data analytics, data visualisation is spreading

in a variety of applications, including scientific applications, etc. Although official statistics is

not an exception to this trend, data visualisation has not already developed its full potentiality

in this domain. Despite the recent advances of visualisation in official statistics, mainly in the

application of map visualisation, there are relevant challenges that National Statistical

Institutes (NSIs) need to face for a proper application to data visualisation in the analysis and

dissemination of official statistics.

Workshop objective

The approach to the ESS Visualisation Workshop held in Valencia (17-18 May 2016) was

thematically focused. Beyond the exchange of experiences and exploration on good practices

for implementation, it also had the goal to increase the awareness of visualisation within the

ESS and the motivation for the NSIs to improve their visualisation systems.

The workshop aimed at increasing awareness of the issue of visualising statistics; identifying

emerging best practices to identify synergies and options for joint development efforts;

sharing best practices, new ideas, tools, and experiences. These objectives should contribute

to the broader objective of facilitating cooperation within the ESS in the development and

implementation of common methodological solutions, aligned with the implementation

portfolio of ESS Vision 20201.

To achieve all these objectives, the workshop included:

Presentations from experts in different issues related to data visualisation, with application to statistical office. These lectures covered the areas of communication, data journalism, infographics and gamification. The applications not only referred to experiences in the ESS, but also from other statistical systems and from the private sector (including an innovative, information-based artistic display, see Box 1);

The workshop´s backbone was a set of parallel sessions lead by facilitators and devoted to the identification and exchange of relevant experiences in the field of visualisation, within and outside the ESS;

A presentation of the ESS.VIP project DIGICOM and the main conclusions of the Task Force Data Visualisation and Infographics.

1 ESS Vision 2020: http://ec.europa.eu/eurostat/documents/10186/756730/ESS-Vision-

2020.pdf/8d97506b-b802-439e-9ea4-303e905f4255


1 Introduction

Box 1: Creativity and data visualisation

To remind participants of the importance of creativity in the design of visualisations, a data-

managed artistic display was exhibited in the conference premises. The Valencian multi-media

artist and Digital Communication Professor Moisés Mañas proposed a moving display of mock

businessmen performing automatic movements governed by stock exchange data received on

an Arduino-based device.

Report structure

In order to enhance its reading and use, this report first presents the key conclusions from the

Workshop (Section 2). The Workshop sessions are then described in Annex 1, summarising the

presentations and discussions that took place.

Annex 2 includes the Workshop programme. Annex 3 includes the list of acronyms used.

All presentations are available on the CROS portal2.

2 https://ec.europa.eu/eurostat/cros/content/2016-visualisation-workshop_en

https://ec.europa.eu/eurostat/cros/content/2016-visualisation-workshop_en


7

2 Key Conclusions and Recommendations from the Workshop

2. Key conclusions and Recommendations from the

Workshop

This section recalls the main conclusions and recommendations issued from the parallel and

plenary sessions, which are presented in detail in Annex 1.

2.1 Summary of conclusions

Why: is the role of NSIs to produce visualisations of official statistics?

Various opinions were expressed on the question whether NSIs should produce visualisation

tools and displays. Should this task be left to other actors in the information market, such as

data journalists?

Workshop participants concluded that visualisations should be developed both by statisticians

and data journalists. They provide different aspects of information and can complement each

other.

Journalists use various sources of data, and all of them do not have the same degree of

reliability. They can compile information based on mixed data sources and have more freedom

on how they present the data.

NSIs, however, disseminate information within the framework of official statistics and comply

with strict quality requirements. Statisticians are more familiar with the methodology and the

limitations related to data compilation. They are therefore in a better position than the media

to provide the public with quality data and disseminate them more objectively. Information

visualisation may indeed increase the accessibility to and clarity of official statistics, and thus,

it is rightly considered within the current framework of the ESS Vision 2020 as an activity that

adds value to European statistics. In addition, visual dissemination can attract the attention of

citizens and thus increase their awareness about the available statistical information.

The selection of what has to be visually displayed by the ESS (and how) requires careful choice,

though. While the availability of data and tools allow for a rich body of visualisations of all

types, data quality (relevance, precision, comparability, coherence) stays a major criterion in

deciding what to visualise. In this sense, NSIs differ from other information distributors.

The ESS has placed the satisfaction of user information needs at the centre of its strategy. In

relation to visualisation, this requires, to know:

• Who the users of visual information are and how they behave in relation to accessing,

understanding and using it?

• Which are the most suitable visualisation tools for each user segment including those

with special interests (such as the young or the large newspapers, with their own data

visualisation teams)?



• How to set up teams and select technologies that are most adequate for the

production of visualisations?

These tasks need to be undertaken in a well-planned manner, especially given the high

pressure on NSIs to produce more and better information, as well as the budget and human

resources constraints.

For whom: segments of visualisation users

Experts vs non-experts

Visualising data for dissemination is generally targeted towards non-expert users, for a fast

and clear presentation of information. The research shows that expert users do not find

significant value added in the visualisation, except that used in the process of analysing

information (and therefore, one using dynamic, customisable tools for manipulation).

Addressing the special needs of re-distributors of statistical information

Very often, the output of official statistics is directly reproduced and re-distributed by specific

users. This mainly includes the media, but also business associations, interest groups and NGOs

(providing information to their members). For the specific segment of “future users” – current

students – schools and other training centres play an important role in redistributing statistics

(see below on educating users).

Media are one of the important users of statistical information. Major newspapers, which have

established their own data dissemination / data visualisation units, cannot be considered as a

target group for data visualisation: they are more interested in receiving prepared files with re-

usable, quality data in flexible formats, and in producing information independently. However,

smaller newspapers may re-publish statistical visualisations, which helps to spread the

information.

Educating users

It was generally recognised that users need more knowledge of statistics and of how to use the

data. Various user-categories need specific data and a different approach.

NSIs have always made efforts in explaining statistical data, publishing metadata, organising

courses for the media and for policy-makers. But a more consistent approach should be

considered.

Young people are the future consumers of statistics. Educating them to access, understand and

use statistics is crucial for the future of the “official statistics industry”. Developing

programmes to increase statistical literacy, in this and other segments, also requires the

building of new skills in NSIs, and strong cooperation with the educational institutions and the

academia.



Promoting visualisations

Well-designed communication campaigns promoting new visualisations are very important in

order to information reaching a large audience. Unfortunately statistical visualisations are not

always published by all possible dissemination channels or media are not always interested in

re-disseminating the visualisations. In this context, the role of social media should not be

neglected. It could be used as additional dissemination platform.

Measuring the impact of visualisation on users

It was agreed that the development of visualisation tools has an undeniable influence on

users: it increases statistical literacy, attracts new users and improves general public

understanding.

However, measuring the impact visualisations have on users is a complex task. Standard user

metrics can shed light on access to visualisations, and therefore, serve as a proxy in measuring

the impact visualisation has on access to official statistics. The success of visualisation can be

measured by popularity of the visual tool provided: by numbers of page views, page view

times, social media impressions, comments from users, etc. These metrics however do not

explain whether users interpret the information better.

Measuring user behaviour during data visualisation, as well as user understanding, has been

done in experimental environments. Laboratory tests have collected and analysed data on user

behaviour, concluding that there are differences between segments, and between the

perception and understanding of information. NSIs can put systems in place, to test the impact

their visualisations have, with techniques that draw from neuroscience, experimental

economics, psychology and game theory. European-scale experiments could prove cost-

efficient to avoid repetition at a national level.

What: the selection of topics and visual displays to be disseminated

With regard to the type of visualisation, it will not only depend on the nature of the data, but

also on the purpose: exploratory, exhibitory or explanatory (see Box 2).



Box 2: Purposes of visualisation

Data visualisation responds to different informational needs. According to Kirk

(www.visualisingdata.com), visualisation can have exploratory, exhibitory or explanatory

purposes:

- Exploratory visualisations aim at discovering features by interrogating the data, generally

through interactive user-driven experience. Exploratory tools allow the user to find, by

seeing representations of the data, relevant patterns, trends, outlier cases and other

statistical features that will lead to further analysis. A clear example is multiple scatterplots

allowing the analyst to infer relationships between pairs of variables;

- Explanatory visualisations aim at conveying to the reader specific information, based on a

pre-defined narrative. They usually highlight important facts requiring attention. An

example would be a dashboard with company results;

- Exhibitory visualisations are also based on data, but contain an “artistic” element. The

motivation is rather to create an artifact for visualisation, not necessarily with the purpose

of transmitting information in an easy way.

Source: Kirk (2012). Data Visualization: A Successful Design Process. Packt Publishing Ltd

Visualisation catalogues can help NSIs to choose the right visualisation for each topic. It is not

possible to establish a full taxonomy of visualisations of statistical data since increasing

interest by graphic designers, data journalists, programmes and of course statisticians daily

add to the possibilities of the traditional sets offered by usual packages (histograms,

scatterplots, trend lines, pie diagrams, map-based figures, etc.). However, selecting a

visualisation should take into account important features such as interactivity with the

reader/analyst, static or dynamic. Selecting the right topic is of primary importance, in order to

attract readers. Visualisation reaches more users when it accompanies statistical data that

complement emerging open discussions in the media. Analyses of user data requests may be

considered when choosing topics for visualisation.

Which tools: the selection, development, adoption and sharing of visualisation technologies

No clear procedures have been developed yet, as to how decisions are taken towards which

visualisation tools to develop or adapt. Sometimes, preparing a visualisation is experimental

and decisions are based on intuition. Tools are explored, which could better serve user needs.

There are no optimal tools either. ESS partners employ various tools to develop visualisations.

There is no one software which could satisfy all needs for data visualisation, and a range of IT

tools are used in NSIs, including those developed in-house, commercial and free software

packages.

NSIs and other organisations (the ECB, the OECD) have already developed and implemented

some visualisation tools for specific services, data or users. Such activities were isolated, in

order to target specific user categories, or just for testing purposes. One common

http://www.visualisingdata.com/



disadvantage of software produced in-house is that it often lacks detailed documentation and

process descriptions. Staff turnover may therefore negatively impact the tool´s sustainability.

On the other hand, if the commercial product adopted is not widely used, the developer can

stop maintaining the software, which will have similar negative effects on sustainability. The

existence of a community of users and developers was identified as being a major criterion in

selecting tools.

In the particular case of geo-spatial information, GIS technologies are mature and they can be

adapted to existing files in NSIs. Developing them in NSIs does not look an interesting option.

It was recommended, in any case, to keep visualisation tools simple and to pay attention to

the quality of the data being displayed.

ESS institutions strongly support the idea of exchanging visualisation tools, as it has been

demonstrated, in the case of Eurostat visualisations that were adapted by NSIs. The GITHUB

code repository, which is largely used by programmers, is already being used as a repository

for the software. Member State NSIs that have not yet developed the necessary tools can

easily re-use tools developed by other NSIs. Currently, the supply of various tools is bigger

their re-use.

The Visualisation Task Force´s activities will continue within the DIGICOM project and it is

expected that more tools will be shared at ESS level. The Task Force´s inventory of visualization

tools should also include national visualisation tools, as well as an analysis of possible ways to

share them.

With whom: how to acquire visualisation skills

Building skills

Producing visualisations is costly, time-consuming and specialised. In order to build a

professional team for data visualisations, a diverse set of expertise fields are needed: graphic

designers and artists, programmers, statisticians and methodologists, journalists, and even

psychologists and neuroscientists, etc.

There is a need to build expertise in NSIs, while resources are limited. Some NSIs acquire the

skills externally, by outsourcing the production of visualisation to specialised companies. Other

NSIs are building up skills internally, through training and learning-by-doing with existing

solutions. There is no optimal solution, since both options have positive and negative aspects.

Collaborating with external stakeholders

Specialised communications consultants, graphic designers and data journalists can add value

to NSIs´ work in producing high-quality visualisations. The interaction is especially positive with

data journalists, who may acquire better awareness about the statistical quality of data, and

therefore improve their role as disseminators.

Cooperation with teachers and academia should be two-sided: official statistics should be



made part of educational curricula, with statisticians acting intensively as invited lecturers in

schools and universities, while teachers help NSIs to address the educational needs for

increasing the statistical literacy of the young.

2.2 Recommendations

Recommendations are structured around the four main objectives of the DIGICOM project:

• User analysis:

o Consider that non-expert users are those who benefit most from visualisation

(compared with expert users), and accompany visualisation with actions to

increase their statistical literacy (more awareness of available information and

better understanding of the techniques, limits and uses of the statistical

information);

o Develop specific visualisations for the young, who are the future users of

statistical information. Gamification can play a role in improving the

attractiveness of statistics;

o Measure the impact visualisation has on users;

o Foster the use of experimental/behavioural techniques for a better analysis of

user behaviour, given visualisation, and a better understanding of the results.

• Innovative and shareable products and tools:

o Identify external skills in graphic design, perceptual/cognitive analysis and

other skills (graphical storytelling, art?) not necessarily available in NSIs, to be

able to count on their expertise to increase internal capacity;

o Develop internal technical skills (mainly in IT) sufficient for testing already-

existing visualisation solutions (available on the market or shared across the

ESS). Only if there are specific, non-satisfied needs, should NSIs invest in

developing in-house technological solutions;

o Encourage the creativity – and identify the skills – of ESS staff by running

visualisation competitions;

o Develop shareable knowledge such as visualisation catalogues, tools, good

practices, providing the necessary hands-on tools for quick re-use;

o When developing tools, NSIs should consider as an objective that of being

shareable across the ESS (by using EU statistical standards, multiple languages,

etc.).



• Open Data Dissemination:

o Consider that the media are essential partners for the onward distribution of

statistical information. For those that have the necessary capacities to produce

visualisations, NSIs can facilitate “ready-to-visualise” datasets.

• Communication and promotion:

o Select “nice to know” topics for visual display;

o Prepare visualisation tools that can be easily shared (such as embeddable

graphics, printed posters, free videos, etc.);

o Include visualisation in literacy tools;

o Promote within the ESS the awareness about the impact visualisation has.


14

Annex 1 Description of Workshop sessions

Annex 1- Description of Workshop sessions

This section describes the contents first of the opening, then the parallel and the general

session. Sessions were organised around specific topics selected by Eurostat as follows

(detailed descriptions are given in the corresponding sections):

- Opening session

- The general sessions included a presentation on uncertainty visualisation, enhancing

dissemination trough gamification and uses and abuses of data visualisation in mass

media. Finally, a panel of experts was convened during a general session to conclude.

- Parallel session 1A & 1B: Visualisation for data analysis

- Parallel session 2A & 2B : Visualisation for data dissemination

- Parallel session 3A: Strategy for visualisation in statistical institutes

- Parallel session 4: Visualisation methods and IT tools

- Parallel session 5: Visualisation for user data literacy

- Parallel session 6: Geospatial visualisation

1.1 Opening session

Participants were welcomed by representatives of the Valencia City Hall. Mr José I. Pastor,

Head of the Valencia Open Government service and Mr Rafael Monterde, Director of the

Innovation Foundation of the City Hall (INNDEA), who presented initiatives linking to better

use of data, including the Transparency and Open Data Portal3 and Smart City projects such as

VLCi4 (see Figure 1).

Figure 1: Visualisations on Valencia City Hall’s Open Data Portal

3 http://gobiernoabierto.valencia.es/es/

4 http://inndeavalencia.com/iciudad/promocion-estrategica/smart-city-valencia-vlci



Ms Martina Hahn, Head of Unit Methodology and Corporate Architecture in Eurostat,

welcomed the participants on behalf of Eurostat, and recalled that the event is organised in

the context of the DIGICOM project and to contribute to the goals defined in the ESS Vision

2020. The event will allow participants to discuss how well the ESS is equipped with the

necessary skills and technology for visualisation of official statistics, how much this really helps

users and how efficient is the investment by NSIs and Eurostat in this “new” activity. Ms Hahn

stressed that the presence in the event of a mix of statisticians, IT experts, data journalists, as

well as the presence of several international institutions such as OECD, ECB, the JRC, DG Trade,

the Court of Auditors was a good sign of interest in this topic. The objectives of the event are

therefore twofold:

• To inspire, increase awareness, and get experience from NSIs that have already

developed visualisations;

• To share, find synergies and see what can be implemented in each NSI.

Ms Christine Kormann (Eurostat) presented the ESS.VIP DIGICOM project5 as one of the

flagships of the ESS Modernisation strategy. The project's objective is to modernise the

communication and dissemination of European statistics, by making the most of available

technologies and systematic dialogue with users. The project - and in particular this workshop

- will contribute to capacity building and will review shareable good practices in the ESS. It

involves Eurostat and 18 National Statistical Institutes.

The main components of DIGICOM are, in brief, the following four:

• User analysis: conduct a user profiling exercise and set up a social network platform

for users of European statistics;

• Innovative and shareable products and tools: develop customised products and

services for users, including visualisation;

• Open Data Dissemination: facilitate the access to micro-data and other statistical data,

including with the use of automated tools for re-using them (APIs);

• Communication and promotion: adopt a new dissemination and communication

strategy, increasing awareness on sources, users’ literacy and trust in European

Statistics.

Thus, visualisation of statistics is a key component of the DIGICOM project.

5 https://ec.europa.eu/eurostat/cros/system/files/DIGICOM-BC-v1.0.0.pdf_en

http://ec.europa.eu/eurostat/cros/content/digital-communication_en



1.2 General Sessions: Expert lectures

1.2.1 Organisation of the general sessions

The general sessions were organised as “expert lectures” delivered by three selected experts

from different fields (NSI, academia and consultancy), showing diverse viewpoints but

converging in the idea that correct visualisation enhances the communication of statistics. In

addition, one final expert panel was organised. The sessions were followed by discussions with

the participants. The three lectures discussed:

- The visualisation of uncertainty (precision) of statistical estimates;

- The use of gamification techniques to improve the effectiveness of data collection and

dissemination;

- The uses and abuses of data visualisation in mass media.

The panel included four experts who summarised their personal impressions6 about the

presentation and discussions throughout the event.

1.2.2 Expert lecture: Uncertainty visualisation

Speaker: Mr Edwin de Jonge (DevStat)

Uncertainty measures are an important means to communicate the accuracy of statistics or to

indicate the stochastic volatility of the collected data. However, most official statistics do not

explicitly publish or show uncertainty, despite the European Code of Practice recommendation

12.2: “Sampling and non-sampling errors should be systematically documented”. The

presentation by Mr Jonge focused on visualisation techniques used in the CBS (The

Netherlands) of uncertainty measures, as a tool to enhance the understanding of the accuracy

of statistical estimates.

The speaker mentioned that one implicit assumption for not publishing/visualising accuracy

measures is that users do not understand or need uncertainty margins. However, uncertainty

around estimates is displayed even on the Dutch TV, as shown in Figure 2.

6 And therefore are not discussed in the report.



Figure 2: Display of uncertainty around weather forecast in the Dutch TV

Source: capture made by the speaker.

A first example of how visualisation is useful for the analysis of data was given, based on the

Anscombe’s quartet7, a set of 4 two-dimensional datasets that present the same values for

several aggregates (mean, variance, correlation between variables and linear regression

estimates). The visualisation of such sets, as in Figure 3, show the importance of seeing the

data before trying to analyse, interpret or adjust any model.

Figure 3: Visualisation of Anscombe’s quartet

7 See for instance https://en.wikipedia.org/wiki/Anscombe%27s_quartet for the values of the dataset.

https://en.wikipedia.org/wiki/Anscombe%27s_quartet



The presentation showed different visualisation research results8 that indicate that laymen do

grasp uncertainty, and that it helps to “better” understand the data. The findings showed that:

- Non-experts can read probability intervals, and users with high numeracy are better at

this task

- There is no significant difference in response time when interpreting uncertainty

For example, with line charts, showing an uncertainty band improves the validity of statements

user make on the data. For bar charts users are more careful in comparison tasks. Figures 4

and 5 show different visual displays of uncertainty.

Figure 4: Display of line charts with uncertainty (based on confidence intervals)

8 See for instance the following papers:

Tak, Toet, van Erp (2014). “The perception of visual uncertainty representation by non-experts”. Transactions on Visualisation and Computer Graphics. https://www.computer.org/csdl/trans/tg/preprint/06654171.pdf

Van der Laan, de Jonge, Solcer (2015). “Effect of displaying uncertainty in line and bar charts”. In Proceedings of the 6th International Conference on Information Visualization Theory and Applications, 225-232. http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=KF6zpM65ZV4=&t=1

https://www.computer.org/csdl/trans/tg/preprint/06654171.pdf

http://www.scitepress.org/DigitalLibrary/ProceedingsDetails.aspx?ID=NZuq4MRpdW4=&t=1

http://www.scitepress.org/DigitalLibrary/ProceedingsDetails.aspx?ID=NZuq4MRpdW4=&t=1

http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=KF6zpM65ZV4=&t=1



Figure 5: Display of bar charts with uncertainty (based on confidence intervals)

Another technique that was demonstrated to improve the communication of results, is that of

small multiples: graphs comparing trends for different segments or time periods, which are

displayed close together, to allow for the better detection of patterns.

As a summary, the expert stressed that showing uncertainty in visualisation improves the

(statistical) quality of the communication.

1.2.3 Expert lecture: Enhancing dissemination trough gamification

Speaker: Dr José Vila (DevStat)

The main goal of this presentation by Dr Vila was to increase the awareness of the role that

gamification can play to enhance the statistical production and dissemination in the ESS. To

this end, the lecture introduced the key concepts of behavioural economics required to

understand how and why gamification can influence respondents’ and users’ cognitive

processes and evidence-based behaviour, and showed some specific applications of

gamification for NSIs.

Gamification is defined as the application of game mechanics and game techniques to engage,

motivate and facilitate people to achieve specific goals in non-game contexts. The

presentation introduced the behavioural levers supporting the effectiveness of gamification,

mainly dual thinking (“systems one and two”, see Figure 6) and the cognitive biases that can

be managed with gamification.



Figure 6: The base of gamification: dual thinking

Source: Dr J. Vila (DevStat)

The core of the presentation was the discussion of two key applications of gamification in the

ESS: gamification for respondents (enhancement of data collection) and gamification for users

(enhancement of data dissemination).

In data collection, gamification can be used to generate ‘nudges’ to users to reduce item and

case non-response rates. Specific examples, such as the design of a gamified framing for the

Household Budget Survey, were discussed. This could take the form of a gamified

questionnaire or the use of an avatar family.

As regards users, the presentation analysed how a behavioural-experimental approach of

gamification can provide relevant insights on how users process statistical information and can

be applied to reduce the cognitive biases and enhance visualisation for dissemination. Two

specific examples, taken from papers published by the lecturer and involving eye-tracking

analysis and economic experiments, were presented and discussed. Figures 7 and 8 show an

eye-tracking report based on users’ examination of a statistical visualisation, distinguishing

between an effective one (i.e. one that requires little time to focus on relevant information)

and an ineffective one.



Figure 7: Two displays of the same statistical information, with and without added

percentages

Figure 8: Heat-map of the eye-tracking for two displays of the same information

The effective visualisation requires less time (red areas) to focus on relevant information

As concluding remarks, the expert highlighted the following four ideas:

(1) gamification is a powerful tool to influence the behaviour and cognitive processes of

respondents and users of statistical information;

(2) gamification is much more than adding points or levels to the NSI website: it should

activate actual behavioural levers;

(3) gamification can play a relevant role in the ESS by improving data collection and

dissemination;

(4) to guarantee a successful application, gamification processes should be designed and

tested under a behavioural-experimental approach.

The discussion was very fruitful and the participants raised several potential applications of

gamification and the behavioural-experimental approach for data collection and

dissemination, such as introducing experimentation along user metrics to distinguish segments

of users based on their behaviour. The discussion showed the existence of a promising area of

improvement of the ESS methodologies based on a sound application of the behavioural

approach to manage the behaviour and cognitive processes of respondents and users.



1.2.4 Expert lecture: Uses and abuses of data visualization in mass

media

Speaker: Mr Pablo Rey (Open Evidence)

Mr Rey recalled that data visualizations are a powerful way to display and communicate data

that otherwise would be impossible to transmit in effective and concise ways. The spread of

broadband Internet, the easier access to reusable datasets, the rise in read/write digital media

literacies, and the lower barrier to generate data visualizations, are all making mass media to

intensively use of infographics. Newspaper and online news sites are taking advantage of new,

affordable and easy to access data visualization tools to broadcast their messages.

Storytelling is not at all new in journalism, but it is a useful resource to help communicate,

especially for NSIs, in more effective ways. Public institutions have to disseminate their data

and information in meaningful and understandable ways. It is not enough to publish datasets

in open, reusable and standardized formats (material transparency), NSIs must engage in the

explanation and clarification of the data (cognitive transparency). The use of contextualized

and interactive data visualisations together with explanatory text is becoming a powerful and

effective practice to display information in newspaper articles. It allows the reader to interact

with the data and follow an explanation/story at the same time. Data-driven journalism

multidisciplinary teams (developers, designers, journalists) in newsrooms are an interesting

example to learn from for institutions engaged in the dissemination on data. Some offices are

already going in that direction (CBS in The Netherlands).

The low barrier to quickly and easily create data visualizations, thanks to widely available and

affordable software, is leading to a broad production of good examples in mass media.

However sometimes it produces poorly designed or mistaken visualizations. Bad selection of

data visualization type or the use of default options provided by software are among its

reasons. Errors can also result from the lack of skills in data visualization, statistics or basic

arithmetic.

The speaker introduced and updated some key concepts popularized in the 80's by Edward

Tufte about data visualization to help review some data visualization examples:

- Data-Ink Ratio, defined as the proportion of a graphic’s ink/pixels devoted to the non-

redundant display of data information (see Figure 9);

- Chart Junk, purposeless use of ink;

- Data density of a graphic, number of entries in data matrix per area of data graphic.



Figure 9: Examples of graphs with low, medium and high data-ink ratio

Source: Kevin Mc Curgan “Data-ink ration and task complexity in graph comprehension”

During the presentation the expert reviewed good and bad practices while using tables, bar

charts, tree maps and geospatial information. Just to name a few typical problems:

- the use of circles to display one-dimensional quantities, due to the difficulty to

understanding and comparing areas;

- the incorrect use of choropleth maps that provide incorrect importance to certain

regions if their area is not taken in account

- the use of non-monochromatic scales to display a range of values.

The lecturer closed the talk with a review of the key concepts and recommendations:

• Carefully select the type of visualisation: do not let the tool take decisions on the most

suitable visualisation;

• Show the context (data);

• Combine multiple skills in a visualisation team: statisticians, designers, journalists,

developers and artists.

1.2.5 Expert panel

Speakers: Mr Philippe Bautier (Eurostat), Dr Jose Vila (DevStat), Mr Guillaume Mordant

(INSEE), Ms Laura Dewis (ONS UK)

Moderator: Mr José Cervera-Ferri

The panel members appreciated the workshop and expressed strong interest in participating in

similar events in the future, as the workshop was considered an important contribution to the

efforts to intensify the collaborative partnership of the ESS.

The panellists stressed that users should be at the centre of the dissemination policy and

practice, so any strategy for visualisation should take into account their needs. Since they are

of very different types, they may need different visualisations. Segmentation of users is critical,

and therefore identifying the behaviour online of users may be a solution to offer them the

most adequate one. Providing training on how to interpret visualisations may also be



considered. NSIs should also make efforts in measuring the impact of visualisations on the

capacity of users in interpreting and using the data.

For the ESS, there are enormous possibilities to enlarge the dissemination and increase the

value of statistics by adding visualisations to the current publication portfolio. However, the

skills for developing good visualisations are mixed (technical and also soft skills such as

communication and storytelling) and this is a challenge for NSIs. Since a lot of work has already

been done, establishing catalogues and sharing tools can lower the entry barriers for

producing good visualisations. This will give invaluable input into the DIGICOM project.

The strategies of NSIs for visualisation should consider user-orientation (segmentation of

users, the testing of solutions), acquisition of skills (internal development and external

collaboration, including with other ESS institutions), and reuse of tools developed by other

NSIs.



1.3 Parallel sessions 1A & 1B – Visualisation for data

analysis

Facilitator: Mr José Cervera (DevStat)

1.3.1 Objective of the session

Session 1 was devoted to the use of visualisation tools to enhance the process of production of

statistics, as means for better analysis of the data. This includes using visualisation to discover

relevant questions for the analysis, to assess the quality of data (e.g. detecting patterns in

missing values) and proposing adequate dissemination. The session included as well a

presentation of the evolution of visualisation tools, from dedicated hardware to democratic

web-based, open tools.

The speakers gave examples using in-house developed tools (based on R or other languages) as

well as commercial packages.

1.3.2 Summary of presentations

Prof. Pedro Valero-Mora, from University of Valencia, delivered a first presentation on the

evolution of dynamic-interactive graphics for statistics.

Dynamic-interactive graphics, or statistical graphics that can be directly manipulated by users

so they can draw their own conclusions, have received much attention in the last years. One

factor that has fostered this attention is undoubtedly the interest in providing citizens with

public data so that they can customize their analysis to fit their own needs, encouraging a

democratization of statistical analysis, distanced of the model in which everything is managed

by experts with their own agendas.

Dynamic-interactive graphics is the point of coincidence of at least three different disciplines:

Statistics, Computing, and Human–Computer Interaction. Most of the time, it has been

“statisticians playing with computers” (see Figure 10), with or without help from computer

experts, which have developed software that addressed the needs of this audience but in

other occasions, the push has stemmed from the computing side, with the result of technically

more advanced but statistically less sophisticated software – although possibly more apt for a

wider audience.



Figure 10: John W. Tuckey and his dedicated hardware for statistical analysis and

visualisation

Source: provided by Prof. Valero-Mora.

The presentation made a short review of the history of the field of Dynamic-Interactive

Graphics, from the dedicated hardware only accessible to selected few, to commercial desk-

top applications, to non-commercial, web-based graphic applications which have made

popular the preparation and dissemination of visualisations.

There are still many barriers to overcome in order to make this enterprise successful, namely,

technological, conceptual, educational and so forth. Indeed, it is of great importance to

understand the foundations of these barriers so that these graphics can achieve the potential

that has been associated with them. Therefore, a research programme developing the theory

behind the application of these graphics is of great relevance. This research topic has origins

that can be traced back to John W. Tukey (1915-2000), however, the speaker recalled that a

systematic approach to evaluate how users utilize these graphics for reasoning and what are

the rules of good design are still in its infancy (see Expert Lecture on Gamification).

Prof. Rivero from KAMPAL, a spin-off of the University of Zaragoza (Spain), presented the

visualisation of trade data as a complex network, using specific analytical techniques for this

type of data. Network data can arise from the study of flows usually studied by official

statistics such as transport, trade or migration or input-output tables (see Figure 11). It can

also come from data matrices that link individuals together (e.g. publications and their

authors).



Figure 11: Network visualisation of the Input-Output table of the economy of the region of

Aragon (Spain)

Source: KAMPAL, http://research.kampal.com/visualization/comtrade/

Network representations allow for identifying similar or related records (communities),

outliers, strength or relationships and thus helping the data analyst discover important

features of the data file.

A presentation by Ms Cristina Versino of the JRC stressed on the role of visualisation for the

analysis of large sets of data. She recalled that visual perception is a component of fast

thought, complementary to the slow process of reasoning (cf. Daniel Kahneman's "Thinking,

Fast and Slow"). The process of visual learning is based on pattern recognition by human

intelligence, and thus, different from machine learning.

Visual encoding transforms "invisible data" into representations that can be perceived by

human eye is based on the choice of forms (length, width, size, shape, orientation, enclosure),

colour (hue, intensity) and spatial position. The encoding facilitates the perception of patterns.

Graphically, the speaker described the changes in the role of data to answer any question (see

Figure 12) in an interactive way, so that the analyst refines the research question after

visualising the data.

http://research.kampal.com/visualization/comtrade/



Figure 12: the role of data in answering questions

Source: author.

Ms Versino described the process of preparing a visualisation, including 4 steps (see Figure 13).

Figure 13: Steps in preparing a visualisation

Source: Stephen Few



The use of visualisation for analysis of data was illustrated with the tools provided by the JRC

to analysts of nuclear trade, using dashboards prepared with the software Tableau. Based on

this experience, she concluded:

- Numerical data are often invisible to the user, so only the visualisation is accessible.

This is particularly the case of Big Data sources;

- Data visualisation helps the analysts ask the right questions, in an easier way than

when data access should start by questions (e.g. by a SQL request from the analyst to a

database). Data queries present only a slice of data filtered. From a learning

perspective, this is compared to "seeing the world only by things we can think of and

ask about";

- (Interactive) data visualisations should allow for progressive detail, providing first a

broad view and then allowing the user/analyst to ask for more precision;

- There is a high value added in the preparation of data for further visualisation. This

may be a task accomplished by the NSIs;

- Data visualisation can also improve the communication with users, by promoting the

awareness of existence of data sources. Interactive functionalities for exploration of

raw and aggregated data improve the comprehension of the information and finding

relevant trends and patterns.

Mr Jorrit Swaneveld from CBS/Statistics Netherlands presented the transition that this

institution is undertaking “from official statistics to a news agency”, developing more diverse

communication tools and introducing the entertainment as a major strategy, to attract the

interest of citizens as data users (see Figure 14).



Figure 14: Website of CBS /Statistics Netherlands

The requirements for fulfilling the role of a news organisation include the simplification of data

presentations, the facilitation of analysis and the adjustment of dissemination to user

segments and needs. A major ingredient for visualisation is the dissemination of open data, so

that different tools can be used to represent the information (such as the in-house developed

StatLine or the commercial package Tableau9).

Finally, Mr Alexander Kowarik (Statistics Austria) presented, and made available for share,

tools for visualisation based on the R language (and therefore free for use). In particular, he

presented a tool for visualising missing data (VIM package) and for generating graphical tables

(sparkTable package).

VIM can be used to discern patterns in missing data, which is a fundamental step in the data

validation process. The package VIM has been developed10 to explore and analyse the

9 see for instance https://public.tableau.com/views/DASHBOARDTOPSECTOREN2015/WELKOM

10 Templ, M., A. Alfons, A. Kowarik, and B. Prantner (2013). VIM: Visualization and Imputation of Missing Values. R

package version 4.0.0.

https://public.tableau.com/views/DASHBOARDTOPSECTOREN2015/WELKOM

https://public.tableau.com/views/DASHBOARDTOPSECTOREN2015/WELKOM



structure of missing values in data using graphical methods, to impute these missing values

with the built-in imputation methods and to verify the imputation process using visualization

tools, as well as to produce high-quality graphics for publications.

An important step in understanding a specific data set and its quality is visual analysis. With

the R package11 sparkTable tables presenting quantitative information can be enhanced by

including sparklines and sparkbars (initially proposed by Tufte12, 2001). Sparklines and

sparkbars are simple, intense and illustrative graphs, small enough to fit in a single line.

Therefore they can easily enrich tables and continuous texts with additional information in a

comprehensive visual way (see Figure 15).

Figure 15: Statistical table complemented with sparkTable

Source: author

11 Kowarik, A., B. Meindl, and M. Templ (2012). sparkTable: Sparklines and graphical tables for tex and html. R

package version 0.9.7.

12 Tufte, E. R. (2001). Visual Display of Quantitative Information. Graphics Press.



1.4 Parallel sessions 2A & 2B – Visualisation for data

dissemination

Facilitator: Ms Valdone Kasperiuniene (DevStat)


The objective of sessions 2A and 2B was to discuss the role and impact of visualisation for data

dissemination. The users’ perspective was considered in all presentations and discussions.

Users of statistics are the core element in data dissemination policies and processes. The ESS

Vision 2020 enshrines the user centrality in the official statistics activity (see Box 3).

Box 3: Identifying user needs and cooperation with stakeholders in the ESS Vision 2020

“We will engage proactively in a regular dialogue with users to understand deeper their needs.

Our strategic alliances with both public and private partners will help to respond flexibly to

users’ needs. We recognize that different user groups have different needs and we will address

this diversity by offering the right information in the right way. […]”.

Source: ESS Vision 2020

The better users’ needs are understood, the better statistical products can be created to

satisfy those needs. Visualisation used in data dissemination of official statistics helps reach a

wider audience and to present the data in a more attractive way. Visualisation should ease

interpretation of data and present a broader and clearer picture of economic, social or

environmental issues to the audience.

But what is the impact of visualisation on the users? Which user groups are most interested in

data visualisation? How can NSIs measure the success of data visualisation for the satisfaction

of user informational needs?

These and other questions formed the centre of the discussions of parallel sessions 2A and 2B.

In addition, the sessions addressed issues such as the relationship between mass media and

official statistical producers, provided room for presenting success stories of infographics,

gamification and personalisation of visualisation, and for sharing the participants’ knowledge

about the capabilities of specific tools for producing visual displays of statistical information.


Session 2A was opened by data journalists Ms Marta Ley and Ms Paula Guisado from “El

Mundo”, one of the largest Spain’s newspapers, who talked about the use of official statistics

for informative purposes by media.



The analysis of official statistical information is changing in the media. Recently, many

newspapers have established dedicated units, such as El Mundo Data, aiming at presenting

information in a visual, more attractive way to the readers.

The recent fast development of data journalism as a specialty within the information job

market has enlarged the scope of usage of official statistics beyond traditional press releases.

Data journalism requires the combination of diverse skills: linkage of data from different

sources¸ proper data analysis, and data visualization. Therefore, data journalists are

confronted with certain challenges:

- Finding (discovering, identifying and accessing) the relevant data;

- Formatting the data for analysis and visualisation;

- data availability and timeliness, good knowledge of statistical data sources and their

content;

- Applying correct data analysis to different types of data;

- Displaying the information in a comprehensive and attractive way for the readers.

The speakers highlighted positive aspects of cooperation between media and statistical offices,

such as: good communication (frequent, open, direct), the practice of disseminating reusable

data releases and the development by NSIs of tools for data analysis. Additional ideas on how

to enhance this cooperation were presented as well:

- Undertaking joint efforts to compile all statistics in a dedicated channel;

- Updating the availability of all countries’ data for international comparisons;

- Ensuring the open format for raw data.

These proposals are in line with the objectives of the ESS Vision 2020 and the DIGICOM

project’s work package on Open Data, which focuses on the development of statistical

products giving as much freedom as possible to active users to create their own products

based on official statistics data.

The session was continued with the presentation of Mr Guy Zacharias (STATEC, Luxembourg)

on infographics about Luxembourg13 (see Figure 16). This infographics, displayed in the

conference premises, aimed at presenting in a new way quantitative information about the

country to the broad public during the Luxembourg Presidency of the European Union in 2015.

The speaker explained the creative idea, the tools used to prepare this infographics and the

ways in which it was communicated to the audience. For the implementation of the project,

several partners were attracted: the government information service and an agency

specialised in data visualisation. A well-designed communication campaign facilitated the

success of the project.

13

http://www.luxembourg.public.lu/en/le-grand-duche-se-presente/luxembourg-tour-horizon/14-infographies/index.html

http://www.elmundo.es/periodismo-de-datos.html

http://www.luxembourg.public.lu/en/le-grand-duche-se-presente/luxembourg-tour-horizon/14-infographies/index.html



Figure 16: Infographics about Luxembourg (selection)

Source: http://www.luxembourg.public.lu/en/le-grand-duche-se-presente/luxembourg-tour-

horizon/14-infographies/index.html

The infographics life-time is not too long: it shall be updated with the new figures for two or

three years before releasing a new edition. The presentation was concluded with the

discussion on the impact of the infographics on the users and the importance of monitoring of

the feedback.

Session 2B started with the presentation of Mr Robert Fry (ONS, United Kingdom), on

connecting official statistics with the wider audience. He shared his experiences about

establishment of a new site of the ONS14 dedicated to provide visual information to the

general public. The decision to implement such projects goes in line with the “Better Statistics,

Better Decisions” strategy for UK statistics (Figure 17).

14

http://visual.ons.gov.uk

http://visual.ons.gov.uk/

http://visual.ons.gov.uk/



Figure 17: Visual display of the ONS’ “Better Statistics, Better Decisions” strategy

The presenter explained the reasons and driving forces why visualisation techniques are more

and more used in official statistics, and highlighted the importance of being up to date with

the data presentation tools.

He further emphasised that good quality, easy understandable and well-presented statistical

data can lead to better decisions. However, this requires delivering statistical content with a

level of quality necessary to engage with the general public (who are increasingly demanding

and time-pressured). The skills for ensuring the success of visualisations requires various skills

be developed (Figure 18, see also Session 4).

Figure 18: Data Design skills

Source: Scott Murray (@alignedleft)



It is hardly possible to collect the needed skills in only one person; therefore building a team of

people with required skills facilitates development of statistical products of required quality.

Providing a number of different examples, the speaker explained how different techniques,

such as gamification, personalisation and topicality have been used to connect the audience

with official statistics. He concluded the presentation explaining the ways in which success of

the visualisation is measured (pages views, dwell times, messages conveyed…).

Lars Knudsen and Ditte Karoline (Statistics Denmark) presented the new project Municipality

indicators on maps15. They explained the current status of usage of maps for the visualisation

of various statistical indicators by municipalities in Statistics Denmark and recent efforts to

introduce the new technology HighCharts16 and HighMaps which produces visualisations such

as in Figure 19.

Figure 19: Visual display of municipal indicators by Statistics Denmark

Source: screenshot of

http://www.dst.dk/da/Statistik/kommunekort/kommuneregnskaber/kulturudgifter-netto

They highlighted the advantages of this technology for the data users: easiness to extract

information needed, and possibility to save the query and automated update of the map with

the new data. However there are further improvements which need to be implemented, such

15

http://www.dst.dk/da/Statistik/kommunekort

16 http://www.highcharts.com/






as the possibility to sort municipality by data size, overview all indicators for selected

municipality, automated change of map over time, etc.

The discussion on the use of Highcharts for visualisation of data was continued further by

Daniel von Burg from the Swiss Federal Statistical Office (FSO). He highlighted that FSO follows

European Statistical Code of practice, in particular its principle 15 on accessibility and clarity

(see Box 4).

Box 4: European Code of Practice, principle 15

“European Statistics are presented in a clear and understandable form, released in a suitable

and convenient manner, available and accessible on an impartial basis with supporting

metadata and guidance.

Indicators:

15.1: Statistics and the corresponding metadata are presented, and archived, in a form that

facilitates proper interpretation and meaningful comparisons.

15.2: Dissemination services use modern information and communication technology and, if

appropriate, traditional hard copy.

15.3: Custom-designed analyses are provided when feasible and the public is informed. […]”

Source: European Code of Practice

Therefore data visualisation, implementation of modern communication technologies, user-

friendly presentation of information to wide audience is seen as priority.

The visualisation software Xact has been used for chart production at the FSO for many years.

However this software is old-dated and has got many shortcomings. Therefore, for the

modernisation of its chart production, FSO chose Highcharts after careful evaluations: an out-

of-the box visualization tool that combines an easy to use concept with high flexibility in

realising a corporate design as well as fulfilling all scientific needs.

This software is in use by other government bodies of Switzerland, too. The presenter

explained the advantages and the major functionalities of the software. Some examples of

data visualisation were presented with the lessons learned: evolution of the share of the votes

by canton and by party (for tablet computers); a web interface for editing standard charts; a

dashboard addressed to occasional users of statistics.

The problems related to the development of the new skills for data dissemination team, and

the importance of correct definition of the target groups for visualisation were discussed as

well.



1.5 Parallel session 3 – Strategy for visualisation in

statistical institutes

Facilitator: Mr Victor Dinculescu (DevStat)


The objective of the session was to share various approaches of NSIs on adopting a strategy for

visualisation, having in mind the ESS Vision 2020 objectives. It was recalled that the strategy

itself is not enough if not followed by concrete actions towards implementation.

While visualisation sounds particularly related to new ways of dissemination and

communication part of NSIs’ activities, it should be underlined that preparing new tools, new

formats or new data for users’ satisfaction it was always a matter of great importance for NSIs.

NSIs are building an information infrastructure, starting with data gathering, processing,

storing and providing the information, as a public good, to a large category of users - citizens,

policy makers, researchers and journalists and entrepreneurs. In this context several projects

aiming at sharing visualisation tools were presented.

The main questions raised during the sessions were about how to measure better the use of

existing data by user category, to continue investigating user needs and their satisfaction with

data content, quality, format and timeliness, to be prepared internally to face new challenges

and developments and to stay updated with our tools for communication, marketing and

dissemination at the same level with other actors outside official statistics, if not even better

than.


The session was started by a presentation from Mr Christiaan Laevaert (Eurostat). He

presented the results of the Task Force for Dissemination Working Group and countries’

experience in sharing visualisation tools in the ESS.

The presentation made reference to a pilot exercise for sharing the existing Eurostat

interactive infographics tools (see Figure 20), already adopted by some of EU NSIs, mainly the

members of Task Force: Economic trends, Young Europeans, and Quality of life which were

translated and integrated in national websites (see Box 5).



Box 5: Eurostat visualisations shared by Member States

“Economic Trends”: Estonia: http://www.stat.ee/public/eurostat/economic-trends/index.html?lang=et Poland: http://stat.gov.pl/infografiki-widzety/wskazniki-ekonomiczne Romania: http://www.insse.ro/cms/economy/desktop/index.html?lang=ro Spain: http://ine.es/infografias/tendencias/desktop/index.html?lang=es Hungary: http://www.ksh.hu/interaktiv/eurostat/ecotrends/desktop/index.html?lang=hu Croatia: http://www.dzs.hr/economy/desktop/index.html?lang=hr Italy (en): http://www.istat.it/en/economic-trends/international-analysis-and-forec “Young Europeans”: Croatia: http://www.dzs.hr/youth/index_hr.html Italy: http://www.istat.it/infografiche/giovani-europei/ Ireland: http://www.cso.ie/en/interactivezone/eurostatvisualisations/youngeuropeans/ Poland: http://stat.gov.pl/mlodziez/index_pl.html Spain: http://www.ine.es/infografias/jovenes/index_es.html Romania: http://www.insse.ro/cms/youth/index_ro.html Portugal: https://www.ine.pt/scripts/young_europeans/index_pt.html “Quality of life”: Portugal: https://www.ine.pt/scripts/qdv/index_pt.html Croatia: http://www.dzs.hr/qol/index_hr.html Slovenia: http://www.stat.si/qol/index_sl.html Spain: http://www.ine.es/infografias/calidadvida/index_es.html Romania: http://www.insse.ro/cms/qol/index_ro.html

Figure 20: Screenshots of Eurostat Visualisations

http://www.stat.ee/public/eurostat/economic-trends/index.html?lang=et

http://stat.gov.pl/infografiki-widzety/wskazniki-ekonomiczne

http://www.insse.ro/cms/economy/desktop/index.html?lang=ro%20

http://ine.es/infografias/tendencias/desktop/index.html?lang=es%20

http://www.ksh.hu/interaktiv/eurostat/ecotrends/desktop/index.html?lang=hu

http://www.dzs.hr/economy/desktop/index.html?lang=hr%20

http://www.istat.it/en/economic-trends/international-analysis-and-forec

http://www.dzs.hr/youth/index_hr.html

http://www.istat.it/infografiche/giovani-europei/

http://www.cso.ie/en/interactivezone/eurostatvisualisations/youngeuropeans/

http://stat.gov.pl/mlodziez/index_pl.html

http://www.ine.es/infografias/jovenes/index_es.html

http://www.insse.ro/cms/youth/index_ro.html

https://www.ine.pt/scripts/young_europeans/index_pt.html

https://www.ine.pt/scripts/qdv/index_pt.html

http://www.dzs.hr/qol/index_hr.html

http://www.stat.si/qol/index_sl.html

http://www.ine.es/infografias/calidadvida/index_es.html

http://www.insse.ro/cms/qol/index_ro.html



It was recalled that EUROSTAT visualisation tools, were successfully shared in 5 to 7 NSIs of the

Visualisation Task Force, to date. More countries are in implementation phase for translation

into national language. Until now, the tools are implemented in 9 languages. This

demonstrates the potential for sharing solutions across the ESS.

The visualisations have also been shared with redistributors.

The next actions will be taken within the DIGICOM project.

New available tools at EUROSTAT were also presented such as “Youth in the EU”, “Government

expenditures in the EU (see Figure 21).



Figure 21: New Eurostat visualisations

“Youth in the EU”

Source: http://ec.europa.eu/eurostat/cache/infographs/youineu/index_en.html

“Government expenditures in EU”

Source: http://ec.europa.eu/eurostat/cache/infographs/cofog/

“Smart Maps”: http://ec.europa.eu/eurostat/web/waste/transboundary-waste-shipments

http://ec.europa.eu/eurostat/cache/infographs/cofog/

http://ec.europa.eu/eurostat/web/waste/transboundary-waste-shipments



A joint project of ISTAT and ISFOL aiming to set an information system on labour market and

occupations, using a standard classification17 was presented by Ms Cristina Freguja (ISTAT) and

Maria Grazia Mereu (Isfol - Institute for the Development of Vocational Training of Workers),

accessible from the Ministry of Labour.

The information system includes a network of institutions producing information regarding

professions, and is providing data regarding each occupation thanks to the availability of

statistical indicators. A major feature of this visualisation tool is that it is based on a diversity of

statistical sources. The data sources for the system are from:

Surveys:

ISTAT Labour force survey

- Unioncamere Excelsior (Union of Chambers of Commerce) survey for the short-term employment forecasts

- the ISTAT-ISFOL survey on Occupations

- the ISFOL Professional Development Requirements Audit

Econometrics studies (ISFOL):

- on the future trends of the economy and employment (forecast) and on the medium-term professional structure changes in economic sectors, using scenario methodologies (foresight).

Administrative sources, e.g.:

- Ministry of Education: data relative to university offers information

- Social Security Service (INPS): information regarding wages

17 http://professioni.istat.it/sistemainformativoprofessioni/cp2011/

https://www.cliclavoro.gov.it/Pagine/default.aspx

http://professioni.istat.it/sistemainformativoprofessioni/cp2011/



- National Institute for Insurance against Work-related Injuries (INAIL) on workplace and work-related accidents and their consequences

The characteristics of this tool on the labor market in Italy are the following:

- use of web linked open data;

- data stay with the producers, but are connected with a simple widget;

- each of the partners' websites is a gateway to the system;

- similar and non-homogeneous data are correlated with each other through the web exchange of a unique key (occupation code);

- the system promotes the adoption of standards for the production of information across institutions.

Main users of the system are: young people looking for their first job, workers, businesses,

operators of the job training and job market fields, families, decision makers and public

institutions.

The last presentation of session 3 was delivered jointly by Mr Per Nymand-Andersen

(European Central Bank) and Matthias Rumpf (OECD) on “Digital communication strategy for

statistics” and it was an excellent example of shared visualisation tools used for improving

communication.

The participants had indeed the opportunity to see solution implemented on two websites

(ECB and OECD) with a shared set of visualization templates to compare national statistics

across countries and other geographical areas.

The solution characteristics are as follows:

• Use of effective distribution channels;

• Share/embed for re-use in digital media, 3rd party websites, tweets and blogs;

• Builds on SDMX and therefore easy to plug-in new statistics;

• A shared back-office to set up new statistics (projects);

• Common structure to enrich statistics with metadata, indicator titles and detailed

definitions;

• Integration of available tools such as the Commission service for maps;

• Easy to use interface for multiple languages and integration into corporate workflow;

• Shared software project. Components are published under CC licence;

• Very limited use of proprietary software;

• Easy and low maintenance costs – use of external webhosting.

After a promotional 2 weeks campaign the impact was measured by the following user

metrics: additional total visits: ~ 21,833 (~ 42,000 page views), approx. 3 times as many visitors

as before the campaign. It also received many comments on media.



1.6 Parallel session 4 – Visualisation methods and IT

Facilitator: Mr Edwin de Jonge (DevStat)


The objective of session 4 was to provide an overview of available methods and IT tools to

visualize data. Visualisation can be explorative, exhibitive or explanatory, (see Figure 22) which

may need different kind of charts and maps. Various web and visualisation technology choices

were discussed so attendees increased their awareness on the technologies that may be

relevant.

Figure 22: A typology of visualisations


The presentations from Business Innovations and Skills (BIS) from the United Kingdom and

Istat (Italy) were showcases of implemented visualisation solutions, describing the IT-tools

used, the user acceptance and skill set needed to implement the visualisation. The publication

office of the European Union presented its project of cataloguing the various visualisation

initiatives from over 60 EU institutions.

Mr Hiren Bhimjiyani from the BIS presented three examples of visualisations with existing data

sets in the UK. The created a visualisation that was optimized for Web pages used HTML5, CSS,

SVG and javascript libraries, including d3.js, bootstrap and d.js. All the technology used is open

source. Their COMTRADE visualisation example showed that a visualisation increases data

awareness. Most users were surprised that this data was available, and were overall pleased

with the visualisation created. They used quick developments cycles, in which user feedback

was taken into account.



Visualisation can be created quickly, however the skill set is diverse: technical, data and

statistical. The speaker recalled that they had to host the visualisation front-end on the data

back-end because of cross-origin (CORS) security issues with web browsers. This may place

restrictions on using visualisation dashboard on multiple data sources.

Agnieska Zajac, from the EU publication office presented a project of cataloguing the

visualisation projects of EU institutions. They completed a thorough survey in 2015 in which

60 institutions reported their initiatives. In 2016, they will design a catalogue describing the

various aspects of the visualisations and put it online. In 2017 they intend to develop a generic

visualisation framework that will be published in the catalogue.

Alessandro Capezzuoli and Emanuela Recchini from Istat presented StatView18, a web platform

for disseminating statistical data and geospatial analysis (see Figure 23). Their platform uses a

REST interface to retrieve data in JSON-STAT format from various datasources (SDMX, DDI, DB,

PostGress and PostGIS).

Figure 23: Screenshot of ISTAT’s StatView

18

www.statview.eu

http://www.statview.eu/

http://www.statview.eu/



1.7 Parallel session 5 – Visualisation for data literacy

Rapporteur: Mr Victor Dinculescu (DevStat)


Knowledge building is particularly important for users of statistical information. There are two

type of users’ knowledge on which NSIs focus.

• The first one is referring to increase the awareness of users on the available statistical

data - many times users don’t even know that data of their interest are already

available;

• The second one targets the increasing of users’ capacity in “reading” and

understanding statistical data, statistical procedures, data sources and many others

which are named with general, more comprehensive “data literacy”.

Data literacy is not a reserved topic for the users of statistics, but also for data providers which

create and communicate data as information. The objective of this session was to investigate

how NSIs used visualisation for increasing ‘data literacy of users’.


The session started with a presentation from Ms Hannele Orjala (Statistics Finland). She shared

the tools and ideas for improving statistical literacy in Finland and how to make statistics

attractive for young people (see Figure 24).

Figure 24: Segmentation of users according to Statistics Finland

Source: Hannele Orjala (Statistics of Finland)



The goal of the presentation was to share the experiences and ways of cooperation with the

educational institutions and the academia, to show a couple of short videos and introduce

newest cases in this area, in particular how they engaged pupils through collecting and

presenting statistics about their school environment. An additional aim of the presentation

was to encourage the ESS members to join ISLP19 (International Statistical Literacy Project -

under the International Statistical Institute).

Figure 25: Screenshot of an animation video by Statistics Finland

Statistics Finland´s cooperation with educational institutions takes place in collaboration with

different partners, such as Forum Virium, City of Helsinki Urban Facts, Helsinki Mathland,

Summamutikka centre, University of Helsinki, and teacher trade unions. Cooperation with

educational institutions and schools includes such activities as the statistical literacy

competitions for students, a Statistical Yearbook for children, training of teachers and

developing animations20 (see Figure 25).

Statistics Finland also participates in exhibitions actively, visits schools and invites pupils and

teachers to office. In the Communication and Information Services Department, a responsible

person is tasked for developing cooperation with educational institutions.

With the second presentation, Mr Martin Martensson (Statistics Sweden) introduced a new

way of presenting statistics “Sweden in figures” targeting young people as well.

Statistics Sweden recognized the need to find new strategies to ensure that young people

know about statistics, having in mind that they will became later full users of information, why

it is important to contribute to official statistics and how they could benefit from using it

themselves.

A new concept website has been published in October 2015, “Sweden in figures“, making

statistical data more accessible to non-expert users, in particular non-expert users of a young

19

http://iase-web.org/islp/

20 http://www.stat.fi/tup/tilastosuomalainen/index.html

http://iase-web.org/islp/

http://www.stat.fi/tup/tilastosuomalainen/index.html

http://www.stat.fi/tup/tilastosuomalainen/index.html



age. The website was created in close collaboration with teachers and their students, and

includes a selection of statistics relevant to the Social science curriculum for students between

10-18 years old, all presented in a simple and interesting way using infographics and

interactive diagrams (see Figure26).

The website was presented during the session along with additional material and the use of social media channels in reaching the projects target groups. The main topics of the website use the dissemination database and cover to date the following domains:

- Elections and political parties - Education, professions, wages - Economy – GDP, trade, unemployment, etc.

Figure 26: Sceen shot of the interactive “Sweden in Figures”

Source: (( http://www.scb.se/sv_/ ) web translation of original page)

http://www.scb.se/sv_/



1.8 Parallel Session 6 – Geospatial visualisation

Facilitator: Mr Edwin de Jonge (DevStat)

1.8.1 Objective of the Session

The objective of the session on geospatial visualisation session is to provide a state-of-the-art

overview of statistical cartography. Besides presentation geographic information, geospatial

visualisation supports complex analytical tasks. The session discussed the combination of data

sources with geographic information.


The presentations of the NSIs of Finland and Slovenia showed geospatial analysis systems that

were developed by their offices using open source GIS tools. The Spanish company CartoDB

presented a geospatial analysis problem which was solved using a navigation/routing system

and their platform. All presented solutions allowed (advanced) users to do geospatial analysis.

Carto DB provides cartographic services, but publishes all developed software as open source.

They presented the geospatial problem where a train connection was closed, and how many

users would be affected. For this problem, the Mapzen routing engine and US Census blocks

were used to find which users were nearby train stations. CartoDB put effort into developing

an intuitive user interface (see Figure 27).

Figure 27: Interactive tool for geospatial analysis using CartoDB tools



Statistics Finland implemented a geospatial platform based on the Open Source OSKARI suite.

It allows for many geospatial tasks including spatial joining and multiple layer calculus, making

it a powerful platform. User feedback suggests that users that are GIS experts find the user

interface difficult. Their tool provides detailed grid data: one of the concerns is disclosure

control.

Statistics Slovenia provided a presentation of their dissemination system STAGE, which is also

based on open source technology. Their input data are mostly PC-axis files, but grid data is also

supported up to 100m blocks. Their platform has less geospatial analysis options, but was

found useful by their users. Users can delineate areas and retrieve statistics.

During the discussion the following remarks were made. All solutions use open source GIS

technology, which is apparently mature. Creating (advanced) spatial analysis tools for users

that are intuitive takes a lot of effort. Providing very detailed geospatial data together with

geospatial operations creates statistical disclosure risks which need to be controlled.

Annex 2 – Programme of the Workshop

Tuesday 17 May 2016

11:30-13:00 Registration, Networking

13:00-14:00 Buffet Lunch

14:00-15:00

Opening Session

Welcome address Mr José Ignacio Pastor Perez (Valencia Open Government service) and Mr Rafael Monterde Díaz ( InnDEA Foundation)

Opening address

Ms Martina Hahn (Eurostat)

Presentation of the Project: Digital communication, User Analytics and Innovative products Ms Christine Kormann (Eurostat)

Introduction to the parallel sessions: objectives and procedures Mr José Cervera (Devstat)

15:00-15:45 Expert lecture: Uncertainty visualisation

Speaker: Mr Edwin de Jonge (Statistics Netherlands)

15:45-16:15 Coffee Break

PARALLEL SESSIONS

Visualisation for data analysis Visualisation for data dissemination Strategy for visualisation in statistical

institutes Visualisation methods and IT tools

Facilitator: Mr Jose Cervera

Facilitator: Ms Valdone Kasperiuniene

Facilitator: Mr Victor Dinculescu

Facilitator: Mr Edwin de Jonge

16:15-17:45

Session 1A Session 2A Session 3 Session 4

“Visualisation of large data sets: From design to analysis and back”

Ms Cristina Versino (JRC)

“The evolution of dynamic-interactive graphics for statistics”

Mr Pedro Valero-Mora (University of Valencia)

“Global Commerce as a complex

network” Mr Alejandro Rivero (Kampal Data

Solutions)

“Statistics for informative purposes” Ms Marta Ley and Ms Paula Guisado

(El Mundo Data)

“Luxembourg told by infographics” Mr Guy Zacharias (STATEC)

"Experience in sharing visualisation tools in the ESS"

Mr Christiaan Laevaert (Eurostat)

"The Italian information system on occupations: navigating the data

deluge" Ms Cristina Freguja (ISTAT)

“Digital communication strategy for statistics”

Mr Per Nymand-Andersen (ECB) and Mr Matthias Rumpf (OECD)

"New Methods of Disseminating Statistics"

Mr Hiren Bhimjiyani (Department of Business Innovation and skills - UK

Government)

“Open data and related visualisation tools in the EU institutions”

Ms Agniezka Zajac (Publication Office EU)

“STATVIEW : a web platform for visualisation and dissemination of

statistical data and geospatial analysis” Mr Alessandro Capezzuoli and Ms

Emanuela Recchini (ISTAT)

20:00 Social Dinner

Wednesday 18 May 2016 9:00-9:30 Networking

9:30-10:15 Expert lecture: Enhancing dissemination trough gamification

Speaker: Mr Jose Vila (Devstat)

PARALLEL SESSIONS

Visualisation for data analysis (continued)

Visualisation for data dissemination (continued)

Visualisation for user data literacy Geospatial visualisation

Facilitator: Mr Jose Cervera

Facilitator: Ms Valdone Kasperiuniene

Facilitator: Mr Victor Dinculescu

Facilitator: Mr Edwin de Jonge

10:15-12:15

(including

coffee break

at

11:00-11:30)

Session 1B Session 2B Session 5 Session 6

“Facilitating visualisations to the public - visualisations and analysis in official

statistics” Mr Jorrit Swaneveld (CBS NL)

“The R-Packages VIM (Visualisation of Missing Values) and sparkTable (Generating Graphical Tables)”

Mr Alexander Kowarik (Statistics AT)

Visual.ONS - Connecting Official

Statistics with a wider audience"

Mr Robert Fry (ONS UK)

“Municipality Indicators on Maps”

Mr Lars Knudsen and Ms Ditte Bechsgaard

(NSI DK)

“Highcharts @ Federal Statistical Office”

Mr Daniel VonBurg (FSO CH)

“Statistics on everyday life with the eyes

of young people - Tools and ideas for

improving statistical literacy in Finland”

Ms Hannele Orjala (NSI FI)

“Sweden in figures – a new way of presenting statistics”

Mr Martin Martensson (Statistics Sweden)

"Making visible the invisible: the

L Train closure"

Mr Jorge Sanz (CARTODB)

“Spatial statistics in the national

geoportal”

Ms Marja Tammilehto-Luode (NSI FI)

“STAGE – integrated system for dissemination of geospatial statistical

data” Ms Mojca Merc (NSI SI)

General Session - Modernisation of Official Statistics though visualisation

12:15-13:00 Expert lecture: Uses and abuses of data visualisations in mass media

Speaker: Mr Pablo Rey (Open Evidence)

13:00-14:00 Buffet Lunch

14:00-15:45

Conclusions from the parallel sessions and panel

Mr Jose Cervera, Ms Valdone Kasperiuniene, Mr Victor Dinculescu, Mr Edwin de Jonge

Panellists: Philippe Bautier (Eurostat), Mr Jose Vila (DevStat), Mr Guillaume Mordant (INSEE), Ms Laura Dewis (ONS UK)

Moderator: Mr Jose CERVERA

15:45-16:00

Conclusions from the Event

Mr Jose Cervera (DevStat)

Closing address

Mr Philippe Bautier (Eurostat)

16:00-17:00 Coffee Break and networking

Annex 3 – Acronyms

CROS Web-based Portal for Collaboration in Research and Methodology for

Official Statistics (www.cros-portal.eu)

ECB European Central Bank

ESS European Statistical System

ESS.VIP ESS Vision Implementation Project

ESS.VIP DIGICOM ESS Vision Implementation Project on Digital Communication

ESTP European Statistical Training Programme

EU European Union

GIS Geographical Information System

GUI Graphical User Interface

ICT, IT Information and Communication Technologies, Information Technologies

ISLP International Statistical Literacy Project

JRC Joint Research Centre

MS Member State of the EU

NSI National Statistical Institute (generic denomination)

NSS National Statistical System (generic denomination)

OECD Organisation for the Economic Cooperation and Development

TF Task Force

Other acronyms used include the usual names of statistical offices of the EU Member States.

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