Jean-Yves Blaise
email: jean-yves.blaise@map.archi.fr

Iwona Dudek
email: iwona.dudek@map.archi.fr

Picturing what others know: towards a dashboard for interdisciplinarity

Keywords: Measurement, Design, Experimentation, Human Factors Methodology, Spatio-temporal data, Information visualization, Knowledge management, Interdisciplinary research

In many complex, multi-faceted, problem-solving situations there is a growing need to pull together various competences and backgrounds, i.e. a need to better balance specialisation and interdisciplinarity. But when scientific backgrounds, skills, references, specialized languages and specialized knowledge are too far apart people involved in an interdisciplinary process face a real communication challenge. Communication is here not the best word -it minors the challenge. What actors face is the challenge to reach a common understanding of facts and of pictures of facts, in other words to build a common, unambiguous, knowledge space. Not making this effort often results in misunderstandings, and accordingly in delays and inefficiency. At the end of the day, results may turn out as a nice patchwork rather than as new knowledge. In this contribution we show that it can be worth having actors get aware of distances between them, of overlaps and non-overlaps in terms of methods, specialised language, references and concepts. We present a series of short experiments that are an attempt to "measure" and then visualize the above mentioned distances, and can act as eye openers for a community.

GENERAL ISSUE

Back in the early age of enlightenment, or during the renaissance, some scientists could claim they had a global understanding of all scientific fields, and, furthermore, of technologies and philosophy. A sort of "universal knowledge", as worded by [START_REF] Schuller | Designing universal knowledge[END_REF], may have been, at that time, within reach (although soon enough the move for encyclopaedias, co-written by several authors, paved the way for modern, collaborative but dispersed science).

This time has obviously more or less passed, and better balancing specialisation and interdisciplinarity appears today as a growing challenge, in the R&D community and more generally in decision making processes. Analysing complex systems -in environmental sciences for instance -is known to require pulling together various competences, encompassing various scientific fields and sub-fields. A good illustration is the analysis of urban particle pollution patterns. Understanding these patterns, deciding on public policies to cope with the inconveniences, requires an observation of the facts (i.e. developing real-time survey methods so as to measure amounts of particles in the air) but also requires analysing possible causes (i.e. knowing of technologies such as coal-fired plants or diesel engines and catalytic converters) and putting the whole picture into its context (depicting contemporary lifestyles, mobility patterns, commuters, etc.). Hence the introduction of terms such as multidisciplinary, pluridisciplinary, interdisciplinary approaches in the vocabulary of research, and in this of public or private funding calls. But what do these terms refer to? How do they differentiate from one another? Are we talking about slogans, or about practical challenges? Intuitively we would tend to consider that unlike multidisciplinarity, that seems to refer to layers of skills added on to the other in order to tackle a multi dimensional problem, interdisciplinarity refers to some kind of "in-between". This is confirmed in [START_REF]Disciplines in Combination: Interdisciplinary, Multidisciplinary, and Other Collaborative Programs of Study CAAA Briefing paper[END_REF] with the following definitions: -Multidisciplinary: Juxtaposition of various disciplines, sometimes with no apparent connection […] -Pluridisciplinary: Juxtaposition of disciplines assumed to be more or less related […] -Crossdisciplinary: Imposition of the approaches and axioms of one discipline on another […] -Interdisciplinary An adjective describing the interaction among two or more different disciplines […] from simple communication of ideas to the mutual integration of organizing concepts, methodology, procedures, epistemology, terminology […] A contribution like [3] where two recent computer technologies are combined in order to back up archaeological research can be considered as a typical multidisciplinary experience. Eventually, with time and with the growing introduction of 3D platforms in the enhancement of archaeological findings, it might even turn out to be a pluridisciplinary experience. A contribution like [START_REF] Blaise | Terminology analysis inspires relations in a knowledge structure[END_REF], where concepts stemming from the field of linguistics structure the creation of a specialised ontology for historic architecture, can be seen as a typical crossdisciplinary experience.

An interdisciplinary research is something a bit different and [START_REF] Hennessy | Promoting multi-and interdisciplinary ageing research in the United Kingdom[END_REF] says it very clearly: Interdisciplinary research requires shared models, concepts and terms best developed through incremental working. A contribution like [START_REF] Aigner | Visual Methods for Analyzing Time-Oriented Data[END_REF], where the focus is put on the convergence of user-centered, visually driven analysis methods for time-oriented data, underlines some of the specific challenges of interdisciplinarity: an ambition to " bridge disciplinary gaps" [START_REF] Blaise | From artefact representation to information visualisation: genesis of informative modelling[END_REF], an ambition to reach some kind of "in-between". So what exactly is that in-between, supposedly specific to interdisciplinarity? One thing that does bring different actors together is computerbased tools, formalisms and methods such as GIS systems [START_REF] Zakšek | Path modelling and settlement pattern In Layers of perception[END_REF], or surveying platforms [START_REF] Porcelli | Integration of 3D Laser Scanning and Virtual Reconstructions as research and educational tools for representing the past[END_REF]. But is sharing a tool enough to make something of that in-between? Some languages share an alphabet -does that alphabet constitute an in-between helping people understanding one another in an efficient way? Certainly not: languages remain apart, incomprehensible if one is not taught them. The tool some languages share-an alphabet -cannot be considered as this in-between we expect when we mention interdisciplinarity. Let us take another example. Let us imagine two people driving along the same road. Well the in-between that makes it possible for them to drive on that same road safely is not the road itself, a tool, but the Highway Code, a common knowledge. In other words, analysts who wish to conduct an interdisciplinary research should simply not take for granted that all they will need is basically to share a goal or a tool. Presumably, and we hope this contribution will back up our claim, they will, prior to the actual co-operation, need to get aware of distances between them in order to build their in-between, made of models, concepts and terms.

OBJECTIVE, CONTEXT, CONTENT

Difficulties of interdisciplinary research are known to many, and convincingly described in [START_REF] Baker | Perils and pleasures of multidisciplinary research Urban Ecosyst[END_REF] who also underlines what is at stake -a synergy, resulting in enhanced productivity for all participants over the long run. But in this contribution we shall not discuss interdisciplinarity as such, in general terms: a number of surveys of issues at institutional level have been carried out such as [11] or [START_REF] Rhoten | Interdisciplinary Research: Trend or Transition[END_REF]. Instead, we present a collaborative experiment through which we have tried to measure and visualise distances between actors inside a given community of scientists who analyse spatial dynamics from a variety of points of views (history, archaeology, environment, geography, urban planning, etc.). That community does share a concern for spatio temporal data, and quite often a tool -GIS platforms. But a number of misunderstandings between actors and /or of biases needed to be pointed out, proven, and debated so as to have the community go ahead, such as:

• A stronger concern for the space variable than for the time variable. • polysemous terms that actors interpret differently ("dynamic", "representation", "timing", "accuracy", "generalisation" for instance).

•

Cartographic representations widely privileged over any other visual formalism.

•

Scarce integration of infovis-inspired reasoning tools.

It is clear that most actors involved in interdisciplinary research practices are conscious of potential non-overlaps, but our goal was to come out with surveys, numbers, facts, visualisations that would help them go beyond a vague consciousness. Accordingly, we propose practical means to assess disciplinary distances so that actors get a clearer view of overlaps and non-overlaps in terms of methods, specialised language, references and concepts, etc., More precisely, we chose to focus on two prominent potential biases and/or misunderstandings in the above research context:

-is there today a sort of "spatial bias" in the way we handle such data sets (in other words, a predominance of space-centric concepts and tools, with time seen as a secondary concern)?

-are there linguistic biases that prevent actors from understanding one another in a clear-cut way (in other words, do discipline create jargons that result in potential misunderstandings, with the same word corresponding to different notions here and there)?

Accordingly, we developed two different survey protocols.

The first protocol (section 3.2.1) focuses on analysing a potential spatial bias. It results in a visual measure of impact, meaning here a measure of how familiar actors are of some classic visual solutions. The hypothesis is that if analysts have a far better knowledge of classic space-oriented visualisations than of classic time-oriented visualisations then it is likely that they do have a bias. Additionally, this first protocol gave us the opportunity to also measure the impact of infovis (Information Visualisation) as a discipline, and as a set of practices and solutions, on actors having no or very little implication in that field.

The second protocol (section 3.2.2) focuses on an analysis of similarities and differences in the way actors from various disciplines interpret terms. The hypothesis here is that some terms, or some classes of terms, may have been interpreted in narrow ways here and there, threatening the capacity of actors to get an in-depth understanding of the positions and methods of others.

Both protocols build on methods and practices that are of rather common use: qualitative questionnaires to be filled in by a target group (see for instance [START_REF] Derntl | An Embeddable Dashboard for Widget-Based Visual Analytics on Scientific Communities[END_REF] and [START_REF] O'keeffe | Personalized activity based eLearning[END_REF]). Some significant differences in terms of practical implementation however need to be pointed out. Accordingly in the next section we present the conditions of the experiment: who was targeted and why, what exactly actors were asked to do, what methods were used, and ultimately how we compared this first "benchmark" experiment with some further surveys. We then focus on the visual results: how the visualisations were designed, and what picture they helped us draw of interdisciplinarity. Finally, conclusions and perspectives underline what we consider was fruitful (or needs to be further developed).

We shall however make no claim that the method we have developed can be used in another context without proper adaptations.

EXPERIMENTAL SETUP 3.1 Test group

The experiment was carried out as part of a thematic school (workshop & tutorials) organised by the MoDyS research group, and entitled "Modelling and visualising spatial dynamics : Reasoning on long time spans and uncertainty" [START_REF] Blaise | Modelling and visualising spatial dynamics : Reasoning on long time spans and uncertainty[END_REF]. The event's aim was primarily to identify and measure similarities and differences in the way events, processes, and transformations are handled across various disciplines, and when facing various real cases, datasets and research goals. The MoDyS (Modelling spatial dynamics) research group itself started as a forum where researchers focused on the intersection of Geomatics and Archaeology. This move followed the massive introduction for more than ten years now of space-oriented concepts and tools both in the acquisition stages (Laser scanning, LIDAR, infra-red, etc.) [START_REF] Takase | A Development of 3D Urban Information System on Web[END_REF] and in the management and post-processing of the data (GIS, CAD, Spatial analysis) [START_REF] Robles-Ortega | Automatic Street Surface Modeling for Web-Based Urban Information Systems[END_REF] [START_REF] Chardonnel | Time-geography: individuals in time and space[END_REF].

But the group was then extended to other disciplines or fields of concern -typically History, Computer science, Information visualisation, Architecture, Urban planning, Landscape analysis, etc. Accordingly the topics covered evolved, switching from space-centric approaches to broader issues such as understanding and modelling the time variable, dealing with uncertainty, introducing concepts and solutions from the Information visualisation & visual analytics communities, etc. As a result, the community knew a significant move, from a face to face situation between two communities, and two competences, to a wider (sortof) melting pot, thereby opening new communication challenges. Furthermore, the experiment was carried out as part of an event that was open beyond the research group itself (more than half of the participants were not members of the research group). In other words, possible biases that could have resulted from a test group composed of people having been in contact previously are limited.

The experiment's target audience should therefore be understood as the 46 participants of the thematic school, and not only members of the pre-existing MoDyS community. A majority that target audience already had opportunities to carry out multidisciplinary research. Profiles were recorded as such, with a "main" disciplinary field corresponding to a participant's main education or field of competence, and one or several areas of interest corresponding to his other involvements (Figure 1). The experiment's initial aim was to try and make the target audience aware of potential communication difficulties resulting from divergent terminological habits, to underline unsaid biases, and to illustrate the benefits of infovis-inspired solutions (in assessing the above statements as well as more generally in reasoning on the time variable). The experiment was later prolonged with other groups so as to pull together different benchmarks.

Tasks

Measure of impact

Here the test group was shown a series of 44 classic visualisations, distributed into three categories:

-visualisations of quantities (ranging from Playfair's line charts to Mosaic displays [START_REF] Friendly | A brief history of the mosaic display[END_REF]), -visualisations of space-oriented data (ranging from Dupin's choropleth maps to Galton's weather maps), -visualisations of time-oriented data (ranging from M.Homma's candlestick chart to the spiralGraph display [START_REF] Weber | Visualizing Time-Series on Spirals[END_REF].

Naturally a number of these visualisations bridge several categories: Minard's flow maps for instance handle both space and time. The test group was not aware of this categorization, used only for the post-processing of their answers.

We chose to combine classic, pre-computer visualisations with more recent solutions since a vast majority of the test group was quite unfamiliar with discipline-specific trends and solutions (notably with infovis formalisms). Each participant was asked to tick, for each of the 44 visualisations, one of the following answers:

-I know this kind of visualisation and consider myself as concerned.

-I know this kind of visualisation and consider myself as not concerned.

-I do not know this kind of visualisation. -I do not know this kind of visualisation but I think it could be of use for me.

In other words, what was tested here was first the impact of a series of visualisations across the test group, and accordingly to some extent across the disciplines the test group corresponded to. But what we were measuring beyond that was on one hand whether or not time-oriented data visualisations differed, in terms of impact, from others; and on the other hand whether recent infovis solutions had crossed disciplinary borders.

Overlaying of concepts and terms

Here the test group was given a sort of lexicon inside which we pulled together 57 terms corresponding to notions that could appear at first glance as straightforward ("process", "time interval", "reliability", "hypothesis").

For each term, we proposed from 2 to 7 alternative definitions (in French and English) taken from the literature (overall numbers: 250 definitions, 53 references). Terms were distributed in four main categories: phenomenon, time parameter, uncertainty, general vocabulary. Each participant was asked to tick, for each of the 250 definitions, one of the following answers:

-I use this term in the sense given by that definition.

-I use the notion given by that definition, but I use another term for it.

Participants who felt unconcerned by a term and its definitions were asked to leave the question unanswered. They could also add their own definitions for each term.

A fifth category, called "tabula rasa" was proposed that gave participants the opportunity either to propose a definition for four terms we left definition-less in that category, or to add a new term (result : 11 terms and 21 definitions added).

In other words, we tested here semantic overlaying: divergent terminological habits, controversial uses of one term that may refer inside one or another discipline to different notions.

In addition, the density of answers for each term, and each definition, can also be a fruitful indicator, illustrating a strong or weak concern for the notion itself (for instance a notion like "multivocal time", that one would expect to be a key concept when dealing with uncertain temporal data sets, surprisingly got only 10 votes out of 46 voters).

A "no computer" survey method

After some debate, and despite its cost at post-processing time, we decided for an "old times" survey method, based on printed material rather than on interactive platforms -and this for several reasons:

-If most of the participants had a good or very good knowledge of computer platforms, some were by contrast less at ease with such environments, possibly biasing the results.

-Since the experiment was carried out during a continuing education event, we wished to foster discussions in small workgroups, in informal contexts.

-Filling in forms pages long on a computer screen often goes with a good deal of boredom. Since the amount of questions we asked testers was rather high we considered necessary to provide a more playful, and debate-enhancing survey modality.

As a result, both the above experiments were conducted basing on a comparable scenario. The data -may it be lists of visualisations or of terms and definitions-was presented inside A3 format leaflets, left for use on a large table for the whole duration of the event (4 days). Participants could come and go around that table anytime during the whole period, fill in leaflets in one go or little by little. In order to "vote", each participant was given a specific inked stamp pad (two colours of ink were used to differentiate the meanings of the vote). Beyond the somewhat playful aspect, the idea was to have votes kept anonymous.

Further surveys

This experimental setup was reused in later experiences in order to try and compare results obtained on the initial test group with others, and to identify possible biases. These additional surveys were however only conducted on the measure of impact test. Groups targeted were last-year students in specific courses preparing them for a PhD. These groups were this time consistent in terms of education -students in mechanical engineering or in architecture. The same method was applied (except timing constraints, stronger here) and results are discussed in the conclusion section.

ANALYSIS OF THE RESULTS

Following the survey, we analysed the results through four graphics aimed at having actors get aware, through visual means, of distances between them, of overlaps and non-overlaps. The paper-based survey was transferred in an RDBMS structure that is queried on the fly in order to output SVG (Scalable Vector Graphics) interactive visualisations. For didactic purposes (illustrating the potential and variety of infovis-inspired solutions), we chose to use two classic, easy to read visualisations (BubbleViz and sparklines) and to develop two specific visual displays, with strong interaction modalities. It is clear that alternative designs could (and probably should) be tested, with possibly better results. However the four solutions were duly decoded and commented by actors during post-event meetingsand accordingly played their expected role.

Measure of impact

The first visualisation we developed was aimed at analysing globally the "quantities" of votes. It builds on the classic "bubbleviz chart" formalism, with each line corresponding to one of the four possible answers (Figure 3, from top to bottom, do not know but could be concerned, do not know, know and not concerned, know and concerned). Each column corresponds to one of the 44 visualisations in the survey, and the size of circles or bubbles corresponds to the number of votes. -What is more, when voters claim they know visualisations dedicated to time-oriented data these solutions are all derivatives of the traditional timeline paradigm (1, 2 , 3, 4).

-Some interesting exceptions strike out -milestones in the history of data visualisation that seem forgotten (a') -Finally, some poorly known concepts are pointed out as potentially interesting (a', b'). But at this stage the disciplinary field of voters is still not taken into consideration. This is done through another visual formalism (Figure 5) in which what is observed is, for each of the 44 classic visualisations, whether there is a relation of the votes to the discipline of voters. In the centre of this concentric graphics two pieces of information are encoded: the category (or categories) to which the classic visualisation corresponds, and its date of creation (Figure 5,a,b). Each angular sector then corresponds to a given voter, and the main discipline of voters is represented by the coloured exterior rings (Figure 5,c). Some interaction is added (on/off switches for textual data or links to in-depth descriptions of the visualisation). The visual formalism helps pointing out differences in terms of background, of references, of expectations, inside the target group. Figure 5 represents votes corresponding to a mediaeval map that appears in Matthew Paris's 'Book Of Additions', a typical ribbon map building on the notion of ordinal time. Naturally here archaeologists and historians do know, if not this specific map, at least this type of representation.

By contrast, a majority of geographers and computer scientists apparently never came across such graphics. But a more intriguing observation can be made: almost one archaeologist or historian out of two declares he feels unconcerned by such ribbon maps. The formalism can also be used to compare answers collected for several visualisations. At times the result is nothing like a surprise (Figure 6) -some classics are known by a vast majority of voters. But this result still gives a useful indication, since it ultimately corresponds to what references people have in common. The result can also be hard to analyse, with rather chaotic answers (Figure 7) and no real discipline-specific behaviours striking out (except in the computer scientists group -bottom, blue ring). However one clear indication is given: inconsistencies inside each group, and a rather poor impact of classic infovis solutions in the test group. In short this concentric visual formalism helps reading behaviours and backgrounds, notably: -Discipline-specific knowledge patterns -Common references across the group -Discipline-specific behaviours (fields of concern that are privileged, solutions that raise an interest).

But in many test groups some people are likely to give fast, and somewhat untrustworthy answers -typically here possibly a systematic "I know that kind of visualisation" answer. This is detected through a third visual formalism (Figure 8), inspired by E.R Tufte's sparklines [START_REF] Tufte | Beautiful Evidence[END_REF].

Each line corresponds to a voter (a coloured dot on the left identifies the discipline), each column corresponds to one the 44 visualisations. Read horizontally, the visual formalism helps spotting individual behaviours -voters who often abstain from giving a vote, voters who almost always give the same answer, etc. This last visual formalism also has an additional role, this of assessing "how thirsty to know or use" voters are. An interactive menu allows to remove all answers from the sparklines except one (Figure 9). On the left part of Figure 9 only the "I do not know this type of visualisation (and do not want to)" answers are shown. On the right part of Figure 9 only the "I do not know this type of visualisation but I think it could be of use for me" answers are shown. The contrast is striking -with some voters definitely not convinced of the potential of the collection of visualisations, and others clearly willing to go further. This visual formalism also underlines a rather weak interest by the test group for timeoriented visualisations (right columns of the sparklines) -once again an eye-opener for the community, and a point we definitely needed to check. The tendency is confirmed in a variant of the sparklines formalism where the 44 classic visualisations are distributed in three groups (categories quantities, space and time). A visual solution like the peopleGarden metaphor [START_REF] Xiong | PeopleGarden: creating data portraits for users[END_REF] for instance apparently raised very little interest. This is an indication that infovis as such remains something like a terra incognita for the test group.

To sum it up, the bubbleviz formalism is used to point out a common trend inside the whole group of testers (space privileged over time); and the concentric visual display underlines biases of sub-groups of testers (discipline-specific behaviours). Both can be used to share a common understanding, as unveilers (more than as statistical indicators, naturally).

Finally, the sparklines formalism is used to detect individual, specific behaviours and therefore rather corresponds to an "expert" view of the survey's results.

Overlaying of concepts and terms

The above measure of impact is supposed to help measuring what tools for visual reasoning actors have in common. In an interdisciplinary problem-solving situation there are earlier stages when what is at stake is reaching a common understanding of concepts, notions, terms that will form the basis of the common investigation.

This is precisely what we have tried to analyse in this second experiment. The resulting visualisation is loosely inspired both by the franco-polish mnemonic method [START_REF] Rosenberg | Cartographies of Time Princeton Architectural Press[END_REF] and by the postHistory visual formalism [START_REF] Viégas | Digital Artifacts for Remembering and Storytelling: PostHistory and Social Network Fragments[END_REF]. It combines a reading of the collection of terms and definitions, a reading of votes, and a reading of votes per discipline.

The collection of terms and definitions is what is presented to users by default. Terms are distributed into categories (phenomenon, time parameter, uncertainty, general vocabulary, tabula rasa), forming sort-of tiles (Figure 10). Inside a category, each term is presented as a smaller, square tile inside which definitions for the term are presented as even smaller tiles. A different outline colour is used to differentiate definitions in French or in English. Basic interactions allow users to read the definitions. At this stage the collection itself is analysed, with for instance a rather high density of definitions in some categories. Votes are introduced on user demand, as an additional layer composed of small red or green squares for each definition (Figure 11). The surface of those squares is indicative of the number of votes. Colours correspond to the meaning of the vote: Green: I use this term in the sense given by that definition. Red: I use the notion given by that definition, but I use another term for it.

The visualisation can be analysed term by term, with for instance cases when one definition is privileged by voters (a large, dominating green square for one of the term's definitions, see Figure 11, a, "cyclic time"). In other cases several definitions are considered as equally relevant (green squares of similar sizes for several definitions of a term, see Figure 11,b, "phenomenon"). As a consequence actors are made aware of an overlaying challenge (If the definitions all agree then the notion behind the term is a shared one, but if they differ then various interpretations of the term are present in the test group).

Controversy is clearly assessed (red squares) and some definitions are rejected by a large majority of voters (red square bigger than green square, see Figure 11,c, "dynamics", "duration", "event"). What is shown here is a sort of common interdisciplinary behaviour: the notion depicted by the rejected definition is used, but another term is used for it.

Finally there are cases when one definition is equally rejected and agreed with (see Figure 11,d,"generalisation", "subjectivity", "representation, "information"). Now this is typically the notions an interdisciplinary community will indeed have to discuss: not making that effort would most probably result in misunderstandings, and accordingly in delays and inefficiency.

The amounts of votes for each definition, and each term, also give an indication. The number of votes in the "time parameter" category (top right, blue outlined tile) is visibly small, at least smaller that in the other categories: the test group seems to pay less attention to time than to space, and as a consequence feels unfamiliar or unconcerned with a wide range of terms such as "chronon", "temporal operator", "branching time", etc. In addition, categories can be observed as such, and Figure 11 shows that the uncertainty category (black outline) concentrates a high number of definitions that are questioned -an indicator that the community would need a thorough debate if it was to co-act in an interdisciplinary analysis process where such notions are key. This visualisation is used in a fine grain analysis in order to try and spot discipline-specific interpretations of terms. It helped us uncover patterns we would not have expected -for instance controversy over the term "information". One would imagine there is no need to have a debate over what it means, but one of the definitions we provided "information [is the] result of the interpretation of raw data in a specific context" was rejected by geographers and geographers only -an indication that debate is needed so as to have the notion clarified inside the community.

LIMITATIONS AND PERSPECTIVES

We have presented an approach aimed at an awakening by actors to the practical and partly avoidable difficulties of an interdisciplinary research. The protocol includes two major steps:

-A survey method highlighting overlaps and non-overlaps in terms of background, references, terms and notions (a method willingly undemanding in terms of learning curve, or of familiarity with computer platforms).

-A series of visualisations helping to pinpoint discipline-specific behaviours, and more generally to analyse the survey's results from a variety of standpoints.

It has to be stated clearly that a number of biases were introduced.

The first and prominent one is the data set itself: both the collection of classic visualisations and the collection of terms are subjective collections. Other samples may have resulted in different observations. Another bias to point out is the survey method: we privileged a (supposedly) playful and debateenhancing survey modality -but we acknowledge answers given by participants may have been influenced by the fact that some chose to go though the survey with a friend or colleague, and accordingly may have changed their intuitive vote following a debate.

But in parallel, it should also be said that our goal was nothing like reaching general conclusions. Our intent was but to encourage debate, a debate we nourished by numbers and facts, and additionally to publicize infovis-inspired solutions in a community where they are not that influential. We make no claim on the generic aspect of the survey protocols we have implemented, of the visualisations we propose, or of how representative the target groups are. Yet we tend to consider that the very approach -having people, if they are to co-act in an investigation process, say out where they stand, in practical terms, through concrete and structured interrogations -can be a plus in the context of complex, multi-faceted, problem-solving situations.

Following this first experiment, where can we go next? One thing we did is compare results with a second target group on the measure of impact experiment (see section 3.3.1). Globally behaviours remain consistent and somehow expected: the second group are pre-PhD students, not directly involved in the handling of spatio-temporal data sets, and accordingly are less familiar with many of the visualisations we had selected. There are some more interesting differences though, such as the "thirst for knowledge" indicator -significantly higher in the second group on some specific visualisations (TimeWheel formalism or peopleGarden metaphor for instance). A further enlargement of the target group, notably through the development of a crowdsourcing platform, is one of the directions we investigate. This would call for a significant rethinking of the technical platform and would require in particular more interactivity, and more updatability.

CONCLUSION

Our intent with this experiment was to use a survey protocol and a set of visualisations as eye-openers for a community. The outcome of this experiment is therefore neither a protocol nor a set of visualisations, but the influence they had on participants, i.e. a greater awareness of:

• unthought-of biases in the community (predominance of space-centric solutions), • lacks in the handling of the time parameter, • potential support of infovis-birthed formalisms in the handling of spatio-temporal data.

In short, the experiment shows that in an interdisciplinary investigation process, or in a problem-solving situation, actors will better and faster communicate with one another if they have been given practical means to assess disciplinary distances. But this can only occur if a scenario of interaction has been designed, here consisting of a survey + a set of visualisations. The scenario we propose is certainly questionable, but a lack of scenario simply results in an ignorance of disciplinary distances. Accordingly, this paper calls for the emergence of sort-of dashboards for interdisciplinarity, consisting of statements of distances between actors, prior to their actual co-acting. Such dashboards can be seen as tools to initiate a move from interdisciplinarity to what [START_REF] Schuller | Designing universal knowledge[END_REF] calls transdisciplinarity, i.e. […] a common system of axioms for a set of disciplines.

Figure 1 :

 1 Figure 1: An overview of the profiles of participants. Each column corresponds to a participant. Shades of grey correspond to disciplines or competences. Below the middle horizontal line are squares corresponding to the initial competences or education of participants -several squares, several competences. Above the middle horizontal line are squares corresponding to their current fields of concern or research field. Only 12 participants out of 46 can be said to be "mono-disciplinary" (columns on the right side of the figure).

Figure 2 :

 2 Figure 2: The survey material -printed material and inked stamp pads.

Figure 3 :

 3 Figure 3: Partial view of the bubbleviz chart, with the four lines of votes. Bottom glyphs correspond to categories (from top to bottom, quantities, space, and time). Categories (quantities, space-oriented data, time-oriented data) to which each visualisation corresponds are shown as coloured glyphs in a bottom line, and some interaction added. The visualisation does reveal some interesting patterns (Figure 4): -Voters are clearly more familiar with visualisations of quantities or of space-oriented data (left and middle parts of the figure) than with visualisations of time-oriented data (right side of the figure).-What is more, when voters claim they know visualisations dedicated to time-oriented data these solutions are all derivatives of the traditional timeline paradigm(1, 2 , 3, 4).-Some interesting exceptions strike out -milestones in the history of data visualisation that seem forgotten (a') -Finally, some poorly known concepts are pointed out as potentially interesting (a', b').

Figure 4 :

 4 Figure 4: The bubbleviz chart showing votes per visualisation.

Figure 5 :

 5 Figure 5: Votes and profiles of voters for a given visualisation. Left, results for W.Paris's London-Jerusalem itinerary, Right, legend of the graphics.

Figure 6 :

 6 Figure 6: Comparison of votes for two classic solutions -Minard's flow maps and Marey's chronological chart. Unsurprisingly, both are familiar to the whole test group.

Figure 7 :

 7 Figure 7: Comparison of votes for M.Homma's candlestick charts and F.Galton's weather maps.

Figure 8 :

 8 Figure 8: The sparklines visual formalism -two individual, specific behaviours are spotted.

Figure 9 :

 9 Figure 9: Sparklines showing (left) only the "I do not know" answer and (right) only the "I do not know but want to" answer (partial view, two opposite behaviours spotted).

Figure 10 :

 10 Figure 10: Visualisation of the collection of terms and definitions, distributed by categories.

Figure 11 :

 11 Figure 11: Visualisation of votes -definitions in the "uncertainty" category, outlined in black, appear as quite often controversial.At this stage what the visualisation shows is a global picture of where controversies are. But the discipline of voters is not visible. This is done though a third level of interaction, allowing users to read for each term, and each definition, each individual vote represented by a coloured vertical bar (Figure12).

Figure 12 :

 12 Figure 12: Visualisation of individual votes for a given term. To each definition (written above the tiles) correspond lines of vertical bars -each bar is a voter, and the bar's colour represents the main discipline of the voter. "Green" votes are aligned in the top row, "red" votes in the bottom row.

ACKNOWLEDGMENTS

Survey conducted in the framework of an event co-funded by the MoDyS research group, and by CNRS.


Jean-Yves Blaise
email: jean-yves.blaise@map.archi.fr

Iwona Dudek
email: iwona.dudek@map.archi.fr

Can simplicity help?

Keywords: H.3.3, H.5.2, J.5, H.1, H.2.5, H.2.8, H.3.1 Documentation, Design, Experimentation, Human Factors, Historical sciences, Methodology, Spatio-temporal data, Information visualization, Visual formalisms

Analysts are today given (or develop) a wide range of powerful visual reasoning tools, of leading-edge platforms where services like high interactivity, high computing capacity, structured knowledge, processing of complex reasoning tasks on massive data, are duly implemented. Classic XVIIIth or XIXth century dataviz solutions may then appear as respectable, but deprecatedfar too simple to match today's challenges. In this paper we try to show that the very simplicity of these solutions can still be of help in problem-solving situations. We first revisit some great classics in order to uncover patterns and exceptions inside a data set consisting of incomplete historical evidence on groups of stalls that used to be located on the market square in Cracow. We show that, once reinterpreted and re-implemented, classic visualisations like Charles De Fourcroy's tableau poléométrique or Munehisa Homma's candlestick chart do help the analyst to re-read the data, and to augment his level of knowledge. We then introduce an online, free to use implementation of these classics that we expect to weigh through a sort-of crowdsourcing approach to which extent they can act as relevant and generic visual formalisms. Finally, we underline the potential contribution of this research in terms of methodology, and discuss in what simplicity proves here helpful.

CONTEXT & MOTIVATION

With the introduction of computer platforms, a wide range of new solutions have little by little emerged and now allow analysts to manipulate, structure, and re-read temporal or spatio-temporal data sets. A number of leading-edges approaches and examples can for instance be found in state-of-the-art compilations like [START_REF] Aigner | Visualization of Time-Oriented Data Springer: Human-Computer Interaction Series[END_REF], [START_REF] Keim | Mastering the Information Age -Solving Problems with Visual Analytics[END_REF], [START_REF] Spence | Information Visualization: Design for Interaction[END_REF], [START_REF]Visualizing Information Using SVG and X3D[END_REF], etc. And indeed the introduction of computer-based visualisations has clearly dramatically changed the way we design and produce graphics, the way we use vision to think, to say it with B.Scheidermann's words [START_REF] Card | Information vizualisation: Using vision to think[END_REF].

High levels of interactions, as well as updatability, are obviously key factors in this shift from "hand-drawn" graphics to nowadays complex information visualisation and visual analytics platforms. But other computer-dependent services could also be quoted like the handling of massive and/or real-time data sets, data preprocessing, user configurations, etc. Accordingly, visual reasoning solutions have been introduced in a wide range of activities and processes, from for instance exploratory analysis of spatiotemporal data [START_REF] Andrienko | Interactive Cluster Analysis of Diverse Types of Spatiotemporal Data In SIGKDD Explorations[END_REF] to massive text data analyses [START_REF] Seifert | Visual Analysis and Knowledge Discovery for Text[END_REF], or from the real-time detection of events [START_REF] Krstajic | Getting There First: Real-Time Detection of Real-World Incidents on Twitter[END_REF] to the handling of uncertain data [START_REF] Gschwandtner | A Taxonomy of Dirty Time-Oriented Data[END_REF]. Animated graphics, although not necessarily suitable in many analytical processes can also be used as complement, or to trigger attention -a convincing evaluation of the respective pluses and minuses of static and animated graphics can be found in [START_REF] Boyandin | A Qualitative Study on the Exploration of Temporal Changes in Flow Maps with Animation and Small-Multiples[END_REF].

In short, as a result of this evolution, not only the making but the use of visualisations has known a very deep change. Yet, some most respected authors in the data visualisation community have put a strong focus on old, hand-drawn, pre-computer era visual solutions. The first name that will come to the mind of the reader will probably be this of E.R Tufte [START_REF] Tufte | The Visual Display of Quantitative Information[END_REF] [START_REF] Tufte | Envisioning Information[END_REF][13] [START_REF] Tufte | Beautiful Evidence[END_REF]: his influence is indeed a major one. But we definitely can also mention here the works of M.Friendly [START_REF] Friendly | A brief history of data visualization[END_REF] or G.Palsky [START_REF] Palsky | Des chiffres et des cartes -La cartographie quantitative au XIXème siècle CTHS[END_REF], and on temporal data the Cartographies of Time by D.Rosenberg and A.Grafton [START_REF] Rosenberg | Cartographies of Time Princeton Architectural Press[END_REF].

Naturally a number of past-time innovations these authors quote are today used on an everyday basis: typically Playfair's pie charts or bar charts, or Von Mayr's star plot for instance.

By contrast, some of these innovations seem still unrivalled: Minard's Russian campaign, described by [START_REF] Tufte | The Visual Display of Quantitative Information[END_REF] [START_REF] Tufte | Beautiful Evidence[END_REF] as "the best graphics ever", has been commented and challenged many times [START_REF] Friendly | Visions and Re-visions of Charles Joseph Minard[END_REF] [START_REF] Kraak | Geovisualization illustrated In[END_REF]. Blanchet's 1840 catholic ladder [START_REF] Rosenberg | Cartographies of Time Princeton Architectural Press[END_REF] introduces a multiple-granularity representation of time that still acts as food for thinking for designers.

Finally, some concepts seem to be more or less forgotten, or are used in narrow disciplines, although they actually are generic solutions -Marey's train schedule could be quoted here [START_REF] Tufte | The Visual Display of Quantitative Information[END_REF].

Yet our motivation is not to comment on hand-drawn visual formalisms, or to eulogize great Masters. Our intent is to show, in a specific problem-solving situation, how a reinterpretation of "old" formalisms helped us to re-investigate a data set.

Simplicity the way we shall discuss it should therefore not be interpreted as specific to hand-drawn graphics: it should be understood in the sense of Maeda's laws of simplicity [START_REF] Maeda | The Laws of Simplicity[END_REF]: "a way to recognize design itself" [START_REF] Schuller | Designing universal knowledge[END_REF].

In the next section we briefly present previous works, so as to position this contribution inside its context. We then detail the data set and the issues behind it. Following, we present our reinterpretation and implementation, and the findings they helped us reach. Finally, the evaluation section introduces a generic, online, free to use implementation of these classics. The conclusion section will focus on how revisiting classics can be considered in terms of workflow, how it can act as food for rethinking a data set, and ultimately in what exactly we think simplicity does help.

BACKGROUND AND PREVIOUS WORK

We have over the years implemented a rather exhaustive information system on the evolution of the Market Square in Cracow (40 built structures, over a hundred documented changes, time span of approximately a millennium) and proposed a series of visualisations dedicated to various reasoning tasks [START_REF] Dudek | Visual assessment of heritage architecture life cycles[END_REF][24] [25][26]. Typically, we have tried to describe and compare lifelines, spatial layouts, amount and quality of historical evidence, types of changes over time, correlations between the construction activity and external factors, etc. This fine-grain analysis proved here and there to be difficult, due to the very nature of historical evidence (incomplete, unevenly distributed in time, imprecise, untrustworthy, etc.), but it did lead to a series of global (visual) pictures of what is actually known about that site. The system is developed on a robust but rather elementary implementation, combining RDBMS and/or XML data sheets, Perl scripts, SVG / XML/ HTML outputs produced on the fly. The inputs and results presented in this paper are handled inside this technological context.

Coming back to Cracow's Market Square, it hosted, like most such urban spaces inside European medieval towns, a variety of buildings and functions, ranging from administrative structures to commercial facilities. Here specifically, as a result of the Market Square's dimensions (200m*200m, one of the largest in Europe), quite a lot of functions co-existed: the town hall compound alone comprised housing for the town authorities, a granary, a prison, a tavern, a garrison. The Market Square comprised in addition two municipal scales, a church, and a large number of commercial facilities.

Furthermore, the size and type of construction strongly varied, from the huge brick-built Cloth hall (over 100 meters long) to small more or less temporary wooden structures when talking about commercial facilities alone. Naturally, not only do the edifices differ in terms of size, construction, function, but our level of knowledge about each of them strongly differs.

Among the 40 built structures we have analysed over the years, several small-scale wooden commercial facilities (Figure 1) share rather challenging particularities:

-They cannot be precisely localised within the market square (known only in archival sources by expressions such as "close to the town hall" or "located in the northern sector"). Moreover, they were wooden structures, and were demolished during the XIXth century. There is accordingly no archaeological indication we can count on. -Almost no direct visual or textual archival material can be found that would help analysts say what were their construction and spatial layout. In fact, these facilities may even not have been "buildings" in the proper sense: they could have been rows of movable stalls, not physically connected to one another, but considered as a consistent group basing on their common function (goods/services sold). We shall focus here on eight such structures, namely: "Lithuanian stalls", "stalls near the town hall", "Hatters' stalls", "Glass stalls", "Food stalls", "stalls near the minor traders' hall", "butter stalls" and "gingerbread stalls" (Figure 2). If very little is known about these commercial facilities (in fact temporal aspects are better known than spatial aspects, which is not that common), we do have one document that gives useful indications: an 1760 inventory conducted by Cracow's municipal authorities of the time. The inventory does not give indications on where the facilities were, how they looked like, but provides the following information:

-name of the structure, -a "sequence number" for each stall inside the facility (however the inventory does not mention what this number actually means -it can be interpreted as the chronological order of the inventory, as well as the spatial order from the beginning to the end of a row for example). -name of owner -width and depth of each stall inside a structure (given in a local unit, ells, with a granularity of a quarter of ell) -tax (amount) before 1760, called "census antiquus" -tax (amount) after 1760, called "census novus" Some other indications are given, but not for each stall (typically, what product is sold). Finally there are some specific stalls for which crucial data is missing (size or tax amounts) -a rather expectable pattern in historical data sets.

It has to be said at this stage that reasoning in one go both on complete and incomplete data (and without asking the analyst to "fill in the missing data") is crucial in Historical Sciences. Our research question can now be laid down openly: could these scarce pieces of information help us gain some understanding of the architecture, of the use and of the urban layout of these facilities (spatial layout, consistency of stalls inside a facility, economical background and rules for a given structure)? More precisely, two main questions emerge: -can something be said about patterns of distribution in space ? -can something be said about the tax policy in general, and the tax increase in 1760?

With such a limited amount of data, and rather basic research questions, we have considered it was worth trying to start by revisiting some great classics -Charles De Fourcroy's tableaux poléométriques and Munehisa Homma's candlestick charts. In a later experiment we also tested C.J Minard's tableaux graphiques -this we mention in the conclusion section.

These visual formalisms turned out to be quite efficient, provided some interaction is added and the incomplete data issue is dealt with. As will be shown, they helped us identify a number of clear patterns and exceptions in the data set.

THE EXPERIMENT 3.1 Tax / surface proportionality: Charles de Fourcroy's tableaux poléométriques

In 1782, Charles de Fourcroy -a French mathematician and scholar -designed what he called a tableau poléométrique (translated by [START_REF]Des cartes générales aux cartes spéciales[END_REF] in "poleometric table") -nested geometric figures (squares, Figure 3) used to compare urban surfaces of major European cities (in his own words, "a kind of table that visually represents an idea of the actual proportion that can be found between the surfaces of these different cities" [START_REF]Des cartes générales aux cartes spéciales[END_REF]).

His design, that M.Friendly [START_REF] Friendly | Milestones project[END_REF] calls "an early tableau graphique", illustrates a key movement into abstraction [START_REF] Palsky | Des chiffres et des cartes -La cartographie quantitative au XIXème siècle CTHS[END_REF]: the figure ceases to represent an object, a "real thing". But his design introduces other unsaid breakthroughs, and notably the concept of cluster: not only are cities "grouped" and "sub-grouped" by ranges of sizes ("very big", "big", etc.) but the values themselves are likely to have been approximated so as to create sort-of clusters (Milan, Naples and Madrid for instance are shown as cities of perfectly equal size).

The influence of Charles de Fourcroy's poleometric table is seemingly difficult to measure but his concept is, as will be shown, quite easy to reuse. One of the reasons for that is that he simply underestimated the capacity of the figure to convey additional information, beyond proportions of squares (in particular, colors are used only to differentiate a square from the next/previous one). Coming back to our data set, we did have an indication about the surfaces of wooden stalls (width and depth, in ells) -and naturally a first thing we did is draw a poleometric table for each commercial facility, where each stall is represented by a square.

Figure 3 : Charles de Fourcroy's Tableau Poléométrique [START_REF] Friendly | Milestones project[END_REF].

We also produced a poleometric table for the whole collection of commercial facilities, where each square represents the overall surface of all stalls in the facility. Once some interactivity is added, the result is a figure enhancing differences / similarities in surfaces inside one commercial facility, and across commercial facilities (Figure 4). On each of them it appears quite clearly that surfaces of stalls inside each facility widely differ -and in a way that could even be seen as totally chaotic. However, when looking from closer, a majority of surfaces are grouped, in all of those four facilities, around the green line's value (more or less 3 square meters, or 9 squares ells). Also noticeable, in each of the four edifices a stall stands out significantly as bigger in surface than the rest. Finally, the left tableau -corresponding to the Lithuanian stalls, shows a wider range of surfaces, which is quite understandable when knowing their overall number (33, far more than the others) and its approximate localisation, around the south-east corners of the market square (meaning most likely a facility composed of several, spatially independent, groups of stalls). Colour here still means nothing particular: it is only used to facilitate visual contrasts. But beyond surfaces our data set includes indications on tax levels before/after 1760 for each stall, and thereby on tax increases. This information is represented by a second poleometric table, that we somehow "mirrored" along the X axis (Figure 5), and inside which the colour now means either the tax level, or the tax increase (the brighter the red is red, the higher the value). Each stall is represented by a square in the top poleometric table (where the focus is put on surfaces and their variability), and by a corresponding square of same surface in the bottom poleometric table (where the focus is put on the variability of an additional parameter, here tax levels / increase, toggle on user demand). As mentioned earlier, since we deal with historical data sets some information is missing: the issue is dealt by providing specific glyph or adapting the colour scheme (Figure 6). Quite surprisingly if one considers the absolute simplicity of the visual formalism, some clear conclusions do emerge, in particular on tax / surface proportionality (Figure 7). Since the size of squares represents surfaces, if tax levels had been proportional to surfaces we would have bright red colours on the bigger squares, and progressively darker reds as the size of squares diminishes.

Interpreting the visualisation becomes straightforward: bright and dark reds are NOT organised progressively, tax levels are NOT proportional to surfaces.

If that is so maybe the tax increase in 1760 was a sort of catchingup process? This possibility is also denied. Note that the fourth group of stalls' mostly highly taxed (Figure 7) is also the one for which the increase is the biggest (Figure 8). In short, there is apparently no simple relation of surface to tax levels or increase. The implementation includes various user interaction modalities (we do not mention here general functions like zoom/pan). "Mouseover" events are used to provide additional visual or textual information on each stall -order of appearance in the inventory, owner, approximative localisation, surface in square metres, width and depth in ells (represented by a rectangle), tax levels before and after 1760, increase rate given by value and represented as a slope (Figure 5). Values corresponding to the colour gradients are displayed on user demand (Figure 7, under the second group of stalls). Users may also decide on what to show in each table: surfaces, tax levels before and after 1760, tax increase (Figure 9). Finally, two other graphics can be displayed, on user demand, for a given group of stalls. The first is a sort-of histogram (Figure 10) showing on the right side stalls widths in descending order (top part) and stalls depths in descending order (bottom part). The scale of grey represents a proportionality indicator -the darker the grey, the more "dominant" the current dimension. A dark grey in the top part means width is significantly higher than depth.

Bars are then mirrored of the left side and tax levels represented with a gradient of red as in the poleometric tables (the brighter the red, the higher the tax level). The surface of the small squares aligned vertically in between the bars represents the tax ratio (tax per linear meter of width -top part -or depth -bottom part). The extreme diversity of tax ratios in the bottom part would suggest that if the tax was calculated basing not on a surface but on a length (linear meters of selling space), it was probably calculated on the width (if not, taxation policies had to be very unfair…). The second additional graphic can be seen as a 2D spatial puzzle, helping the analyst to test various spatial arrangements of stalls. It builds on the hypothesis that stalls sharing a common dimension (may it be width or depth) could have been aligned as a consistent row. Stalls are in aligned horizontally and vertically -the x value is then the width, the y value the depth. A "mouseover" event allows users, on selection of a given stall, to check "which stalls in the group have the same width and the same depth". Then, once a given stall is selected, all stalls of comparable width OR depth are aligned, and the remaining stalls presented as a group (Figure 11). These additional graphics are designed to help analysts think aloud in workgroups, and, as such, complement the poleometric tables in later stages of the analysis process.

Tax /position interrelations: Munehisa Homma's candlestick charts

Candlestick charts originate from Japan where in the early XVIIIth a rice trader called Munehisa Homma used them to represent visually the movement over time of rice prices (debate over the actual origin of candlesticks can be found in [START_REF] Fogle | The Art of Japanese Candlestick Charting[END_REF]).

Traditional candlestick charts (Figure 12) represent the changes of value for a "good" during a time interval, and summarize four indications:

-price of the good at the open and the close, represented as a box called the body (filled black when prices fall, white when they raise) -maximum and minimum prices during the interval, called upper and a lower shadow (wick).

Figure 12: The "classic" candlestick

When wanting to observe changes over time on a longer period, candlesticks are presented along the X axis as a time series (Figure 13). Unlike poleometric tables, candlestick charts are known and used worldwide: they remain today a popular visual tool in the world of trading and financial analysis. The concept, however, can be adapted to other fields of concern -the wikipedia page on candlestick charts for instance illustrates an application to meteorological data. The concept could in fact be adapted to any situation where a limited number of quantitative variables need to be observed in ordinal time. But our re-vision of candlesticks introduces a quite significant shift. Each candlestick represents, for a given stall, the following information (Figure 14):

-tax levels before and after 1760 (height of the body, when levels are unchanged a specific glyph is displayed), -tax increase (colour of the body), -ratio per square meters before and after 1760 (upper and a lower shadows), -average and maximum values for that ratio across the group of stalls (small horizontal grey lines above and below the body).

Figure 13: The information encoded in a candlestick

When information on tax levels is missing, we have the stall represented by a grey dot on the X axis, and when information on surfaces is missing the shadows of the candlestick are replaced by a small grey square. Candlesticks for each stall inside a facility are then represented side by side, in the inventory's order of appearance. In other words, what we show is not a time series any more but a "spatial" series -with each candlestick corresponding to a stall, to a location (an order) in space.

Interactivity is here quite basic -on user demand values for tax levels can be displayed, along with the legend and its colour scale (the colour scale is recalculated for each group of stalls so as to enhance differences, but can be blocked to fixed values when comparing several groups). A rectangle representing the surface and shape of the stall can also be displayed on user demand, along with the stall's owner name. Both the poleometric tables and the candlestick charts have proven efficient in workgroup discussions, where they allow a fast and clear-cut invalidation of biases, easy judgments, popular misconceptions and generally accepted ideas. It should be said though that these graphics help us to spot questions, rather than to give answers (the latter being often out of direct reach in Historical Sciences). Those questions then act as potential evidence, needing cross-examination, and nothing more.

EVALUATION AND FUTURE WORK

Evaluation is here somehow arduous: proving the visual formalisms can be helpful and efficient is something like a mere pleonasm once we have shown how they impacted our understanding of the data set. Besides, the context of the research -a specific question, addressed by a small group of "experts"was such that issues like the quality of the interfacing and of the interactivity were probably not key to test (what we did check though, but informally, is the capacity of such simple visual design to foster discussions in workgroups). We could of course also have compared the performances of other suitable but more recent solutions like mosaic plots or treemaps (although newcomers in visual reasoning -historians for instance, at ease with Latin texts but not with abstract graphics -may not feel familiar with such formalisms). Instead, what we did is consider the evaluation issue from another point of view -this of the capacity of our two classic visual formalisms to be considered as efficient generic solutions. We therefore developed an Internet implementation allowing users to post a data set (CSV format) and retrieve an SVG poleometric table. The data set must include for each item three parameters: -a label (qualitative), -a surface parameter (any quantitative parameter, may it correspond or not to a physical surface), -a density parameter (quantitative) represented in the bottom table (the more red, the more dense).

The idea is to try and learn from users, in this sort-of crowdsourcing approach, whether the simplicity of the concept, and whether the actual implementation we propose, can really be adapted to a variety of situations. A preliminary feasibility test was conducted within our research unit. Several data sets were tested: city sizes vs. population; computer disk use vs. number of files, size of forces engaged in a battlefield vs. duration of the battles or vs. losses (Figures 16), size of book vs. number of pages for various editions, farmland use vs. size of farms, etc. These tests already allowed us to harvest a certain number of feedbacks (minuses, open questions or expected improvements), notably:

-readability and scale of values: a good readability requires a range of surfaces (first parameter) between 1 and 20, thereby limiting the use of poleometric tables to such situations; -clustering: the current implementation creates automatic kind-of clusters by approximating decimal values. A critical improvement would be to allow user-monitored clustering so as to avoid visual discomfort when surfaces are "almost" overlapping; -comparison enhancement : a proposition was made to display the density parameter "as expected if it was in a linear scale relation with the surface parameter" (or any other relation), so as to gauge the differences between a benchmark and the real pattern (Figure 16); -user configuration and interaction : the actual colour gradient is system-defined, an improvement would be to allow user-choices for the colour and the orientation of the gradient;

This approach is primarily intended at having the needs of users resurface, and the range of potential reuses of the formalisms assessed. But what we more generally expect from this experiment, with time, and expectably a number of faulty attempts, is to get a better understating of the actual benefits of Charles de Fourcroy's solution: a nice old thing, or something worth re-examining?

CONCLUSION

In this contribution we introduced a reuse of "great classics" in a new context: the need to uncover patterns and exceptions inside a data set consisting of incomplete historical evidence on groups of stalls that used to be located on the market square in Cracow.

The experiment shows that once reinterpreted and reimplemented, equipped with ad-hoc interactivity, classic visualisations like Charles De Fourcroy's tableaux poléométriques or Munehisa Homma's candlestick charts do help the analyst to re-read the data, to augment his level of knowledge, and ultimately can boost workgroup discussions. It should be said clearly though that our objective was not to prove that poleometric tables and candlestick charts are unavoidable research tools when facing incomplete historical data sets. In particular, we make no claim other visual solutions could have helped us reach exactly the same conclusions concerning the tax/surface interrelations or the tax /position interrelations. What we did is show that, although they are very simple concepts, and not related to our field of application, they can be fruitfully revisited. We then proposed a generic implementation aimed at weighing to which extent what was true for us could be true elsewhere.

By this, we believe this contribution underlines a more general issue: testing out a sort of "reverse workflow". In other words, instead of observing a data set, then formulating questions, and finally deriving from these questions some visual tool, why not try and do precisely the contrary:

-observe and understand a classic visualization, -think out how it could be applied in the context of a data set, -ultimately, verbalize a question that had not even come to the mind, or introduce new metrics for the data set under scrutiny.

J.Bertin wrote that "Graphics […] an answer to a question" [START_REF] Bertin | Semiology of Graphics: Diagrams, Networks, Maps[END_REF], and we acknowledge he was perfectly right. But what if we tested it the other way round: "Graphics: a potential question yet unthought-of?" Naturally it has to be said strongly that in no way do we consider this "reverse workflow" as a potential central part of the analysis process. But this research shows that it is worth trying it out. This is what we did in yet another experiment, this time revisiting C.J Minard's tableaux graphiques (in a rather loose way, however).

The original solution was meant to analyse commercial traffic along a canal in Burgundy (France). The X axis corresponded to distances from a fluvial port to another and the Y axis corresponded to the quantity of goods transported. Accordingly the surface of rectangles represented the price of the delivery [START_REF] Palsky | Des chiffres et des cartes -La cartographie quantitative au XIXème siècle CTHS[END_REF].

In our interpretation, applied to major edifices of the market square in Cracow, the X axis corresponds to each edifice's surface (a quantity, in square meters) and the Y axis to its lifeline (a quantity, in years). The surface then represents an unthought-of metrics dedicated at historic architecture: a "sustainable usability" measure (Figure 17). The idea is simple: an edifice can be considered as more "useful" if it is big, but also if it lasts long. In our interpretation, of C.J Minard's "tableaux graphiques", the surface of a rectangle corresponds to that idea: the bigger the edifice the bigger the rectangle, and the more long-lasting the edifice the bigger the rectangle. To conclude, one of this paper's claims is that the power of many hand-drawn masterpieces probably lies somewhere in the following aspects:

-a simplicity of design, something like a "less is more" rule, -a high readability for non-experts, -a potential of reuse, in close relation with simplicity of design.

Besides, this contribution underlines some other expected benefits of simplicity (simplicity fosters a fast, clear-cut, and sharable invalidation of biases and easy judgments, and facilitates workgroup discussions with people rooted in Human Sciences, not familiar with abstract graphics or highly interactive environments).

But it also underlines another benefit, and a key one when rereading data sets: simplicity compels the analysts to a dramatic dimensionality reduction and, as a consequence, uncovers lines of thought, open issues, unthought-of metrics. In that sense, revisiting simple classics did not only remind us that simplicity, in the sense of Maeda, can help -reducing, organizing, "subtracting the obvious, and adding the meaningful" (three of Maeda's laws of simplicity) are rather consensual aims. Revisiting classics acted as food for rethinking a data set, and re-questioning it.

Figure 1 :

 1 Figure 1: Some of the many possible layouts and shapes of wooden stalls.

Figure 2 :

 2 Figure 2: Approximate position of our groups of wooden stalls in Cracow's Market Square.

Figure 4 :

 4 Figure 4 : Poleometric tables for 4 four groups of stallsIn the example above facilities of very different overall sizes are considered. On each of them it appears quite clearly that surfaces of stalls inside each facility widely differ -and in a way that could even be seen as totally chaotic. However, when looking from closer, a majority of surfaces are grouped, in all of those four facilities, around the green line's value (more or less 3 square meters, or 9 squares ells). Also noticeable, in each of the four edifices a stall stands out significantly as bigger in surface than the rest. Finally, the left tableau -corresponding to the Lithuanian stalls, shows a wider range of surfaces, which is quite understandable when knowing their overall number (33, far more than the others) and its approximate localisation, around the south-east corners of the market square (meaning most likely a facility composed of several, spatially independent, groups of stalls). Colour here still means nothing particular: it is only used to facilitate visual contrasts. But beyond surfaces our data set includes indications on tax levels before/after 1760 for each stall, and thereby on tax increases. This information is represented by a second poleometric table, that we somehow "mirrored" along the X axis (Figure5), and inside which the colour now means either the tax level, or the tax increase (the brighter the red is red, the higher the value). Each stall is represented by a square in the top poleometric table (where the focus is put on surfaces and their variability), and by a corresponding square of same surface in the bottom poleometric table (where the focus is put on the variability of an additional parameter, here tax levels / increase, toggle on user demand).

Figure 5 :

 5 Figure 5: Bottom tables can show (left) tax increases or (right) tax levels for each stall in the top table.

Figure 6 :

 6 Figure 6: Handling missing information: left, unknown tax levels for the largest stall (represented by a grey surface). Right, unknown surface for two stalls (represented by two small rectangles and the tax info remains available).

Figure 7 :Figure 8 :

 78 Figure 7: Surfaces and tax levels

Figure 9 :

 9 Figure 9: In this configuration the tables represent tax levels before (top row) and after (bottom row) the 1760 inventory, showing patterns of stability and change.

Figure 10 :

 10 Figure 10: Top bars: stalls arranged by growing widths, bottom bars, stalls arranged by growing depths. Small squares in between represent tax ratios per linear meter of width (top part) or of depth (bottom part, far greater variability).

Figure 12 :

 12 Figure 12: On user demand a combination of stalls that match a regular alignment constraint is drawn, and the remaining stalls are represented as greyish rectangles. Used as support for workgroup discussions, the visualisation helped weigh the odds of a classic, regularly aligned spatial organisation.

Fig 14 :

 14 Fig 14: Candlesticks for the "Stalls near the Town Hall": stall 13 clearly appears as an Outlier. Wrong data? Specific position? When observing the shape of the stall, its width also strikes out, suggesting this specific stall might have been a lasting wooden structure resting against a wall of the Town Hall itself when the rest of the stalls were mobile structures installed in the same area.

Fig 15 :

 15 Fig 15: Candlesticks for the food Stalls. The two first stalls significantly stand out, which can be connected to specific goods, but could also suggest a possible prominence in terms of spatial layout (maybe something like today's "aisle head" position used to promote products in supermarkets). Also note that in most cases the tax ratio before and after 1760 remains the same (lower and upper shadows).

Figure 16 :

 16 Figure 16: A generic application to size of forces engaged in a battlefield vs. losses (data extracted from Wikipedia). The bottom left table shows the linear benchmark -(linear proportionality of forces engaged to losses).

Figure 17 :

 17 Figure 17: A "sustainable usability" measure (the bigger the rectangle, the more bright red its filling colour). Proportionality of surfaces (represented by the red colour scale) remains the same whatever unit is used on the axes.