Vincent Garcia

Olivier Commowick

Grégoire Malandain

A Robust and Efficient Block-Matching Framework for Non Linear Registration of Thoracic CT Images

The registration of thoracic images is a challenging problem with essential clinical applications such as radiotherapy and diagnosis. In the context of the EMPIRE10 challenge, we briefly introduce a general robust and efficient algorithm to register automatically any type of scalar images (CT, MRI, ...) on virtually any location (brain, thorax, ...). Although fully automatic and generic, the proposed algorithm reached the 17 th place in the EMPIRE challenge over 34 algorithms evaluated. Moreover, we have since optimized further the parameter set used for the challenge and we demonstrate the ability of the algorithm to recover much better large displacements of the lungs boundaries.

Introduction

Registration of thoracic CT images is a challenging problem encountered in many clinical applications. For instance, one of the most important aspect in radiotherapy planning is to precisely determine the target volume to be treated and the surrounding critical structures. The elastic nature of the lung tissue deformation and the physiological movement of patient (e.g. breathing cycle) affects these localizations. Image registration may therefore greatly improve the treatment accuracy by compensating for the inevitable anatomical changes between two acquisitions. To tackle this complicated registration problem, we propose a fully automatic non linear registration method, which may be applied to a large variety of medical images. It is based on a pyramidal block-matching approach, which allows to recover large displacements. To improve the robustness of the registration, the block-matching is coupled with an outlier rejection scheme to remove incorrect pairings that may occur due to several factors such as noise or lesions. This algorithm was evaluated in the EMPIRE10 challenge, which provides a platform for in-depth evaluation and fair comparison of thoracic CT image registration algorithms. Although being generic and fully automatic, the proposed algorithm appears to be very efficient and competes with many state of the art registration methods specialized in thoracic CT image registration. Moreover, since the evaluation results, better registration parameters were tested and we demonstrate the ability of our algorithm to recover much better large displacements of the lungs boundaries.

General Block-Matching Algorithm for Non Linear Registration

We developed a general algorithm to register a floating image F to a reference image R, i.e. find the dense transformation T (one displacement vector per voxel) so that F resampled by T locally matches R: R ≈ F •T . Note that these reference and floating images may also be referred respectively as fixed and moving images in the literature. Our method is built on the principle of the block-matching based algorithm developed by Ourselin et al. for rigid registration [START_REF] Ourselin | Block matching: A general framework to improve robustness of rigid registration of medical images[END_REF]. However, its purpose is different and we therefore developed an algorithm to estimate a robust non linear transformation between the two images.

To compute this transformation, we follow an iterative framework using a multi-resolution scheme illustrated in Algorithm 1. At each iteration l, a transformation correction δT l is evaluated using pairings obtained through blockmatching, so that F • T l gets closer to R than F • T l-1 . As the block-matching algorithm may produce incorrect block pairings, the second step is then to detect and remove these outliers in order to compute an outlier-free transformation correction δ T l . Finally, the transformation correction is composed with the current transformation T l-1 to obtain T l , which may be regularized using an elastic-like regularization scheme.

Algorithm 1 Block-Matching Based Non Linear Registration Algorithm

1: Initialize the transformation to identity T 0 ← Id. 2: for p = 1...M , iterations on pyramid levels, do 3:

for l = 1...L, iterations, do 4:

Estimation of pairings: C ← blockmatch(R, F • T l-1 ).

5:

Interpolate of a dense correction field: δT l ← interp(C).

6:

Rejection of outlier pairings: C ← prune(C, δT l ). 7:

Interpolate the outlier free transformation correction: δ T l ← interp( C). 8:

Composition of the correction T l = T l-1 • δ T l . 9:

Elastic-like regularization of the current transformation T l . 10: end for 11: end for

Transformation Correction Estimation

At each iteration l, a dense correction field δT l is computed to get a better correspondence between the images R and F • T l-1 . We detail in the following first how sparse pairings are obtained between the current images. Then, we present how this sparse information is used to compute a dense correction field.

Estimation of Correspondences

To estimate correspondences between the images R and F • T l-1 , we have chosen to use a block-matching algorithm. It allows to search for large displacements between the images, while being robust to the possible local minima of the similarity measure.

Blocks are placed regularly on the whole reference image R. Then, for each block B(x v ) ⊂ R, centered in x v , we look for its best match B(y v ) in a local neighborhood V (x v ) ⊂ F •T l-1 (this neighborhood can be small as we are working on an image pyramid). The best match is defined according to a similarity measure S, i.e. the best match is defined as :

y v = arg max y∈V (xv)⊂F •T l-1 S(B(x v ), B(y)). (1)
where S(B(x v ), B(y)) corresponds to the similarity measure between the two blocks B(x v ) and B(y). The choice of the similarity measure is crucial and must depend on the assumed relationship between the intensities of the two images.

We have used small blocks in our experiments (5 × 5 × 5 voxels). This means that a small number of tissue classes are present in each block (usually two or three) [START_REF] Roche | Unifying maximum likelihood approaches in medical image registration[END_REF], which fits perfectly the use of a squared correlation coefficient as a similarity measure. We define S v as the best value of the similarity measure for the block B(x v ):

S v = S(B(x v ), B(y v ))
. At the end of this step, we obtain a set of pairs of points (x v , y v ) corresponding to the pairings between the images R and F • T l-1 . These pairs are associated to the corresponding values of the similarity measure S v , that can be used for characterizing the confidence in each pairing.

Transformation Interpolation

The obtained pairings can be seen as a sparse displacement field C, where each couple (x v , y v ) implies the displacement C(x v ) = y vx v . Similarly, a scalar sparse map of pairing confidence k may be built from the corresponding values

S v : k(x v ) = S v .
The next step of the algorithm is then to interpolate a dense correction field from these two sparse fields. As we are estimating a dense non linear transformation, previous schemes (least squares) for linear transformation estimation may not be applied here. We therefore compute the dense transformation from the pairings by convolving C with a 3D Gaussian:

δT l = G σ * kC G σ * k . ( 2
)
The interpolation can be seen as a fluid regularization of the transformation as proposed in [START_REF] Christensen | Deformable templates using large deformation kinematics[END_REF] in order to limit the influence of outliers. It is defined as a local mean of the pairings weighted by the confidence k to emphasize the pairings for which the confidence is high. This equation enables us to compute efficiently a dense correction field from the sparse displacement field C. σ corresponds to the standard deviation of the Gaussian, allowing to compute a smoother or sharper displacement field. Note that other methods could have been used to interpolate the dense correction field such as kriging [START_REF] Ruiz-Alzola | Nonrigid registration of 3D tensor medical data[END_REF] or thin-plate splines [START_REF] Bookstein | Principal warps: Thin-plate splines and the decomposition of deformations[END_REF].

Outlier Rejection

From the block pairings, we have now obtained a dense correction field δT l . However, the pairings obtained through the block-matching process may be erroneous in some regions such as regions where a lesion is present in one of the images, and in homogeneous regions. To prevent these outliers from corrupting the estimated transformation, we present an outlier rejection process. Unlike previous work on block-matching [START_REF] Ourselin | Block matching: A general framework to improve robustness of rigid registration of medical images[END_REF], our algorithm is designed for non linear registration, and techniques such as Least Trimmed Squares [START_REF] Rousseeuw | Robust Regression and Outlier Detection[END_REF] may not be used. We therefore introduced an effective measure for the robust detection of outliers with respect to a non rigid transformation. We based our approach on the comparison of the original pairings (x v , y v ) available in C and the interpolated displacements in δT l . From this comparison, the mean displacement difference over the whole image e is computed as well as the variance σ 2

e :

e = 1 N v C(x v ) -δT l (x v ) , σ 2 e = 1 N -1 v (e -C(x v ) -δT l (x v ) ) 2 (3)
where N is the number of blocks in the image. Then, we define a criterion to determine whether a couple (x v , y v ) is an outlier or not:

C(x v ) -δT l (x v ) > e + ασ e . (4)
In this equation, all the pairings whose difference with respect to the interpolated corrections field δT l is higher than e + ασ e are considered as outlier pairings and removed from the sparse field C. The α parameter allows more variable displacements when its value is large, and highly constrains the pairings when it is small. As with the LTS outlier rejection framework, this process could be iterated. However, only one iteration was sufficient to take into account the outliers and we therefore chose to use only one iteration.

After this outlier rejection step, the remaining pairings are used to compute a new sparse displacement field C, which is interpolated. This gives as a result a final correction field δ T l that will be composed with the current transformation T l-1 : T l = T l-1 • δ T l . Finally, the current estimate of the transformation T l may be regularized using an elastic-like regularization, to provide a smoother deformation field. This amounts to convolve the current estimate T l with a Gaussian kernel of standard deviation σ el .

EMPIRE10 Challenge -Results

Challenge Context and Implementation Details

The EMPIRE10 challenge consisted in evaluating the state of the art in chest CT registration. 20 pairs of chest CT scans (intra-subject) had to be registered. The provided data were very challenging since they encompassed many of the problems faced by researchers developing registration algorithms for this application (variation in image/voxel size, scans taken at various phases in the breathing cycle, etc.). In addition to the CT scans, binary lung masks were also provided for each pair of scans. The reader may find the proposed registration method referenced as "Asclepios2" in the EMPIRE10 challenge.

The implementation used for the challenge was done in C++. To register each pair of images, we first performed a global affine registration between the reference and floating images using the method proposed by Ourselin et al. [START_REF] Ourselin | Block matching: A general framework to improve robustness of rigid registration of medical images[END_REF]. As explained in Section 2, this method is also based on a block-matching scheme. It is able to estimate a linear transformation (rigid or affine) between two images. In this paper, we used the affine version of the method. The parameters used for the challenge was:

-Number of pyramid level: 4 -Number of iteration at each level: 6 -Type of estimator: weighted least trimmed squares The value of other parameters was set by default. This first registration allowed to compensate for the global deformation induced by change in position of the patient or by the lung position in the breathing cycle. To focus on the lung part, the affine registration was performed on the masked images. The resulting affine transformation was then given as an initial transformation to our non linear registration algorithm. The parameter set used to register (non linear registration) each couple of images was:

-Number of pyramid levels: 4 -Size of blocks: 5×5×5 -Search window size: 7×7×7 -Number of iterations per pyramid level: 10

We found that the impact of the last pyramid level (finest resolution) brought only negligible registration quality improvement in comparison to the computation time increase. The last level of pyramid was therefore skipped in order to speed-up the computation. To further decrease computation time and since only the lungs registration was evaluated, we also decided to use the lung mask of the reference image to define blocks only where needed in the reference image. Since the parameters used are the same for every pair of scans, the method is considered as fully automatic.

Numerical Results

Table 1 and Fig. 1 present the results of the EMPIRE10 challenge for the proposed registration method. For each pair of images, the registration method was evaluated by four criteria:

-Alignment of lung boundaries -Alignment of major fissures -Correspondence of annotated point pairs -Analysis of singularities in the deformation field Overall, the proposed registration method appeared to be a very efficient method for thoracic CT images with a rank of 17 among the 34 competing algorithms. Three major conclusions may be drawn. First, the proposed method obtains a perfect score on singularities in the deformation field. This comes both from the outlier rejection scheme and from the interpolation of sparse displacement fields over the whole image. This allows our algorithm to compute deformation fields with no singularities. Second, our method is very efficient to align the fissures and landmarks, ranking at the 13 th place in average for this evaluation. Finally, the alignment of the lung boundaries does not perform as well as we had expected for some scan pairs. This issue comes from the fact that we adapted the parameters from a set that was designed for brain registration, where displacements are much smaller. We since investigated this problem and 1. Results for each scan pair, per category and overall. Rankings and final placement are from a total of 34 competing algorithms.

Lung Boundaries

Fissures Landmarks Score Fig. 1. Scores of the proposed registration method: box-and-whisker plot for categories "lung boundaries", "fissures", and "landmarks". Information about "singularities" were not displayed since the score was 0 for every scan pair.

propose in Section 3.4 a simple modification of the parameter set that settles the boundary precision problem while improving the global registration quality.

Visual Results

In this section, we present some visual results illustrating the performance of our method. The presented images correspond to axial slices of the lungs. In order to focus the reader attention onto the lungs, the contrast in these regions has been artificially increased while the contrast outside has been decreased. This manipulation on the contrast was possible thanks to the masks provided for both reference and floating images. Since the registered image is supposed to perfectly match the reference image, the contrast change of the registered image has been performed using the mask of the reference image. As a consequence, a misalignment of the lung boundary will appear as a thin white band in the lung mask and as a dark band outside of the mask. According to Table 1, the registration process using the proposed set of parameters was particularly efficient on the pairs of scans #2 and #19. Fig. 2 illustrates for both scan pairs, respectively from left to right, the floating image, the registered image, and the reference images. One can see that the initial data (reference and floating images) are clearly not registered (lung boundaries, vessels, bronchus, etc.). Using our algorithm, the registered image appears to be very similar to the reference image: the lung boundaries are correct and most of the structures in the reference image are present and well positioned in the registered image.

According to Table 1, our algorithm is however less efficient on the scan pairs #1 and #18 (see Fig. 3). One can notice that the lung boundaries have not been retrieved as precisely as we expected. However, inside the lungs, the two registered images are very similar to their corresponding reference images. The proposed method is therefore reliable and allows to precisely compare visually the reference and registered floating images.

Improvements

Thanks to the results of the evaluation, the boundary alignment issue, illustrated in Fig. 3, was detected. As mentioned above, this comes from the fact that we adapted the parameter set used in the experiments from one used for brain registration. However, the displacements encountered in brain registration are much smaller than in lung registration. Since the submission of the results, we further improved the parameters and found a single set of parameters allowing for a much better visual correspondence of the lung boundaries. This improvement resides in two changes. First, we increased the number of iterations per pyramid level to 15 to ensure that all displacements are recovered. Then, we gave the algorithm a dilated mask of the lungs as the area on which to create blocks. This was done to ensure that blocks are created on the border of the lungs, allowing for their precise registration. Fig. 4 illustrates the improvement between the two sets of parameters. The new set of parameters visually corrects the lung boundary matching issue. In addition, the alignment of fissures also seems to be improved: some fissures visible in the reference image were lost in the registered image with the submitted method and are well recovered using the improved registration method.

Computation time

The computation time of a given registration method depends mainly on the hardware configuration of the computer used. For our experiments, the configuration was the following: DELL laptop E6400, CPU double core at 2400MHz, 8GB of DDR3 memory, Linux Fedora 10. Using this configuration, and using the set of parameters of the initial submission, the computation time was distributed between the affine registration and the non linear registration:

-Affine registration: 123 seconds -Non linear registration: 623 seconds These computation time was obtained for the image #1. Using the improved set of parameters, the computation time of the non linear registration slightly increased and was, on the same image, 844 seconds. One should note that the code can be distributed on a multiple core architecture. In order to compare the distribution of the computation time between the linear and the non linear registration processes, we used one of the two cores available. Indeed, the affine registration was optimized for single core processors.

Conclusion

The registration of thoracic images is a challenging problem having critical clinical applications such as radiotherapy and diagnosis. In this context, the EM-PIRE10 challenge had a simple but essential goal: objectively compare the thoracic CT image registration algorithms proposed by the research community.

In this paper, we introduced an efficient algorithm to register automatically any type of scalar images on a large variety of locations. Associated to an outlier rejection scheme, this method is also robust to incorrect pairings that may occur. Although fully automatic and generic, our algorithm performed well and reached the 17 th place in the challenge over 34 algorithms evaluated. Moreover, we presented a new set of parameters that drastically improves the visual results compared to the challenge results, allowing to recover much better large displacements of the lungs boundaries.

Finally, this method is currently being integrated into the MedINRIA software platform3 to make it available to clinicians and researchers.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Image registration results (two of the most accurate examples according to Table 1). The registered image (central column) is the floating image (left column) re-sampled in the geometry of the reference image (right column). Slice numbers are 317 for the scan pair #2, and 194 for the scan pair #19.

Fig. 4 .

 4 Fig. 4. Improvement of the proposed registration method. The lung boundary issue has been fixed. The slice numbers are 148 for the scan pair #1, and 170 for the scan pair #18.

http://www-sop.inria.fr/asclepios/software/MedINRIA/


Is there a relation between frequency where a country is included in Europe and the number of people declaring to have visited it? The hypothesis could be that when a large number of people have visited a country, this country is better known. This knowledge could modify the previous perception on the proximity level of this country from European ones.

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Is their relation between the drawing of Europe and the age / nationality / country of birth of the members of the ESPON community? Those questions are in relation with the fact people are born in Europe Union, live in an old or recent European Union country. For example: do people from the first six countries of Europe Union have a narrower of broader view of Europe than people from new member states?

1.4

Replicable methodology, problems, questions 1.4.1.

Steps of the realisation of the survey

The survey has been realised in three stages:

- Unluckily all 123 answers can not be used because of some mistakes or some oversights that lead to a usable number of answer equal to:

-117 for the individual attributes -116 for the limits of Europe -110 for the World divisions

The question of drawing limits

We asked to the ESPON community member to draw limits of Europe and of World regions on two specific maps. Concerning Europe it was the European Neighbourhood Template (cf. First Interim Report of the ESPON project 3.4.1.). Concerning the World it was a circular map with projection on North Pole. This then avoid putting Europe in the centre of the World. More, the map was nonoriented and people can handle it as they want as it was a dish of paper not attached with the rest of the survey.

To use a drawing on a map rather than a list of country raise a problem and even more when the name of the countries are not specified. That implicitly implies that people know to name the countries from a map and therefore where they are located. We are not sure that people know the location and the name of the countries in the World map and maybe neither in the Europe map.

However we deliberately chose this procedure because it is far more precise in the inclusion of a country in a region. A country can belong to a region as a whole. But the drawer can also split it in two or more parts and affect it to many zones. This gives precious indications on transition zones between two World regions.

Building the databases

The building of the databases has been a very long process. Many problems have been faced during the building of the database for the Europe map as well as for the World one.

Specific problems for the Europe map

Concerning the Europe map the codification was rather simple. The answers to the questionnaire are codified as follow:

-"1" if the country belongs to Europe in a questionnaire -"0" if the country does not belong to Europe.

-"0.5" if the country is cut in two part one of them belonging to Europe.

However, many problems have been faced during the codification, as some of people did not following the instructions and imagined creative unexpected solution… First in some of the questionnaires two "European" delimitations have been proposed introducing a gradation in the belonging to Europe (figure 01). The question was more restrictive and it was assumed that the gradation in the intensity of belonging to Europe would be found thanks to the frequency of attribution of a country to Europe or if a country is cut in two parts. It has been then decided to take into account the larger zone as the delimitations of Europe. This problem has been found in 6 questionnaires.

Another problem was incoherence between the drawing of Europe and the written explanation in the part reserved for the criteria used to draw the limits. For example, many times the criterion "ESPON space" has been used to justify the drawing of Europe. But sometimes the drawings of the limits include the Balkans countries, i.e. Albania and the countries from the former Yugoslavia which are not actually ESPON members. In that case we used the map and therefore the drawing instead of the justification.

Specific problems for the World map

For the World each country has been affected to a region according to the answer of each participant. The fact that a country can be cut in two or more part raise therefore a new problem (figure 02): the multiplication of the spatial references. Then, if a country is divided, as much new spatial units as the number of parts are introduced in the table . A number has been attributed to the new country line: the larger zone gets the first number, the smaller one the last one. For example if Iran (figure 02 & table 01) is divided in three part, the larger one being "Central Asia", the second one "Oil countries", the smallest one being "India World" the codification will be :

Iran_01

Central Asia

Iran_02

Oil countries

Iran_03

India World

After the first preliminary survey, it has been discovered that it was very difficult to deal with zones recovering each others (figure 03). It was then specified in the instructions of the following surveys that "one place should belong to only one region". However some people draw zones recovering each other. In order to take them into account in the statistical analysis it was then decided to draw a median line between the intersections points of two recovering zones. This new line is thus considered as the limit of both zones (figure 04)

A very problem was raised by the fact that some people from the ESPON community refused to divide the World. As we asked them to divide the World in at least two parts, few of them draw a small zone in the North Pole area and then considered the rest of the World as the second zone. This point of view is very interesting and has to be taken into account in the interpretation of the results. However it was not possible to include them in the statistical analysis because that kind of division was not suitable with the methodology used. It was the same problem for survey proposing an original point of view in the division of the World but whose divisions was covering a too small part of the World. For example one questionnaire (figure 05), shows a World in three parts: 1: "my" vacations space, 2: "my" work space, 3: the rest of the World. Another questionnaire (figure 06), shows a World in two parts: 1: "my" house, 2: the rest of the universe. The inclusion of such original answers in the statistical analysis should have introduced too much bias. They have not included in the study but they will be taken into account in the interpretation of results. The matrixes have been built with the same methodology used than for the study presented in the Key Question 01 on mental map. However additional matrixes have been built:

-The divided and forgotten countries matrix

This matrix aims to take into account the countries that have been divided between two regions. For each country it establishes the frequency of the division of a country in two, three or four part. More, some countries have been forgotten in the drawing of World regions and put in a so-called "rest of the World" implicit region. This matrix aims also to establish how many times a country has been forgotten.

-The links and discontinuities matrix

This matrix establishes for each couple of contiguous countries the intensity of the link or of the discontinuities between them.

Criteria

After the drawing of the limit of Europe a question were asking: "Which criteria did you used for this delimitation?" One hope was that the use of a particular criterion could explain the spatial extension of the drawn Europe or the inclusion of a country or not. Six questionnaires did not indicate which criteria were used in the drawing of Europe and in consequence could not be used in the following analyses.

No precise criterion was proposed in this open question and therefore many criteria were quote. In order to make the analysis easier they have been grouped in 8 categories presented in the table 02. The most frequent criterion used is the geophysical / geographical one. This criterion is typically used by people who want to let know that Europe is fact that can be defined by objective factors. However as is as been shown in the First Interim Report of the ESPON project 3.4.1. and in the attempt to define Europe in the Second Interim Report, the physical criterion is in fact subjective one and more, often promote with a political purpose. The culture criteria, more often defined by religion, would join the Huntington point's of view on civilisations. The "politic" criterion was more often used in order to justify the drawing of the present delimitations of European Union. Last, the History criterion was used to gather countries that have a common past, even if this past is made both of confrontations and war as well as peaceful exchanges. That explains the ambiguity of Russia and Turkey status. -Specific tests have been made on specific countries (first bivariate analysis and then maybe a logistic model will be developed). The choice of countries is very sensitive but it is first determined by the frequency at which they have been included in Europe focussing on "in between area". If the number is too high or to small it will be not possible to make the analysis. Researcher Administrator

Christian religion

Male Female

Live in the country corresponding to its nationality

Do not live in the country corresponding to its nationality

Those profiles underline some trends that seem significant:

-Researchers from the six founder countries are rather young (between 26 and 45 years old) and therefore born in Europe. Some of them are not even researchers yet as it is from their part that the category "other" (that represents mainly PhD or post PhD) can be found. But the younger are the people coming from the very new European countries (adhesion after 1990). They are mainly policy makers and administrators. The researchers coming from countries of the second enlargement (between 1958 and 1989, i.e. United Kingdom, Ireland, Denmark, Greece, Spain and Portugal) are older and therefore not born in European Union.

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The number of female is more important in the younger age. That can underline the feminization of the European research.

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The residential mobility of young researchers and administrators. That can be due to two different facts. First, the residential mobility of the younger can be due to the opportunity to find a job in another country when there is a lack of opportunity in their countries. As older researchers live in their countries of birth that could mean that they ever work in their country or that they are back after some years abroad. The second solution would underline the existence of a kind of cycle of the research activity with few years aboard and a return to the country. But this mobility of young researchers could be also due to the increasing number of opportunities to work and travel in Europe thank to European exchanges programs or research programs and to the willing to discover other European countries. The cause of the residential mobility of young researcher in Europe is probably a mix of the facts proposed above.

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Globally, the punctual mobility, for work or leisure is rather important among the ESPON community. It is true that they have to go to the ESPON seminar twice a year, to LP meeting if they are Lead partners and to transnational project group meetings…

ESPON seminar particiants' view of Europe

Concerning the delimitation of Europe, a clear definition has not been provided.

It could be either the present "European Union" as much as a geographical Europe, historical one, a wish for the political construction of European Union or more an utopia of what should be Europe in a more or less far future.

Description

The first surprise has been to discover how large was the Europe drawn by the ESPON seminar participants in Luxembourg. With an average of 37.9 countries, it is far larger than the present European Union. In fact the more frequent numbers of countries included in Europe are 37 & 38. That means that about 40% of the questionnaires add 12 or 13 countries to European Union and 61% between 11 and 14 countries. Only one questionnaire draws a Europe with the European Union countries and three have been influenced by the ESPON template and draw a 29 countries Europe. Other countries, in light green colour, belong sometime to Europe sometime not or they can be divided in too parts. Eastern and Southern countries could be consider as a transition area, were the probability to be part of Europe have to be more precisely evaluate. Indeed, their situations are quite different from one country to another one. Some of them like Ireland, Greece belong to European Union and are nearly always placed in Europe. Others, like Norway, Switzerland or Balkan countries are very often placed in Europe. And the situation is quite different for other countries: some of them being put in Europe in only one questionnaire.

Frequency of integration to Europe

Map 2 highlights the situation of countries that were placed sometime in Europe sometime not. A gradation from West to East is very clear showing that when one draws the limit of Europe on a map the proximity with the core of Europe makes it highly probable for a country to be included in Europe. The more impressive example is related to Albania and the countries from the former Yugoslavia. It is very rare when they are excluded from Europe mostly because they are located between the core of Europe, Greece and two candidate countries Bulgaria and Romania. Consequently maybe the drawer does not take time to ask himself if Serbia or Bosnia belong to Europe. If the question would have been posed rather than asking for a drawing it is likely that those countries would have not been so frequently included in Europe or would have been more differentiated (Croatia). The situation of Ukraine, Turkey, Georgia and Armenia is quite different as they have often been divided in two parts, one belonging to Europe, the other not. In consequence could those countries be considered as a first sign of a limit of Europe?

Map 2 : Frequency of inclusion of a country (as a whole) in Europe

The inclusion of all Russia in Europe is a plunge that few questionnaires dare to take, maybe because the country is too large and because a big part of it is located in what people consider as Asia. Less than 17% of questionnaires placed it fully in Europe.

The Mediterranean Sea acts like a frontier between the core of Europe and other countries. There is no gradation like the one that could be observed in the East and the number of questionnaires that include them in Europe is small or very small.

In-depth analysis of margin

This analysis aims to go further than the simple description of the frequency by defining profile of inclusion of group of countries in Europe.

The first component of the multivariate analysis (graph n°02) presents a gradation of the frequency of inclusion of countries in Europe. A group of 19 countries are very often put in Europe. The second component globally makes an opposition between questionnaires that draw a Europe toward the East (Belarus, Serbia, Albania, Ukraine and Moldova) and those making it toward the South and Middle East. The following map (03) presents a classification that allows to stress on two great trends in the inclusion of countries in Europe. First, the frequency of inclusion in Europe. The first group is composed of countries that are often or very often included in Europe (red & orange). The second group is composed of the countries that are not often included in Europe with different intensities (green, light greens) and the last one of Central Asia Countries (blue) that is only once included in Europe.

The second trend that can be observed is a regionalisation of countries. The redorange cluster is clearly the drawing of Europe towards the East whereas green clusters concern Southern Mediterranean Sea countries and Middle East ones. This regionalisation is also visible in the subdivision of the green category as Iraq and Jordan (medium green) are close from Syria, Israel and West-Bank & Gaza in the classification even if the frequency of inclusion in Europe is equivalent to the one of Egypt, Libya and Algeria. The case of Tunisia and Morocco are specific as their smaller size allow people to put them in Europe as a whole without going too much to the south as it is the case for Algeria (that is very often cut).

Explanations

Criteria used to draw the limits Europe

Among all the criteria presented in table 02 only the "Physical / Geographical" criteria has a significant relation with the variation of the number of countries included in Europe. (Chi Square value is 6.9 and the "α" value is 0.032). The relation is the following one: people using the geographical criteria drawn a medium Europe (37 & 38 countries) and people not using it draw a small Europe (less than 36 countries). An analysis on criteria used could explain their use in relation with the drawing of Europe.

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The "Politic" criteria is significant but on the other side of the group significant variable describing the integration of different countries in Europe suggesting that to draw a large Europe the "Politic" criteria is not used.

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The physical criteria is used to included in Europe the Eastern countries and the political one by people including Southern Mediterranean countries and middle East that sounds strange according to the fact that the previous interpretation of this criteria as the "European politic construction".

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The "feeling" criteria is used by people considering Russian federation, Egypt, Algeria, Libya, Kazakhstan, Turkmenistan and Uzbekistan as being a part of Europe.

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Functional and economical criteria are used by people considering Estonia, Albania, Croatia, Iraq and Jordan as being a part of Europe.

.Could individual attributes explain the drawing of limits of Europe

The individual attributes we gathered are not only used to describe the typical ESPON seminar member, but also to try to find if there is a relation between them and the drawing of Europe i.e. the number of countries included in Europe.

Unfortunately, as it was the case previously concerning the criteria used, very few tests have significant result. Only one was very significant, but it is very difficult to interpret: the gender (significance level of 0.011). Men draw a larger Europe than women. The average number of countries included in Europe is 38.7 for men and 36.4 for women. This result seems actually very difficult to explain.

Turkey and Ukraine

Specific tests have been conducted for two countries that are in an intermediate position, both in the survey and in the geopolitical point of view: Turkey and Ukraine.

Turkey is now an official candidate country to be part of the European Union.

Ukraine is in a sensitive position between its historical links with Russia Federation and the proximity of the European Union. More, both countries have sometimes been divided in two or more pieces in the survey (table 07). For both countries two different tests have been made: one using the three modalities (In & Out and Cut) and one other using two modalities (In & Out). For the second one, only 66 questionnaires were taken into account for Turkey and 107 for Ukraine.

Turkey in Europe

Absolutely none of the criteria used has a significant relation with the fact to included Turkey in Europe or not. However significant relations can be found when studying the personal attributes. The question can be raise also for people who visit Turkey: Are they more likely or not to include it in Europe? The relation is significant for both tests and show that people who have not visited Turkey are over represented among the people that exclude it from Europe and vice-versa. Would that mean that a better knowledge of Turkey lead to include it in Europe? But maybe that people that went to Turkey was previously more well-disposed toward Turkey.

Ukraine in Europe

Concerning Ukraine, again the same gender variable can significantly explain its belonging to Europe or not and again: women are more likely tempted to exclude it from Europe than men. The new discovery for Ukraine is that some criteria have a significant relation with its inclusion in Europe and that for both tests. The first criterion to be very significant is the "politic" one. People who declare to use this criterion exclude Ukraine from Europe that is quite logical because Ukraine does not politically belong to European Union and remains clearly in the geopolitical influence of Russia from economic point of view (cf. recent gas crisis).

The other criterion to be significant is the "geographical" one. People who use the geographical criteria are more likely to include Ukraine in Europe. That seems quite logical too because, the physical criteria that is traditionally used to define the East frontier of Europe is the Ural Mountain one despite the lack of evidence of real physical frontier (c.f. dictionary of concepts).

A travel explanation?

A better knowledge about a specific country could lead people to more easily consider it as belonging to Europe or the contrary. So the following question is: is their a relation between the number of people that visit a country and the number of people considering this country belongs to Europe?

This map has to be compared with map 2 showing the frequency of inclusion of a country to Europe. There is no a strong tendency showing that more a country is visited more it is included in Europe. For example countries from former Yugoslavia are not very much visited but they are included in Europe in more than 85% of questionnaires. It is the same for countries like Belarus or Moldova.

The trends of tourism flows can explain the cases of Egypt and Jordan that are more visited than the number of questionnaires that put them in Europe.

Map 4 : Number of people who give back a questionnaire who have visited the country

The existence of a relation can be easily checked with a simple correlation analysis. For this analysis only the intermediate countries (i.e. countries that are not always or never placed in Europe) are taken into account. The correlation index in not high and the relation between the two variables could explain only 20% of the information. That could be a good beginning in the try to explain the fact that a country is included in Europe or not, but the pattern of the correlation plots shows that in fact there is no relation at all. In consequence, the frequency of visit of a country can not be consider as a way to explain the fact that people consider that country belonging or not to Europe.

Figure 11 : A relation between the visits of a country and the appreciation of its belonging to Europe?

ESPON seminar view of the World

Our main objective when doing this survey at the World level was to define if the individual representation of the World regions of ESPON members could allow to identify strong groups of countries always put together in the same region. That would mean those countries are perceived as linked either by flows, similarity or accessibility. But then for which reason are they so perceived and could those countries be seen as a core group of countries that could be used in regionalisation process? One other question was to identify "blurred areas". By blurred area we mean areas that are put sometime with one group of countries sometime with another group. We should have then to define those areas. Are they transitions areas between two relatively homogeneous or integrated groups of country?

1.7.1.

The significant steps in the division of the World.

We asked the ESPON members to draw on a round map the limits of 2 to 15 World regions according to them. The following maps were obtained thanks to a hierarchical ascendant classification1 . This part is based on the comment of two more significant steps (cf. figure 08) as it was presented in the key question on the Mental Maps plus the last limit of fifteen zone because it was the maximum number of zones that it was allowed to draw.

World in four regions

The first space to be clearly identified by the ESPON members is a World region formed by Europe plus the English speaking North America. Those two spaces are considered here as only one maybe on cultural criteria: this region look like the Huntington's Western civilization except the fact that Australia is not included and Europe is larger than Huntington's one because it includes Eastern Europe, Greece and Turkey that are not included by him. But it is also likely that this zone have been drawn by using wealth and development level criteria as they were often put in the same "north" or "developed" region in some questionnaires. More, this region is isolated and in the first step all the reminding countries belong to the same region "Rest of the World". This regionalisation reminds the strong tendency to Euro-centrist point of view of the World that is in the Key Question on mental maps of global actors.

The second entity to be also clearly revealed is a large South America that includes Mexico and the Caribbean Islands. This region is associated to the continents point of view but it is mainly a cultural one with the extension of the region to Caribbean and Mexico that do not belong to the South American Plate. Mexico and some of the Caribbean belongs to the North American plate and other Caribbean Islands and Central America form a specific plate (See Figure 31 in the ESPON 3.4.1. First Interim Report).

Map 5 : The World in four regions

Africa is also perceived with an evident continental existence and it is defined in the third step. This regionalisation follows very precisely the tectonic plate shape as Madagascar and Mauritius are nearly always included in this Africa region. At this stage a first significant step is reached in the partition The remaining countries form a very large contiguous region that can be considered as Asia, as it is mainly composed by Asiatic countries plus Russia Federation and Oceania.

One should just notice that Turkey and Georgia are rather considered as European countries. Those four parts of the World can be considered as "strong realities". That means those spaces are the basic entities that are identified firstly by the majority of the ESPON Community and that will be further divided.

A World in eight regions

Turkey appears then, with Georgia, as a small transition zone between Europe and Middle East and the rest of Asia. Turkey is then placed in the peripheral of Europe but more to serve as a "buffer zone" vis-à-vis other Middle East countries of the region. That position of Turkey underlines that Turkey has been more often placed in Europe in the questionnaires than in Asia or another region. The next country to be isolated is the Papua New Guinea. Its level of development and the geographical share of an Island with Indonesia make it closer from it and Philippine. But it seems that Papua New Guinea is cultural more similar to Oceania countries and the former dependency toward Australia plays a great role when one put it in the same region as Australia. However the hesitation is strong and that is what it is underlined by this quite isolated position on the World map produced by the ESPON community. It is quite the same situation for Greenland that is divided between its proximity to North America, and mainly Canada, and its institutional belonging to the Denmark sovereignty. The two very small Caucasian countries of Armenia and Azerbaijan are then split between the Russian, Turkish and Persian influence. Finally the Central America and Caribbean split from the rest of South America, maybe because of specific cultural features of more likely to the geographical characteristics. The countries of the Central America peninsula are very small ones and the other are islands that distinguished clearly them from the large and continental countries present on the Southern America Peninsula like Brazil, Argentina and Peru…

The last step has no statistical meaning. We decided to stop to study the partition here only because it was asked to the ESPON seminar participant to divide the World in fifteen regions. The previous significant partition would have been in 11 regions that means before the identification of Greenland like a region. However the last step is an interesting one because it may mark that we reach a new stage where once more medium size regions are identified. The remaining Asia region is then divided in two large regions, one that comprise China, Japan, both Korea and Mongolia and the other one that comprise all the South Asia Countries (except Pakistan), and the South-eastern Asia Countries.

Map 6 : The World in eight regions Map 7 : the World in fifteen regions

Now that we have our fifteen regions some remarks can be done on the divisions that one could except and that did not appear in the study.

One can note than despite the disparities of development Japan is, in those stages, not separated from China neither North African countries from sub Saharan countries; neither North African countries from the rest of Africa. In both cases the mental map regionalisation is different of what has been proposed in WUTS (Cf. Vol 1) by using expert criteria and statistical analysis of flows and similarities. For Japan, maybe its particularity in term of development vis-à-vis the rest of the region would suggest to put it alone in a region, but its relative proximity with China in cultural term, and from a European point of view, lead to put it in the same region than China. Concerning North Africa, one can note than in fact none region has been identified within Africa. That underlines the fact that Africa is badly known in Europe and that the continental perception is dominant to define African countries. It seems that few among us know Africa sufficiently to draw African sub regions even if the differences between African countries and populations are very numerous (see analysis on discontinuities).

To conclude one should notice that very large areas (like Africa our South America) coexist with very small spaces formed by only one country (Greenland, Papua New Guinea), a couple of countries (Turkey and Georgia, Azerbaijan and Armenia) and groups of countries (Europe, Central America and Central Asia). That underlines the existence of well defined regions among the ESPON community and the existence of transition areas for which the knowledge is more hesitant or that are the place of confrontation between two influences or more.

Limits of the case study

The analysis of the questionnaires allows to draw the limits of the regions, according to the number of questionnaires that draw that limit between contiguous countries. Moreover it is possible to draw the links according to the number of time two contiguous countries have been put in the same region. The following map 08 therefore shows the main limits (in blue) and the main links (orange) between two (terrestrial and extended) contiguous countries.

The frontier of Europe

One can observe that the strongest division of the World for the ESPON seminar participants is the one drawn through the Mediterranean Sea between Europe and North African countries. Would that means they feel that European countries are very much different from the North African ones? Could that mean that for the European researchers and policy makers that attend the seminar, the Mediterranean Sea should be considered as the more evident frontier of the World, and consequently the frontier of Europe? The terrestrial western frontier of Russia Federation is also a strong one. It could reveal, from the ESPON members point of view the Eastern frontier of Europe, meaning that for them Belarus, Moldova and Ukraine should be included in Europe. The limit is less strong between Turkey and Ukraine, Greece and the Balkan countries. It is even less strong than the frontier between Turkey and Iraq and Syria. The fact that Turkey is circled by two medium strong limits shows that there are great hesitation in the ESPON community to decide whether Turkey belong to Europe or not. The strength of the limit around Europe and of the links between European countries clearly shows that this map has been drawn by European people. How strong the links between European countries are, comparing with the links with other countries in the World! Even if on the previous synthetic maps it appears that African is not split during the first 15 steps of dividing the World it is not as coherent, according to the ESPON community, than Europe is. Europe, from France to Ukraine, and Norway to Malta is the better defined space in the World.

Map 8 : limits and links 1.8.2.

Continentalisation and development

The second strongest division of the World is the one between Russia Federation and North America (Alaska). It also crosses a maritime strait and could be a remaining of the Cold War. However those two countries belong to two different continental bodies and in consequence this limits could be just considered as a continental limit. In fact, the mains limits proposed by the ESPON seminar participants are continental ones (even the one between Europe and Africa). That is the case for the limit between the Arabic peninsula and Africa and the South Eastern Asia / Oceania one. In that case the difference of development level that exists between Indonesia and Australia could also explain that limit. The limit between North America and Caribbean and Mexico is less due to continentalisation that the fact that despite the trade agreements, Mexico is not perceived as belonging to North America. The number of thin limits in the Iran, Afghanistan and Pakistan neighbourhood show of it is difficult, from our European point of view to define a coherent space in this region.

Correlations of discontinuities

A correlation test has been made in order to understand the meaning of the discontinuities. Is there a relation between the values of the discontinuities and other indicators like economic or social ones?

The following table 08 shows that the most significant relation is between the value of the limit and the absolute value of GDP in 1986-90.

Then comes nearly all indicators related to the GDP either in absolute or relative terms. The Human Development Index in 2002 is also correlated with the value of discontinuities from the survey, but more than each of its components. Two things can been learned from this short analysis: first, it seems that the inertia of knowledge is such that we evaluate the countries on outdated data. So the limits drawn by the ESPON seminar participant are based on old data or knowledge. Second, we have more a global impression about the level of development of a country, than the knowledge of the different indexes that are used to evaluate this development. That could underline an ability to perceive a average situation of a country despite the fact that the details are not known.

One of the first factors of vulnerability is obviously the size of a country (Russia -China). When a country has a large size is more susceptible to be cut in different part as people have knowledge about the internal spatial differentiations. Thus large countries can belong to different cultural areas or at least their different parts can be under different cultural influences. The development disparities between spaces can also lead to cut a country, but in the case of this survey that never happens: surprisingly nobody makes a distinction between the eastern coast of China and the rest of the country. The second factor of vulnerability could be attributed to the shape of the country. That is the case for Indonesia and Malaysia, Philippines. When a country is formed by more than one entity it is then likely to be more often cut in different parts, and even more when this country is an archipelago. Indonesia, that is the larger archipelago of the World (17 000 islands) is the second more often divided country in the questionnaires. Malaysia is also often divided even if its superficies is not so important but because it is formed by one peninsula and the northern part of the Borneo Island that are far one from each other.

Then a third factor could be the geographical position between two distinct zones that are different from a cultural or economical point of view. That is more precisely the case for Turkey, Ukraine, Mongolia and Mexico. People drawing the limits of the World regions hesitate to attribute them to one space or another one and find a solution in cutting them. One can note that, in addition to the countries named above, the main vulnerable countries in this situation are the countries of Central Asia from Kazakhstan to Pakistan and Iraq.

Conclusion

There are more analyses to do in order to exploit all the information get thanks to this questionnaire. For example, it would be interesting in the next step of the work to compare the size and the composition of Europe in the European map and in the World map of the questionnaires. Analysis of the explanations of World regions by the individual attributes of respondents should also be further developed. However this case study is wealthy in term of knowledge we get about the European researchers and policy makers vision of the World and vision of Europe. The analyses made here have been very helpful to the interpretation of the ones made in the Key Question 1 (Volume 1) on mental maps, because we get here information about the criteria that where used and that where not available on global actors websites.

In the final report, the results of this case study will be helpful to, because, as we know what the common representations of Europe and of the World in the ESPON community are, it will be easier to introduce our proposals on World regions. In a psychoanalytic sense, we can consider that further progress in the perception of reality can not be achieved as long that unconscious facts are not put to awareness of actors.

  One on the ESPON project 3.4.1 members during the kick off meeting of the project in January 2005 in Paris (14 answers) -One on the ESPON lead partner during the lead partners meeting in Brussels in February 2005 (21 answers) -The last one during the ESPON seminar in Luxemburg in May 2005 (88 answers)

Figure 1 :

 1 Figure 1 : gradation in the belonging to Europe

Figure 2 :

 2 Figure 2 : example of a problem of codification

Figure

  Figure 3 : dealing with the recovering zones

Figure

  Figure 8 : ESPON members' profile

Figure 9 :

 9 Figure 9 : the size of Europe

Map 1 :

 1 Countries placed in/out Europe and those that are placed sometime in/sometime out or that are divided 1.6.1.2.

Figure 10 :

 10 Figure 10 : MCA on European countries.

Figure

  Figure 12 : hierarchical tree of the classification

  on the following map a line showing your delimitation of Europe. B) Which criteria did you used for this delimitation? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ..................... DIVIDE THE WORLD IN 2 TO 15 REGIONS C) Draw on the following map lines showing your World divisions in 2 to 15 spaces. One place should belong to only one region but a country can be divided between two or more regions. If a country must be divided be sure you know why and explain it below. D) Name your areas (please remind the number and report it on the map) Name E) Which criteria did you used for this delimitation? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... .......................................................................................................................................................

Table 1 : example of codification Country name Zone name

 1

Table 2 : Frequency of use of criteria

 2

	Contiguity	4
	Functional	9
	Economy	13
	Feeling / Personal	18
	History	32
	Politic	40
	Culture	44
	Physical / Geo	55
	Making groups of criteria raise some difficulties, mainly concerning the
	interpretation of the criteria listed in the questionnaire as shown is the following
	figure 07. Religion has always been considered as "Culture". More, the
	interpretation of a criterion raises other problems. For example, does "economy"
	means that people gather countries because they have the same economic
	profile (homogeneity) or because it exist a strong economic relation between it
	and Europe or that an economic relation could be of some interest
	(complementarity)?

Figure 7 : criteria grouping 1.4.4. Methodology to analyse the survey

	-Statistical univariate and multivariate have been applied to the whole database
	in order to reveal main trends related to attributes, frequency of belonging to
	Europe or of a specific area in the World.

Empirical result of the case study 1.5.1. The Typical Espon seminar member 1.5.1.1. The ESPON "ideal-type"

	1.5
	An "ideal type" is a qualitative model constructed out of selected elements of
	reality which can maybe never be found as such in reality. It involves an
	accentuation of typical characteristics of a phenomenon.
	It has been tried here to build an ideal type of the ESPON seminar member. To
	do so it has been decided to put the stress on the most frequent answer in some
	categories (for example age categories). The value of the more important
	frequency could be quite different from one variable to another one because
	categories are quite different. For example 28 categories have been made to the
	"country of birth" variable, and 2 only for the gender. That is why it could
	happen that some statements are contradictory (for the graphs related to each
	variable see the annex 02).
	The typical ESPON seminar member is a man, born between 1970 and 1979
	(however the average date of birth is 1963) in Germany with actual German
	Scepticism nationality. The two other more frequent nationalities are French and Italian and
	Humour people from the 6 founder countries of European Union represent 46% of the Rationality Declining nationalism ESPON Members. However about 60% of the participants were not born in the CULTURE European Union, i.e. in a country that was belonging to European Union at the Declined competitiveness date of the birth. The Typical ESPON seminar member is a researcher who Sport fans (less than American) Football ECONOMY travels abroad more than once a year but less than once a month. In European
	Union he has	Beer, pork meat and wine
		Individualism
		Tolerance

already visited 18 countries mainly Belgium, Italy, France, Luxembourg and Germany. Outside Europe he has visited an average of 4 countries mainly Canada USA, Russia and Turkey. 1.5.1.2. Profiles from a multivariate analysis A

  multivariate analysis on the individual attribute provides a more precise figure of the ESPON community. The first component (15 % of information) (table03) underlines an opposition between older researchers not born in Europe and who travel a lot and younger people, born in Europe travelling less often. The second component (11 % of information) (table 04) introduces a differentiation according to the age, the gender and the date of adhesion of the country of residence or birth.

Table 4 : Oppositions in the second axis Born in the forties or fifties Born in seventies Born before the Roma Treaty Born after the Roma Treaty Live in countries which adhesion occur from 1958 to 1989 Live in countries which adhesion occur after 1990

 4

Table 5 : number of countries included in Europe

 5

	MIN	25
	Median	37,00
	Mode	37 & 38
	Average	37,97
	MAX	55

The Map of Europe: countries in, out or cut 

  "IN" countries are mainly always members of the European Union. The number of countries placed in Europe without being divided is 20 that is less that the number of countries actually being member of the European Union (25). They actually represent what could be called the core of Europe. We can notice that Greece, Ireland, Estonia, Cyprus and Malta have been "forgotten" by at less some participant to the survey.  "OUT" countries are those located on the South of the Sahara desert area and Southern and Eastern countries from the Arabic Peninsula excepted Saudi Arabia which northern part is sometime included in the drawing of Europe. It seems that those countries are clearly excluded from the mental maps of Europe of the ESPON community. 

Table 6 : Size of Europe and Physical geography criterion

 6

		Do	not
		use Phys	Use Phys
		/ Geo		/ Geo	Total
	Large	22,95		34,55	28,45
	Medium	34,43		45,45	39,66
	Small	42,62		20,00	31,90
	Total	100		100	100

Typology of European margins according to how they are perceived in the survey

Table 7 : Ukraine and Turkey in Europe

 7

		In Europe	Outside Europe	Cut
	Ukraine	92	15	9
	Turkey	25	41	50
	Source: Survey ESPON 3.4.1. Europe in the World

  Once again the gender is significant to explain the inclusion of Turkey (in both In & Out and Cut and In & Out tests). Women significantly put Turkey out of Europe and men in Europe. It is coherent with the test of the size of European Union but still very difficult to explain. The two other variables that have significant relations are the number of countries visited in the World and in Europe. Number of countries visited in the World has significant relation with the inclusion of Turkey in the In & Out and Cut test. But the test is not very significant and quite difficult to explain. People that have visited few countries in the World include Turkey in Europe… or exclude it. And people who travelled a lot in the World cut it in two pieces. Does that mean that people travelling a lot are more conscious of the internal differentiations of Turkey or that they are more likely to have a balanced point of view and not a clear-cut one?

Table 8 : Correlation Indexes

 8

	VALDIS

Map 9 : countries divided in two parts

Annexe 01: the questionnaire ESPON 3.4.1. « EUROPE IN THE WORLD » SURVEY REPRESENTATION OF THE WORLD OF THE ESPON COMMYNITY

  Sex: ................................................................... Birth date: ......................................................... Country of Birth: ............................................. Actual Nationality (ies): .............................................................................................................. Former Nationality (ies) if any: ................................................................................................... Other : .......................................................... Could you precise on which frequency you go abroad for your job or your vacations?Indicate in which following countries you've been and add those that are not listed.

	___________________________________________________________________________
	I.	INDIVIDUAL ATTRIBUTES
	Activity :	-Researcher or teacher
		-Administrator or policymaker
	-Less then once a year	Once a year	More than once a year	Once a month	More than once a month
	Austria		Greece	Poland
	Belgium		Hungary	Portugal
	Bulgaria		Ireland	Romania
	Cyprus		Italia	Slovakia
	Czech Rep.		Latvia	Slovenia
	Denmark		Lithuania	Spain
	Estonia		Luxembourg	Sweden
	Finland		Malta	Switzerland
	France		Netherlands	United Kingdom
	Germany		Norway

Actual country of residence: ....................................................................................................... Former countries of residence (more than six months) if any: ................................................... ....................................................................................................................................................... What is your higher degree level ? ...............................................................................................

The methodology is detailed in the Key Study on the Division of the World in this report.

Gender repartition Nationality of the participants Country of birth according to its date in European Union Date of birth of the participants Frequency of travel abroad Number of people born in European Union Activity of the participants Number of participants having visited… in Europe Number of participants having visited… in the World