ESPON Typology Compilation

Transcription

1 Version 15/06/2009 The ESPON 2013 Programme ESPON Typology Compilation Scientific Platform and Tools 2013/3/022 Interim Report EUROPEAN UNION Part-financed by the European Regional Development Fund INVESTING IN YOUR FUTURE ESPON

2 This report presents the interim results of a Scientific Platform and Tools Project conducted within the framework of the ESPON 2013 Programme, partly financed by the European Regional Development Fund. The partnership behind the ESPON Programme consists of the EU Commission and the Member States of the EU27, plus Iceland, Liechtenstein, Norway and Switzerland. Each partner is represented in the ESPON Monitoring Committee. This report does not necessarily reflect the opinion of the members of the Monitoring Committee. Information on the ESPON Programme and projects can be found on The web site provides the possibility to download and examine the most recent documents produced by finalised and ongoing ESPON projects. This basic report exists only in an electronic version. ESPON & Spatial Foresight GmbH, Printing, reproduction or quotation is authorised provided the source is acknowledged and a copy is forwarded to the ESPON Coordination Unit in Luxembourg. ESPON

3 List of authors Kai Böhme, Spatial Foresight (lead partner) Tomas Hanell, Eurofutures Finland Kai Pflanz, IRS Sabine Zillmer, IRS Petteri Niemi, Eurofutures Finland ESPON

4 Table of contents Summary Introduction Urban / metropolitan areas Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Rural regions Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Sparsely populated regions Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Regions in industrial transition Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Cross-border regions Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Mountainous regions Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Islands Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Coastal regions Review of existing typologies Proposed methodology for the final typology Testing of the proposed typology Next steps List of Abbreviations Annex 1: Urban Audit Large City Audit variables and data coverage Annex 2: Typology Compilation ESPON

5 Lists of maps and tables Map 1 Major cities in the enlarged ESPON space (draft)...11 Map 2 Border regions according to different types of formal borders...27 Map 3 Border regions according to different simplified types of formal borders...29 Table 1 Summary proposed typology of urban / metropolitan areas...14 Table 2 Summary testing of the typology of urban / metropolitan areas.14 Table 3 Summary proposed typology rural regions...17 Table 4 Summary testing of the typology of regions in industrial transition...19 Table 5 Summary proposed typology on sparsely populated areas...21 Table 6 Summary testing of the typology on sparsely populated areas...21 Table 7 Summary proposed typology of regions in industrial transition...24 Table 8 Summary testing of the typology of regions in industrial transition...25 Table 9 Summary proposed typology of cross-border regions...30 Table 10 Summary testing of the typology of cross-border regions...30 Table 11 Summary proposed typology of mountainous regions...33 Table 12 Summary testing of the typology of mountainous regions...34 Table 13 Summary proposed typology of islands regions...36 Table 14 Summary of the testing of the typology of islands regions...37 Table 15 Summary proposed typology of coastal regions...39 Table 16 Summary testing of the typology of coastal regions...40 ESPON

6 Summary The study on the ESPON Typology Compilation started in March The purpose is to provide a compilation of existing territorial typologies and to propose a set of eight territorial typologies which can be used throughout the ESPON 2013 Programme. Following the Terms of Reference the fields to be addressed are (1) urban / metropolitan regions, (2) rural regions, (3) sparsely populated regions, (4) regions in industrial transition, (5) cross-border regions, (6) mountainous regions, (7) islands, and (8) coastal regions. This is the First Interim Report, following an Informal Progress Report presented in May The report provides a first overview on the 56 existing typologies identified and the proposals for the eight envisaged typologies. In general one can conclude that the project did not find any typology which we would like to propose as ESPON typology for one of the eight thematic fields. Accordingly, we have developed proposals for the typologies which bring together elements form the various typologies reviewed and which compose a coherent set of eight homogenous ESPON typologies. These proposals have been tested regarding their feasibility and will be implemented until November Development, testing and implementation are interactive processes. This implies that during the implementation phase modifications of the proposed typologies might be made. For the development of the typologies following aspects have been main concerns: They need to be methodological sound and robust. They need to have policy acceptance and relevance, e.g. illustrated by the earlier use of similar aspects in important policy documents. There ought to be no conflict between the different typologies. Every typology should have a clear internal differentiation. They need to have ESPON coverage, i.e. data of sufficient quality needs to be available for the ESPON space and preferably beyond (on a sufficient regional level). The typologies should be simple and easy to use for cross-analysis with other ESPON results. Therefore a focus has been on typologies which are rather open and can be easily spiced with different kinds of socio-economic data. ESPON

7 1 Introduction A typology is the study or systematic classification of types that have characteristics or traits in common. A typology is not an end in itself. Rather, it is a tool enabling meaningful analysis and comparison. This understanding of a typology as a tool for comparative analysis differs from the way in which typologies mainly have been used in the ESPON 2006 Programme. There typologies were mainly a tool for communicating different aspects of policy. In the present study emphasis is on the understanding of a typology as an analytical tool. This implies that the typologies to be proposed have to be of limited complexity as they are meant to be used by other projects to assist the analysis of their own data and typologies. Thus, complexity will stepwise increase as the typologies are applied by various ESPON projects. Past experience shows clearly the challenges of regional typologies as concerns methodology, data availability, communication value and policy relevance. Various projects have taken different directions to overcome these challenges and developed a wide range of different typologies. They are the result of an integrated analysis based on several factors and often building on more than one methodological approach. In addition to the ESPON typologies, also other actors, such as OECD, DG Regio, EEA, JRC, CMPR or AEBR have developed territorial typologies within their domains, which are relevant for European territorial development. This study sets out to provide a compilation of these existing typologies and proposes a set of eight territorial typologies which can be used throughout the ESPON 2013 programme. These typologies need to be based on sound methodologies, built on robust data, have a high explanatory power and communicative value and be of policy relevance. The typologies envisaged in the Terms of Reference are: 1) Urban / metropolitan regions 2) Rural regions 3) Sparsely populated regions 4) Regions in industrial transition 5) Cross-border regions 6) Mountainous regions ESPON

8 7) Islands 8) Coastal regions For each of these types of regions one typology is to be developed, which (a) does not conflict with the other typologies (external heterogeneity), (b) differentiates between different categories within the typology (internal homogeneity), (c) is methodologically robust, (d) relies on data which is available and of sufficient quality, (e) can be applied at an appropriate geographical level, and (f) shows policy relevance and acceptance. To achieve this, the project team firstly conducted a survey on existing territorial typologies in the eight thematic fields. In total 56 existing typologies have been scrutinised. Each typology has been reviewed with regard to the possibility to propose it as an appropriate ESPON typology. The typologies and the facts sheets on the review are provided in Annex 2. In principle, the project team did not find any typology to be proposed the way it is. Based on the existing typologies the team has developed proposals for new typologies for each of the eight themes. Most of them combine different elements of the existing typologies. The following chapters provide an overview on the status of the existing typologies, the proposals developed by the project team, and the first testing of the feasibility of these new typologies. Currently, the team is positive that it will deliver eight interesting typologies in its Final Report in November 2009 which fulfil the above mentioned requests (a to f). As the implementation and final development of the typology may pose new questions and challenges concerning data availability or the robustness of the approach, the final outcome might differ from the typologies presented in this report. It has to be acknowledged, that the development, testing and final implementation of the typologies are iterative processes. ESPON

9 2 Urban / metropolitan areas Urban typologies were a difficult topic already during the ESPON 2006 Programme. Following the review of various existing typologies, the project team proposes a new typology which will bring together the typology developed within the Urban Audit and results from ESPON typologies in the field. 2.1 Review of existing typologies A wide range of different actors have developed various types of urban and/or metropolitan typologies. Not at least within the ESPON 2006 Programme various such typologies have been developed by the two projects on functional urban areas. For the typology compilation eight such typologies have been selected, mainly developed by the European Environment Agency (EEA), the Urban Audit and ESPON. These typologies show that both the delineation of urban areas and their typology are issues to be considered. Indeed, some of the examples picked are not typologies but rather delineation definitions. This might also be of relevance for the rural-urban typologies. Furthermore, none of the typologies uses NUTS 2 or 3. Most of them have defined own geographical levels and statistical units. With regard to the territorial coverage, only the ESPON typologies have been developed for the full ESPON space of the previous period. The other typologies focus mainly on the territory of the EU, although for some additional information from other countries might be available. In addition to the typologies presented in the compilation, the ongoing ESPON project FOCI (Future Orientation of Cities) is developing new typologies. The final typology of the FOCI project will only be presented when this study is already concluded. However, the project team advises the ESPON CU to follow up on this and to consider at a later moment possible adjustments of the typology proposed below in order to ensure the harmony with the FOCI project. Furthermore, the work on rural-urban typologies (reflected in the next chapter) needs to be considered when discussing urban / metropolitan typologies. This regards both the existing typologies and the work currently under way by DG Regio and also within ESPON. Indeed, it might be sensible to even consider a joint urban and rural typology instead of two separate typologies. However, even in the case of two separate typologies, the congruence between these two needs to be ensured. ESPON

10 2.2 Proposed methodology for the final typology Based on the reviewed typologies, we suggest to develop a typology which (a) at LAU 2 level distinguish between major cities (approx 660) and more regional cities (approx 820), and (b) further differentiates the major cities using additional socio-economic data (most likely at NUTS 3 level). To ensure policy acceptance and relevance the typology builds clearly on the work carried out under the Urban Audit and the EEA work on the Degree of Urbanisation (DGUR). We acknowledge that for any urban typology there is a need to capture the entire urban system in the ESPON space on the one hand so as to provide for a coherent picture of the entire urban palette. On the other hand there is also a need to further diversify the description of at least the upper hierarchy of the urban system. Due two these two requirements our proposed typology is grounded in three different approaches to urban areas: functional, structural and morphologic. Upper tier urban centres We take as a starting point the unique and currently underutilised capabilities of the Urban Audit data base. The extended Large City Audit data base contains limited statistical information (50 variables, see Annex 1) for 592 large and medium-sized European cities. Apart from the entire EU27, this collection includes all major cities in Norway, Iceland, Switzerland, Turkey and Croatia, and has thus a very good coverage in terms of the ESPON space. The statistical level utilised here is LAU 2. Although this data base provides a very good coverage of all larger cities and a fairly good one of the medium-sized ones (pop. roughly ), it needs to be amended for ESPON purposes. We therefore propose to supplement it (i.e. the Large City Audit) with information on larger cities as identified in the GISCO STEU point geodata base. All Urban Audit cities are thus initially included and the set is completed with the STEU points. As an initial size limit, we have opted for a LAU2 population of inhabitants. The combined Urban Audit and STEU data base on 695 larger European cities (592 Urban Audit cities, marked in blue, 103 additional STEU cities, marked in red) would look as indicated in the (draft) map below. ESPON

11 Map 1 Major cities in the enlarged ESPON space (draft) Croatian, Irish and Turkish Urban Audit cities are not included in the current cartographic data set of the Urban Audit, and are thus here added manually by copying their location from the STEU data base. In the Urban Audit city set there are a certain number of cities that are rather small in terms of population and have been added to the urban Audit on other grounds. It is an open question whether we would apply a population limit as a drop-out criteria for the smallest Urban Audit cities. In the STEU data base we are initially working with inhabitants, and a threshold of e.g inhabitants on LAU2 level for the uppermost tiers of the ESPON space urban system could thus be applied also to the Urban Audit. All in all this would imply some 660 cities. This selection would finally be overlaid on the EEA based Degree of Urbanisation (DGUR) typology to confirm a coherence with urban land ESPON

12 use. In the (very unlikely) case a selected city does not display any urban land use characteristics, this city will be removed from the selection. Western Balkans and Turkey are not included in the EEA DGUR typology. For these, we would instead confirm the feasibility by means of the urban land use classes in PELCOM (Pan-European Land Use and Land Cover Monitoring), which covers all of Europe. This extended city collection would thus constitute that part of the urban tier where we propose to apply more advanced statistical distinctions of the different types and functions of cities (see below). Lower tier urban centres Apart from the upper tiers of the urban system, we propose to utilise the remaining STEU cities that are both outside the Urban Audit and have less than inhabitants for identifying an urban tier of regional centres. There are approximately 820 such cities that have a population between and inhabitants. Depending on how these are distributed regionally (one such city per region, or several), we would adjust the lower limit in order not to classify the entire remaining ESPON space into this category (see description of the rural typology below). The STEU data lacks population numbers for about 400 cities, of which the majority can be ignored, but some, commonly known larger cities, call for attention here. Among these are Antibes and Cannes (FR), which will be considered individually. Further differentiation of the upper tier urban centres For the approximately 660 larger cities a further differentiation is envisaged enabling distinction between different types of cities. Ideally, this could be done on LAU 2 data, e.g. deriving from the Urban Audit. However, the data screening has shown that the current data situation does not allow for this (cf. annex 1). Therefore we will mainly rely on NUTS 3 data and ensure the appropriateness of that information through some statistical checks. By comparing the population of the selected large cities and their surrounding NUTS 3 region, we could obtain a picture of the urban characteristic of this, and thus a final verification that we could use the NUTS 3 based information to describe that particular city in the work with the typology. Should the population of the city be too low compared to the NUTS3 region, the area would not be considered urban at this stage. A tentative limit here would be set to the city having less than one third or a quarter of the total population of the ESPON

13 region. Regions containing more than one of these cities would be excluded from this comparison and automatically considered being urban areas. Already for this rather simple approach the current data situation needs to be improved. (a) There are (expectedly) gaps in population data. On NUTS 3 level, data are missing for the whole Former Yugoslavian Republic of Macedonia, Croatia, Denmark and Iceland. In Poland we lack data for 44, in Germany for six (6) regions, in Spain for the Illes Balears & Canarias, and in the Netherlands for Arnhem & Achterhoek regions on NUTS level 3. We believe however that we are able to overcome this by substituting this information with a national data collection. (b) The Urban Audit population data again is missing information for 35 Spanish cities, Zagreb, Slavonski Brod, Osjiek and Split (HR), Siauliai and Klaipeda (LT), and the whole Bulgaria. A part of these gaps can be filled with STEU data. As a result of the above, we would thus have a selection of cities for which their either exist Urban Audit based data, or for which there exist corresponding information based on the NUTS nomenclature. In a next step some basic NUTS 3 data sets on e.g. GDP per employee, employment figures, total GDP, total population and total area can be applied to differentiate cities. Following the work of the previous ESPON projects and the Urban Audit also some more functional information will be applied in order to fine tune the categorisations. The ambition is to develop a typology which by combining LAU 2 information for the delineation of urban areas and appropriate NUTS 3 information for differentiation urban areas according to some basic characteristics. The final categories envisaged are e.g. (a) global centres, (b) European players possible with a sub-category for this with global aspirations, (c) marco-regional players, (d) national players, and (e) regional players. The final categories will be closely linked to the existing categorisations in ESPON projects and the Urban Audit, to ensure the policy acceptance. This approach to the typology can also facilitate the future use of Urban Audit data by ESPON projects as Urban Audit data can be easily integrated into the further analysis using this typology. ESPON

14 Table 1 Name: Approach: Geographical level: Categories (examples): Summary proposed typology of urban / metropolitan areas Urban and metropolitan areas Definition of urban areas at LAU 2 based on Urban Audit and GISCO STEU and checked against the EEA Degree of Urbanisation. Differentiation of urban areas based on NUTS 3 data following the approaches taken by previous ESPON projects and the Urban Audit. Initially LAU2, but for purpose of further usability, scaled upward to NUTS 3. (a) Global payers (b) European players possible with a sub-category for this with global aspirations (c) marco-regional players (d) national players (e) regional players. 2.3 Testing of the proposed typology The above description of the shows that as expected there are severe data challenges for this typology. However, by bringing together results from different accepted sources, we are confident that these challenges can be overcome. Building on existing typologies will also facilitate the robustness and policy acceptance of the proposed typology. Table 2 Summary testing of the typology of urban / metropolitan areas Name of the typology External heterogeneity Internal homogeneity Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Data quality Geographical level (appropriate & feasible) Policy relevance Urban / metropolitan areas Very high, as it is mutually exclusive with the typology of Rural regions. T.b.d., aiming at a high rate. Principally clear-cut (decision tree possible), albeit some subjective choices will have to be made (especially regarding the cut-off rates) There are considerable data challenges, as described in further detail above. However, the project team is confident that these can be overcome. As described above the data quality is moderately high. Initially LAU2, but for purpose of further usability, scaled upward to NUTS 3. As the typology is based on the work of the Urban Audit and previous ESPON experience, policy acceptance should be given. ESPON

15 3 Rural regions Rural typologies or urban-rural typologies have been a challenge to the ESPON 2006 Programme as well as to Eurostat and DG Regio. Following the review of various existing typologies, the project team proposes a new typology which draws on the work carried out by Eurostat, the OECD, DG Regio, and DG Agri. Furthermore, we have undertaken efforts to ensure that the rural typology does not conflict with the urban typology proposed above. 3.1 Review of existing typologies Rural typologies, which mostly concern both rural and urban areas, constitute the largest field of typologies addressed in this study. Indeed, there are more such typologies than one might imagine. The 17 typologies presented in the annex are only a selection, i.e. the compilation is not complete. However, it shows a series of approaches both for differentiating and delineating urban and rural areas. Most typologies relate to population density referring to the OECD typology, but often they build only on the general rule without corrections concerning larger cities. Furthermore, most of them do not catch the full complexity of rural-urban settings. Population density, accessibility / peripherality and land use are the most common features but often only one or two of them are taken into account. Those typologies which come closer to the rural-urban complexity are usually overly complicated. Furthermore, it can be noted that the agricultural dimension is rather prominent in some discussions of rural typologies. In general, the geographical level is extremely challenging for these typologies. Only a few typologies use LAU 2 or NUTS 5, some NUTS 3 and a substantial number of typologies builds on a mixture of NUTS 2 and 3. This results in various statistical artefacts in the maps. In addition to these existing typologies, DG Regio is currently working on a new or revised typology in co-operation with DG Agri and Eurostat. The aim is to establish a typology which works for both regional and agricultural policies. Furthermore, the current ESPON project EDORA (European Development Opportunities in Rural Areas) is also developing a new typology. Both DG Regio and the EDORA project will finalise their typologies only when this study is already finished. Therefore, the project team has developed an own proposal, but advises ESPON strongly to follow the outcome of the work carried out by DG Regio and the EDORA project. ESPON

16 3.2 Proposed methodology for the final typology Based on the reviewed typologies, the following proposal has been elaborated by the project team. Our proposed rural typology would build upon the previously described urban typology. As a starting point we thus envisage to include as rural areas primarily 1 only such regions that are not classified as urban (cf. urban typology). Taking into account the overall demand for external heterogeneity of the entire palette of typologies, this would be a justified choice. An overlap between urban regions on the one hand, and rural ones on the other, could thus be avoided. We are well aware that this approach will lead to differing conclusions as to what could be considered as rural than would be the case if we a priori would not at all take into account the urban categories. We however feel that the demand for heterogeneity vis-à-vis other typologies specifically on this point is too large to oversee it. Putting it simply: what is urban cannot be rural, and vice versa. This approach also ensures that the end result is concept wise similar to the OECD rural typology, which is class wise mutually exclusive. Also, there exist a strong link between this approach and the OECD one regarding the introduction of kick-in or kick-out criteria based on population at the LAU 2 level and size of urban centres. As for the demand for internal homogeneity of the rural typology, we propose to take as a starting point the methodical assessments developed under the SPESP (Study Programme of European Spatial Development, project 2.3 A Typology of Rural Areas in Europe,) with some alterations and the urban-rural typology developed by DG Regio. We propose primarily to address the issue from an economic point of view. As input data we would use two different data sets. (a) We would assess each regions relative position vis-à-vis larger urban centres. This is a critical factor from the point of view of rural development strategies. Rural regions that are located close to larger urban centres do have the possibility to build upon a strategy implying connecting up with these large urban centres in order to broaden the employment base of the region. Regions that lack this possibility will either have to further focus on developing primary production as a source of livelihood and economic development, or they will have to develop rural economic diversification strategies that are not or very little connected to 1 We should stress the word primarily here, since it may well be that the initial test of the proposed method reveals a need to introduce also regions principally classified as urban into this category on account of their substantial rurality (as operationalised by us). For the most part of it, one could assume that these exceptions would principally concern regions in the lower tiers of the urban hierarchy. ESPON

17 accessibility to larger centres. For this, we propose to overlay the above data with the accessibility to urban areas. Several tentative sources for this exist (e.g. DG Regio typology or ESPON areas within 45 minutes reach from urban centres). (b) To cast some more light on which rural regional actually are also agricultural regions, we would look at the Gross Value Added or GVA 2 of the primary production branches (A-B) as a share of total GVA. This would provide us with an assessment of how important the primary production branches are to the overall regional economy. This data is available in New Cronos, regional accounts, branch accounts. Furthermore, we would look at the corresponding data on employment, i.e. how large a share of total employment stems from primary production (branches A-B). This data is also available in New Cronos, regional accounts, branch accounts. It must be stressed though, that the employment data contained herein is primarily intended to be used as a denominator in regional/branch/household accounts only. However, as there are no other data sources on this territorial level (only on NUTS 2), we would nonetheless opt to use this. By combining the employment and the GVA data, a picture of the regional economic structure vis-à-vis primary production can be obtained. It has to be stressed that neither of the two data sets are separately enough to provide for a clear picture of the overall importance of primary production to the regional economy. Hence the need for this combined approach. This method implies more generally that we do not plan to introduce any variables on land use in this typology. Although at first sight utterly tempting, the reasons for omitting these are rather selfexplanatory. Differences in land use stemming e.g. from the type of crops (grain, fruit orchards, and so on), the type of animal husbandry (sheep, pig, dairy, and so on), or the fact that fishing or forestry as basic economic branches are to a large extent not reflected in prevailing land use classifications, imply that an introduction of such a variable could tentatively lead to ambiguous or misleading conclusions. We propose to identify cut-off rates for the regional GVA/employment importance either in connection to the country average (worse method) or in terms of a de facto universal cut-off rate (better option, but might not work due to large differences across the ESPON space). The two variables would then be merged with the accessibility 2 The difference between GVA and the in the ESPON context more familiar GDP is as follows. GDP (at market prices) plus subsidies on products minus taxes on products equals Gross Value Added (at basic prices). GVA specifically at basic prices is the best suited and most commonly used indicator of an individual producer s, industry s or sector s contribution to the overall economy. ESPON

18 information (definite technique still to be decided) and hence provide a principally two-dimensional rural typology based on (a) the relative position of the rural region vis-à-vis larger urban centres and (b) importance of primary production both to the overall regional economy (GVA) as well as a source of livelihood (employment). Table 3 Name: Approach: Geographical level: Categories (examples): Summary proposed typology rural regions Rural regions This typology will only include those regions not covered by the urban typology. For the further differentiation of these regions two dimensions will be used: 1) the relative position of the rural region vis-à-vis larger urban centres (e.g. areas within 45 minutes reach from urban centres), and 2) the importance of primary production to the overall regional economy (GVA branches A-B as a share of total GVA), combined with the importance of primary production as a source of livelihood (employment in branches A-B as a share of total employment). NUTS 3 (in a very limited number of cases, NUTS 2 due to lack of data) (a) rural areas close to urban centre without agrarian profile (b) rural areas close to urban centres with agrarian profile (c) remote rural area without agrarian profile (d) remote rural area with agrarian profile 3.3 Testing of the proposed typology A first testing of this approach shows that there are some data challenges which we, however, believe can be overcome. An initial data screening of required variables on NUTS 3 level reveals that there are, with considerable differences in temporal availability, but nonetheless, data available on both GVA and employment roughly for the time period For GVA we lack NUTS 3 data for the entire countries Switzerland, Iceland, Liechtenstein and Former Yugoslav Republic of Macedonia. For Turkey, we only have data for the years For employment, we lack NUTS 3 data for the entire countries of Switzerland, Iceland, Liechtenstein, Former Yugoslav Republic of Macedonia and Turkey. Additionally, we lack NUTS 3 data for the following regions: Verviers (BE), Sachsen-Anhalt (DE), Illes Balears & ESPON

19 Canarias (ES), Sardinia (IT), Poland, 45 NUTS 3 regions) and entire North Eastern Scotland. We feel that these rather small gaps in data on this point are possible to overcome by supplementing New Cronos data with nationally available data. We will first explore the OECD Territorial Indicators data base to see what we can extract from there, and only after that make an effort for purely national data collection. As regards the methodological robustness, the proposed typology builds on experience form various other typologies and is fully inline with the OECD and DG Regio typology. Therefore we are confident both as regards methodological robustness and policy acceptance. Table 4 Name of the typology External heterogeneity Internal homogeneity Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Data quality Geographical level (appropriate & feasible) Policy acceptance Summary testing of the typology of regions in industrial transition Rural regions Very high, as it is mutually exclusive with the typology of Urban / metropolitan areas. T.b.d., aiming at a high rate. Clear-cut in relation to the typology of Urban / metropolitan areas. Internal classification can be made on unequivocal ground which renders the approach very robust. Only the identified cut-off rates are subjective, Principally covers all EU27, with data lacking only for Switzerland, Iceland, Liechtenstein, Former Yugoslav Republic of Macedonia and Turkey as well as scattered other NUTS 3 regions. For full details, see text. The general data quality is high. However, data on employment are Eurostat estimates primarily intended to be used as a denominator in regional/branch/household accounts. The chosen NUTS 3 level (in a very limited nr of cases, NUTS 2 due to lack of data) is the most appropriate. At lower level it would be difficult to apply a robust methodology and overcome the data challenges. As the typology is based on the work of the Urban Audit and previous ESPON experience, policy acceptance should be given. ESPON

20 4 Sparsely populated regions Sparsely populated areas are generally debated in the context of areas with geographical specificities. Whereas the basic definition as inhabitants per square kilometres is mostly clear, the thresholds and the geographical level are main issues. In the proposed typology the thresholds are taken from the EU Structural Funds regulation and the geographical unit suggested is NUTS 3 as this is the lowest level sensible in this context. 4.1 Review of existing typologies Sparsely populated areas are addressed within EU Cohesion Policy and have been defined within the EU Structural Funds when Sweden and Finland joined the EU. These areas have been addressed and defined under Objective 6 of the Structural Funds. The basic feature is the number of inhabitants per square kilometre. This has been used by four of the five typologies respective identified in the compilation. The remaining typology addresses the population potential by considering the amount of people living within 50 km from a given place. The geographical level of the typologies differs widely. It ranges from grid level information (2.5 km x 2.5 km) to NUTS 2 level information. Also the threshold used for defining sparsely populated regions differs between the typologies. 4.2 Proposed methodology for the final typology Based on the reviewed typologies, the project team proposes to use population density (i.e. number of inhabitants per square kilometre) to assess sparsely populated regions. For total population a current and complete dataset (elaborated in the ESPON database project) is available down to NUTS 3 level (NUTS version 2006) which also seems to be the most appropriate geographical level for this analysis. The latest available data is from The proposed thresholds to define sparsely populated areas follow the Structural Funds regulation: (a) Very sparsely populated: Regions with less than 8.0 inhabitants per km² (b) Sparsely populated: Regions with less than 50.0 inhabitants per km² ESPON

21 (c) Non-sparsely populated: Regions with 50.0 and more inhabitants per km² These thresholds allow to categorise the European regions adequately. Furthermore, the typology is politically relevant as the used thresholds are coherent with the thresholds used by Structural Funds policies. This way the typology provides a sound basis for policy oriented analyses. A further differentiation with socio-economic data can be easily added to enrich this typology, e.g. GDP per capita, accessibility or population change. However, we would suggest to leave this to the projects applying the typology so that they the can use socio-economic data which is most appropriate for the purpose of the analysis. Table 5 Summary proposed typology on sparsely populated areas Name: Sparsely populated regions Approach: Number of inhabitants per km 2 Geographical level: Categories (examples): NUTS 3 (a) very sparsely populated (b) sparsely populated (c) non-sparsely populated 4.3 Testing of the proposed typology The typology is based on a robust approach and a good quality of data. Furthermore, linked to the Structural funds regulations it provides a sound basis for further policy oriented analyses. Table 6 Summary testing of the typology on sparsely populated areas Name of the typology External heterogeneity Internal homogeneity Sparsely populated regions The typology does not conflict with any other typology proposed. Because only one indicator is used to categorise the regions, internal homogeneity solely depends on the chosen thresholds. These thresholds are linked to the European reality of different understandings of what is considered as sparsely populated (e.g. a sparsely populated region with around 40 inh./km² in a Nordic country might not gain the same attention as a similar populated region in a state in central Europe). The chosen thresholds take these different understandings in account. Thus, the thresholds are reasonable and internal homogeneity is high. ESPON

22 Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Data quality Geographical level (appropriate & feasible) Policy acceptance As sparsely populated can be clearly linked to the only chosen indicator population density, the approach is very robust as long as the thresholds are set reasonably. Despite demographic change, population density can be considered to be a relatively stable socio-economic indicator. Data is currently not completely available in the regional statistic database of Eurostat, but the ESPON Database project managed to build a complete dataset of total population for the whole ESPON space. Nearly complete data for Macedonia und Turkey is availably from Eurostat. In general, the necessary data is collected, checked and harmonised by Eurostat. Accordingly, data quality is high with regard to further updates. For the current dataset the ESPON Database project had to estimate few records. The chosen NUTS 3 level is the most appropriate level for the indicator population density. Analyses on NUTS 5 level would produce many statistical artefacts and might lead to some kind of urban/rural typology. Furthermore current and complete data on NUTS 5 level is not available. Due to the availability of NUTS 3 data the chosen level is also feasible. As the thresholds are linked to the Structural Funds regulations policy acceptance can be assumed. ESPON

23 5 Regions in industrial transition Regions in industrial transition have been one of the most challenging topics when it comes to finding existing typologies. As we basically have not found any usable typology, we have developed an own proposal. 5.1 Review of existing typologies No European typology of regions in industrial transition could be identified. The closest to a typology has been an identification of some 20 old industrial (i.e. mining areas) areas in EU12, but this material is not feasible for any present-day European endeavour on the subject. 5.2 Proposed methodology for the final typology As the existing material as well as the exact definition and data availability for this topic are extremely challenging, the project team has developed following proposal. The typology is constructed in two stages. In the first stage, it is decided whether a particular region is industrial or not. In the second stage, those regions that were classified as being industrial are divided into two groups: stable or in transition. Eventually, it is only the latter group of regions that will constitute the typology. We propose primarily to address industrial transition from an economic and a labour market point of view. As input data we would use two primary data sets. On the one hand we would look at Gross Value Added (GVA), on the other at employment. We feel these two variables are able to capture the principal aspects of industrial transition. Neither of them alone are able to provide a complete picture of the structure of the economy or its change. Hence the need for combining these two data sets. Firstly, we would look at the share of manufacturing branches (C-F) GVA and employment as a share of total GVA and total employment respectively. This would provide us with an assessment of how important the manufacturing branches are to the overall regional economy and the labour market. This data is available in New Cronos, regional accounts, branch accounts. The employment data contained herein is primarily intended to be used as a denominator in regional/branch/household accounts only. However, as there are no other data sources on this territorial level (only on NUTS 2), we would nonetheless opt to use this. ESPON

24 Based upon this, we would either identify global cut-off rates for these shares in identifying what is to be considered an industrial region and what is not. Alternatively, we could treat each country as a specific entity and adjust the cut-off rates to these. Secondly, for those regions that are classified as being industrial, we would for a given period look at the annual rate of change of industrial GVA and employment separately in relation to the total rate of change of both variables in these regions, in relation to the country average. By setting minimum annual rates for this change we can identify those regions where this structural change is occurring most rapidly. The minimum rates could be set either globally, or country wise. The preferred time interval would follow data availability and tentatively span the period The outcome of this exercise could be a classification of industrial-nonindustrial regions on the one hand, and stable industrial regions or industrial regions undergoing structural transition at different speeds on the other. It is expected that the overall structural change of the European economy would imply that that for most industrial regions at least employment, tentatively also GVA, would increase more slowly or decrease more rapidly than overall employment (or GVA). It is however as of yet unclear to us whether regions that display the opposite pattern should be classified as regions undergoing a (positive) industrial transition, or something else. Table 7 Name: Approach: Geographical level: Categories (examples): Summary proposed typology of regions in industrial transition Regions in industrial transition Stepwise approach: a) identify industrial regions in general; and b) identify those industrial regions that are undergoing a structural economic transition. NUTS 3 (in a very limited number of cases, NUTS 2 due to lack of data) a) industrial regions non-industrial regions b) industrial regions undergoing structural economic transition; (for example: very rapidly, rapidly, moderately, slowly) 5.3 Testing of the proposed typology The testing of the data availability showed that we lack data on both total and industrial GVA for Switzerland, Iceland, Liechtenstein and Former Yugoslav Republic of Macedonia. For Turkey there are only data available for the period For the UK, we lack data on ESPON

25 industrial GVA alone. Furthermore, no data on this are available for the following NUTS 3 regions: Sachsen-Anhalt (DE), Illes Balears (ES), Canarias (ES) as well as 45 NUTS 3 regions in Poland. For Norway we lack dynamic data (only 2004 available). By and large, similar data on employment is missing. We feel that these rather small gaps in data on this point are possible to overcome by supplementing New Cronos data with nationally available one. We will first explore the OECD Territorial Indicators data base to see what we can extract from there, and only after that make an effort for purely national data collection. Overall, the proposed approach is rather simple and straight-forward and should guarantee a robust result. As regards the policy acceptance, the proposal builds on accepted standard indicators. Therefore we do not expect any major problems in that field. Table 8 Name of the typology External heterogeneity Internal homogeneity Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Summary testing of the typology of regions in industrial transition Regions in industrial transition Very high. Only industrial regions are concerned. T.b.d., aiming at a high rate. Very robust. Only the identified cut-off rates are subjective. Principally covers all EU27, with data lacking only for Switzerland, Iceland, Liechtenstein, Former Yugoslav Republic of Macedonia and Turkey as well as scattered other NUTS 3 regions. For full details, see text. Data quality High. However, data on employment are Eurostat estimates primarily intended to be used as a denominator in regional/branch/household accounts. Geographical level (appropriate & feasible) Policy acceptance NUTS 3 (in a very limited nr of cases, NUTS 2 due to lack of data) As the proposal builds on largely accepted indicators, and is very transparent, policy acceptance should be given. ESPON

26 6 Cross-border regions Cross-border situations and relations are rather complex. Among others the previous ESPON INTERACT project has illustrated some features of this complexity. Building on that experience the proposed typology suggests a basic differentiation of the most important border characteristics. 6.1 Review of existing typologies A range of typologies of cross-border areas and regions has been developed in various studies. Often the focus is on the quality and dynamics of interactions across the borders in selected areas. An example for this is the 2008 report by the Association of European Border Regions reviewing cross-border co-operation in Europe. Also under the current ESPON priority 2 a study on cross-border areas is carried out. Only very few studies have covered more than single examples. For the compilation, we selected the few studies which actually have a more European coverage or use indicators for which data can be assessed for the entire ESPON space. In total 12 relevant cross-border typologies have been identified and are presented in the compilation. First of all, these typologies differ with regard to their geographical level. Most of them use the NUTS classification, others use INTERREG cross-border cooperation areas or smaller units like cross-border FUAs. The thematic features of the typologies differ widely. Some use geographical or political border characteristics. Others focus on the permeability of the border, economic differences on either side of the border, the participation in cross-border co-operations or cross-border urban structures. 6.2 Proposed methodology for the final typology Regarding the cross-border typology the first commitment to be made is the choice of geographical level (NUTS regions, cross-border cooperation areas, cross-border FUAs). These different levels tend to represent different functional units. But as the typology should serve as a basis for further analysis, enriched with more socio-economic data, only NUTS regions ensure the availability of such data. Therefore the cross-border typology uses NUTS 3 as geographical level. Concerning the special situation of cross-border regions the main issues seem to be the cross-border integration potential and the border porosity. As borders are political agreements, the type of political border has an important influence on the integration potential. ESPON

27 In the European context it is for example possible to differentiate between EU internal and EU external borders, or between internal and external Schengen borders. Furthermore, borders can be subdivided in land borders and maritime borders. A definition of maritime borders could follow the general rule provided by Council Regulation (EC) No 1083/2006. According to this there is a maritime border if the land mass of another country can be reached within a maximum of 150 km. A first intermediate approach for such a typology is shown in map 2. Map 2 Border regions according to different types of formal borders ESPON

28 However, limiting a typology to the political type of border is not sufficient in the context of ESPON. To further define the typology, the barrier effect or importance a border has for a region needs to be considered. Therefore following indicators will be added to illustrate the cross-border characteristics of the regions and should be included in the typology. The ratio between the size of a NUTS 3 region and the length of its border. The density of border crossings. It is envisaged to combine the type of border with these two additional features. As the final number of types of cross-border regions should not exceed six categories it is necessary to simplify the typology of map 2 and eventually integrate both additional features as a combined one. This simplification is necessary because the typology otherwise misses the target to provide a sound basis for further analysis. A possible reduction of border-types is shown in map 3. It is assumed that the integration of the EU and EFTA (European Free Trade Area) has become so close that the main barriers are the borders to regions outside these countries. These borders comprise also all external Schengen-borders (although there exist still at least until 2011 some few external Schengen borders inside the EU). For further simplification it might be useful to skip the sea borders, since they represent a type of cross-border which would always constitute a separate category, which can hardly be related to the other categories of cross-border regions. Deliberately the focus has been put on the characterisation of the border which implies different development conditions for these regions as compared to other regions. Other socio-economic factors can be added later on, depending on the purpose of the further analysis. This includes also the dimension of cross-border integration where we did not find a convincing typology with data availability for the entire ESPON space. A first attempt to such a typology has been elaborated by the Association of European Border Regions, however, only for selected cross-border regions. ESPON

29 Map 3 Border regions according to different simplified types of formal borders ESPON

30 Table 9 Name: Approach: Geographical level: Categories (examples): Summary proposed typology of cross-border regions Cross-border regions Differentiation of border regions with regard to the simplified type of formal border and the density of landborder crossings while at the same time considering the relation of the size of a region and the length of the border. NUTS 3 The final categories still have to be developed and will be less intricate than the following table which gives a first overview on the possible richness of information: EU / EFTA internal EU / EFTA external EU / EFTA internal and external high border exposure Low border exposure many border crossings few border crossings many border crossings few border 6.3 Testing of the proposed typology The envisaged typology is a promising approach to provide a sound basis for further analysis of cross-border regions, although there have to be made compromises between accuracy and feasibility. Table 10 Name of the typology Summary testing of the typology of cross-border regions Cross-border regions External heterogeneity Internal homogeneity The typology does not conflict with any other typology proposed. To choose the type of border as a starting point of the typology secures a quite high degree of internal homogeneity. Further distinctions concerning the barrier characteristics and the importance of the border for the region even strengthen this. But of course, any simplification which is made reduces internal homogeneity. ESPON

31 Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Data quality Geographical level (appropriate & feasible) Policy acceptance This classification by types of border as a basis for the typology can be regarded as very robust. The integration and combination of the additional indicators has to be conducted very carefully to avoid statistical artefacts. The type of border is easy to identify in the ESPON states and the Balkan states plus Turkey. The size of the region and length of the border are known as well or can be calculated based on GISCO layers. Border crossings are only available for the ESPON space at the moment, thus there might be need to complement the existing data sets. As the data is based mostly on political agreements and basic topographic criteria the quality is high. Regular updates are not necessary for most indicators. The only exception is the density of border crossing which is a larger effort to collect and calculate. With regard to further analyses based on the proposed typology NUTS 3 is an appropriate as well as feasible geographical level. Lower geographical levels miss a regional character and would raise problems of data availability and quality. As the proposal builds on largely accepted and simple indicators, policy acceptance should be given. ESPON

32 7 Mountainous regions Mountainous areas are generally debated in the context of areas with geographical specificities and have been studied e.g. by the DG Regio study of Mountain Regions. The proposed typology builds on the results of this study by up-scaling it to NUTS 3 level and focusing on the share of a region s population living in mountainous municipalities. 7.1 Review of existing typologies Definitions and typologies of mountain areas have been developed by various organisations. For the typology compilation we have collected six different typologies of mountainous regions. Most commonly altitude and slope are used to define mountain areas. The work of the UNEP-WCMC (United Nations Environment Programme - World Conservation Monitoring Centre) is a common point of reference for typologies of mountainous areas. In the Mountain Study carried out for DG Regio also climate has been used as defining criteria. Among others, this approach has also been used in the ESPON Atlas. In detail, the DG Regio Mountain Study followed the UNEP-WCMC approach, which uses altitude alone to define mountain areas above 2,500m and combines altitudinal and slope criteria to define mountains above 1,000m. For lower elevations ( m), an additional criterion based on local elevation range is used to identify mountainous areas. In contrast to the UNEP-WCMC approach also areas for elevations lower than 300m are considered. Beyond, temperature contrasts function as an additional criterion to define mountain areas. Small isolated mountainous areas are not included, the same holds for non-mountainous areas within mountain massifs. Finally, a municipality is considered as mountainous if at least 50% of its area within the area is delimited as mountainous. The criteria used by the various typologies for distinguishing different types of mountain regions range form the elevation via the share of mountainous areas within a region to totally different indicators such as potential multimodal accessibility. The geographical level applied for the typologies ranges from grid level (1 x 1 km) via LAU 2 to NUTS Proposed methodology for the final typology The UNEP-WCMC approach is widespread and widely accepted. Therefore it is reasonable to develop a typology linked to this approach. Again NUTS 3 regions are chosen as the geographical level. ESPON

33 At the moment the socio-economic data on LAU 2 level is only available from the DG Regio Mountain Study and represents old municipality boundaries. Furthermore, for further use and cross analysis within ESPON NUTS 3 is more adequate. Based on the existing typologies, the project team proposes to develop a typology at NUTS 3 level which is based on the LAU 2 level data deriving from the DG Regio Mountain Study. As this typology should focus on mountains, only municipalities related to topographic criteria are considered as mountainous. Municipalities defined as mountainous by climatic criteria are not considered. The internal differentiation would then follow the population share of people living in mountainous LAU 2 regions within each NUTS 3 region. The aim is to assess how strong the population living in a NUTS 3 region is affected by the mountains. The typology will e.g. distinguish between NUTS 3 regions with (a) a large share of the population living in mountainous municipalities; (b) a predominate share of the population living in mountainous municipalities; (c) a minor share of the population living in mountainous municipalities; and (d) non-mountainous municipalities. The specific thresholds to be chosen will depend on an appropriate categorisation of the European regions. This typology can be easily enriched with additional socio-economic data by the ESPON projects applying the typology. Thus, depending on the focus of the analysis population development, GDP, or data on various economic sectors can be used for further differentiations. Table 11 Summary proposed typology of mountainous regions Name: Mountainous regions Approach: Population share of mountainous LAU 2 regions (according to DG Regio Mountain Study) within a NUTS 3 region Geographical level: Categories (examples): NUTS 3 (a) Mountainous regions / population (b) Predominately mountainous population (c) Partly mountainous population (d) Non-mountainous regions / population ESPON

34 7.3 Testing of the proposed typology The typology is an innovative approach to examine the mountainous characteristic of a region. Due to the use of LAU 2 data as an input it is possible to reduce MAUP issues which are especially relevant for topographic and natural phenomena. Table 12 Name of the typology Summary testing of the typology of mountainous regions Mountainous regions External heterogeneity Internal homogeneity Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Data quality Geographical level (appropriate & feasible) Policy acceptance The typology does not conflict with any other typology proposed. The choice of only one indicator differentiating between mountainous regions allows for a high level of internal homogeneity. The concrete degree of homogeneity depends on the chosen thresholds. The approach is robust as long as the thresholds are set reasonably. Problems can occur due to MAUP, although due to the use of LAU 2 data as input these problems are relatively small compared to analyses which are only based on NUTS 2/3 data. The necessary topographic and population data is delivered by the DG Regio Mountain Study, thus the data is only available for the ESPON space. For 98.4 % of the more than 115,000 LAU 2 regions total population data is currently available. The population data collected by the mountain study covers 2001 as latest year available. Also the latest changes of the boundaries of LAU 2 regions are not reflected in the dataset and the LAU 2 regions are assigned to NUTS 3 regions of the NUTS 1999 code. Therefore several LAU 2 regions have to be reassigned. At the moment the ESPON database project is working on a more current dataset, so that the typology may be eventually updated once this work is done. With regard to further analyses based on the proposed typology NUST 3 is an appropriate as well as feasible geographical level. Lower geographical levels miss a regional character and would raise problems of data availability and quality. As the typology largely builds on the results of the DG Regio study in the field, policy acceptance should be given. ESPON

35 8 Islands Islands are generally debated in the context of areas with geographical specificities and have been scrutinised e.g. by the respective DG Regio study. This study uncovered a series of challenges concerning the Eurostat definition of island regions. Our proposal builds on a different definition of islands which should avoid similar problems. 8.1 Review of existing typologies Typologies of islands have predominantly been developed within the DG Regio Islands Study. Following the Eurostat definition of islands, this study has used a wide range of different indicators to develop various typologies. Most of them cover only a selected number of islands. The Eurostat statistical definition of an island uses five criteria. An island must have an area of at least one km²; be at least one kilometre from the continent; have a permanent resident population of at least 50 people; have no permanent link with the continent; does not house an EU capital. According to the Eurostat definition the territory of the EU comprises 286 islands. The typologies consider islands as well as island regions. The latter are NUTS 2/3 regions entirely consisting of island territory. They identify 24 of such island regions. For a differentiation within the 286 islands the ESPON project EUROISLANDS proposes to use population size as single criterion to distinguish between big, intermediate and small islands. 8.2 Proposed methodology for the final typology The project team proposes that this definition and delineation might be too narrow to be useful within the context of ESPON and especially for further socio-economic analysis of the European territory. The 286 islands contain many very small units resulting in large gaps of socioeconomic data. Therefore the separate consideration of the 24 island regions does not take into account the majority of European islands. To be useful for other projects the proposed typology needs to take into account a larger number of islands but also have a feasible geographical level. ESPON

36 We therefore intend to develop a new typology which focuses on regions containing and or being islands. Similarly to the mountain typology the number of inhabitants living in island municipalities as share of the total population of the NUTS 3 region is used to build the typology categories. This allows to build a typology at NUTS 3 level, which is useful for further socio-economic analyses, and simultaneously comprises those islands which are not entirely NUTS 3 regions. In relation to the Eurostat delineation of islands the project team proposes to define an island municipality by the following criteria: LAU 2 region which is entirely island territory, which is located at least one kilometre from the continent and which has no permanent link with the continent. Differing from the Eurostat approach Malta and Cyprus will be included in the analyses although both islands house EU capitals. But due to their relatively small size these islands are influenced by their island characteristics to a great extent. Based on LAU 2 population data, the population share of a NUTS 3 region living on islands will be identified. This allows then to distinguish between regions with a high, medium or low part of its total population living on an island. This approach includes a first indication about the weight of the islands features to the socioeconomic development of an area. Additional socio-economic features can be easily integrated later on by the ESPON project applying the typology. Again, this allows adding those indicators which are of relevance for the analysis within the specific project. Table 13 Summary proposed typology of islands regions Name: Islands regions Approach: Share of islands population (in municipalities at least 1 km from the mainland) within a NUTS 3 region plus NUTS 2 and 3 regions which are islands Geographical level: NUTS 3 Categories (examples): (a) Island regions (b) Regions with a high share of islands population (c) Regions with a low share of islands population (d) Non-island regions ESPON

37 8.3 Testing of the proposed typology The typology is an innovative approach to allow analyses of islands and island regions in Europe. Due to the use of LAU 2 data as an input it is possible to extend to number of islands included in the typology. Table 14 Summary of the testing of the typology of islands regions Name of the typology External heterogeneity Internal homogeneity Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Data quality Geographical level (appropriate & feasible) Policy acceptance Island regions This typology does not interfere with any of the other typologies proposed. The choice of population as the only indicator to delineate the categories allows a high level of internal homogeneity. The concrete degree of homogeneity depends on the chosen thresholds. The approach is robust as long as the thresholds are set reasonably. Problems can occur due to MAUP, although due to the use of LAU 2 data as input these problems are relatively small compared to analyses which are only based on NUTS 2/3 data. The necessary data is delivered by the DG Region mountain study, thus the data is only available for the ESPON space. For 98.4 % of the more than 115,000 LAU 2 regions total population data is currently available. The population data collected by the mountain study covers 2001 as latest year available. Also the latest changes of the boundaries of LAU 2 regions are not reflected in the dataset and the LAU 2 regions are assigned to NUTS 3 regions of the NUTS 1999 code. Therefore several LAU 2 regions have to be reassigned. At the moment the ESPON database project is working on a more current dataset, so that the typology may be eventually updated once this work is done. The geographical level is a very special subject of the island typology. With regard to further analyses NUTS 3 is an appropriate as well as feasible geographical level. Lower geographical levels miss a regional character and would raise problems of data availability and quality. The choice of the NUTS 3 level implies that the proposal is not an island typology but a typology of regions containing islands. As the typology is simple and will most likely overcome the main problems deriving from the Eurostat definition of islands and major MAUP issues, we assume that policy acceptance will be given. ESPON

38 9 Coastal regions Coastal regions are generally debated in the context of areas with geographical specificities. When mapped on NUTS level coastal regions are one of the most prominent examples to illustrate the MAUP. By focusing on a NUTS 3 region s population share living in coastal municipalities, we hope that a simple typology can be developed which avoid major MAUP disturbances. 9.1 Review of existing typologies Typologies of coastal regions, in particular, illustrate very well the challenges of existing statistical units. A region with a small coastal line and a large share of the region being far away from any coast, is usually classified as coastal region although most of the region is clearly non-coastal. Three out of the four typologies of coastal regions presented in the compilation face this problem as they are based on NUTS 3 or 2 data. The only typology avoiding this is the EEA typology based on grid data. This typology considers only the coastal zone defined by an area extending 10 km landwards from the coastline. As concerns the further differentiation of coastal areas usually population information has been applied, e.g. differentiating coastal areas according to their population density or according to their population development over a certain period of time. Depending on what a typology of coastal regions shall be used for at a later stage, the use of total population data is not necessarily relevant for the analysis, and if so, can easily be added later on. 9.2 Proposed methodology for the final typology Therefore, the project team proposes a typology which focuses solely on the aspect of coast. To make the typology easy-to-use for further analysis within ESPON, it is suggested to use NUTS 3 regions and differentiate them according to the share of the population living in coastal municipalities. For each coastal NUTS 3 region, the population share of the municipalities (LAU 2) lying at the coast or no more than 10 km from the coast will be calculated. The coastal municipalities will be identified with the help of GIS. The typology will distinguish between NUTS 3 regions according to the share of population living in coastal municipalities. Thus regions with a long and densely populate coast strip can be differentiated from regions with a relatively short coast strip or coastal regions where the main part of the population lives further inland. In addition, islands ESPON

39 can be identified as a particular category if this turns out to be reasonable. Therefore, a possible categorisation could be: (a) island regions (b) regions with a high share of population living in coastal municipalities (c) regions with a medium share of population living in coastal municipalities (d) regions with a low share of population living in coastal municipalities (e) non-coastal regions This approach is in particular suitable as many coastal regions stretch far from the coast to the inland and thus are not necessarily coastal in their overall character, whereas other coastal regions are very much oriented to the coast. Thus, categorising the regions according to the share of their coastal population is a first step to overcome the MAUP in this typology. This typology can be easily enriched with additional socio-economic data by the ESPON projects applying the typology. Thus, depending on the focus of the analysis population development, GDP, or data on various economic sectors can be used for further differentiations. Table 15 Name: Approach: Geographical level: Categories (examples): Summary proposed typology of coastal regions Coastal regions Share of the population living in coastal municipalities (LAU 2) within each coastal NUTS 3 region NUTS 3 (a) Island regions (b) Regions with a high share of coastal population (c) Regions with a low share of coastal population (d) Regions with a medium share of coastal population (e) Regions without any coastline 9.3 Testing of the proposed typology The typology is an innovative approach to handle MAUP which is an important issue especially for coastal regions typologies. It offers a basis for further analyses on NUTS 3 level but is able to differentiate the degree of coastal character of the coastal NUTS regions. ESPON

40 Table 16 Summary testing of the typology of coastal regions Name of the typology External heterogeneity Internal homogeneity Robustness of the approach Data availability (ESPON space plus Turkey & Balkan) Data quality Geographical level (appropriate & feasible) Policy acceptance Coastal regions The typology does not conflict with any other of the proposed typologies. Possible conflicts with the islands typology can easily be incorporated. The choice of population as the only indicator to delineate the categories allows for a high level of internal homogeneity. The concrete degree of homogeneity depends on the chosen thresholds. The approach is robust as long as the thresholds are set reasonably. Problems can occur due to MAUP, although the use of LAU 2 data as input reduces these problems compared to analyses which only base on NUTS 2/3 data. But especially in the Nordic countries also the LAU 2 regions are of a considerable size and some stretch deep into the inland. But this is a problem which could only be overcome by using grid data. The necessary data is delivered by the DG Region mountain study, so the data is only available for the ESPON space. For 98.4 % of the more than 115,000 LAU 2 regions total population data is currently available. The population data collected by the mountain study covers 2001 as latest year available. Also the latest changes of the boundaries of LAU 2 regions are not reflected in the dataset and the LAU 2 regions are assigned to NUTS 3 regions of the NUTS 1999 code. Therefore many LAU 2 regions have to be reassigned. At the moment the ESPON database project is working on a more current dataset, so that the typology may be eventually updated once this work is done. With regard to further analyses NUTS 3 is an appropriate as well as feasible geographical level. Lower geographical levels miss a regional character and would raise problems of data availability and quality. The typology is based on a simple and accepted indicator and should avoid some standard MAUP disturbances, thus policy acceptance should be given. ESPON

41 10 Next steps This Interim Report presents the results of the review of existing typology and the proposal and testing of eight typologies. Until the Draft Final Report in November 2009 the eight proposed typologies will be developed and further tested. As these are iterative processes, this may also involve some changes in the exact proposals. Thus the Final Report will present the typologies incl. meta data and information on how to use them. For single typologies we may also provide examples of how they can be used for cross-analysing with other ESPON results or socio-economic data. In parallel the Annex 2 on the typology review will be further polished. The results provided in November should be reviewed by selected ESPON stakeholders concerning its usability before the Final Report is presented in January Any suggestions or remarks concerning the improvement, usability and acceptance of the proposals made in this report are most welcomed. ESPON

42 List of Abbreviations AEBR CMPR EEA EFTA EU FUA GIS JRC MAUP NUTS OECD Association of European Border Regions Conference of Peripheral and Maritime Regions European Environmental Agency European Free Trade Area European Union Functional Urban Area Geographic Information Systems Joint Research Centre (European Commission) Modifiable Area Unit Problem Nomenclature of Territorial Units for Statistics (French: nomenclature d'unités territoriales statistiques) Organisation for Economic Co-operation and Development ESPON

43 Annex 1: Urban Audit Large City Audit variables and data coverage Variable % of cities for which data is available Total Resident Population 91.9 Total Resident Population Total Resident Population Total Resident Population Total Resident Population Total Resident Population Total Resident Population Total Resident Population Total Resident Population 75 and over 81.1 Total Resident Population Total Resident Population Total Resident Population Residents who are Nationals 42.7 Residents who are Nationals of other EU Member State 34.0 Residents who are not EU Nationals 34.3 Total Number of Households 42.6 Households with children aged 0 to under Number of dwellings 72.5 Households owning their own dwelling 49.5 Number of practising physicians 52.0 Total Economically Active Population 61.7 Residents Unemployed 51.9 Total Full-Time Employment 57.6 Male Full-Time Employment 60.6 Female Full-Time Employment 60.6 Total Part-Time Employment 55.4 Male Part-Time Employment 57.4 Female Part-Time Employment 57.6 Total Economically Active Population Total Economically Active Population Residents Unemployed Gross Domestic Product of city / region / country 58.8 Total employment of area [country] relating to reported 61.1 GDP ESPON

44 Median disposable annual household income 23.1 City Elections: Total electorate (registered) 52.5 City Elections: Total votes counted 60.3 Total Municipality Authority Income 64.4 Municipality Authority Income derived from local taxation 64.2 Total Municipality Authority Expenditure 62.2 Number of children 0-4 in day care 42.6 Number of residents (aged 15-64) with ISCED level 0, or 2 as the highest level of education Number of residents (aged 15-64) with ISCED level 3or as the highest level of education Number of residents (aged 15-64) with ISCED level 5 or as the highest level of education Total land area (km2) according to cadastral register 83.1 Land area of core city based on modelling 0.0 Land area of morphological city 0.0 Land area of the morphological city within the 0.0 boundaries of the core city Number of private cars registered 48.0 Number of cinema seats ( total capacity) 57.3 Total annual tourist overnight stays in registered 44.9 accommodation ESPON

45 Annex 2: ESPON Typology Compilation The picture is taken from a presentation by Jan-Erik Petersen (European Environment Agency) Overview on existing typologies Urban areas 8 typologies Region regions 18 typologies Sparsely populated regions 4 typologies Regions in industrial transition 1 typology Cross-border regions 12 typologies Mountainous regions 6 typologies Islands 3 typologies Coastal regions 4 typologies

46 Urban typologies

47 Source: EEA 1.1 Title: Urban Morphological Zones (UMZ)

48 Fact Sheet Typology Review URBAN ID Name of typology Author of the typology Sources Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-1 Urban Morphological Zones (UMZ) European Environment Agency (EEA) EEA (2006) Urban sprawl in Europe EEA (2007) Urban Morphological Zones Definition and procedural steps Urban / definition of urban areas Raster data (25 ha units) EU27 plus Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Liechtenstein and Macedonia. Urban morphological zones (UMZ)are defined as built up areas lying less than 200 m apart. Urban areas defined from land cover classes contributing to the urban structure and function are (a) continuous urban fabric, (b) discontinuous urban fabric, (c) industrial or commercial units, and (d) green urban areas. In addition port areas, airports, and sport and leisure facilities, are also included if they are neighbours of the core classes or are continuous with the core classes. 1. urban morphological zones (UMZ) 2. areas outside these UMZ As the grid cells are relatively small, the methodology seems quite robust. The utilisation of two classes allows for a definition of urban areas. GIS layer The utilisation of grid data makes it less attractive for socio-economic analyses as these use mostly NUTS regions as geographical level. Question whether this can be translated into LAU 1 or LAU 2 entities. The typology is easy to understand and so to communicate. But the lack of the missing match to NUTS units reduces political relevance regarding socio-economic issues. The typology gives a good and detailed overview of the European territory. The utilisation of grid data helps to overcome/reduce statistical artefacts. The explanatory power seems to be high. 1.1

49 Source: EEA 1.2 Title: Degree of Urbanisation

50 1.2 Fact Sheet Typology Review URBAN ID Name of typology Author of the typology Sources Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-2 Degree of urbanisation Eurostat Eurostat Regions: Statistical yearbook Urban / definition of urban areas and also to the rural-urban typology LAU2 EU27 plus Norway, Iceland, Switzerland and Croatia The focus is on population density. It is based on the CORINE, supplemented with estimated raster population density classifies 2004 European census communes. The classification algorithm considers the population density and total population of a commune and of its surrounding areas. This classification is used by the European Union Labour Force Survey, a quarterly large sample survey. The classification facilitates the derivation of other indicators according to the commune classification. 1. Densely populated areas (>500 inhabitants per km 2 ) 2. Intermediate areas ( inhabitants per km 2) 3. Thinly populated areas (<100 inhabitants per km 2 ) As the grid cells are relatively small, the methodology seems quite robust. The utilisation of three classes allows for a definition of urban areas. GIS layer In general, the approach should be feasible in case the data sets are made available. The typology is easy to understand and so to communicate. But the lack of the missing match to NUTS units reduces political relevance regarding socio-economic issues. The typology gives a good and detailed overview of the European territory. The utilisation of grid data helps to overcome/reduce statistical artefacts. The explanatory power seems to be high.

51 Source: ESPON Title: Functional Urban Areas according to their population

52 1.3 Fact Sheet Typology Review URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-3 Functional Urban Areas according to their population IGEAT / ESPON Final Report ESPON Urban / definition of urban areas (morphological) functional urban areas EU27 plus Switzerland and Norway Delineation of morphological urban areas, are defined as NUTS 5 areas with more than 650 inhabitants / km 2. Then all the contiguous municipalities with this threshold of density, and municipalities enclosed by them are added to define morphological urban areas. In addition municipalities with more than 20,000 inhabitants, whenever they have a concentrated morphological core are considered. The single morphological urban areas are merged into FUAs based on labour market information. 1. Metropolitan FUAs (> 500,000 inhabitants) 2. Large FUAs (> 250,000 inhabitants) 3. Medium FUAs (> 100,000 inhabitants) 4. Small FUAs (< 100,000 inhabitants) The methodology seems to be quite robust. Morphological urban areas seem to work rather well, but the composition of functional urban areas is difficult. Within ESPON the use of functional urban areas proved to be difficult. Previous discussions within ESPON have shown that the policy acceptance is difficult to achieve. The typology gives a good overview of the European territory, but raises also many questions. The project team who has developed this typology is also in charge for the present ESPON project on urban areas (FOCI). Here the team does not follow this approach, but started by looking at the Urban Audit approach as point of departure.

53 Source: ESPON Title: Large cities and metropolises (cities gathered inside the polycetnric areas) according to their population

54 1.4 Fact Sheet Typology Review URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-4 Large cities and metropolises according to their population IGEAT / ESPON Final Report ESPON Urban (morphological) functional urban areas EU27 plus Switzerland and Norway Delineation of morphological urban areas, are defined as NUTS 5 areas with more than 650 inhabitants / km 2. Then all the contiguous municipalities with this threshold of density, and municipalities enclosed by them are added to define morphological urban areas. In addition municipalities with more than 20,000 inhabitants, whenever they have a concentrated morphological core are considered. The single morphological urban areas are merged into FUAs based on labour market information. This typology focuses on how the FUAs are composed by morphological areas. 1. MEGAs and other metropolises 2. Large cities 3. Polycentric areas The methodology seems to be quite robust. Morphological urban areas seem to work rather well, but the composition of Functional urban areas is difficult. Within ESPON the use of functional urban areas proved to be difficult. Previous discussions within ESPON have shown that the policy acceptance is difficult to achieve. The typology gives a good overview of the European territory, but raises also many questions. The project team who has developed this typology is also in charge for the present ESPON project on urban areas (FOCI). Here the team does not follow this approach, but started by looking at the Urban Audit approach as point of departure.

55 Source: ESPON Title: Typology of Functional Urban Areas (FUAs)

56 1.5 Fact Sheet Typology Review URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-5 Typology of Funcational Urban Areas IGEAT / ESPON Final Report ESPON Urban Functional urban areas EU27 plus Switzerland and Norway Following a characterisation of functional urban areas (cf. earlier typologies) according to (a) population, (b) decision making, (c) administration, (d) transport, (e) knowledge, and (f) culture and tourism, typologies have been developed. For each of the topics various indicators have been used. 1. Metropolitan European Growth Areas (MEAGs) 2. Transnational / national FUAs 3. Regional / local FUAs The methodology seems to be quite robust. Morphological urban areas seem to work rather well, but the composition of Functional urban areas is difficult. Within ESPON the use of functional urban areas proved to be difficult. Previous discussions within ESPON have shown that the policy acceptance is difficult to achieve. The typology gives a good overview of the European territory, but raises also many questions. The project team who has developed this typology is also in charge for the present ESPON project on urban areas (FOCI). Here the team does not follow this approach, but started by looking at the Urban Audit approach as point of departure.

57 Source: ESPON Title: Typology of Funcational Urban Areas (FUAs)

58 1.6 Fact Sheet Typology Review URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-6 Typology of Functional Urban Areas Nordregio / ESPON Final Report ESPON Urban Morphological functional urban areas EU27 plus Switzerland and Norway This is a pre-successor of the previous typology of functional urban areas. It varies slightly as concerns the delineation of the areas and the selected functions taken into account. 1. Metropolitan European Growth Areas (MEAGs) 2. Transnational / national FUAs 3. Regional / local FUAs The methodology appeared to be rather weak as the exact delineation and application of various data sources depend largely on subjective impressions. Morphological urban areas seem to work rather well, but the composition of Functional urban areas is difficult. Within ESPON the use of functional urban areas proved to be difficult. Previous discussions within ESPON have shown that the policy acceptance is difficult to achieve. The typology gives a good overview of the European territory, but raises also many questions.

59 Source: Urban Audit 1.7 Title: City-types mapped

60 1.7 Fact Sheet Typology Review URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-7 City-types mapped Urban Audit State of European Cities Report (DG Regio) Report: 2007 / data: 2004 Urban Core cities according to Urban Audit definition approx 200 cities in EU27 The criteria for selecting a city type are based on some 15 indicators, primarily from the sphere of economy. Key factors were among others (a) size, (b) economic structure, (c) economic performance, and (d) key drivers of competitiveness. 1. Knowledge hubs / 2. Established capitals / 3. Re-invented capitals / 4. National service hubs / 5. Transformation poles / 6. Gateways / 7. Modern industrial centres / 8. Research centres / 9. Visitor centres / 10. De-industrialised cities / 11. Regional market centres / 12. Regional public centres / 13. Satellite towns The methodology takes a pragmatic approach which is not entirely consistent both as regards the delineation of cities and also the categorisation. GIS / city level Using the Urban Audit City or LUZ level might work for ESPON the present typology is however to too complex. Furthermore, the data availability is limited, which challenges the use of typology for the full ESPON space. Too many categories to be useful as an analytical typology. The typology gives a good overview of the European territory, but raises also many questions. Due to the highly diverse nature of political boundaries in the European Union, for some cities the political boundary does not correspond to the general perception of that city. In a few cities, Dublin for example, the political boundary of the city is narrower than the general perception of that city. The core cities approach could tentatively be extended o other territorial units.

61 Source: Urban Audit 1.8 Title: Participating cities

62 Fact Sheet Typology Review URBAN 1.8 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 1-8 Urban Audit Participating cities Urban Audit Data 2006 / 2007 Urban Larger Urban Zones / Core cities / Sub-city level according to Urban Audit definition 357 cities in EU27 plus Norway, Switzerland, Croatia and Turkey The Urban Audit aims at a balanced and representative sample of cities in Europe. To obtain such a selection, a few simple rules were followed: 1. Approximately 20% of the national population should be covered by the Urban Audit. 2. All capital cities were included. 3. Where possible, regional capitals were included. 4. Both large (more than inhabitants) and medium-sized cities (minimum and maximum inhabitants) were included. 5. The selected cities should be geographically dispersed within each Member State. The selection of cities was prepared in close collaboration between DG Regio,, Eurostat and the national statistical institutes. To ensure that large and medium-sized cities are equally represented in the Urban Audit, in some of the larger Member States not all large cities could be included. 1. Urban Audit cities 2. Large City Audit cities The methodology takes a pragmatic approach which is not entirely consistent both as regards the delineation of cities. GIS / city level Using the Urban Audit City or LUZ level might work for ESPON in particular as about 250 indicators have been collected for these cities and will be updated approx every third year. The main challenge is however that not all indicators will be collected for all cities. Not all (large) cities are included which is a serious hinder for an analysis of the complete ESPON territory. - Due to the highly diverse nature of political boundaries in the European Union, for some cities the political boundary does not correspond to the general perception of that city. In a few cities, Dublin for example, the political boundary of the city is narrower than the general perception of that city.

63 Rural-urban typologies

64 Source: ESPON Title: Areas within 45 miniutes of reach from urban centres ESPON Synthesis Report 3, p. 45

65 2.1 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-1 Areas with 45 minutes of reach from urban centres Nordregio / ESPON ESPON Final Report 2004 Delineation of rural and urban also relevant for typologies of peripheral areas NUTS 5 EU27 plus Norway and Switzerland Accessibility of areas within 45 minutes by car from a functional urban area (FUA), according to typology Area in 45 minutes reach from an urban centre (FUA) 2. Area more than 45 minutes from the nearest urban centre (FUA) The methodology takes a coherent and clear approach which appears to be robust. NUTS 5 This approach might be feasible for defining urban and rural areas in combination with other factors. For some countries the picture might be too little nuanced. The typology has an high explanatory value. A similar approach is also inherent in the DG Regio typology (cf. 2.17). Peri-urban areas cannot be identified at this stage.

66 Source: ESPON Title: Urban-rural typology ESPON Synthesis Report 3, p. 49

67 2.2 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-2 Urban-rural typology CURS / ESPON ESPON Final Report 2003 Rural-urban typology NUTS 3 EU27 plus Norway and Switzerland The typology is based on two dimensions (a) the degree of urban influence, which is defined on the basis of population density and the functional ranking of urban centres, and (b) the degree of human footprint, which is defined on the bases of land cover, which means the share of artificial surfaces and of agricultural land. 1. High urban influence and high human footprint 2. High urban influence and medium human footprint 3. High urban influence and low human footprint 4. Low urban influence and high human footprint 5. Low urban influence and medium human footprint 6. Low urban influence and low human footprint The methodology takes a coherent and clear approach which appears to be robust. This has not at least been illustrated by the NUTS 5 examples for Austria and Belgium. A major problem is that the use of NUTS 3 creates a series of artefacts and that the EU average creates nationally counterintuitive results. NUTS 3 The approach is fully feasible, although it would be preferable to take it down to LAU level in order to exclude some of the statistical artefacts. Because of its statistical artefacts it has created a number of hiccups in policy terms. The typology has an high explanatory value.

68 2.3 Source: ESPON Atlas Title: Rural areas and their regional diversification ESPON Atlas, p. 33

69 Fact Sheet Typology Review RURAL-URBAN 2.3 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-3 Rural areas and their regional diversification BBR / ESPON Atlas ESPON Atlas 2008 (data ) Rural-urban typology NUTS 3 EU27 plus Norway and Switzerland Typology based on two dimensions (a) the degree of urban influence, which is defined on the basis of population density and the functional ranking of urban centres, and (b) the degree of human footprint, which is defined on the basis of land cover, which means the share of artificial surfaces and of agricultural land. This is overlaid with information on the infectivity of agricultural and the employment in agriculture, forestry and fishing. The latter two points are not really integrated in the typology 1. High urban influence and high human footprint 2. High urban influence and medium human footprint 3. High urban influence and low human footprint 4. Low urban influence and high human footprint 5. Low urban influence and medium human footprint 6. Low urban influence and low human footprint Overlay A. High share of agricultural land B. High share of employment in agriculture, forestry and fishing The methodology takes a coherent and clear approach which appears to be robust, which has not at least been illustrated by the NUTS 5 examples for Austria and Belgium. A major problem is that the use of NUTS 3 creates a series of artefacts and that the EU average creates nationally counterintuitive results. NUTS 3 The overlay on the ESPON typology makes things rather complex, and decreases the usability. Because of its statistical artefacts it has created a number of hiccups in policy terms. On top of this, the overlay reduces the communication value. The approach is to complex to have an explanatory value.

70 2.4 Source: BBR Title: Spatial strutures of Europe Maps on European territorial development, p. 17

71 Fact Sheet Typology Review RURAL-URBAN 2.4 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-4 Spatial structure of Europe BBR Maps on territorial development (background document to the Territorial Agenda) 2007 Rural-urban typology Smoothed presentation based on NUTS 3 data EU27 plus Norway, Switzerland, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Macedonia, Albania Typology based on two dimensions (a) population density and (b) access to urban centres within 50 km. This is overlaid with the ESPON FUAs of national and internal importance. 1. Inner central area 2. Outer central area 3. Intermediate areas with agglomeration tendencies 4. Intermediate areas with low density 5. Peripheral areas with agglomeration tendencies 6. Peripheral with very low density Overlay A. MEGAs and transnational/national functional urban areas (FUAs) Whereas the overall impression is that this approach gives a coherent picture, some results appear rather questionable. This might be an effect of the smoothening technique. GIS layers Depending on which data is behind the smoothening, the approach might be feasible for the development of a typology. The current map will certainly raises a number of questions as it contains a series of counter intuitive results. Examples for this are Lisbon, Porto, Sofia, Bucharest etc. which appear to be rather off whereas Norrköping appears to be rather central. The approach might have an explanatory value if the counter intuitive results can be resolved. Possibly the colour coding is a major problem. If one would swap the colours of category 4 and 5 things might look more reasonable.

72 2.5 Source: ESPON Atlas Title: Urban areas ESPON Atlas, p. 29

73 Fact Sheet Typology Review RURAL-URBAN 2.5 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-5 Urban areas BBR / ESPON 3.1 ESPON Atlas 2006 Rural-urban typology NUTS 3 EU27 plus Norway and Switzerland Typology based on the share of artificial surfaces in a region and an overlay of the ESPON FUA & MEGA classification 1. very low share of artificial surfaces 2. low share of artificial surfaces 3. medium share of artificial surfaces 4. high share of artificial surfaces 5. very high share of artificial surfaces Overlay A. Global nodes B. European engines C. Strong MEGAs D. Potential MEGAs E. Weak MEAGs F. Transnational/national FUA G. Regional/local FUA The main indicator using the share of artificial areas shows a high homogeneity and robustness. The overlay involves all the problems of the ESPON results. The two dimensions are not integrated. NUTS 3 plus GIS layers An overlay approach is not suitable for the development to typologies, thus the feasibility concerns mainly the basic indicator on the share of artificial surfaces. This one is in principle feasible. Policy relevance is probably rather low in terms for a rural typology. Explanatory power is rather low for differentiating various types of rural areas.

74 Source: SPESP (DE) 2.6 Title: Types of rural-urban regional settings Study Programme on European Spatial Planning, p. 29

75 2.6 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-6 Types of rural-urban regional settings BBR / Study Programme on European Spatial Planning Study Programme on European Spatial Planning (p. 29) 2000 Rural-urban typology NUTS 3 EU15 The classification typifies larger units of urban-rural regional settings, i.e. the commuter catchment-areas of metropolises, medium-sized centres and smaller centres. Three criteria have been used: (a) size of the city, (b) distance between administrative centres of regions in minutes by road, and (c) the primacy rate. This rate is the share of the region s total population which is found in the biggest city of a region. It indicates whether metropolitan areas are polycentric (low primacy rate) or monocentric (high primacy rate). 1. Metropolitan area polycentric 2. Metropolitan area - monocentric 3. Regions with medium-sized cities polycentric 4. Regions with medium sized cities monocentric 5. Regions dominated by smaller cities polycentric 6. Regions dominated by smaller cities monocentric 7. Regions without a city larger than inhabitants Overlay concerning the cities and their size only for illustration purposes The approach appears to be robust. NUTS 3 In general this approach should be feasible. Policy relevance should be given, although the differentiation between polycentric and moncentric might be less relevant today. The typology has a high explanatory power.

76 Source: SPESP (FR) 2.7 Title: Regional types of urban-rural spatial patterns Study Programme on European Spatial Planning, p. 25

77 Fact Sheet Typology Review RURAL-URBAN 2.7 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-7 Regional Types of urban-rural spatial patterns GDR LIBERGEO / Study Programme on European Spatial Planning Study Programme on European Spatial Planning (p. 25) 1999 (data 1994) Rural-urban typology NUTS 3 & 2 mixture EU15 plus Poland, Czech Rep., Slovakia Hungary, Slovenia, Romania, Bulgaria, the Western Balkans, Switzerland and Norway. Six indicators of urban-rural spatial patterns of settlements were involved in this exercise: (a) urbanisation rate, i.e. the urban population in relation to the total population; (b) rural population density per square kilometre; (c) the degree of contrast in the distribution of settlement size; (d) average distance to any urban settlement, weighed by population; (e) the primacy of the largest city, measured in terms of population size; (f) the size class of the main centre, also measured in terms of population size. 1. Regions dominated by a large metropolis 2. Polycentric regions with high urban and rural densities 3. Polycentric regions with high urban densities 4. Rural areas under metropolitan influence 5. Rural areas with networks of medium-sized and small towns 6. Remote rural areas The approach appears to be robust albeit it is has a certain level of complexity. NUTS 3 & 2 In general this approach should be feasible, although data availability has to be checked in detail. The typology has been used in the EU Cohesion Report of 2001 and thus has proven a policy acceptance and relevance already. A challenge in this context is the complexity of the typology. The typology appears rather complex and the colour coding is not very intuitive. This hampers the communicative value.

78 Source: SPESP (IT) 2.8 Title: Final selection SPESP 2.3 A Typology of Rural Areas in Europe, p. 36

79 2.8 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-8 Final Selection Politecnico di Milano / Study Programme on European Spatial Planning Study Programme on European Spatial Planning Study Rural-urban typology NUTS 3 EU15 without UK and Sweden Three indicators of urban-rural spatial patterns of settlements were involved in this exercise: (a) productivity of agriculture (GVA per unit utilised agricultural area), (b) importance of agricultural area (share of agricultural land), (c) diversification of activities (activities number of workers in small industrial activities), and (d) urban sprawl (discontinuous urban fabric). 1. Strong 2. Under pressure 3. Weak The approach is conceptually interesting, however the results are less convincing. Possibly this relates to the strong focus on agriculture which is only in some areas in Europe is strongly linked to rural areas. NUTS 3 Doubtful, as even under the SPESP it has not been possibel to further develop this into a typology achieving full coverage of the the EU15. At European level, the policy relevance is supposedly low. The strong focus on agriculture results in a low explanatory power.

80 Source: DG Agri Title: Rural Communities 2.9 DG Agri Factsheet, p. 3

81 2.9 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-9 Rural Communities DG Agri DG Agri Factsheet 2004 Rural-urban typology NUTS 3 EU25 The typology builds on population density at the level of municipalities, where a population density below 150 inhabitants per km 2 is defined as rural. A region is defined as predominately rural if more than 50% of its population lives in rural communities, predominately urban if less than 15% of the population lives in rural communities, and intermediate if the share of the population living in rural communities is between 15% and 50%. 1. Predominately rural 2. Significantly rural 3. Predominately urban The approach seems robust and internally consistent. NUTS 3 In general this approach is feasible It has a great communication value as the typology is easy to understand. With regard to regional policies, result might make Europe looking too rural. Population density might not fully explain the rural-urban settings. This typology follows basically the OECD approach.

82 Source: Scenar 2020 Project (DG Agri) 2.10 Title: HARM2 Regional Classification

83 2.10 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-10 Rural typology Scenar 2020 Project for DG Agri LEI Wageningen UR - Ida J. Terluin 2004 Rural-urban typology 475 HARM2 regions (i.e. mix of NUTS 2 & 3) EU25 The typology builds on population density at the local level and the share of local units of a certain type within a region. In this context local units (probably municipalities) with a population of 150 inhabitants / km 2 or below are considered as rural communities. 1. Most rural 2. Intermediate rural 3. Most urban The approach seems robust and internally consistent. NUTS 2 & 3 mixture In general this approach is feasible It has a great communication value as the typology is easy to understand. With regard to regional policies, result might make Europe looking too rural. Population density might not fully explain the rural-urban settings. Similar approach as OECD but slightly different regional units.

84 Source: PLUREL 2.11 Title: RUR-type (ruralurban-regions)

85 2.11 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-11 RUR-type (rural-urban regions) ARC System Research / PLUREL Project (FP 6) PLUREL Newsletter no 4 (September 2008) 2008 Rural-urban typology NUTS3 (merged into 900 RUR or functional areas) EU27 The typology builds on (a) EEA s Corine land cover 2000 data, (b) population data at NUTS 3, and (c) population data from the GISCO urban centre point data base (STEU) updated with recent World Gazetteer population figures. 1. Monocentric very large 2. Monocentric large 3. Monocentric medium 4. Urban polycentric 5. Dispersed polycentric 6. Rural The approach seems robust and internally consistent. NUTS 3 In general this approach is feasible. It has a good communication value as the typology is easy to understand, albeit is has rather many categories. Combining land cover and population density seems to give a good explanatory power for rural-urban settings. The approach has some similarities with the one taken in ESPON

86 Source: OECD 2.12 Title: Regional Typology - Europe

87 Fact Sheet Typology Review RURAL-URBAN 2.12 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-12 Regional typology Europe OECD OECD Regions at a Glance 2005 Rural-urban typology TL3 (i.e. a mix of NUTS 2 and 3 regions) EU15 plus Poland, Czech Republic, Slovakia, Hungary, Island, Norway, Switzerland and Turkey The typology builds on population density at municipal level, where a population density below 150 inhabitants per km 2 is defined as rural. A region is defined as predominately rural if more than 50% of its population lives in rural communities, predominately urban if less than 15% of the population lives in rural communities, and intermediate if the share of the population living in rural communities is between 15% and 50%. A region that would be classified as rural on the basis of the general rural is classified as intermediate if it has an urban centre of more than 200,000 inhabitants representing no less than 25% of the regional population. A region that would be classified as intermediate on the basis of the general rural is classified as predominantly urban if it has an urban centre of more than 500,000 inhabitants representing no less than 25% of the regional population. 1. Predominantly rural regions 2. Intermediate regions 3. Predominantly urban regions The approach seems to be robust and internally consistent. A general issue is that even small municipalities (e.g. of 10,000 inhabitants or less) with a high population density can be classified as urban. NUTS 3 & 2 mixture In general this approach is feasible. The approach has a high policy acceptance as it is used in most of the rural-urban typologies within DG Agri and DG Regio, albeit often with modifications. The explanatory power of population density solely involves a number of artefacts and might not fully cover the complexity of rural-urban settings as the general population density in Europe varies greatly between countries. The addition to the general rules taking into account the deviations due to the size of single cities seems to be very important in this context. The OECD has undertaken efforts to complement this typology with data on the economic structure and accessibility, but it seems that these efforts have been stalled in 2002.

88 2.13 Source: SERA Project (A. Copus et al.) Title: Desingation of rural regions: Combination of OECD classification combined with Peripherality Index

89 2.13 Fact Sheet Typology Review RURAL-URBAN ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-13 Combination of OECD classification combined with Peripherality Index SERA Project SAC (2006) for DG Agri, p Rural-urban typology NUTS 3 EU27 The typology builds on population density according to the OECD approach (but without the adjustment due to large cities) plus a peripherality index by Schürmann and Talaat based on European accessibility to population. 1. Predominantly rural regions 2. Significantly rural peripheral 3. Significantly rural accessible 4. Predominantly rural peripheral 5. Predominantly rural accessible The approach seems to be robust and internally consistent. NUTS 3 In general this approach is feasible The approach builds on to approaches which are accepted in European policy cycles, (a) the OECD approach to rural typologies, and (b) the European accessibility model. The explanatory power seems to be good, although in some parts it is counter intuitive. It is not clear why the approach takes only the population density component of the OECD approach and not also the adjustments.

90 Source: SERA Project (A. Copus et al.) 2.14 Title: Modified OECD classification combined with Peripherality Index

91 Fact Sheet Typology Review RURAL-URBAN 2.14 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-14 Modified OECD classification combined with Peripherality Index SERA Project SAC (2006) for DG Agri, p Rural-urban typology NUTS 3 EU25 minus UK and Slovakia The typology builds on population density according to a modified OECD approach (but without the adjustment due to large cities) plus a peripherality index by Schürmann and Talaat based on European accessibility to population. The modification of the OECD approach implies that municipalities with less than 10,000 have not been classified as urban. 1. Predominantly rural regions 2. Significantly rural peripheral 3. Significantly rural accessible 4. Predominantly rural peripheral 5. Predominantly rural accessible The approach seems to be robust and internally consistent. NUTS 3 In general this approach is feasible The approach builds on to approaches which are accepted in European policy cycles, (a) the OECD approach to rural typologies, and (b) the European accessibility model. The explanatory power seems to be good, although in some parts it is counter intuitive. Adding the 10,000 inhabitants threshold led to an increase in NUTS 3 areas classified as predominately rural. It is not clear why the approach takes only the population density component of the OECD approach and not also the adjustments.

92 Source: SERA Project (A. Copus et al.) Title: Designation on rural areas based on area share of densely populated areas and maximum number of inhabitants 2.15

93 Fact Sheet Typology Review RURAL-URBAN 2.15 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-15 Designation on rural areas based on area share of densely populated areas and maximum number of inhabitants SERA Project SAC (2006) for DG Agri, p Rural-urban typology NUTS 3 EU25 minus UK and Slovakia The typology builds on the areas share of rural and urban municipalities within a NUTS 3 region. In a first step, based on population density the municipalities have been classified as rural (<150 inh./km 2 ), intermediate (> inh./km 2 ) or urban (> 500 inh./km 2 ). In a second step, the share of the regional area accounted for by densely populated municipalities is taken as the first criterion for classifying NUTS 3 regions into four categories. Thirdly, a second criterion is the maximum municipal population size within the NUTS 3 region. This allows regions with a significant urban centre to be distinguished from those which are more homogenously rural. (deep rural max 50,000 inh. etc.) 1. Mainly urban areas 2. Intermediate areas with significant urban influence 3. Intermediate areas with limited urban influence 4. Deep rural The approach seems to be robust and internally consistent. NUTS 3 In general this approach is feasible. The general approach appears reasonable. However, the main result appears to counter intuitive in many regards, which will limit the policy acceptance and thus relevance. It appears that this approach does not fully capture the complexity for rural and urban. Germany turns out to be mostly deep rural, whereas Sweden does not have this category at all.

94 Source: SERA Project (A. Copus et al.) Title: Designation on rural areas based on area share of densely populated areas and maximum number of inhabitants combined with Peripherality index 2.16

95 Fact Sheet Typology Review RURAL-URBAN 2.16 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-16 Designation on rural areas based on area share of densely populated areas and maximum number of inhabitants combined with peripherality index SERA Project SAC (2006) for DG Agri, p Rural-urban typology NUTS 3 EU25 minus UK and Slovakia The typology builds the areas share of rural and urban municipalities within a NUTS 3 region. In a first step, based on population density the municipalities have been classified as rural (<150 inh./km 2 ), intermediate (> inh./km 2 ) or urban (> 500 inh./km 2 ). In a second step, the share of total regional area accounted for by densely populated municipalities is taken as the first criterion for classifying NUTS3 regions into four categories. Thirdly, a second criterion is the maximum municipal population size within the NUTS 3 region. This allows regions with a significant urban centre to be distinguished from those which are more homogenously rural. (deep rural max 50,000 inh. etc.) This approach is combined with the peripherality index by Schürmann and Talaat based on European accessibility to population. 1. Mainly urban areas 2. Peripheral - intermediate areas with significant urban influence 3. Accessible - intermediate areas with significant urban influence 4. Peripheral - intermediate areas with limited urban influence 5. Accessible - intermediate areas with limited urban influence 6. Peripheral - deep rural 7. Accessible - deep rural The approach seems to be robust and internally consistent. NUTS 3 In general this approach is feasible This approach is too complex. It appears that this approach does not fully capture the complexity for rural and urban. Furthermore, the introduction of the accessibility component makes the typology too complex.

96 Source: DG Regio 2.17 Title: Urban-rural typology Fourth report on economic and social cohesion, p. 58

97 Fact Sheet Typology Review RURAL-URBAN 2.17 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-17 Urban-rural typology of NUTS3 regions DG Regio Fourth Cohesion Report, p Rural-urban typology NUTS 3 EU27 The typology combines the OECD approach concerning population density (but probably without correction of the general rule) with an accessibility factor, indicating whether an area is close to a city (i.e. at least 50% of the region s population lives at less than 1 hour travel by road to a city of at least 100,000 inhabitants). 1. Predominantly urban regions 2. Intermediate rural regions, close to a city 3. Intermediate rural, remote regions 4. Predominantly rural regions, close to a city 5. Predominantly rural, remote regions The approach seems to be robust and internally consistent. NUTS 3 In general this approach is feasible. This approach is used in the fourth cohesion report. The approach has some counter intuitive results. DG Regio is still working on the further development of this approach.

98 Source: ESPON - EDORA 2.18 Title: Draft Typology of Rural Development Environments

99 Fact Sheet Typology Review RURAL-URBAN 2.18 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 2-18 Draft Typology of Rural Development Environments ESPON EDORA Interim Report, p Rural-urban typology NUTS 3 ESPON Space The following data sources are currently incorporated into the decision tree: (a) The Dijkstra and Poelman (2008) urban-rural codes, (b) The population trend typology produced by Mats Johansson (ESPON Programme 2008), (c) Gross value added by sector (the Eurostat REGIO Database), (d) European size units data from the European Farm Structures Survey (the Eurostat REGIO database), (e) Farm holders with Other Gainful Activities (OGA) from the European Farm Structures Survey (the Eurostat REGIO database). 1. Urban 2. Depleting rural 3. Primary sector dominated rural economy 3.1 with semi-subsistence agriculture 3.2 with pre-productivist agriculture 3.3 with para-productivist agriculture 4. Fordist mixed rural economy with strong manufacturing sector 5. New rural economy The approach seems to berobust and internally consistent. NUTS 3 In general this approach is feasible. This approach is still under development by the project, thus a final results needs to be awaited. dito.

100 Sparsely populated regions

101 Source: EU Parliament 3.1 Title: Population Potential in Europe (Raster Cells)

102 Fact Sheet Typology Review Sparsely populated regions ID Name of typology Author of the typology Source 3-1 Population Potential EU Parliament (study by NORDREGIO) Study: Regional disparities and Cohesion: What strategies for the future, pages Year Related topic Geographical level Geographical coverage Methodology used 2006 Sparsely populated regions Raster data (2.5 km x 2.5 km grid cells, except Nordic countries 1 km x 1 km grid cells) EU except CH and CY People living within 50 km radius from each point in Europe, standardised to the EU27 + 2, no thresholds are set to define sparsely populated Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous As the grid cells are relatively small compared to the 50 km radius, the methodology seems quite robust. The utilisation of 10 classes allows a detailed distinction of different types of regions. GIS layer The underlying population data should be relatively easy to access and is a quite robust indicator. But the utilisation of grid data makes it less attractive for socio-economic analyses as these use mostly NUTS regions as geographical level. Aggregation to this level might end up in population density. The typology is easy to understand and so to communicate. But the lack of the missing match to NUTS units reduces political relevance regarding socio-economic issues. Also missing is a clear definition if a region is sparsely populated or not. The typology gives a good and detailed overview of the European territory. The utilisation of grid data helps to overcome/reduce statistical artefacts. The explanatory power seems to be high.

103 Source: Nordregio 3.2 Title: Population Potential

104 Fact Sheet Typology Review Sparsely populated regions 3.2 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 3-2 Population Potential Nordregio (study is the basis for the former typology of the EU Parliament) Nordregio Report 2006:2. Study on Northern peripheral, sparsely populated Regions in the European Union and in Norway, page Sparsely populated NUTS 2 regions NUTS 2, based on Raster data (2.5 km x 2.5 km grid cells, except Nordic countries 1 km x 1 km grid cells) EU except CH and CY NUTS 2 regions with highest proportion of areas where less than 100,000 persons can be found within a maximum commuting distance of 50 km. This population potential corresponds to a population density of 12.5 inh/km². Population potential: People living within 50 km radius from each point in Europe, standardised to the EU Densely populated areas 2. Intermediate areas 3. Thinly populated areas The calculation of the population potential seems to be quite robust (see typology 3-1). But the aggregation to NUTS 2 level depends heavily on the shape of the administrative units. Many sparsely populated areas regarding to population potential are not included in the respective NUTS 2 regions. GIS > Regional codes > GIS layer The underlying population data should be relatively easy to access and is a quite robust indicator with high quality of data. Regularly updates should be no problem. Analyses of population density are already common in ESPON and the methodology is easy to adopt. The typology does not apply any threshold currently used in EU policies. The typology is not very complex but due to the 2-step-approach of aggregation it is not fully self-explanatory. Communication is possible but the typology needs explanation. The typology identifies only the most sparsely populated NUTS 2 regions in Europe. Many smaller areas and areas with sparse but not very sparse population potential do not become apparent. Therefore, the explanatory power is not very high. Miscellaneous

105 Source: EU Parliament 3.3 Title: Sparsely populated areas

106 Fact Sheet Typology Review Sparsely populated regions 3.3 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage 3-3 Sparsely populated areas EU Parliament (study by Nordregio) Study: Regional disparities and Cohesion: What strategies for the future, page Sparsely populated regions NUTS 3 and NUTS 5 EU Methodology used Population density of the NUTS 3/5 region, thresholds are set at 12,5 inh/km² and 50 inh/km² Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power The class building follows common thresholds in Europe. Although, they are still randomly not justified in this study. Regional codes, GIS layer The underlying population data should be relatively easy to access and is a quite robust indicator with high quality of data. Regularly updates should be no problem. Analyses of population density are already common in ESPON. The typology is easy to understand and so to communicate. But eventually the thresholds used need to be justified. The match with NUTS units could be a basis for political decisions. At first impression the typology gives a good overview of sparsely populated regions in Europe. But when comparing the maps of the different regional levels NUTS 3 and 5, the weakness of the utilisation of administrative units as geographical level becomes apparent, as the pictures of sparsely populated regions in Europe differ. The authors of the study state: Average population density figures are complex to handle as the results are largely determined by the size of the regions or municipalities. It is therefore important to relate the scale of statistical observation to the concrete policy issues at stake. Labour market policies will not have the same demographic sparsity issues as land use measures. Miscellaneous

107 Fact Sheet Typology Review Sparsely populated regions 3.4 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage 3-4 Sparsely populated areas EC DG Regio Structural funds regulations, article Sparsely populated regions NUTS 2 EU27 Methodology used Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Population density, thresholds are set at 8 inh/km² (very sparsely populated) and 50 inh/km² (sparsely populated) The 8 inh/km² threshold follows the old Objective 6 regulation concerning Sweden and Finland. However, as always when fixed thresholds are used, they might be not very robust. Regional codes > GIS layer The underlying population data should be relatively easy to access and is a quite robust indicator with high quality of data. Regularly updates should be no problem. Analyses of population density are already common in ESPON. The typology is easy to understand and so to communicate. It is already in use to constitute funding of the European Union. The match with NUTS units delivers the basis for such political decisions. At first impression the typology gives a good overview of sparsely populated regions in Europe. However, the geographical level might not be appropriate (see also typology 3-2), surely it is not for all political issues. Miscellaneous

108 Regions in industiral transition

109 Source: Centre for Public Policy for Regions 4.1 Title: Delineation of old industrial regions

110 Fact Sheet Typology Review Industrial transition ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used 8-1 Delineation of old industrial regions Centre for Public Policy for Regions, University of Glasgow Working Paper: Revisiting the Old Industrial Region: Adaptation and Adjustment in an Integrating Europe 2006 Industrial transition NUTS 2 EU15 The definition of old industrial regions based upon old mining areas. 4.1 Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power No types, just a delineation The delineation is very simple and quite robust Regional code, GIS The approach is easy to adapt Policy relevance is low because old mining areas are not in the centre of attraction anymore Explanatory power is low as there are clearly areas outside the identified regions in textile, shipbuilding and engineering industries that could also be identified as old industrial areas. Miscellaneous

111 Cross-border regions

112 Source: Association of European Border Regions 5.1 Title: AEBR - cross-border areas in Europe

113 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-1 AEBR-Typology for border / cross-border areas in Europe Association of European Border Regions (AEBR) Towards a more uniform and comprehensive typology of border / cross-border regions in Europe (internal working paper) 2005 Border regions Specific cross-border regions Europe Socio-cultural / economic cohesion and cross-border co-operation intensity were combined in order to determine the overall degree of cross-border integration achieved in a given border / crossborder area. Type 1: Integration Forerunners Type 2: Areas catching up to integration forerunners Type 3: Integration Candidates Type 4: Areas catching up to integration candidates Type 5: Areas still searching for integration perspectives The typology is based on previous analyses of cohesion and integration in the areas. These analyses seem not to be very robust at first glance. None Updating and integrating of new areas is nearly impossible without extensive analyses in the forefront. Policy relevance is relative high concerning the funding of cross-border cooperation. At the moment explanatory power is relatively low as the typology only considers some of the cross-border regions in Europe. Furthermore, only the cross-border characteristics are integrated in the typology. NB: The map illustrates the AEBR cross-border regions, whereas the above typology is taken from another AEBR document. 5.1

114 Source: Topaloglou et al 5.2 Title: Border Regions Typology

115 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-2 Border Regions Typology Topaloglou et al Journal of Borderlands Studies, Volume 20 No. 2 Fall Border regions Nuts 3 EU27 Cluster analysis and factor analysis with 12 socio-economic variables a) Highly integrated border regions with advanced economic performance, mainly cultural similarities and small size. b) Border regions that enjoy agglomeration economies but need significant structural adjustments in order to deal with the increased competition c) Highly integrated border regions that present significant economic performance, though much cultural dissimilarity d) Border regions with high development potential due to their favourable geographic position, but with low economic performance e) Border regions with low market potential and no prevailing positive characteristics The methodology is sound but even small changes in socio-economic development could produce differing results with even new types. Regional codes, GIS Not feasibly as a basic typology as it needs to be updated too often. Policy relevance is at most medium as the typology is too complicated to communicate. The typology gives a good overview of the socio-economic setting of cross-border regions. 5.2

116 Source: ESPON Interact 5.3 Title: Level of economic disparities

117 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-3 Level of economic disparities between areas of INTERREG IIIA programmes approximated to NUTS 3 regions KTH ESPON Interact, final report, page Border regions Nuts 3 EU27+2 The GDP in euro per capita as a percentage of the EU25 average for the highest NUTS3 region within the programme and the lowest region in the programme were considered, as well as the spread of difference between them. 1) no border regions 2) areas without significant disparities 3) areas with low level of disparities 4) areas with high level of disparities 5) areas with very high level of disparities a) nuts 3 regions included in more than one Interreg IIIa programme GDP per capita is a relatively homogenous indicator, so the typology seems quite robust but needs regularly updates. Regional codes, GIS Feasibility regarding ESPON is low as the typology only bases on economic disparities Regarding very special issues the typology may be of political relevance but overall the opportunities of application are low. The explanatory power is limited. 5.3

118 Source: ESPON Title: geographical-physical border typology

119 Fact Sheet Typology Review Border regions 5.4 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage 5-4 Draft Typology # 1: border typology for integration potential geographical-physical border typology ÖIR ESPON 1.1.3, final report, page Border regions Nuts 3 Accession countries Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 1) Forerunners of integration 2) Hardworkers of integration 3) Candites of integration 4) Handicapped for integration Easy and therefore robust methodology Regional codes, GIS Feasibility regarding ESPON is low as the typology only bases on crossing points. Regarding very special issues the typology may be of political relevance but overall the opportunities of application are low. The explanatory power is limited as only one aspect of integration is considered. Miscellaneous

120 Source: ESPON Title: socio-economic border typology

121 Fact Sheet Typology Review Border regions 5.5 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage 5-5 Draft Typology # 1: border typology for integration potential socio-economic border typology ÖIR ESPON 1.1.3, final report, page Border regions Nuts 3 Accession countries Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 1)Forerunners of integration 2)Hardworkers of integration 3)Candites of integration 4)Handicapped for integration Easy and therefore relatively robust methodology. But transnational activities are only considered by their number and not their extent Regional codes, GIS Feasibility regarding ESPON is medium. The typology would need to be enhanced to the whole ESPON space. Regarding very special issues the typology may be of political relevance but overall the opportunities of application are low. The explanatory power is limited. Economic disparities are considered as hindering to co-operation which seems not to be the case (see study ESPON Interact) Miscellaneous

122 Fact Sheet Typology Review Border regions 5.6 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage 5-6 Types of cross-border regions Perkmann European Urban and Regional Studies 2003 Border regions CBR in Europe EU15 Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 1)Integrated micro-cbrs 2)Emerging micro.cbrs 3)Scandinavian groupings 4)Working Communities To less types for such a complex phenomena, especially the rough distinction between small and large None Updating and integrating of new areas is nearly impossible without extensive analyses in the forefront. Policy relevance is relative high concerning the funding of cross-border cooperation. At the moment explanatory power is relatively low as the typology only considers some of the cross-border regions in Europe. Furthermore, only the cross-border characteristics are integrated in the typology. Miscellaneous

123 Source: ESPON Interact 5.7 Title: Type of borders

124 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-7 Typology of borders in NUTS3 regions participating in INTERREG IIIA Programmes KTH ESPON Interact, cross-border cooperation, page Border regions Nuts 3 EU27+2 The border regions are typed according to their political borders (EU25 and the former accession countries). 1) internal border 2) external border 3) mixed: internal and external border 4) accession countries 5) non-participants in INTERREG IIIA programme Very easy and so very robust data. Regional codes, GIS Feasibility is given Regarding special issues the typology may be of political relevance but overall the opportunities of application are limited as there is no differentiation between internal borders. As it is a very basic typology the explanatory power is limited but it could serve as a starting point for further analysis. 5.7

125 Source: ESPON Interact 5.8 Title: geographic type of land border and sea borders

126 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-8 Geographic type of land border and sea borders of NUTS 3 INTERREG IIIA programme areas KTH ESPON Interact, cross-border cooperation, page Border regions Nuts 3 EU27+2 The typology is based on the geographic type of land border between NUTS3 land regions, as well as the sea borders of crossborder regions (based on INTERREG IIIA programmes). 1) river border 2) high mountain border 3) low mountain border 4) green border 5) sea border Very easy and so very robust data which needs no update. Regional codes, GIS Feasibility is given. Regarding very special issues the typology may be of political relevance but overall the opportunities of application are strongly limited as the geographical type of border is a very special issue. The explanatory power is limited regarding to socio-economic policies. 5.8

127 Source: ESPON Interact 5.9 Title: density of border crossings

128 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-9 Density of border crossings (roads and rail crossings per 100 km) in INTERREG IIIA areas approximated by NUTS3 regions KTH ESPON Interact, cross-border cooperation, page Border regions Nuts 3 EU27+2 Typology is based on the density of border crossings by land (roads and rail crossings per 100 km of border) in INTERREG IIIA areas approximated to NUTS3 regions. 1) no border regions 2) no density (no international rail/road crossings) 3) very low (0-3 crossings per 100 km) 4) low (3-5 crossings per 100 km) 5) medium (5-10 crossings per 100 km) 6) high (10-15 crossings per 100 km) 7) very high (more than 15 crossings per 100 km) The classbuilding seems to be random but ok. The methodology is simple and robust. Regional codes, GIS Feasibilty seems to be medium. The typology needs regular updates. Regarding special issues the typology is of political relevance but it provides no general survey. No other crossings than road and rail are considered and the definition of the border line is not clear. As well the crossing are not weighted by their importance. 5.9

129 Source: ESPON Interact 5.10 Title: Level of economic disparities and relative economic strength

130 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-10 Level of economic disparities and relative economic strength with areas of INTERREG IIIA programmes approximated to NUTS 3 regions KTH ESPON Interact, cross-border cooperation, page Border regions Nuts 3 EU27+2 The GDP in euro per capita as a percentage of the EU25 average for the highest NUTS3 region within the programme and the lowest region in the programme were considered, as well as the spread of difference between them. 1) very high performing vs. very high performing 2) very high performing vs. high performing 3) high performing vs. high performing 4) very high performing vs. low performing 5) very high performing vs. very low performing 6) high performing vs. low performing 7) high performing vs. very low performing 8) low performing vs. low performing 9) low performing vs. very low performing 10) very low performing vs. very low performing GDP per capita is a relatively homogenous indicator, so the typology seems quite robust but needs regularly updates Regional codes, GIS Feasibility regarding ESPON is low as the typology only bases on economic disparities Regarding very special issues the typology may be of political relevance but overall the opportunities of application are low. The explanatory power is limited. 5.10

131 Source: ESPON Title: Transborder FUAs

132 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-11 Typology of Transborder FUAs IGEAT / ESPON ESPON 1.4.3, page Border regions FUAs EU27+2 The transborder FUAs are characterised by the relation of the cities at both sides of the border. 1) twin-cities, generally quite small, sometimes a former single city, cut by a border, each with their own FUA even if some transborder commuting is present. 2) a metropolis or large city, with a morphological area extending across the border in the neighbour country, through suburban areas or small cities, more included in the FUA of the main city. mixed: internal and external border 3) a metropolis or large city, with a contiguity in the neighbour country to smaller cities with their own FUA or sending quite few commuters to the main city in the other country. 4) a small transborder urban area with a quite well integrated common commuting basin. 5) a metropolis or a large city, with its FUA extending in the neighbour country, possibly with a scattered network of secondary centres. 6) two metropolises or large cities, on each side of the border, with tangential MUAs. 7) two or more metropolises or large cities, on each side of the border, with tangential FUAs. Categorisation seems to be based on the assessment of the authors. Not very robust. Regional codes, GIS It would be possible to adopt this typology in ESPON. The number of 7 types and the unclear methodoly hinder policy oriented communication. Explanatory power is especially limited by the limitation to FUAs as the only type of cross-border area. Type 8 as shown in the map is not specified in the report. 5.11

133 Source: ESPON Title: Potential transnational urban areas

134 Fact Sheet Typology Review Border regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 5-12 Potential transnational urban regions Nordregio / ESPON ESPON & ESPON Synthesis 2, p Border regions FUAs EU27+2 Functional urban areas which are located less than 45 min. from a national border and thus potentially can extend beyond them. 1) 4 countries 2) 3 countries 3) 2 countries 4) 1 countrty Categorisation is based on the PUSH system developed by ESPON and thus it should be fairly robust Regional codes, GIS It would be possible to adopt this typology in ESPON. The map has been used e.g. in the ESPON Synthesis Report 2. Simple approach with a good explanatory power. 5.12

135 Mountainous regions

136 Source: UNEP-WCMC 6.1 Title: Mountains of Europe (Delineation)

137 Fact Sheet Typology Review Mountain areas 6.1 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 6-1 Mountains of Europe (Delineation) UNEP-WCMC Mountain Areas in Europe: Analysis of mountain areas in EU member states, acceding and other European countries, pages Mountain areas Grid (1 km²) Theoretically the whole world The UNEP-WCMC approach uses altitude alone to define mountain areas above 2500m and combines altitudinal and slope criteria to define mountains above 1000m. For lower elevations ( m), an additional criterion based on local elevation range is used to identify mountainous areas. 7 classes with varying thresholds. No description of types. The small grid cell size allows a detailed analysis of altitude, slope and elevation. The vast majority of mountain areas in Europe can be identified this way. But the delineation does not consider the mountain areas below 300m altitude. GIS layer The required data and accordingly the delineation are available. As topographic data does not change significantly over time an update is not necessary. The grid data needs to be aggregated to policy relevant geographical levels. Beyond the communication level seems quite high. The delineation gives a good and detailed overview of mountain areas in Europe but not all areas are included (<300 m)

138 6.2 Source: Nordregio (DG Regio Study) Title: Delineation of mountain municipalities in Europe

139 Fact Sheet Typology Review Mountain areas 6.2 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 6-2 Delineation of mountain municipalities in Europe Nordregio (DG Regio Study) Mountain Areas in Europe: Analysis of mountain areas in EU member states, acceding and other European countries, pages Mountain areas NUTS 5 EU27 +2 The methodology follows the UNEP-WCMC approach, which uses altitude alone to define mountain areas above 2500m and combines altitudinal and slope criteria to define mountains above 1000m. For lower elevations ( m), an additional criterion based on local elevation range is used to identify mountainous areas. In contrast to the UNEP-WCMC approach also areas for elevations lower than 300m are considered. Beyond, the temperate contrast functions as an additional criterion to define mountain areas, considered as less favoured areas. Small isolated mountainous areas are not included, similarly non-mountainous areas within mountain massifs were are. Finally, a municipality is considered as mountainous if at least 50% of its area within the area delimited as mountain. 1. Municipalities defined as mountainous according to topographic criteria 2. Municipalities defined as mountainous according to climatic criteria The identification of mountainous municipalities bases on a sound delineation of mountain areas. But the threshold of 50 % is a little bit random. Maybe a typology with more classes would be appropriate. GIS layer The required data and accordingly the delineation are available. As topographic data does not change significantly over time an update is not necessary. The communication value seems to be quite high. But municipalities according to topographic and to climatic criteria should be handled separately. Depending on the special policy issue they describe different phenomena. For many issues an aggregation to NUTS2/3 will be necessary. The delineation gives a good and detailed overview of mountainous municipalities in Europe. Due to the low geographical level the results should be quite reasonable. Based on this delineation 3 thematic typologies had been elaborated: social and economic capital infrastructure, accessibility and services land use and land covers

140 Source: Centre for mountain studies 6.3 Title: Europe s mountain area

141 Fact Sheet Typology Review Mountain areas 6.3 ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 6-3 Europe s mountain area Centre for mountain studies; SAC rural policy group Presentation: A Preliminary Characterisation of the Mountain Area of Europe 2002 Mountain areas NUTS 3 EU The methodology follows the UNEP-WCMC approach, which uses altitude alone to define mountain areas above 2500m and combines altitudinal and slope criteria to define mountains above 1000m. For lower elevations ( m), an additional criterion based on local elevation range is used to identify mountainous areas. The regions are typed by their share of mountainous area within their boundaries. 1. Mountain regions 2. Predominantly mountainous regions 3. partly mountainous regions 4. non mountainous regions The data itself is robust but the thresholds at 40, 60 and 90 % to define the types are set quite randomly. With other, also reasonable thresholds easily differing result could be obtained. GIS layer + regional codes The required data and accordingly the delineation are available. As topographic data does not change significantly over time an update is not necessary. The communication level seems to be quite high. But the NUTS3 level is inadequate for several countries as is produces some fallacies when displaying topographic data. The delineation gives an overview of NUTS 3 regions in Europe with a high share of mountain areas. Due to the MAUP problem and the too large scale in many countries the explanatory power is reduced. Miscellaneous

142 Source: ESPON Atlas 6.4 Title: Mountain Areas and their accessibility

143 Fact Sheet Typology Review Mountain areas ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 6-4 Mountain Areas and their accessibility BBR (EPSON Atlas) ESPON Atlas, p Mountain areas NUTS 3 EU27+2 The delineation is based on the delineation of Nordregio (typology 4-2). NUTS 3 regions with at least 50 % mountainous areas (on the level of municipalities) are set as mountainous. The regions are categorized by their accessibility (potential multimodal). 1. NUTS 3 regions with at least 50 % mountainous areas (5 categories by accessibility) 2. NUTS 3 regions with less than 50 % mountainous areas The identification of mountainous municipalities bases on a sound delineation of mountain areas. But the appliance of a twofold aggregation may develop statistical artefacts. Furthermore the level of NUTS 3 seems not appropriate for this topic. GIS layer, regional code The required data and accordingly the delineation are available. As topographic data does not change significantly over time an update is not necessary (except if NUTS regions change). The communication level is medium. There is no differentiation between topographic and to climatic criteria and depending on the special policy issue they describe different phenomena. The geographical level NUTS 3 is beneficial for policy issues but only a rough estimation. The delineation gives an overview of mountainous regions in Europe. The geographical level and weaknesses in methodology reduce the explanatory power 6.4

144 Source: EC 6.5 Title: Less favoured areas Only the thin orange lines!

145 Fact Sheet Typology Review Mountain Areas ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 6-5 Less favoured areas according to Art. 23 of the regulation (EC) 950/97 EC Study of the EC, Nordregio: Mountain Areas in Europe, p Mountain areas local government districts or parts thereof EU15 The classification of Less Favoured Areas according to Article 23 of regulation (EC) 950/97 (Mountainous areas) was made according to different criteria in each country. The areas should be characterised by a considerable limitation of the possibilities for using the land and an appreciable increase in the cost of working it. This regulation includes a climatic criterion that states that areas north of the 62nd parallel and certain adjacent zones are to be included as mountain areas. Only delineation between mountainous and non-mountainous areas As the categorization criteria differ from country to country the data is neither homogenous nor robust. GIS layer Feasibility is given but not recommended The political relevance is high since the delineation was/is used to define eligible regions. For further use it has to be updated and extended to the EU27. The explanatory power is low as the delineation follows political considerations. 6.5

146 Source: EEA 6.6 Title: Mountain Areas

147 Fact Sheet Typology Review Mountain ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 6-6 Mountain Areas EEA EEA environmental issues, p Mountain areas Unlcear Europe (a total of 35 countries) mountain areas are defined to include locations above 1 000m sea level, as well as all areas having a slope greater than 5 degrees, but excluding areas with a surface area less than 100 square kilometres Only delineation between mountainous and non-mountainous areas Robust methodology, but it takes no areas into account with a surface area less than 100 km² No map The required data should be available. As topographic data does not change significantly over time an update is not necessary. Although the delineation is used by a European organisation the political relevance is relative low. The methodology is simple but maybe not accurate enough. Hard to assess without a map. The non-consideration of smaller areas lowers the explanatory power. 6.6

148 Islands

149 Source: EC 7.1 Title: Classification des territoires insulaires

150 Fact Sheet Typology Review Islands ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 7-1 Classification des territoires insulaires EC, study conducted by Planistat Europe Analyse des régions insulaires et des régions ultrapériphériques de l'union européenne 2003 Islands Islands (in total 286, following the EUROSTAT definition of islands) EU15 Variables considering the topics size, natural conditions an distance have been combined using statistical methods. The analyses resulted in 7 classes. No description of the classes is available As most of the variables are changeable, changing of the classes in further analyses with updated data is likely GIS A lot of data is missing for several variables, and the available ones are not easy to obtain and update Only of minor policy relevance due to its intricate methodology. Hard to communicate. As the classes are not discussed in detail the explanatory power remains low Based on this typology 5 major categories are identified: 1) Sicilia and Sardinia 2) Islands on the open sea, with high population, high share of mountain area. Mostly Mediterranean islands and islands from the north except Bornholm und Wight Island 3) Islands on the open sea, with medium population, high share of mountain area. Mostly Mediterranean islands plus Bornholm und Wight Island 4) Island with medium population near the continent, less mountainous, without territorial particularism 5) Small islands near the coast with low population density and less mountainous. Typically Finnish, Swedish and French 7.1

151 Source: EC 7.2 Title: Classification des régions insulaires

152 Fact Sheet Typology Review Islands ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 6-2 Classification des régions insulaires EC, study conducted by Planistat Europe Analyse des régions insulaires et des régions ultrapériphériques de l'union européenne, p Islands NUTS 2/3 (19 islands) EU15 Variables considering the topics size, natural conditions an distance have been combined using statistical methods. The analyses resulted in 3 classes. No description of the classes is available As most of the variables are changeable, changing of the classes in further analyses with updated data is likely GIS As only the large islands on NUTS 2/3 level are considered data accessibility is quite good Only of minor policy relevance due to its intricate methodology. Hard to communicate. As the classes are not discussed in detail the explanatory power remains low 7.2 Miscellaneous

153 ESPON Project (Euroislands) typology approach is going to take into account variables regarding the following topics: 7.3 Elements Economic efficiency Variables Proxy variables Economic output GDP per capita GDP per employee Change rate of GDP per capita Change rate of employment Economic development and fragility Weight of competitive economic branches (in GDP) Degree of dependence on main activity(ies) (in GDP) Characteristics of the main branches (evolution of supply competition; added value) Economic leakages Weight of competitive economic branches (employment) Degree of dependence on main activity(ies) (employment) Elements Social justice/equity Variables Proxy variables Structure and evolution of the population Population change (absolute change and growth rates) Natural movement, replacement rate Migration Active population Dependent population Aged population Gross migration = population natural movement % of population in active age (15-65) Social cohesion Unemployment percentages: total, female, young Long term unemployment Income per capita, distribution of income Life expectancy Early school leavers Social capital Trust towards institutions and society Membership and voluntarism in NGO s

154 Environmental conservation 7.3 Elements Variables Proxy variables Availability and quality of drinking water % of population receiving water suitable for drinking % of drinking water emanating from natural resources % of drinking water coming from desalination, artificial lakes/dams, transported water Areas with problems in water quality Naturalness of the area Evolution of rainfall patterns Density of tourism beds Quality of sea water Change of temperature of sea water Pressures from land activities Air quality / climate change Change of temperature, patterns of rainfalls CO2 emissions Land quality Desertification, erosion Cultivation practices Biodiversity Species under extinction, land cover Change of land use, parcelling, cultivation practices, management plans of protected areas Landscape quality Construction beyond the designated urban areas Atrificalisation of coastal zone Preservation of cultural capital Classified settlements and monuments Management plans of protected monuments Quality of urban space Noise, % of non built area, traffic (pedestrian zones, cycling) Population density

155 Coastal regions

156 Source: EEA 8.1 Title: Delineation of coastal zone Example population density

157 Fact Sheet Typology Review Coastal regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power Miscellaneous 8-1 Delineation of coastal zone EEA The changing faces of Europe's coastal areas 2006 Coastal regions Grid EU expect for UK, Greece, Cyprus, Poland, Lithuania, Latvia, the Swedish/Finnish archipelago and the outer most areas. The terrestrial portion of the coastal zone is defined by an area extending 10 km landwards from the coastline. This assessment is enhanced by comparisons between the immediate coastal strip (up to 1 km), the coastal hinterland (coastal zone between 1 and 10 kilometre line) and the noncoastal national territory, called inland. 1) coastal strip 2) coastal hinterland 3) non-coastal territory Due to the good quality of data, the methodology seems quite robust. GIS Delineation is determined from the corine land cover data base. So, data is available. The delineation covers only non statistical areas which are smaller than the regional policy relevant NUTS regions. For coastal zone relevant policies the relevance might be high. Explanatory power is relatively low as the typology only delivers a delineation of coastal and noncoastal territory. There exist several other thresholds for delineation of coastal areas using the distance from the coast. In the EEA report a threshold of 100 km is mentioned, in a document concerning the ICZM the threshold is 50 km. Unfortunately I haven t found better descriptions or maps of these delineations so far. 8.1

158 Source: EEA 8.2 Title: Delineation of coastal regions Example population trends

159 Fact Sheet Typology Review Coastal regions ID Name of typology Author of the typology Source Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 8-2 Delineation of coastal regions EEA The changing faces of Europe's coastal areas 2006 Coastal regions NUTS 3 EU expect for UK, Germany, Poland, Lithuania, Latvia, Greece and Cyprus All regions bordering the coastline are regarded as coastal regions. No further differentiation. 1. coastal regions 2. non-coastal regions Easy and robust methodology GIS, regional codes High feasibility Could be high, but NUTS 3 seems not to be an appropriate geographical level for this topic. Explanatory power is low and the big size of several NUTS 3 regions misleading 8.2 Miscellaneous

160 Fact Sheet Typology Review Coastal regions ID Name of typology Author of the typology Source 8-3 Delineation of coastal regions CSIL Centre for Industrial Studies, study conducted for the European Parliament The impact of tourism on coastal areas: regional development aspects 8.3 Year Related topic Geographical level Geographical coverage Methodology used Types Homogeneity / robustness Organisation Feasibility Policy relevance Explanatory power 2008 Coastal regions NUTS 2 EU27 All regions bordering the coastline are regarded as coastal regions. No further differentiation. 1. coastal regions 2. non-coastal regions Easy and robust methodology GIS, regional codes High feasibility Could be high, but NUTS 2 is no appropriate geographical level for this topic. Explanatory power is low and the big size of several NUTS 2 regions misleading Miscellaneous

161 Source: ESPON Title: Typology of coastal regions