




Projects
Peripherality (2000) 
Towards a European Peripherality Index E.P.I. 


Carsten Schürmann and Ahmed Talaat
The purpose of this study for the General Directorate XVI Regional Policy of the European Commission was to calculate indicators of peripherality of the 'potential' type for the fifteen Member States of the European Union and the twelve candidate countries in eastern Europe. The economic or population potential of a region is the total of destinations (firms or population) in all regions weighted by a function of distance from the origin region. The peripherality i ndicators calculated will be based on Level 3 of the Nomenclature of Territorial Units for Statistics (NUTS) of the Statistical Office of the European Union and equivalent geographical units as for the candidate countries and aggregated to NUTS levels 2, 1 and 0 as weighted average of the corresponding NUTS3 regions. Distance measures used are average road travel times of passengers and goods taking account of road types, speed limits for cars and lorries, congestion in urban regions and of delays due to mountainous areas, national borders and maximum driving hours of lorry drivers.
The indicator system developed can be differentiated by the following criteria:
 Spatial aggregation: All calculations of peripherality indices are based on level 3 of the Nomenclature of Territorial Units for Statistics (NUTS) and are then aggregated to levels 2, 1 and 0 of the NUTS for the EU member states and equivalent geographical units as identified by Eurostat for the candidate countries by averaging over NUTS3 regions weighted by NUTS3 region population.
 Modes: Since speed limits for cars and trucks differ and statutory drivers' resting periods affect freight transport, all indicators were calculated separately for passenger and freight road transport.
 Mass terms: Peripherality indices were calculated for each origin region by adding up the mass of each destination region weighted by a function of distance from the origin region. Usually, the mass is measured in terms of gross domestic product (GDP). In this study, also GDP in Purchasing Power Standards (PPS), employment and population were used as mass terms. Distance was measured as the average travel time from one region to every other region in the form of a matrix.
 Type of indicator: All peripherality indices are derivatives of potential accessibility. Two different types of peripherality indices were defined: Peripherality Index 1 (PI1): The region with the highest potential accessibility, i.e. the most central region, was defined to have a peripherality index of zero. The region with the lowest potential accessibility, i.e. the most remote region, was defined to have a peripherality index of one hundred. The peripherality index of all other regions is a linear interpolation between zero and one hundred proportional to their potential accessibility. The higher the peripherality index, the higher the peripherality. Peripherality Index 2 (PI2): The average potential accessibility of all regions weighted by regional population was defined to be one hundred. The peripherality index of all regions was calculated as potential accessibility expressed in percent of average accessibility. The higher the peripherality index, the lower the peripherality. Peripherality Index 2 is therefore in fact a standardised accessibility indicator.
 Spatial scope of standardisation: The standardisation was done for three different territories: EU member states, EU member states plus five candidate countries (Estonia, Poland, Czech Republic, Hungary, Slovenia) and EU member states plus twelve candidate countries (first five plus Latvia, Lithuania, Slovakia, Romania, Bulgaria, Cyprus, Malta). This implies that the values of the regional peripherality indices differ depending on the territory covered.
Based on the above classification (4 NUTS levels, 2 modes, 4 mass terms, 2 types of indicators, 3 territories), 4 x 2 x 4 x 2 x 3 = 192 possible output indicators were calculated.
The results showed that the general spatial patterns of peripherality are very similar across all indicators calculated, reflecting the fact that distant geographical location cannot be fully compensated by transport infrastructure. However, each indicator emphasises certain aspects of peripherality. So, the choice of the type of peripherality index to be used becomes a matter of concern. Depending on the purpose of the study, a certain indicator type may be more appropriate than another type in that certain subsets of regions and yield slightly better or even worse results with respect to peripherality.
In general, the following conclusions can be drawn:
 The overall spatial patterns of all peripherality indices are very similar, so correlation between different indicators are rather high. This reflects the fact that, irrespective of the kind of peripherality index used, the distant geographical position of peripheral regions cannot be fully removed by transport infrastructure improvements.
 Peripherality with respect to population by car is less polarised than peripherality with respect to GDP by lorry.
 Peripherality with respect to lorry favours regions around the Channel coast, since for lorries the ‘barrier effect’ of the Channel Tunnel is much less than for cars.
 Candidate countries benefit more if peripherality with respect to population by car is used; conversely, central regions benefit more if peripherality with respect by lorry is used.
 The type of indicator has relatively little influence on the results. Standardisation between the minimum and maximum shows slightly more differentiation among peripheral regions, whereas standardisation on the European average shows slightly more polarisation between the central regions.
 GDP in PPS has slight balancing effects compared in Euro, but nevertheless peripherality with respect to both is more polarised than peripherality with respect to population or employment.
 The greater the territory used for standardisation is, i.e. the more candidate countries are taken into account, the lower will be the European average, and the more will regions in EU member states improve their relative position.
 The higher the NUTS level, the greater will be the loss in spatial differentiation. Studies based on the NUTS3 level yield a great number of detail and differentiation between and within peripheral and central regions. This is particularly true for the relatively small German, French and Italian regions.
The study comprised not only the calculation of peripherality indicators but also the development of an interactive programm systems based on the ArcInfo Macro Language (AML) for the calculation of the European Peripherality Index (E.P.I.).
Selected results have been incorporated in the recently published Cohesion Report of the European Commission. The Final Report and the Software UserManual can be downloaded as reports 53 and 52 from the IRPU Home Page.




