XXI. MEZINÁRODNÍ KOLOKVIUM O REGIONÁLNÍCH VĚDÁCH. SBORNÍK
PŘÍSPĚVKŮ.
21ST
INTERNATIONAL COLLOQUIUM ON REGIONAL SCIENCES.
CONFERENCE PROCEEDINGS
Place: Kurdějov (Czech Republic)
June 13-15, 2018
Publisher: Masarykova univerzita, Brno
Edited by:
Viktorie KLÍMOVÁ
Vladimír ŽÍTEK
(Masarykova univerzita / Masaryk University, Czech Republic)
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AUTOR, A. Název článku. In Klímová, V., Žítek, V. (eds.) XXI. mezinárodní kolokvium
o regionálních vědách. Sborník příspěvků. Brno: Masarykova univerzita, 2018. s. 1–5.
ISBN 978-80-210-8969-3.
AUTHOR, A. Title of paper. In Klímová, V., Žítek, V. (eds.) 21st
International Colloquium
on Regional Sciences. Conference Proceedings. Brno: Masarykova univerzita, 2018. pp. 1–
5. ISBN 978-80-210-8969-3.
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© 2018 Masarykova univerzita
ISBN 978-80-210-8969-3
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DOI: 10.5817/CZ.MUNI.P210-8970-2018-4
SPATIAL DATA ANALYSIS AS SELECTED APPROACH TO
MEASURING OF THE EU DEVELOPMENT POTENTIAL
Prostorová anlýza jako vybraný přístup k měření rozvojového potenciálu
EU
EVA POLEDNÍKOVÁ
Katedra evropské integrace
Ekonomická fakulta
Vysoká škola báňská - Technická univerzita Ostrava
Department of European Integration
Faculty of Economics
VŠB - Technical University of Ostrava
 Sokolská třída 33, 702 00 Ostrava, Czech Republic
E-mail: eva.polednikova@vsb.cz
Annotation
Development potential of the European territory (at the level of country, region or local unit) in the context of the
cohesion and competitiveness is frequently discussed in the European Union (EU.) The EU’s internal diversity
and inequalities are reflected in the quality of living standard, different pace of development of the European
territory and also spatial organization of economic and social activities. In this context it is necessary to analyse
the possibilities and seek new directions of development that can contribute to increasing the dynamism and
development potential of economies. There is no uniform theoretical or quantitative approach to measuring the
development and development potential of European territory. The main aim of the paper is to analyse the usability
of spatial data analysis as theoretical and quantitative approach to regional development (potential) evaluation
in the European Union. Based on the conducted literature review, spatial analysis and particularly method of
autocorrelation can be considered as the suitable tool in regional development evaluation that helps to understand
the spatial processes in the EU area.
Key words
European Union, development potential, methods, region, spatial data analysis
Anotace
Rozvojový potenciál evropského území (na úrovni země, regionu nebo místní jednotky) je v Evropské unii (EU)
často diskutován, a to v kontextu posilování soudržnosti a konkurenceschopnosti. Vnitřní rozmanitost EU a
existující rozdíly se odráží v kvalitě života, v rozdílném tempu rozvoje evropského území a také v prostorovém
uspořádání hospodářských a sociálních aktivit. V této souvislosti je nutné analyzovat a hledat nové možnosti a
směry rozvoje, které mohou přispět ke zvýšení dynamiky rozvojového potenciálu ekonomik. K měření rozvoje a
rozvojového potenciálu evropského území neexistuje jednotný teoretický ani kvantitativní přístup. Hlavním cílem
příspěvku je analyzovat využitelnost prostorové analýzy dat jako teoretického a kvantitativního přístupu k
hodnocení regionálního rozvoje a jeho potenciálu v Evropské unii. Prostorovou analýzu a zejména metodu
prostorové autokorelace lze, na základě literární rešerše, považovat za vhodný nástroj pro hodnocení
regionálního rozvoje jeho potenciálu, jelikož pomáhá pochopit prostorové procesy. Metoda prostorové
autokorelace může být použita pro hodnocení stavu, změny a vývoje prostorové struktury.
Klíčová slova
Evropská unie, rozvojový potenciál, metody, region, prostorová analýza dat
JEL classification: C31, C80, O18, R11, R12
1. Introduction
In recent years, the interest in the issue of territorial imbalances in the European Union (EU) has increased and
has been analysed in numerous studies using a variety of different approaches. The reasons can be seen in the fact
that economic growth theory has advanced greatly over the last decades-. Also there is the need to reduce the
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existing differences in terms of economic, social and territorial development across the various European regions
(Ezcurra, Gil, Pascual, 2005). The EU’s internal diversity and inequalities are reflected in the quality of living
standards, different pace of development of the European territory and also spatial organization of economic and
social activities. Assessment of regional (spatial) disparities (mainly at the level of NUTS 2 regions) and
identification of key development factors, that may contribute to increasing the dynamics and development
potential, is crucial to adopt the measures supporting the long-term growth of regional economies (Poledníková,
2017b). There is no uniform quantitative method for disparities evaluation in the countries and regions in the EU.
Several quantitative and qualitative regional indicators (see e.g. Sucháček, 2015) are processed by different
mathematical, statistical or econometric methods. Several groups of methods can be identified: univariate
statistical methods (e.g. mean, standard deviation, coefficient of variation, correlation, traffic light method);
multivariate statistical methods (e.g. cluster analysis, factor analysis); multicriteria decision-making methods;
composites indices, see e.g. Klímová, Žítek (2015), Melecký, Skokan (2011), Staníčková (2015), Michálek (2012),
Campo, Monteiro, Soares (2008), Ginevičius, Podvezko, Mikelis (2004). In the assessment of the level of socioeconomic
potential development, it prevails the aim to obtain aggregated (synthetic) index that characterizes the
analysed territory in comprehensive way (Cheymetova, Nazmutdinova, 2015). Rivera (2012) compiles index of
regional economic potential to measure the regional economic strength and model is based on a number of
variables of population, work activity, unemployment rate, activity rate, production and income. Miłek, Nowak
(2015) employs Krugman index of dissimilarity to identify potential regional specialisations. Viturka (2014)
evaluates the development potentials of regions based on the synthesis of three components: business environment
quality, innovation potential of companies and use of human resources (UHR). Other authors use less or more
sophisticated statistical methods and econometrics models, e.g. Petrakos (2001) uses statistical analysis as
coefficient of regional variation, β-convergence coefficient, the β-density coefficient estimated from the regression
of various economic indicators on regional population density. This author also uses cartographic analysis that
based on maps allows the detection of possible west-east or core-periphery patterns of change, the formation of
possible development axes. Kalnina-Lukasevica (2003) introduces the Synthesized Model identifying the causation
of regional development trends, the priority areas of policy and recommendations and intervention to stimulate
economic development (Poledníková, 2017a). Despite the advantages of these methods, their application to spatial
data is problematic. Spatial data includes, in addition to attribute information indicating the characteristics of the
observed event, spatial information indicating the location of the given even (Spurná, 2008). Although we can
find studies including the importance of the spatial aspect of data in measuring socio-economic differentiation, in
the vast majority of existing research the non-spatial statistics and indicators still prevail. This is in contradiction
with current regional economy that introduces space into economic theories and to practical procedures and trends
in quantitative geography emphasing the application of spatial analysis (local and exploratory spatial analysis)
(Poledníková, 2017b).
The main aim of the paper is to analyse the usability of spatial data analysis as theoretical and quantitative approach
to regional development (potential) evaluation in the EU. To achieve this goal, the method of a literature review
is used. The method of literature review objectively describes and discusses the state of the science of a specific
topic from theoretical and contextual point of view (Rother, 2007). Literature review has an important role in
continuing education because it provides current thinking and research on a selected area of study, and may justify
future research into a previously overlooked or understudied area (Rother, 2007; Poledníková, 2017a). The
literature review on the specific aspects of spatial data analysis used in the context of evaluation of regional
development is the initial phase before the further empirical research work. Literature review considers the
European, American also Asian research studies, papers and books. In order to analyse quality academic journal
or conference proceedings articles, worldwide renowned citation databases Web of Science, Scopus and EBSCO
database were selected. The date of papers’ publication was limited for the period 2000‒2017. The first part of
the paper introduces the theoretical framework of spatial economics. Based on the review of research studies, the
second part of the paper presents the usability of spatial data analysis for the regional development (potential)
evaluation in the terms of territorial level, used factors/ indicators, methods and main results. Finally, main results
of literature review are concluded.
2. Theoretical framework of spatial economics
Mainstream economics has traditionally paid remarkably little attention to the location of economic activity to the
choices firms and households make about where to produce and consume, and about how these choices interact.
(Fujita, Krugman, J. Venables, 1999) Spatial economics is concerned with the allocation of (scarce) resources
over space and the location of economic activity (Duranton, 2008). Spatial economics should include all branches
of economics dealing with the analysis of economic processes and developments in geographical space (Fujita,
2010, p. 1). Most fundamental theory of spatial economics is the general theory of location and space-economy
using the terminology of Isard (1949), the new economic geography (NEG) initiated by Paul Krugman in the early
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1990s represents the newest wave in the development of general location theory. According to Isard “the general
theory of location and space-economy is conceived as embracing the total spatial array of economic activities,
with attention paid to the geographic distribution of inputs and outputs and the geographic variations in prices and
costs.” (Fujita, 2010, p. 2). Developing general location models help us to understand important features of spatial
economies (at various levels of spatial scale) in a unified manner. Many important features of actual spatial
economies were successfully analysed such as: the formation of core-periphery spatial structures and income
disparities within a country as well as among a system of nations; the “flying geese” pattern of industrial relocation
within a country as well as among countries; the formation of various types of industrial agglomeration and
specialized cities; the emergence of a hierarchical urban system in a country; and the formation of various types
of specialized zones within cities. The spatial economics have been developed periodically by great location
theorists, geographers and economists such as Launhardt (1885), Marshall (1890), Weber (1909), Hotelling
(1929), Ohlin (1933), Christaller (1933), Kaldor (1935), Hoover (1936, 1937), Lösch (1940) and Isard (1949). On
the other hand, the history of spatial economics is somewhat perplexing. Spatial economics remained at the
periphery of economic science until very recently. As suggested by Krugman (1995), this is perhaps due to the
lack in the past of a unified framework, or of a comprehensive general location theory. (Fujita, 2010, p. 2). Johann
Heinrich von Thünen represents the oldest and the grandest attempt to develop a general location theory. He
imagined an “isolated state” where a very large town is located at the centre of a homogeneous plain. He then
attempted to determine simultaneously all variables of the economy through competitive markets of goods, labour
and land, with a special focus on the land use pattern and land rent pattern in the agricultural hinterland. Later
Thünen’s model was widen by Alonso in 1964 to understand land use patterns in cities. (Duranton, 2008, p. 4).
The next stage of general location theory centred on the development of industrial location theory, mostly by
German scholars, was in the late 19th century and the first half of the 20th century. With an explicit consideration
of scale economies or indivisibilities in manufacturing production, they had developed industrial location theory
together with noncompetitive models of firms. Lösch (1940) developed theory of market areas in which
oligopolistic competition among firms producing a homogeneous product under increasing returns leads to the
formative of hexagonal market areas. In England, meanwhile, Marshall (1890) presented study on industrial
agglomeration, in which he examined systematically the reasons for the concentration of specialized industries in
particular localities. According to Marshall, externalities are crucial in the in the formation of economic
agglomerations and generate something like a lock-in effect. (Fujita, 2010, p. 9). Following Marshall, local
increasing returns could arise because of knowledge spillovers, linkages between input suppliers and final
producers, and thick local labour market interactions. (Duranton, 2008, p. 5) Between the early 1940s and the
early 60s, the influence of neoclassical economics was so strong that little progress was made in developing new
general location models based on noncompetitive theory. On one side, the neoclassical general equilibrium school
in the tradition of Walras, Pareto and Hicks maintained that a flexible application of the basic competitive theory
can satisfactorily handle the problem of space. On the other side, the other school, led by Isard, asserted that in
order to capture the essential impact of space on the distribution of economic activities, new models were needed
that were fundamentals different from those found in standard general equilibrium theory based on perfect
competition. In the next stage, several successful attempts were made to formulate general location models in the
context of urban morphology. Theoretically, these urban models served as precursors to the NEG. (Fujita 2010, p.
4). The spatial economy was given new impetus in the 1990s with the work on the new economic geography,
which provided economists with new tools to examine why and where population or economic activity is located.
The concentrations of population or economic activity are subject to agglomeration economies and are thus selfreinforcing.
The new economic geography seeks to understand why such concentrations arise and why they are
self-reinforcing. With this the question is linked: How has spatial concentration evolved with growth and
development, and what are the efficiency implications of too much or too little spatial concentration? (Nallari,
Griffith, Yusuf, 2012). The central topic of NEG has been how to explain the emergence of a core-periphery
structure on a nationwide scale, or on an international scale. The hallmark of the NEG is a general equilibrium
approach to the modelling of endogenous agglomeration forces generated through the three-way interactions
among increasing returns, transport costs (broadly defined), and the movement of production factors. (Fujita, 2010,
p. 2). All early models in the NEG using the modelling framework based on the Dixit–Stiglitz model of
monopolistic competition, see Fujita, Krugman, J. Venables (1999); Combes, Mayer Thisse (2008).
3. Review of spatial data analysis used to regional development potential evaluation in the
EU
We can distinguish between two spatial effects: spatial dependence and spatial heterogeneity. Intuitively,
observations from adjacent regions can on the one hand be correlated (spatial dependence/spatial autocorrelation,
measured by Moran’s I), or on the other hand a functional relationship can vary across the regions (spatial
heterogeneity, measured by Getis-Ord Gi). Spatial autocorrelation can stem from aggregation of variables.
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Because the underlying spatial scale of the variable is not correctly reflected within the aggregated variable, the
result might be exposed to spatial autocorrelation. Although this kind of measurement error is likely to occur it is
not the main source of spatial dependence. Spatial autocorrelation derives to a large extent from the fact that
localities interact with each other. The relationship of correlation and distance is in most instances a negative one.
Spatial heterogeneity can be dealt with by standard econometric methods. In many cases the assumption of a
stable functional relationship across space might not hold. (Feldkircher, 2006)
Following part describes the main findings of analysed literature in terms of the territorial unit, indicators,
approaches and methods and also basic results and findings of studies. Main results of literature review are
summarized in the table 1 in the Appendix.
The method of spatial autocorrelation is mostly applied at the lower territorial level in the European states. Spatial
data includes, in addition to attribute information indicating the characteristics of the observed event, spatial
information indicating the location of the given even (Spurná, 2008). Although we can find studies including the
importance of the spatial aspect of data in measuring socio-economic differentiation, in the vast majority of
existing research the non-spatial statistics and indicators still prevail. This is in contradiction with the current
regional economy that introduces space into economic theories and to practical procedures and trends in
quantitative geography emphasising the application of spatial analysis (local and exploratory spatial analysis), see
e.g. Novák, Netrdová (2011). The theory of spatial autocorrelation has been a key element of geographical analysis
for more than twenty years. A number of measurements of spatial autocorrelation were proposed so that we can
investigate the spatial process of geographical evolution from differing points of view. Spatial autocorrelation is
a property of spatial data that exists whenever there is a systematic pattern in the values recorded at locations in a
map. The term of spatial autocorrelation elaborated authors Cliff and Ord in 1973 and as a method of Exploratory
Spatial Data Analysis. Spatial autocorrelation is the correlation among values of a single variable strictly
attributable to the proximity of those values in geographic space, introducing a deviation from the independent
observation assumption of classical statistics.
The spatial data analysis (and the method of spatial autocorrelation) is mostly applied at the lower territorial level
in the European states. Spatial autocorrelation is used in case of municipalities in Slovak Republic, Czech Republic
by Novák, Netrdová (2011), Slavík, Grác, Klobučník (2011), at the level of NUTS 3 regions in Germany and
Central and Eastern Europe by Dańska-Borsiak, Laskowska (2014), Zierahn (2010), Smętkowski, Wójcik (2010),
Pautelli, Griffith, Tiefelsdorf, Nijkamp (2006). The sample of the higher territorial level of NUTS 2 and NUTS 3
regions, as well as functional regions, used Laskowska, Borsiak (2016), Brasili, Bruno, Saguatti (2012), Chapman,
Meliciani (2012), Ertur, Koch (2006), Feldkircher (2006), Stirboeck (2004), Gezici (2004), Niebuhr (2003), Le
Gallo, Ertur (2000), moreover López-Bazo, Vayá, Artís (2004), Verspagen (2007) analysed the NUTS 1, NUTS
2 and also mix these regions. Unbalanced development and disparities among regional economics are an important
topic at the international, especially in China, where the exploratory data spatial analysis are used at the county
level, by Ma, Pei (2010), at the 31 administrative regions on the provincial level by Min, Chen, (2012), or at the
30 regions on the provincial level by Xie, Liu, Liu, Wang (2014). (Poledníková, 2017b).
The spatial data analysis and the method of spatial autocorrelation involve the wide range of variables. Brasili,
Bruno, Saguatti (2012) analysed the economic convergence among European NUTS 2 regions based on GDP per
capita, regional employment rate and the percentage of agricultural employment. Novák, Netrdová (2011), Slavík,
Grác, Klobučník (2011), Zierahn (2010), Smętkowski, Wójcik (2010) focused on spatial differentiation of social,
demographic and economic variables. Spurná (2008), Pautelli, Griffith, Tiefelsdorf, Nijkamp (2006) focused on
role unemployment rate. Verspagen (2007) discusses the possibility of a spatial hierarchy of innovation and growth
dynamics in Europe where 30 variables of general state of economic development, education and patenting were
used. (Poledníková, 2017b). Ertur, Koch (2006) studied the space–time dynamics of regional GDP in the context
of the enlargement of the EU. Feldkircher (2006) investigated absolute convergence (by GDP per capita as
explanatory variable, yearly average growth rate as the dependent variable) within the EU. Stirboeck (2004)
analyses the sectoral specialisation patterns of the 56 NUTS 2 regions in the EU and focused on the regional
investment and employment shares in relation to an economy of reference (relative specialisation of gross fixed
capital formation and relative specialisation of employment is measured). Niebuhr (2003) analysed the regional
data on unemployment, working population, employment, population and area. Chapman, Meliciani (2012) and
Le Gallo, Ertur (2000) studied the distribution of regional GDP per capita in selected EU countries as well as
López-Bazo, Vayá, Artís (2004) who analyze the influence of externalities on production technology across
regional economies, on steady state levels of income, and on the process of growth (gross domestic product per
worker and labour productivity are main variables). Gezici (2004) examined the spatial dependence of the level
of income and its relationship to regional inequality in terms of GDP per capita in Turkey. Ma, Pei (2010) used
the index of the regional per capita gross domestic product (GDP), while Min, Chen (2012) investigate the possible
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influential factors of spatial disparities of agricultural mechanization in China. Xie, Liu, Liu, Wang (2014) studied
energy consumption and social-economic data (population growth rate, GDP growth rate, urbanization rate,
industrialized rate, percentage of industry production value change, percentage of transportation industry
production value change).
The method of spatial data analysis has been applied in many different views of territorial development evaluation.
For example Laskowska Dańska-Borsiak (2016) used the spatial analysis (Moran’s and LISA statistics, the
bivariate Moran’s statistics-BiLISA) to examine the relationship between human capital and GDP per capita in
the European NUTS 2 regions. The results show that most clusters consist of regions with high income per capita
that are surrounded by regions with high levels of human capital, but the Balkans also show a very large
concentration of regions with low values of both variables. In some EU countries, the NUTS-2 regions with the
national capital cities are characterised by a high-low relationship between human capital and spatially lagged
GDP per capita. These are the regions of Közép-Magyarország (with Budapest), Praha, Mazowieckie (with
Warsaw), Bucuresti-Ilfov, and Área Metropolitana de Lisboa. As expected, region’s GDP per capita is positively
influenced by its values in the neighbouring regions. According to Brasili, Bruno, Saguatti (2012) the spatial
distribution of the regional per capita GDP in 1980 suggests that there was spatial heterogeneity, with two clusters
of richer and poorer regions. The hypothesis that the geographical and economic peripheries in Europe
substantially coincide is thus supported by this result. Chapman, Meliciani (2012) found that spatial correlation in
per capita GDP has not increased over time 1998-2005, thus suggesting that agglomeration forces are not able to
explain the increase in within countries disparities. Le Gallo, Ertur´s (2000) study of the spatial distribution of
regional per capita GDP in Europe over 1980-1995 using exploratory spatial data analysis highlights the
importance of spatial interactions and geographical locations in regional growth and convergence issues. Spatial
data analysis appears therefore as a powerful tool to finely reveal the characteristics of economic development of
each region in relation to those of its geographical environment. Analysis reveals significant positive global spatial
autocorrelation, which is persistent over the whole period: regions with relatively high (resp. low) per capita GDP
are and remain localized close to other regions with relatively high (low) per capita GDP and that the spatial
distribution of regional per capita GDP is not random. The Moran scatterplot and LISA show the persistence of
the high-high and low-low clustering types for regional per capita GDP, confirming the north-south polarization
of European regions. This reveals some kind of spatial heterogeneity hidden in the global positive spatial
autocorrelation pattern and may indicate the co-existence of two distinct spatial regimes. Spatial effects could then
perform differently in Northern Europe than in Southern Europe. Ertur, Koch (2006) continue in Le Gallo, Erturʾs
(2000) study and measure, based on nearest neighbours spatial weight matrix, global and local spatial
autocorrelation (Getis–Ord statistics) of European regional GDP per capita in the period 1995–2000. The results
also highlighted the new North–West/East polarization pattern which appears with the enlargement process to
Central and Eastern European countries and which replaces the previous North–South polarization pattern often
underlined in the literature for EU15. They point out that these results have important implications on the way
regional and cohesion policies have to be designed because the expected effects of such policies on a given region
could be over- or under-estimated depending on the spatial interaction pattern characterizing it. Indeed, spillovers
and spatial externalities underlying the spatial autocorrelation are likely to affect regional development processes
and therefore should be seriously taken into account. Feldkircher (2006) stated that spatial interactions such as
technological spillovers or factor mobility, both being important forces for the process of convergence, should not
be neglected. Results showed that growth performance and convergence depend crucially on the development of
a region’s surrounding. López-Bazo, Vayá, Artís (2004) analyse the influence of externalities on production
technology across regional economies, on steady state levels of income, and on the process of growth. The results
showed the relevance of interdependencies between regional economies by a simple growth model in which
externalities across economies positively influence the process of production. Stirboeck (2004) check the
robustness of the recent findings on the economic and locational determinants of regional specialisation patterns
for spatial correlation. According him spatial data analysis using of the Getis-Ord statistics there was no strong
clusters of sectoral specialisation across regions included in the study. Gezici (2004) looks at the spatial patterns
of GDP per capita in order to examine spillover effects in province in Turkey. Ma, Pei (2010) combine exploratory
spatial data analysis with GIS technology to investigate the influence of the 2008 Olympics Games for the
development of regional economics in Beijing and to explore the possibility that important historical events or
national policy guides may associate with change in spatial patterns of regional economic disparities over time.
Results show not strong evidences of global spatial autocorrelation, but clear evidences of local spatial
autocorrelation and spatial heterogeneity in the distribution of regional per capita GDP. Min, Chen (2012) used
exploratory spatial data analysis as a descriptive step before suggesting dynamic factors to explain the spatial
patterns and before estimating and testing more sophisticated regression models (Spatial Lag Model and Spatial
Error Model). The results showed the spatial distribution of agricultural mechanization in Chinese provinces is
significantly uneven. Agricultural mechanization is much higher in the Northern provinces than in the southern
provinces of China. Since the spatial autocorrelation seems to affect agricultural mechanization. Xie, Liu, Liu,
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Wang (2014) stated that traditional methods measuring the regional disparities ignored the factor of geographical
position, which may not truly reflect the spatial characteristics of regional disparities. ESDA mainly measuring
spatial association can solve the problem of spatial relationship between regions. Energy consumption changes in
China and its driving forces have shown a spatially positive correlation. The residuals of standard regression model
also showed positive autocorrelation, indicating that stand multiple linear regression model failed to consider all
the spatial dependencies.
Conclusion
Most of studies use the comprehensive quantitative approaches to the assessment of the socio-economic potential
of territory applying the statistical method or econometric models. Other important and popular group of used
method represents spatial data analysis, especially method of spatial autocorrelation, have been applied to many
fields. Detailed analysis of literature sources was focused especially on territorial unit, indicators and results used
in the issue of the usability of spatial data analysis for development potential evaluation. Based on the conducted
literature review, spatial analysis and particularly method of autocorrelation can be considered as the suitable tool
in the analysis of regional economic, social and territorial differentiation and variability, which complements the
spatial dimension in the EU area. Spatial autocorrelation can be used as a tool for evaluation of the state, changes
and development of the spatial structure.
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Appendix
Table 1: Review of spatial data analysis used to regional development potential evaluation in the EU
Author (Year, Title of
paper)
Territorial
focus
Indicators Main results
BRASILI, C., F. BRUNO,
SAGUATTI, A (2012). A
spatial econometric
approach to EU regional
disparities between
economic and
geographical periphery.
NUTS 2, NUTS
3 regions
GDP per capita,
regional
employment rate,
the percentage of
agricultural
employment
The spatial distribution of the regional per capita GDP between
1980 and 2006 suggests that there was spatial heterogeneity,
with two clusters of richer and poorer regions. The geographical
and economic peripheries in Europe substantially coincide is
confirmed. Spatial heterogeneity is also evident with regards to
the other variables considered in the model.
CHAPMAN, SA,
MELICIANI V., (2012).
Income Disparities in the
Enlarged EU: Socioeconomic,
Specialisation
and Geographical
Clusters.
NUTS 2, NUTS
3 regions
regional GDP per
capita
Spatial correlation has not increased over time 1998-2005,
agglomeration forces are not able to explain the increase in
within countries disparities. The structural factors are becoming
increasingly important for explaining differences in per capita
GDP across regions. The distribution conditioned by neighbours’
income is not significantly different from the original one in either
year.
DAŃSKA-BORSIAK, B.,
LASKOWSKA, I. (2014).
Selected Intangible
Factors Of Regional
Development: An Analysis
Of Spatial Relationships.
NUTS 3
regions
GDP per capita,
human and social
capital
Spatial clustering of high values (and/or low values) of human and
social capital is significant (clustering is stronger for social capital,
as the Moran’s I-values are higher than for human capital). Social
capital showed the tendency for the clustering of positive values.
The correlation was positive and is of high–high or low–low type.
No significant changes in the spatial patterns occurred in relation
to human and social capital. The next analysis confirmed the
positive correlation between the GNP level per capita and the
human capital measure. However, the results of the spatial
analysis show that some subregions with the high level of
development are surrounded by regions with low human capital
and social capital.
ERTUR, C., KOCH, W.,
(2006). Regional
disparities in the
European Union and the
enlargement process: an
exploratory spatial data
analysis, 1995–2000.
NUTS 2, NUTS
3 regions
regional GDP per
capita
The strong evidence of both spatial autocorrelation highlighted
the fact that the per capita GDP level for a given region is not
independent of neighboring regions per capita GDP levels in the
period 1995-2000. The analysis of average annual growth rates of
per capita GDP also showed strong evidence in favor of spatial
autocorrelation: the economic dynamism of a given region is
highly correlated to the economic dynamism of neighboring
regions. New North–West/East polarization pattern appears with
the enlargement process which replaces the previous North–
South polarization.
EZCURRA, R., C. GIL,
PASCUAL, P. (2005).
Regional welfare
disparities: the case of
the European Union.
NUTS 1
regions
national
component, the
spatial location,
the productive
structure, R&D
expenditure
Regional differences in productivity are the main explanatory
factor behind observed welfare inequality in the European
context. Empirical evidence highlights the importance of variables
such as the national component, the spatial location, the
productive structure and the percentage of GDP devoted to
investment or to R&D expenditure, in explaining the dynamics of
the regional welfare distribution in the EU.
FELDKIRCHER, M. (2006).
Regional Convergence
within the EU-25: A
Spatial Econometric
Analysis.
NUTS 2
regions
GDP per capita
(explanatory
variable), yearly
average growth
rate (the
dependent
variable)
It is shown that growth performance and convergence depend
crucially on the development of a region’s surrounding. The
detected spatial autocorrelation is of substantive form indicating
that least squares estimation of the absolute convergence model
yields biased results. According to Local Moran’s I, Europe is
divided into three growth zones: Clusters of fast growing regions
in the East and West of Europe and in between a cluster of slow
growing regions. Significant growth clusters indicate that regions
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
45
Author (Year, Title of
paper)
Territorial
focus
Indicators Main results
located in a dynamic surrounding of high growing localities are
more likely to show high growth rates than ones that are
neighbors of “slow-growing” areas. This clustering phenomenon
can be due to the existence of regional spillovers.
GALLO LE, J., ERTUR, C.,
(2000). Exploratory
spatial data analysis of
the distribution of
regional per capita GDP
in Europe, 1980–1995.
NUTS 2, NUTS
3 regions
regional GDP per
capita
Spatial distribution of regional per capita GDP highlights the
importance of spatial interactions and geographical locations in
regional growth and convergence issues. Significant positive
global spatial autocorrelation: regions with relatively high (low)
per capita GDP are and remain localized close to other regions
with relatively high (low) per capita GDP and that the spatial
distribution of regional per capita GDP is not random. The
persistence of the high-high and low-low clustering types for
regional per capita GDP, confirming the north-south polarization
of European regions. This reveals some kind of spatial
heterogeneity hidden in the global positive spatial
autocorrelation pattern and may indicate the co-existence of two
distinct spatial regimes. Spatial effects could then perform
differently in Northern Europe than in Southern Europe.
LASKOWSKA, I., DAŃSKABORSIAK,B.,
(2016). The
Importance Of Human
Capital For The Economic
Development Of EU
Regions.
NUTS 2, NUTS
3 regions
GDP per capita,
human capital and
social capital
Most clusters consist of regions with high income per capita that
are surrounded by regions with high levels of human capital, but
the Balkans also show a very large concentration of regions with
low values of both variables. In some EU countries, the NUTS-2
regions with the national capital cities are characterised by a
high-low relationship between human capital and spatiallylagged
GDP per capita. These are the regions of Közép-Magyarország
(with Budapest), Praha, Mazowieckie (with Warsaw), BucurestiIlfov,
and Área Metropolitana de Lisboa. Moreover, influence of
human capital on GDP per capita was assessed by two spatial
regression models (a spatial autoregressive model and a spatial
error model) and basic specifications were used. As expected,
region’s GDP per capita is positively influenced by its values in the
neighbouring regions.
LÓPEZ-BAZO E., VAYÁ, E.,
ARTÍS,M., (2004).
Regional externalities
and growth: evidence
from European regions.
NUTS 1, NUTS
2 regions
GDP per worker,
labour
productivity,
Agriculture,
Energy, Share of
employment in
each sector,
Manufacturing,
Construction,
Market potential,
Patents/GDP,
Temperature
The results showed the relevance of interdependencies between
regional economies by a simple growth model in which
externalities across economies positively influence the process of
production. Growth andinitial productivity in the set of
neighboring regions enhance growth in any region. For instance,
trade and patent citations suggest that the strength of
interactions decreases with distance. As a consequence,
externalities across regions might be behind the features
observed, and already reported in the recent literature, in the
spatial distribution of production and its growth. Geographical
clusters of regions in which the amount of economic activity is
well above or below the average could be, among other things,
caused by spillovers that cross the usually artificial regional
borders.
NIEBUHR, A. (2003).
Spatial Interaction and
Regional Unemployment
in Europe.
NUTS 2, NUTS
3 regions
unemployment,
working
population,
employment,
population and
area
Spatial distance costs as a reason for insufficient equilibrating
forces and persistent disparities between regional labour markets
in Europe. The correlation analysis indicated a strong positive
autocorrelation of both regional unemployment and the change
in regional unemployment. Adjacent regions that form clusters of
high and low unemployment seem to be a central feature of
disparities in Europe. Spatial dependence is not solely the
consequence of national differences since a significant autocorrelation
also characterises relative unemployment rates.
Unemployment clusters are not exclusively national clusters,
covering all regions that belong to the same EU member state.
NOVÁK, J., NETRDOVÁ P.
(2011). Prostorové vzorce
sociálně-ekonomické
diferenciace obcí v České
republice.
municipalities
25 social,
demographic, and
economic
variables
Six types of spatial clusters were found: core regions, Ostrava,
Northern Bohemia, Bohemian-Moravian Highlands, nondevelopment
areas, other territories.
PAUTELLI, R., D.A.
GRIFFITH, M.
TIEFELSDORF, NIJKAMP,
P. (2006). The Use Of
Spatial Filtering
Techniques.
NUTS 3
regions
unemployment
rates, commuting
flows
If shown as graphical visualizations, the spatial filters found in our
analyses provide hints on the geographical distribution of
unemployment trends. It is an example, map can be interpreted
as the visualization of a North-South divide. Results also suggest
differences between East and West Germany.
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
46
Author (Year, Title of
paper)
Territorial
focus
Indicators Main results
SLAVÍK, V., GRÁC, R,
KLOBUČNÍK, M. (2011).
Priestorová autokorelácia
– metóda vymedzovania
a klasifikácie regiónov v
kontexte sociálno-
ekonomickej
regionalizácie Slovenskej
republiky
municipalities
economically
active population,
unemployment
rates
The spatial autocorrelation of economically active population as
well as the unemployment rate is showed. Four types of regions
were defined: areas with a high proportion of the economically
active population and low unemployment rate; regions with low
economically active population and low unemployment rate;
regions with economically active population, but also with high
unemployment rate; municipalities with low economically active
population and high unemployment rate.
SMĘTKOWSKI, M.,
WÓJCIK, P, (2010).
Regional Development in
Central and Eastern
Europe.
NUTS 3
regions
GDP, economic
structure, labour
productivity,
labour market
situation,
condition of
enterprises, R&D
sector, human
capital,
infrastructure,
external
attractiveness
There is decreasing spatial correlation relating to the
development level of regions for the Central European
macroregion. There is a considerable polycentric of the
macroregion since the growth centres in individual countries
were separated from one another by less-developed areas, which
resulted in the lack of statistical significance of Moran's I,
suggesting a random distribution of the growth poles.
Polarisation processes were visible, manifested by a spatial
concentration of the development dynamic, which meant that
regions which were surrounded by faster-developing areas would
grow faster themselves and, conversely, slow development rate
of neighbouring regions, led to the emergence of macroregions
with a low dynamic of growth. This could prove that the regional
hinterland does have some, rather weak, influence on
development processes.
SPURNÁ, P., (2008).
Prostorová autokorelace
– všudypřítomný jev při
analýze prostorových
dat?
municipalities
on age index, the
share of university
educated people,
unemployment
rate and altitude
The empirical examples based on aggregate statistical data at the
municipal level highlight the relevance and usefulness of analysis
of spatial autocorrelation. The proportion of university educated
people shows more pronounced clusters of above average values
- "hot spots". The LISA identified the core or concentration
centers of the university educated population corresponding to
the largest cities and backgrounds. In the case of the age index,
there can be talk of the existence of areas with a higher
proportion of children's population in border areas. The LISA
results for the unemployment rate and altitude illustrate the link
between the high number of municipalities showing significant
local spatial autocorrelation and the high level of global spatial
autocorrelation. In the case of the unemployment rate, the axes
between Prague and the towns of České Budějovice, Liberec and
Plzeň with low unemployment rates were identified, while
problematic areas such as Northern Bohemia.
STIRBOECK, C., (2004). A
Spatial Econometric
Analysis of Regional
Specialisation Patterns
across EU Regions.
NUTS 2, NUTS
3 regions
regional
investment,
employment
shares (relative
specialisation of
gross fixed capital
formation and
relative
specialisation of
employment
There were no strong clusters of sectoral specialisation across
regions. There are some few clusters (e.g. specialisation in
Southern Italy), but these are not very striking. Spatial
interdependencies between the level of sectoral specialisation of
neighbouring regions in the econometric analysis was rarely
detected as significant. The spatial clustering of similar
specialisation in some rather unfavourable sectors in the
peripheral regions is not generally accompanied by significant
spatial interdependencies.
VERSPAGEN, B., (2007).
The Spatial Hierarchy of
Technological Change
and Economic
Development in Europe.
NUTS 1, NUTS
2 regions
30 variables of
general state of
economic
development,
education,
patenting
The positive correlations (58% of the cases) is calculated, positive
spatial correlation is particularly frequent along the row and
column of the GDP per capita. GDP per capita correlates strongly
with services and in particular business services, employment,
and the same patenting sectors as mentioned before. The other
strong correlations are mostly negative (the sectoral employment
shares variables, the general economic variables, and the
education variables). The results point to a hierarchy consisting of
four groups: South Europe, East Europe, West and North Europe.
In the South and East, such interactions have not yet emerged
very frequently. In the South and East, major urban centres exist
in which economic growth and innovation flourishes. But these
cities do not seem to support a surrounding area with which
knowledge interactions are taking place. The metropolis of the
South and East remain isolated centres, not yet capable of
generating enough spillovers.
ZIERAHN, U., (2010). The
Importance of Spatial
Autocorrelation for
NUTS 3
regions
employment, the
number of
employees subject
Regional employment growth is characterised by spatial
autocorrelation, the development of employment in a region is
interrelated with the employment development of nearby
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
47
Author (Year, Title of
paper)
Territorial
focus
Indicators Main results
Regional Employment
Growth in Germany.
to insurance
contribution,
average monthly
wage
regions. This also holds true for major factors of regional
employment, such as wages and qualification.
XIE, H., LIU, G., LIU,
Q.,WANG. P.,(2014).
Analysis of Spatial
Disparities and Driving
Factors of Energy
Consumption Change in
China Based on Spatial
Statistics
administrative
regions
population growth
rate, GDP growth
rate, urbanization
rate, industrialized
rate, percentage
of industry
production value
change,
percentage of
transportation
industry
production value
change
They stated that traditional methods measuring the regional
disparities ignored the factor of geographical position, which may
not truly reflect the spatial characteristics of regional disparities.
ESDA mainly measuring spatial association can solve the problem
of spatial relationship between regions. Energy consumption
changes in China and its driving forces have shown a spatially
positive correlation.
MA, X., PEI, T., (2010).
Exploratory spatial
analysis of regional
economic disparities in
Beijing during 2001-2007.
county level
index of the
regional per capita
gross domestic
product
Results show not strong evidences of global spatial
autocorrelation, but clear evidences of local spatial
autocorrelation and spatial heterogeneity in the distribution of
regional per capita GDP. Since the economic increasing-speeds of
Changping and Shijingshan Districts were significantly lower than
their some neighbouring regions, a new centre-surrounding
polarization scheme was gradually replacing the North-South
polarization scheme in Beijing from 2001 to 2007.
MIN, M..,CHEN, J. (2012).
A Spatial Econometrics
Analysis On Regional
Disparities Of Agricultural
Mechanization In China
administrative
regions
rate of agricultural
mechanization,
rural net income
per capita,
government
financial
investment,
educated
population in
agricultural
machinery
technology,
Cultivated Land
per capita, rate of
agricultural labour
transfer
The results showed the spatial distribution of agricultural
mechanization in Chinese provinces is significantly uneven.
Agricultural mechanization is much higher in the northern
provinces than in the southern provinces of China.
Source: own elaboration based on reviewed references, 2017