The article analyses changes in the regional variability of unemployment rates resulting from the global crisis, at the NUTS 3 and LAU 1 levels in the new member states of the European Union in Central and Eastern Europe. Although the coefficient of variation, the Gini coefficient and the Theil index each show a clear tendency towards convergence, in all of the states and at both of the scale levels observed, the range of variation has increased throughout the region, with the exception of the LAU 1 level in Hungary. The analysis therefore shows the ambiguous nature of the relationship between an economic crisis and the intensity of micro-regional differences or, rather, their dependence, owing to the nature and severity of the crisis as well as the statistical measures of variability. We found a number of mutual and contradictory tendencies concerning regional development during the global economic crisis in the observed countries. Based upon these findings, several groups of countries with similar changes in their regional pattern during the crisis have been identified.

This article aims to contribute to existing analyses of regional development in the post-socialist countries of Central and Eastern Europe (CEE) by conducting a detailed regional analysis at the municipal (LAU II) level in Czechia. The focus on such a detailed regional level allows for the testing of several hypotheses, for which traditional regional analyses based on NUTS II – NUTS III data were insufficient. First, we investigate whether development axes, as used in planning documents, manifest themselves at a higher level of socioeconomic development. Second, we expect the formation of clusters, exhibiting a higher level of socioeconomic development, around strong growth centres, such as the city of Mladá Boleslav. Third, we explore whether increasing differentiation has shifted from the regional level to the microregional/local level during the past 15 years of the post-socialist transformation. To address these hypotheses, we analyze a set of socioeconomic variables with the help of the spatial autocorrelation method, which allows us to generalize the inevitably, highly fragmented patterns found at the local level.

An attempt to provide a procedure for the assessment of the efficiency of various regional systems for the purposes of spatial analysis is presented in this paper. Functional regions as well as approximated functional regions and the existing administrative regions in the Czech Republic are evaluated, as examples of regional systems to be compared and assessed. Functional regions and approximated functional regions are defined according to the adjusted third variant of the CURDS regionalisation algorithm, using the latest knowledge on the operation of the constraint function. The comparisons of individual regional systems are based on LISA maps and particularly on the assessment of regional variability, including the measures of internal homogeneity and external variability in the regional systems.

Cervical cancer screening (CCS) is an important public health measure for early detection of cervical cancer and prevents a large proportion of cervical cancer deaths. However, participation in CCS is relatively low and varies substantially by country and socio-economic position. This study aimed to provide up-to-date participation rates and estimates on educational inequalities in CCS participation in 24 European countries with population-based CCS programmes. This was a cross-sectional study. Using data from the European Health Interview Survey (EHIS) conducted in 2019, 80,479 women aged 25-64 years were included in the analyses. First, standardized participation rates and standardized participation rates by educational attainment were calculated for all 24 countries based on each country-specific screening programme organization. Second, a series of generalized logistic models was applied to assess the effect of education on CCS participation. Screening participation rates ranged from 34.1% among low-educated women in Romania to 97.1% among high-educated women in Finland. We observed that lower-educated women were less likely to attend CCS than their higher-educated counterparts. Largest educational gaps were found in Sweden (odds ratio [OR] = 6.36, 95% confidence interval [CI] = 3.89-10.35) and Poland (odds ratio = 5.80, 95% CI = 4.34-7.75). Population-based screening initiatives have successfully reduced participation differences between women with medium and high educational attainment in some countries; however, persistent disparities still exist between women with low and high levels of education. There is an urgent need to increase participation rates of CCS, especially among lower-educated women.

In this paper, we focus on mapping and analysing the spatial patterns of unemployment in four Central European countries – Austria, Czechia, Germany, and Poland, on municipal level in 2010. Specifically, based on the geo-social differentiation patterns, we are searching for secondary axes stretching from the Blue Banana (the major European economic development axis running from London to Milan) towards Eastern Europe. Unemployment is supposed to approximate economic development, thanks to its close relation to GDP and other economic indicators. To study spatial patterns and development axes on a micro scale, we use the concept of spatial autocorrelation, specifically Moran's I and LISA analysis. While we analyse more than 44,000 units, the resulting maps are very detailed and difficult to interpret on small scales. In this paper, we take advantage of the opportunity to present large-scale maps (1:2,500,000 and 1:6,000,000), which are also more suitable for the analytical conclusions that follow.

The variability and geographical patterns of population characteristics are key topics in Human Geography. There are many approaches to exploring and quantitatively measuring this issue. Besides standard aspatial statistical methods, there is no universal framework for incorporating regional and spatial aspects into the analysis of areal data. This is mainly because complications, such as the Modifiable Areal Unit Problem or the checkerboard problem, hinder analysis. In this paper, we use two approaches which uniquely combine regional and spatial perspectives of the analysis of variability. This combination brings new insights into the exploration of the variability and geographical patterns of population characteristics. The relationship between regional and spatial approaches is studied with models in a regular grid, using variability decomposition (Theil index) as an example of the regional approach, and spatial autocorrelation (Moran’s I) as an example of the spatial approach. When applied to empirical data based on the Czech censuses between 1980 and 2011, the combination of these two approaches enables us to categorise the studied phenomena according to the regional and spatial nature of their variability. This is a useful advance, especially for assessing evolution over time or comparisons between different phenomena.