P Netrdová, V Nosek (2020)
ISPRS International Journal of Geo-Information 9 (6), 401
This paper focuses on the analysis of unemployment data in Czechia on a very detailed spatial structure and yearly, extended time series (2002–2019). The main goal of the study was to examine the spatial dimension of disparities in regional unemployment and its evolutionary tendencies on a municipal level. To achieve this goal, global and local spatial autocorrelation methods were used. Besides spatial and space-time analyses, special attention was given to spatial weight matrix selection. The spatial weights were created according to real-time accessibilities between the municipalities based on the Czech road network. The results of spatial autocorrelation analyses based on network spatial weights were compared to the traditional distance-based spatial weights. Despite significant methodological differences between applied spatial weights, the resulting spatial pattern of unemployment proved to be very similar. Empirically, relative stability of spatial patterns of unemployment with only slow shift of differentiation from macro-to microlevels could be observed.
USING AREAL INTERPOLATION TO DEAL WITH DIFFERING REGIONAL STRUCTURES IN INTERNATIONAL RESEARCH
P Netrdova, V Nosek, P Hurbanek (2020)
ISPRS International Journal of Geo-Information 9 (2), 126
When working with regional data from different countries, issues concerning data comparability need to be solved, including regional comparability. Differing regional unit size is a common issue which influences the results of socio-economic analyses. In this paper, we introduce a strategy to deal with the regional incomparability of administrative data in international research. We propose a methodological approach based on the areal interpolation method, which facilitates the usage of advanced spatial analyses. To illustrate, we analyze spatial patterns of unemployment in seven Central European countries. We use a very detailed spatial (municipal) level to reveal local tendencies. To have comparable units across the whole region, we apply the areal interpolation method, a process of projecting data from source administrative units to the target structure of a grid. After choosing the most suitable grid structure and projecting the data onto the grid, we perform a hot spot analysis to show the benefits of the grid structure for socio-economic analyses. The proposed approach has great potential in international research for its methodological correctness and the ability to interpret results.
P Netrdova, J Blazek (2019)
Journal of Maps 15 (1), 69-76
This study presents an analysis and visualisation of the evolutionary dynamics of unemployment at the municipal level in Czechia during the global economic crisis. The analysis is based on a monthly time series of unemployment data at a detailed territorial level. Namely, there are 6,258 municipalities in Czechia, which makes it particularly suitable for a detailed investigation of the unfolding and evolution of the recent crisis. Our focus is on analysing and mapping the spatiotemporal patterns of unemployment using variability and autocorrelation measures. Given the detailed territorial level of our analysis, large-scale maps will be presented to assist with interpretation and analytical conclusions. The Main Map (1:600 000) shows the categories of municipalities according to the rate of unemployment and its evolutionary dynamics. Three additional maps (1:1 400 000) visualise the results of spatiotemporal analyses.
P Netrdova, V Nosek (2018)
GEOGRAFIE 123 (2), 225-251
The paper is focused on the geographical differentiation of the population in Czechia between the years 1980 and 2011. Data from population censuses were adjusted in all years to the municipal structure in 2011, so an analysis of evolution on a municipal level could be undertaken. Besides analyzing geographical differentiation of the population for different types of phenomena (demographic, social, and economic) and its evolution, we study underlying processes (such as concentration/deconcentration, convergence/divergence) and conditional factors and mechanisms. When studying geographical differentiation, we distinguish between simple regional differentiation measured by standard statistical measures, relative regional differentiation measured by Theil index decomposition, and spatial differentiation quantified by Moran’s I. Thee empirical results show that the geographical differentiation of the population in the transformation period and beyond has been steadily decreasing in a majority of studied variables. Variables with increasing geographical differentiation of the population are always connected with specific conditional factors and mechanisms. Moreover, the geographical differentiation of the population has shifted to lower geographical levels.
EXPLORING THE VARIABILITY AND GEOGRAPHICAL PATTERNS OF POPULATION CHARACTERISTICS: REGIONAL AND SPATIAL PERSPECTIVES
P Netrdova, V Nosek (2017)
Moravian Geographical Reports 25 (2), 85-94
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.
WHAT VALUES OF MORAN’S I AND THEIL INDEX DECOMPOSITION REALLY MEAN UNDER DIFFERENT CONDITIONS: ON THE ISSUE OF INTERPRETATION
V Nosek, P Netrdova (2017)
Letters in Spatial and Resource Sciences 10 (2), 149-159
In recent decades, improved methodological apparatuses and increased data availability have enhanced data analyses in social sciences. Moreover, complex analyses using sophisticated methods take just a matter of seconds nowadays thanks to highly powerful software. However, such methods are often poorly understood from a methodological point of view despite the fact that knowledge of their specific properties is crucial to accurately interpreting the results. In this paper we study methods of spatial aspects of variability and examine a specific property of such methods to demonstrate how it can affect the final interpretation. By modelling data in a regular 100 by 100 grid as well as empirical examples from Czechia based on data from the 2011 Czech census, this paper presents possible interpretation-biases and recommendations for how to avoid them. We use the example of spatial autocorrelation (measured by Moran’s I) and variability decomposition (measured by the Theil index); two basic methods which enable us to measure variability in regions and in space.
SPATIAL PATTERNS OF UNEMPLOYMENT IN CENTRAL EUROPE: EMERGING DEVELOPMENT AXES BEYOND THE BLUE BANANA
P Netrdova, V Nosek (2016)
Journal of Maps 12 (4), 701-706
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 EFFICIENCY OF AREAL UNITS IN SPATIAL ANALYSIS: ASSESSING THE PERFORMANCE OF FUNCTIONAL AND ADMINISTRATIVE REGIONS
P Klapka, M Halás, P Netrdová, V Nosek (2016)
Moravian Geographical Reports 24 (2), 47-59
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.