P Netrdova, 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 Netrdová, V Nosek, P Hurbánek (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.
THE VARIEGATED ROLE OF PROXIMITIES IN ACQUISITIONS BY DOMESTIC AND INTERNATIONAL COMPANIES IN DIFFERENT PHASES OF ECONOMIC CYCLES
V Květoň, A Bělohradský, J Blažek (2020)
Papers in Regional Science
This paper aims at an understanding of acquisition processes in a strongly industrialized and export‐oriented economy in Central Europe. Drawing on a proximity framework and behaviour theory, the paper investigates that the geographical proximity dimension is more influential than the cognitive proximity dimension. At the same time, cognitive proximity matters more for foreign firms investing into the economy than for domestic acquisitions. While the role of cognitive proximity diminished during the economic crisis, geographical proximity keeps its importance throughout the economic cycle. Moreover, cognitive proximity has become more important for acquisitions of large companies and less for SMEs.