Using Areal Interpolation to Deal with Differing Regional Structures in International Research

Abstract

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.

Changes in the Patterns of Population Distribution and Built-Up Areas of the Rural–Urban Fringe in Post-Socialist Context—A Central European Case Study

“…Grid network is appropriate to transform different spatial datasets into a common territorial structure with aggregation or disaggregation [77][78][79][80][81]. In order to create a comprehensive and common basis for the calculations, the point layers were aggregated into the 100 × 100 m grid network which contained the population data; however, in the case of the objects, the whole administrative area was converted into grid cells.…”