Next Article in Journal
A Graph-Based Spatiotemporal Data Framework for 4D Natural Phenomena Representation and Quantification–An Example of Dust Events
Previous Article in Journal
A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data
Open AccessArticle

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

1
Department of Social Geography and Regional Development, Faculty of Science, Charles University, Albertov 6, 128 43 Prague 2, Czechia
2
Department of Geography, Faculty of Education, Catholic University in Ruzomberok, Hrabovska cesta 1, 034 01 Ruzomberok, Slovakia
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 126; https://doi.org/10.3390/ijgi9020126
Received: 14 January 2020 / Revised: 11 February 2020 / Accepted: 20 February 2020 / Published: 22 February 2020
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. View Full-Text
Keywords: areal interpolation; simple area weighting; areal kriging; hot spot analysis; data comparability; international research areal interpolation; simple area weighting; areal kriging; hot spot analysis; data comparability; international research
Show Figures

Figure 1

MDPI and ACS Style

Netrdová, P.; Nosek, V.; Hurbánek, P. Using Areal Interpolation to Deal with Differing Regional Structures in International Research. ISPRS Int. J. Geo-Inf. 2020, 9, 126.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop