<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The delimitation and classification of peripheral settlements using multivariate statistical methods is presented in this article, with a case study of Hungary. A combination of four different methods provided the basis for the delimitation of settlements defined as peripheral. As significant overlapping was detected between the results of the different methods, peripheries – more than one-fifth of the Hungarian settlements – were identified in a common set of the results. The independence of the results from the applied methods points to the fact that peripherisation is multi-faceted, and the peripheries of Hungary are stable and well-discernible from other regions. After the identification of peripheral areas, we classified these settlements into groups based on their specific features. Multiple steps specifying the relevant variables resulted in selecting the most appropriate 10 indicators and these served as the basis for a hierarchical cluster analysis, through which 7 clusters (types of peripheries) were identified. Five of them comprised enough cases to detect the most important dimensions and specific features of the backwardness of these groups. These clusters demonstrated a spatial pattern and their socioeconomic and infrastructural features highlighted considerable disparities. These differences should be taken into consideration when development policies are applied at regional levels or below.</p>
</abstract>

The delimitation and classification of peripheral settlements using multivariate statistical methods is presented in this article, with a case study of Hungary. A combination of four different methods provided the basis for the delimitation of settlements defined as peripheral. As significant overlapping was detected between the results of the different methods, peripheries – more than one-fifth of the Hungarian settlements – were identified in a common set of the results. The independence of the results from the applied methods points to the fact that peripherisation is multi-faceted, and the peripheries of Hungary are stable and well-discernible from other regions. After the identification of peripheral areas, we classified these settlements into groups based on their specific features. Multiple steps specifying the relevant variables resulted in selecting the most appropriate 10 indicators and these served as the basis for a hierarchical cluster analysis, through which 7 clusters (types of peripheries) were identified. Five of them comprised enough cases to detect the most important dimensions and specific features of the backwardness of these groups. These clusters demonstrated a spatial pattern and their socioeconomic and infrastructural features highlighted considerable disparities. These differences should be taken into consideration when development policies are applied at regional levels or below.

Peripheral areas and their distinctive characteristics: The case of Hungary

Department of Social Geography and Regional Development Planning

Moravian Geographical Reports

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Peripheral areas and their distinctive characteristics: The case of Hungary"}

The delimitation and classification of peripheral settlements using multivariate statistical methods is presented in this article, with a case study of...

Peripheral areas and their distinctive characteristics: The case of Hungary

Published Online: Oct 08, 2021

Page range: 217 - 230

Received: Aug 18, 2020

Accepted: Apr 24, 2021

DOI: https://doi.org/10.2478/mgr-2021-0016

Keywords
core-periphery dichotomy, demographic processes, multivariate statistical methods, peripheral areas, peripherisation, regional development planning

© 2021 János Pénzes et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Peripheral areas and their distinctive characteristics: The case of Hungary

Published Online: Oct 08, 2021

Page range: 217 - 230

Received: Aug 18, 2020

Accepted: Apr 24, 2021

DOI: https://doi.org/10.2478/mgr-2021-0016

Keywordscore-periphery dichotomy, demographic processes, multivariate statistical methods, peripheral areas, peripherisation, regional development planning

© 2021 János Pénzes et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
core-periphery dichotomy, demographic processes, multivariate statistical methods, peripheral areas, peripherisation, regional development planning