Is there congruence in the spatial patterns of regions derived from scalar and vector geographical information?

Abstract

Selected traits of the spatial organisation of a geographical environment which stem from two types of human behaviour (locational and interactive) are examined in this paper. An attempt is made to find and account for similarities in the spatial patterns of scalar and vector geographical data. In doing so, the paper analyses a core-periphery dichotomy, based on socio-economic information, and travel-to-work patterns. The paper uses the concept of a region as an integrating and focusing framework for the study. Formal regions (peripheral areas) are defined through the application of principal components analysis and cluster analysis; functional regions are defined by a standard rule-based regionalisation algorithm. The territory of the Czech Republic is used as an area for testing the basic hypotheses. The results show that there is some form of interrelationship and complementarity between the spatial distribution of scalar data and vector data, i.e. between spatial structure and spatial interaction patterns, which together form the spatial organisation of a geographical environment.

Peripheral areas and their distinctive characteristics: The case of Hungary

“…The objectives of the core-periphery models have been determined, on the one hand, by research on the transformation of societies and production modes, and on the other by the effects of these changes on given areas at different territorial levels (Ramírez, 2009). The core-periphery relations tend to be analysed not only with scalar datasets but increasingly within networks (Borgatti and Everett, 1999) and with vector data (Erlebach et al 2019). Peripheral positions might appear in global, national and regional contexts and in local structural relations, with interlinkages among them (Kreckel, 2004;Kühn, 2014;Erlebach et al, 2019) representing a fractallike pattern (Halás, 2014;Erlebach et al, 2019).…”

“…The core-periphery relations tend to be analysed not only with scalar datasets but increasingly within networks (Borgatti and Everett, 1999) and with vector data (Erlebach et al 2019). Peripheral positions might appear in global, national and regional contexts and in local structural relations, with interlinkages among them (Kreckel, 2004;Kühn, 2014;Erlebach et al, 2019) representing a fractallike pattern (Halás, 2014;Erlebach et al, 2019).…”

Population movements based on mobile phone location data: the Czech Republic

“…Examining the map of 'Regular daily movements' shows us that the spatial distribution of regular daily flows, based on the mobile phone location data, fully corresponds to the distribution of daily travel-towork flows as shown in the last census (Erlebach et al, 2019). The Pearson correlation coefficient for regular daily flows based on the mobile phone location data and daily travel-to-work flows from the last census is 0.989 in the case of inflows, and 0.960 in the case of outflows.…”