Geospatial dataset for analyzing socio-economic regional divergence of European regions

Abstract

This data article presents macroeconomic data that can be used for comparative territorial studies. The data cover a sample of 413 regions (national administrative-territorial units corresponding to second level of a common classification of territorial units for statistics of the European Commission – NUTS 2 level region of the European Union, and comparable administrative-territorial units outside the EU) of 48 European countries, including Cyprus, Turkey, the European part of Russia, and two partially recognized states – the Republic of Kosovo and the Pridnestrovian Moldavian Republic. The statistical database covers a five-year period of 2010–2014. This dataset is created to enhance our understanding of the contemporary coastalization dynamics in Europe. Despite the fact that coastal regions of European countries exhibit an extensive level of development and remain attractive to human settlement, industry localization, and investment flows their contribution to the socio-economic development of Europe is unclear. The reported data cover a series of macroeconomic data on key indicators traditionally used in comparative analysis of regional development: average annual population, gross regional product (GRP) in purchasing power parity (PPP), labor productivity, population density and GRP (PPP) values per sq.km. Accounting for differences in geoeconomic position of the European regions enables to distinguish four subtypes of regions with a particular emphasis on the coastal area: coastal border, coastal other, coastal hinterland, and inland other. An additional focus is made on differentiating the performance indicators of regions depending on their border geo-economic position: border regions with a state border over land, lake or river surface, and midland regions – other non-border regions. This data is to be used as a comparative benchmark for the coastal border subgroup of regions against the totality of border and midland regions.

Data describing the regional Industry 4.0 readiness index

“…It is stated that governmental policies focusing on application of Industry 4.0 simultaneously develop the region itself [8] . Macroeconomic open data proved to be suitable to measure regional innovation dynamics [9] , inclusive growth [10] as well as socio-economic performance [11] , however the I4.0-specific assessment has not been studied extensively. The collected, cleaned and analyzed dataset represents the socio-economical and technical standing of the current regional Industry 4.0 readiness.…”

Polarization Assessment of the Regional Socio-Economic Development Based on the Generalized Indicator

“…Òàê, íà ïðèìåðå Ðåñïóáëèêè Áóðÿòèè áûëà âûÿâëåíà äèíàìèêà èçìåíåíèé ÷àñòíûõ èíäåêñîâ è èíòåãðàëüíûõ ïîêàçàòåëåé ñîöèàëüíî-ýêîíîìè÷åñêîãî ðàçâèòèÿ ìóíèöèïàëüíûõ îáðàçîâàíèé 7 . Äëÿ ïîñòðîåíèÿ ñðàâíèòåëüíîãî àíàëèçà òàêaeå ïðåäëàãàåòñÿ èñïîëüçîâàòü ñëåäóþùèå ïîêàçàòåëè: "âàëîâîé ðåãèîíàëüíûé ïðîäóêò ïî ïàðèòåòó ïîêóïàòåëüíîé ñïîñîáíîñòè"; "ñðåäíåãîäîâàÿ ÷èñëåííîñòü íàñåëåíèÿ"; "ïðîèçâîäèòåëüíîñòü òðóäà"; "ïëîòíîñòü íàñåëåíèÿ" 8 . Ó÷åíûå Ðåñïóáëèêè Áàøêîðòîñòàí îöåíèâàþò ñîöèàëüíî-ýêîíî-ìè÷åñêîå ïîëîaeåíèå ðåãèîíîâ ñ ïîìîùüþ ðàçëè÷íûõ ñèñòåì ïîêàçàòåëåé, èçìåðÿþùèõ ïëîòíîñòü íàñåëåíèÿ, îáúåì èíâåñòèöèé, ïëî-ùàäü aeèëûõ ïîìåùåíèé, îáåñïå÷åííîñòü íàñåëåíèÿ áîëüíè÷íûìè êîéêàìè, äîõîäû íà äóøó íàñåëåíèÿ 9 .…”

Database Strategy in Development of Digital Ecosystem for Quality Control in Education

“…Besides, the importance of higher education in modern society requires using valid and reliable instruments to measure the quality of education [8,9].…”