Spatial analysis

Data describing the regional Industry 4.0 readiness index

The data article presents a dataset suitable to measure regional Industry 4.0 (I4.0+) readiness. The I4.0+ dataset includes 101 indicators with 248 958 observations, aggregated to NUTS 2 statistical level) based on open data in the field of education (ETER, Erasmus), science (USPTO, MA-Graph, GRID), government (Eurostat) and media coverage (GDELT). Indicators consider the I4.0-specific domain of higher education and lifelong learning, innovation, technological investment, labour market and technological readiness as indicators. A composite indicator, the I4.0+ index was constructed by the Promethee method, to identify regional rank regarding their I4.0 performance. The index is validated with economic (GDP) and innovation indexes (Regional Innovation Index).

Data accessibility

Are Regions Prepared for Industry 4.0? The Industry 4.0+ Indicator System for Assessment

The concept of industry 4.0 is spreading worldwide and readiness models exist to determine organizational or national maturity. On the other hand, the regional perspective of the digital transformation is yet to be widely researched, although it significantly determines how the concept of industry 4.0 can be introduced to the organisations. This book identifies the regional aspect of industry 4.0 and provides a regional (NUTS 2 classified) industry 4.0 indicator system model that is based on open data sources. This new model serves as a tool to evaluate regional economy to support governmental decisions. It also provides territorial councils with a decision-support tool for field investment decisions. And finally, this model offers investors with a heat map to evaluate regional economies successful implementation of industry 4.0 solutions.

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A multilayer and spatial description of the Erasmus mobility network

The Erasmus Programme is the biggest collaboration network consisting of European Higher Education Institutions (HEIs). The flows of students, teachers and staff form directed and weighted networks that connect institutions, regions and countries. Here, we present a linked and manually verified dataset of this multiplex, multipartite, multi-labelled, spatial network. We enriched the network with institutional socio-economic data from the European Tertiary Education Register (ETER) and the Global Research Identifier Database (GRID). We geocoded the headquarters of institutions and characterised the attractiveness and quality of their environments based on Points of Interest (POI) data. The linked datasets provide relevant information to grasp a more comprehensive understanding of the mobility patterns and attractiveness of the institutions.

The Settlement Structure Is Reflected in Personal Investments: Distance-Dependent Network Modularity-Based Measurement of Regional Attractiveness

How are ownership relationships distributed in the geographical space? Is physical proximity a significant factor in investment decisions? What is the impact of the capital city? How can the structure of investment patterns characterize the attractiveness and development of economic regions? To explore these issues, we analyze the network of company ownership in Hungary and determine how are connections are distributed in geographical space. Based on the calculation of the internal and external linking probabilities, we propose several measures to evaluate the attractiveness of towns and geographic regions. Community detection based on several null models indicates that modules of the network coincide with administrative regions, in which Budapest is the absolute centre, and where county centres function as hubs. Gravity model-based modularity analysis highlights that, besides the strong attraction of Budapest, geographical distance has a significant influence over the frequency of connections and the target nodes play the most significant role in link formation, which confirms that the analysis of the directed company-ownership network gives a good indication of regional attractiveness.