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

Abstract

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.

The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis

“…To group similar variables, our goal is to separate this network into groups of vertices that have fewer connections between them than inside the communities. In the literature, this task [ 17 ] is referred to as modularity-based community analysis (see, e.g., [ 18 ]) or simply community detection (see, e.g., [ 19 ]). Although the proposed method may seem more complicated than traditional model reduction methods, they cannot be used because of the large number of variables and relatively few observations.…”

The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis

“…To group similar variables, our goal is to separate this network into groups of vertices that have fewer connections between them than inside the communities. In the literature, this task [15] is referred to as modularity-based community analysis (see, e.g., [16]) or simply community detection (see, e.g., [17]). Although the proposed method may seem more complicated than traditional model reduction methods, because of the large number of variables and relatively few observations, they cannot be used.…”

A multilayer and spatial description of the Erasmus mobility network

“…subject area, study level of students, participant type, of the mobility database. The attractiveness of nodes can be expressed in terms of a number of socioeconomic indicators and calculated measures 13 that require additional sources of information. The database provides the opportunity to ask such valuable research questions to which the repertoire of the multidimensional network analysis can yield valuable answers.…”