Evaluating the performance of geographical locations in scientific networks with an aggregation - randomization - re-sampling approach (ARR)

TL;DR

This paper introduces a novel method combining aggregation, randomization, and re-sampling of scientific networks to evaluate regional efficiency in knowledge dissemination, providing robust, statistically significant insights into geographical contributions to science.

Contribution

The paper presents a new approach using network analysis with aggregation, randomization, and re-sampling to assess regional scientific performance, applicable beyond spatial data.

Findings

Robust estimates of regional knowledge brokering efficiency.

Method validated with empirical cross-sectional and longitudinal data.

Applicable to various network measures beyond centrality.

Abstract

Knowledge creation and dissemination in science and technology systems is perceived as a prerequisite for socio-economic development. The efficiency of creating new knowledge is considered to have a geographical component, i.e. some regions are more capable in scientific knowledge production than others. This article shows a method to use a network representation of scientific interaction to assess the relative efficiency of regions with diverse boundaries in channeling knowledge through a science system. In a first step, a weighted aggregate of the betweenness centrality is produced from empirical data (aggregation). The subsequent randomization of this empirical network produces the necessary Null-model for significance testing and normalization (randomization). This step is repeated to yield higher confidence about the results (re-sampling). The results are robust estimates for the relative regional efficiency to broker knowledge, which is discussed along with cross-sectional and longitudinal empirical examples. The network representation acts as a straight-forward metaphor of conceptual ideas from economic geography and neighboring disciplines. However, the procedure is not limited to centrality measures, nor is it limited to spatial aggregates. Therefore, it offers a wide range of application for scientometrics and beyond.