Time Dynamics of the Dutch Municipality Network

TL;DR

This study analyzes the evolution of Dutch municipalities from 1830 to 2019 using network science, revealing distribution patterns and proposing a model to predict future demographic and structural changes.

Contribution

It introduces a network science approach to model municipality dynamics, incorporating population growth, migration, and merging processes, with a predictive framework for future distributions.

Findings

Population and area distributions follow normal and logistic patterns.

Population tails exhibit power-law behavior.

Merging, growth, and migration shape distribution dynamics.

Abstract

Based on data sets provided by Statistics Netherlands and the International Institute of Social History, we investigate the Dutch municipality merging process and the survivability of municipalities over the period 1830-2019. We examine the dynamics of the population and area per municipality and how their distributions evolved during the researched period. We apply a Network Science approach, where each node represents a municipality and the links represent the geographical interconnections between adjacent municipalities via roads, railways, bridges or tunnels which were available in each specific yearly network instance. Over the researched period, we find that the distributions of the logarithm of both the population and area size closely follow a normal and a logistic distribution respectively. The tails of the population distributions follow a power-law distribution, a phenomenon observed in community structures of many real-world networks. The dynamics of the area distribution are mainly determined by the merging process, while the population distribution is also driven by the natural population growth and migration across the municipality network. Finally, we propose a model of the Dutch Municipality Network that captures population increase, population migration between municipalities and the process of municipality merging. Our model allows for predictions of the population and area distributions over time.