  Seaports play a crucial role in the global economy, and researchers have
sought to understand their significance through various studies. In this paper,
we aim to explore the common characteristics shared by important ports by
analyzing the network of connections formed by vessel movement among them. To
accomplish this task, we adopt a bottom-up network construction approach that
combines three years' worth of AIS (Automatic Identification System) data from
around the world, constructing a Ports Network that represents the connections
between different ports. Through such representation, we use machine learning
to measure the relative significance of different port features. Our model
examined such features and revealed that geographical characteristics and the
depth of the port are indicators of a port's significance to the Ports Network.
Accordingly, this study employs a data-driven approach and utilizes machine
learning to provide a comprehensive understanding of the factors contributing
to ports' importance. The outcomes of our work are aimed to inform
decision-making processes related to port development, resource allocation, and
infrastructure planning in the industry.
