Elsevier

Space Policy

Volume 59, February 2022, 101444
Space Policy

Examining the Growth of the Global Space Situational Awareness Sector: A Network Analysis Approach

https://doi.org/10.1016/j.spacepol.2021.101444Get rights and content

Abstract

The global space situational awareness sector is rapidly evolving as government and commercial entities around the world increase their ability to monitor the space environment. In many cases, these entities are increasing capabilities by creating or joining international organizations, forging partnerships, and purchasing data or services from new sources. The ways in which these entities engage with one another will affect the quality of SSA data available to civil, commercial, and foreign users around the world. This, in turn, affects the security of key military space assets as well as the sustainability of the space environment as a whole. In this paper, I use social network analysis to map out the current state of the global SSA sector and discuss its potential future evolution.

Section snippets

Introduction: growth of the space situational awareness sector

As of January 2021, there were more than 3,300 operational satellites orbiting the Earth, and this number is rapidly increasing with the growth of ‘mega-constellations’ made up of hundreds or thousands of small satellites [1]. These space assets are critical to both the economy and the military in many nations, yet they are vulnerable to accidental damage. The 3,300 operational satellites are surrounded by more than 20,000 pieces of debris larger than 10 cm, including defunct satellites, rocket

Methodology: social network analysis

Social network analysis is based on the understanding that the structure of an organization or network is important. It allows an examination of how an actor’s position within a network can help determine the constraints and opportunities encountered by that actor. It also posits that ‘what happens to a group of actors is in part a function of the structure of connections among them’. [11] Within international relations, social network analysis often focuses on how access and brokerage, as

Analysis: network approach to the space situational awareness sector

A visutal inspection of the network diagram (see Fig. 1) reveals a number of interesting points. First, as expected, it reflects the current role of the United States as a central actor in this sector. With the most advanced SSA system in the world, a program for providing SSA data to commercial and foreign users free of charge, an active effort to develop data-sharing agreements, and early engagement with the emerging commercial SSA sector, the United States has a broad reach to many entities

Discussion

At present, it is clear visually and through statistical measures that the sector is fairly centralized, with the United States in the middle. The United States should aim to continue to hold this position, as it is associated with both innovation and influence within the network. It may also be beneficial for the system as a whole, as centralized networks have been shown to be more effective in terms of accuracy and speed [16]. For other nations, maintaining a connection to the United States

Conclusions

The global space situational awareness sector is growing rapidly. Because high-quality SSA capabilities rely on a large volume of diverse data, many nations are relying on partnerships and commercial data purchases to efficiently build their SSA capabilities. The types of partnerships formed and the way in which these connections are made will affect the quality of SSA information available to decision-makers and satellite operators, ultimately affecting the sustainability of the space

Author statement

Mariel Borowitz is the sole author of this paper.

Mariel Borowitz: Conceptualization, Methodology, Data Curation, Investigation, Formal Analysis, Writing, Visualization, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by a grant from the Georgia Tech Research Institute (GTRI) HIVES program. I also wish to thank Ben Riley and others involved in the program for their substantive input and feedback in developing this work.

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