Understanding Growth in the Global Space Situational Awareness Sector

Introduction: Growth of the Space Situational Awareness Sector

Space situational awareness (SSA) – the ability to monitor where objects are in space and where they will be in the near future – is critical to predicting and avoiding potential collisions in space, and to ensuring the long-term safety and sustainability of the space environment. This capability is growing in both complexity and importance as the number of space objects increases and as nations continue to develop and test counterspace weapons.

As of 2022, the United States military maintains the most advanced SSA capability in the world. However, there are a number of other nations and commercial entities that are improving existing SSA systems or developing new SSA capabilities. Many of these entities have formed partnerships or other connections to allow SSA data sharing. Such partnerships can provide significant benefits to the entities involved both in terms of improved capabilities and shared understanding.

SSA products and services depend on high-quality observations of objects in space. These products and services can be improved if observations are taken more frequently, from more different locations on the ground, or with more different types of instruments. Partnerships that facilitate data sharing can allow SSA providers to increase the volume and type of observations they use in their analysis, thus improving the resulting data and products. Data is not the only factor that affects SSA capabilities – algorithms and computing capabilities also play important roles, for example – but high-quality data is a key component for creating a high-quality output.

Partnerships also allow entities to create a shared understanding of the space environment. Existing research has shown that existing SSA systems produce different results regarding the current and future location of individual spacecraft.[1] These differences can cause significant issues when spacecraft operators attempt to use this information to identify and avoid potential collisions in space or to plan maneuvers to avoid collisions. Different understandings of the location of space objects could also complicate efforts to attribute attacks on space objects, and this uncertainty can decrease deterrence. Partnerships that enable the sharing of SSA data and products can help alleviate these issues and ensure that space operators have a common understanding of the state of the space environment.

Methodology: Social Network Analysis

To understand how partnerships tie together the global SSA sector, this paper uses social network analysis. Social network analysis emphasizes the structure of an organization or network and asserts that an actor’s position within a network can help determine the constraints and opportunities encountered by that actor. The structure of the network also affects the group as a whole: ‘what happens to a group of actors is in part a function of the structure of connections among them’.[2]

In the field of international relations, scholars have used social network analysis to analyze how access and brokerage, as determined by network position, are related to an actor’s power. It can also be used to examine how an actor can enhance or exploit their network position.[3] A common way to analyze networks focuses on measurements of how ‘central’ an actor is within the network. Studies have shown that various types of centrality are correlated with outcomes such as improved innovation, efficiency, influence, and power.[4]

The two key elements necessary to define a network are nodes and edges. In this study, nodes represent individual entities that operated in the global spaces situational awareness sector in 2020 as either a provider of SSA data, services, or products; a user of SSA data, services, or products; or both. One exception is that individual spacecraft operators are condensed into one node. This is done to enable the study to capture cases where SSA data is provided to satellite operators, without attempting to document each individual data purchase or transfer agreement, which are not typically made publicly available. This study includes a total of 74 nodes, made up of 42 government organizations, 25 commercial entities, 5 international organizations, and 2 nonprofit organizations. The 74 organizations are associated with a total of 44 countries. 52 of the 74 entities operate their own SSA sensors.

The analysis also includes 274 edges. Edges are individual connections between the nodes. In the context of this analysis, an edge represents a transfer of SSA data, information, products, or services from one node to another. For example, a line from Node A to Node B, with the direction indicated by an arrow, would mean that Node A provides SSA data or information to Node B. If Node B also provides information to Node B, a second line with an arrow would be included in the opposite direction. Only exchanges that are clearly documented in publicly available sources or identified in personal interviews are included in this analysis. Using this method, a network analysis diagram was established using Gephi, an open-source software package. (Additional details on the methodology and data collection can be found in the full article.)

Analysis: Network Approach to the Space Situational Awareness Sector

The global SSA sector network diagram shows a number of interesting features of this ecosystem. First, the United States stands out as a central actor in this sector. This is expected, given the significant capacity of the U.S. SSA system, particularly its SSA Data Sharing Program, which facilitates sharing of SSA data with spacecraft operators around the world free of charge. Other clusters are visible, as well. These tend to align with five international organizations involved in SSA activities. There are tight partnerships among nations involved in the European Union and European Space Agency SSA programs. The European Incoherent Scatter Radar (EISCAT) organization ties European countries to a number of other nations. The Chinese-led Asia-Pacific Space Cooperation Organization (APSCO) and the Russian-led International Scientific Optical Network (ISON) have also built a cluster of partnerships, both of which include nations not well connected to other parts of the network.

Quantitative analysis of the network focuses on four different types of measurements of centrality. The first, degree centrality, is calculated based on the total number of edges connected to each node. High degree centrality is typically associated with innovation and influence. The United States leads in this measure, with nearly double the number of connections of any other entity. ESA and the EU come in second and third, respectively, followed by France and Germany. ISON comes in sixth on this list. The Space Data Association, a non-profit organization that facilitates SSA data sharing among spacecraft operators, is seventh on the list. The commercial firm AGI also makes it into the top 10.

Degree centrality can be split into in-degree centrality, which measures the number of edges representing data or information flowing into the node, and out-degree centrality, which measures the number of edges representing data or information flowing out of the node. When considering in-degree centrality, the node representing individual satellite operators jumps to number four on the list. This suggests that individual operators are choosing to take advantage of a wide variety of SSA data and information sources. The willingness of these entities to consider diverse sources of information bodes well for new actors in this sector that will rely on satellite operators to make up a portion of their customer base.

Out-degree centrality is particularly important for measuring influence within the network. The entities that are sending data and information out to others help to establish those entities’ understanding of the space environment. An entity that provides information to a large number of entities will be able to create a shared understanding of the space environment among a larger community of actors, enabling smoother coordination of space traffic issues and more rapid agreement on issues such as attribution. Once again, the United States and ESA top this list. The Russian-led ISON jumps to number five. The commercial firms ExoAnalytic Solutions and LeoLabs also make the top ten, representing the growing influence of the commercial SSA sector.

Another measure, eigenvector centrality, takes into account not only the number of connections an individual node has, but also whether those partner nodes are themselves well connected. The United States remains the leader here, but the rest of the top 10 is filled with European organizations and nations. This reflects the tight interconnections among European nations that tend to have both bilateral partnerships with each other as well as connections through one or more international programs (such as those organized by ESA, the European Union, or EISCAT). These tight interconnections can help to reinforce connections and understanding among partners. However, they can also limit the amount of truly unique information that participants receive, potentially creating an echo chamber that can limit innovation.

Betweenness centrality measures the number of times a node lies on the shortest path between two other nodes. Entities with a high betweenness centrality often act as bridges within a network, partnering with diverse entities and providing unique connections. The fact that the United States, ESA, and the EU still appear in the top 10 on this list suggests that they are succeeding in making connections beyond their traditional partners. ISON jumps to second place in this measure, and the Chinese-led APSCO comes in fourth. This suggests that, while these entities do not always rank highly on more straightforward measures of centrality, they have been successful in bringing in new actors to the SSA sector. Among these groups, they may have outsized influence, serving as a primary source of data as well as an intermediary for access to other parts of the network.

The final measure considered here, closeness centrality, is based on the average length of the path to all other nodes in the network. Closeness centrality is associated with independence and efficiency – a node that is well-connected in this way can access data from many other sources or disseminate its own information quickly with few intermediaries. Under this measure, the United States and European entities once again dominate the top 10. The Space Data Association comes in at number six on the list. These entities should be able to efficiently aggregate SSA data from many sources. They should also have a relative advantage in sharing their understanding of the space environment with many actors.

Discussion and Conclusion

As we look at the future evolution of the global SSA sector, this analysis can help actors understand and affect their position within the network. Entities such as the United States and Europe may choose to further solidify the strong connections they have among their allies. However, they may better increase their global influence by seeking to make connections with non-traditional space actors and other entities not already active in this area, as Russia and China have done. The commercial sector may be buoyed by the apparent willingness of private actors, as well as many nations and other organizations, to seek out diverse sources of SSA information. In addition, commercial actors may have an advantage in making connections among entities that would have difficulty connecting directly due to geopolitical issues. These types of connections could play an important role in generating a shared understanding of the space environment not only among like-minded nations, but among a more diverse set of actors. Close attention to global trends in partnerships and data exchanges will be important. These partnerships will affect global SSA capabilities overall, and ultimately, the sustainability of the space environment for all users.

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.

About the Author

Mariel Borowitz is an Associate Professor in the Sam Nunn School of International Affairs at the Georgia Institute of Technology and head of the Nunn School Program on International Affairs, Science, and Technology. Her research deals with international space policy issues, focusing particularly on global developments related to remote sensing satellites and challenges to space security and sustainability. Her book, “Open Space: The Global Effort for Open Access to Environmental Satellite Data," published by MIT Press, examines trends in the development of data sharing policies governing Earth observing satellites, as well as interactions with the growing commercail remote sensing sector. Her work has been published in Science, Strategic Studies Quarterly, Space Policy, Astropolitics, and New Space. Her research has been supported by grants from the National Science Foundation and the National Aeronautics and Space Administration.

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