Graph databases to support space situational awareness and space traffic management

New innovative technologies and business models are continuing to reduce the cost of launch and manufacture of space infrastructure, paving the way for increased participation in the space industry by corporations, start-ups, academia and emerging space powers in developing countries.  As earth-orbital environments become increasingly populated, improved capabilities in the field of Space Situational Awareness (SSA) and the coordination of international space activities through Space Traffic Management (STM) efforts will be key to mitigating hazards associated with debris and collisions on orbit. STM is an international challenge that intersects both the technical and governance aspects of operating in an already complex and dynamic environment.  Changing technology and growing commercial participation in space adds further complexity to a domain where standard practice has historically been dictated by government and military operations. The existing approach to the management and provision of STM data sources - where data is not necessarily shared, data is incomplete or inconsistent, or datasets for pertinent information simply do not exist in a structured format - limits meaningful progress toward safe and secure space operations. The effects of technical innovation rapidly changing the landscape of space operations, combined with the hurdles to obtaining reliable STM data as these changes occur, is exemplified in this work through an analysis of collision probabilities associated with satellite "mega-constellations". This investigation subsequently establishes the need for new frameworks to support the complex analytics required to address emerging STM challenges. This thesis contributes to the field of STM by demonstrating that a graph database framework can be leveraged as a tool to connect disparate STM datasets, and that the rigorous analysis of these combined STM datasets via graph database queries and graph algorithms leads to new knowledge in the space domain.  In particular, the graph database developed in this work is applied in two case studies.  First, the graph database development methodology is implemented in a space governance application which leverages the flexibility of the graph data structure to connect diverse information relating to space object registration and the physical characteristics of space objects.  This enables a quantitative analysis using graph queries to investigate factors influencing overall compliance to space object registration, which forms part of the existing international legal framework governing outer space activities.  The second study draws on the ability of graph databases to manage densely connected information by demonstrating a graph database development methodology for the synthesis of spatio-temporal data including space object trajectories, geographic coordinates for optical sensors and climate conditions.   This enables the investigation of dynamic relationships with time-varying trends that influence the performance of an optical sensor network for Space Situational Awareness (SSA).  The study also contributes a novel algorithmic solution, informed by outputs of graph similarity algorithms, to select the best sites for an optical SSA sensor network in Australia. The resulting body of work presented in this dissertation represents the very first synthesis of technically-focused SSA data with policy-focused STM data, and demonstrates applications for quantitative research relating to the international coordination of space activities.  The graph database development methodology described in this work is a novel contribution to the field of STM research.  Future work should extend this methodology to applications of graph database technology that focus on critical space sector needs including improved SSA capability, international coordination of space traffic, and monitoring compliance to existing space law, policies, guidelines and standards.