# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
###### Abstract  
With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access within a target area. Accordingly, an original multi-level disaggregated framework for airspace, and its corresponding information management is proposed. Further, a multi-scale grid modeling and coding mapping method of airspace information represented by ADS-B is put forth. Finally, tests on the validity of the 4D airspace-temporal grid we named as the GeoSOT-AS framework were conducted across key areas based on the development of an effective data organization method for ADS-B, or an effective algorithm for extracting relevant spatiotemporal data. Experimentally, it was demonstrated that GeoSOT-AS conforms to the existing Chinese specification of civil aeronautical charting and is advantageous for its low deformation and high practicality; furthermore, the airspace grid identification code modeling was less costly, and improved performance by >80% when used for ADS-B data extraction. GeoSOT-AS can thus provide effective reference and practical information for existing airspace data management methods represented by ADS-B and can subsequently be extended to other forms of airspace management scenarios. airspace grid; ADS-B; GeoSOT-AS; aviation database; airspace management; air traffic management 20232023100871  
geographical area and improve fusion accuracy [7]. In this context, various aspects of the research have enhanced and extended the new navigation system with a very rich set of features, as well as bringing potential challenges, in addition to socioeconomic benefits. With the advent of big data, storage and analysis of stock ADS-B, as well as other kinds of ATC data with spatiotemporal attributes, have been explored in depth. The premise of the above application and analysis operation lies in extracting data from the whole stock database for analysis across target areas and the processing of aircraft positions over time. Although scholars have noticed this association, the traditional approach to the management of aerial information presents a fragmented state, and is to some extent limited by the influence of various data sources and corresponding qualities [8]. Indeed, different data types have led to various databases and data management methods. For example, a new compressed indexing method for managing ADS-B data has been proposed in the literature [9], the basic building blocks of which are spatiotemporal reference partitioning, reordering, and compression. Similarly, there is a compressed storage method for 4D air traffic trajectories [10], as well as the development of an airport navigation service database based on GIS tools, and other notable methods [11]. Under such conditions, ATC flight data exists only in corresponding file databases with their own file management methods, which do not co-align with actual airspace, and cannot quickly respond to the immediate availability of ATC data within a target area. Simultaneously, digitalization and intelligence are becoming the focus of competition in the fourth global industrial revolution [12]. In the context of digital airspace, its management includes not only that of flight space, but internal element information as well, such as basic navigation data, occupancy status, etc. Combining the management of airspace with that of correlated information represented by ADS-B data can comprise a novel applicable data management method [13; 14]; thus, as the management of ATC data is ultimately for flight services, the method of airspace management can be applied to that of ATC data to achieve a direct data mapping of airspace. According to the ATC access requirements for aircraft flights, different types of airspace are subdivided for targeted operational management and control.