Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with AT

LocID - A Unique Object-at-a-Location Identifier; designing a global hierarchical geographical identifier that accounts for spatial inaccuracy and computational performance

Numerous organizations, such as insurance companies, work with global geospatial data. Global spatial operations, including risk assessment or disaster analysis, take time to compute. Spatial identifiers are a possible solution to speed up important spatial operations. Modern spatial identifiers struggle to encode geometries: Distortion of area, found in the common Mercator projection, poses challenges for precise area calculations and comparisons across latitudes. Complex transformation of areas into local Cartesian coordinate systems is thus needed. The added complexity increases errors. Latitude and longitude coordinates, while precise for location, lack the ability to convey object size or globally consistent location accuracy. Thus, a hierarchical system as outlined in this thesis may well be a solution for performant and accurate object identification. Discrete global grid systems (DGGSs), offer global continuous indexing, maintain consistent spatial resolution, and support data aggregation. This thesis compares different DGGS options based on factors like hierarchical structure, tesselation shape, accuracy, and programming language support. This work demonstrates that triangles, as a tesselation shape, provide consistent area sizes and computational efficiency, making them suitable for LocID’s goal of addressing spatial challenges in risk assessment and disaster analysis on the Earth’s surface. Consequently, the triangle-based DGGS Quarternary Triangular Mesh (QTM) is selected as the foundation for building LocID. The goal of LocID is to identify, match, recognize contains, and detect changes in objects using only its ID attribute. The process of designing, conceptualizing, and building a reference implementation of LocID follows a circular development loop, where requirements are defined, researched, designed, implemented, and evaluated iteratively. The encoding uses Quaternary Triangular Mesh (QTM), a DGGS based on an octahedron and on triangles as its cell shape. LocID identifiers consist of encoding digits and separators. LocID’s geometry part is encoded recursively, with the number of digits indicating the number of levels used for encoding an object. This part is also compressed in a tree-like manner, by not double-encoding branches shared by leaves, leaves being the highest-level QTM cells used for encoding. A reference Python implementation, called LocID.py, is provided. Examples and tests are included to verify the correctness of encoding functions and operations. By encoding geometries within Discrete Global Grid System (DGGS) cells, LocID enables spatial operations without the need for additional spatial computation, providing quick string or byte operations such as matching and containment. LocID extends a DGGS, enabling geometry encoding and processing within cells. DGGSs offer a path to more performant geospatial data processing for points, whereas geometries have not yet been considered. LocID addresses this, offering a solution for encoding geometries in a way that allows a few operations to be performed performantly directly on the ID itself

Towards Q-analysis Integration in Discrete Global Grid Systems: Methodology, Implications and Data Complexity

Spatial data is characterized by rich contextual information with multiple characteristics at each location. The interpretation of this multifaceted data is an integral part of current technological developments, data rich environments and data driven approaches for solving complex problems. While data availability, exploitation and complexity continue to grow, new technologies, tools and methods continue to evolve in order to meet these demands, including advancing analytical capabilities, as well as the explicit formalization of geographic knowledge. In spite of these developments Discrete Global Grid Systems (DGGS) were proposed as a new comprehensive approach for transforming scientific data of various sources, types and qualities into one integrated environment. The DGGS framework was developed as the global data model and standard for efficient storage, analysis and visualization of spatial information via a discrete hierarchy of equal area cells at various spatial resolutions. Each DGGS cell is the explicit representation of the Earth surface, which can store multiple data values and be conveniently recognized and identified within the hierarchy of the DGGS system. A detailed evaluation of some notable DGGS implementations in this research indicates great prospects and flexibility in performing essential data management operations, including spatial analysis and visualization. Yet they fall short in recognizing interactivity between system components and their visualization, nor providing advanced data friendly techniques. To address these limitations and promote further theoretical advancement of DGGS, this research suggests the use of Q-analysis theory as a way to utilize the potential of the hierarchical DGGS data model via the tools of simplicial complexes and algebraic topology. As a proof of concept and demonstration of Q-analysis feasibility, the method has been applied in a water quality and water health study, the interpretation of which has revealed much contextual information about the behaviour of the water network, the spread of pollution and chain affects. It is concluded that the use of Q-analysis indeed contributes to the further advancement and development of DGGS as a data rich framework for formalizing multilevel data systems and for the exploration of new data driven and data friendly approaches to close the gap between knowledge and data complexity

Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling

We present the Eco-ISEA3H database, a compilation of global spatial data characterizing climate, geology, land cover, physical and human geography, and the geographic ranges of nearly 900 large mammalian species. The data are tailored for machine learning (ML)-based ecological modeling, and are intended primarily for continental- to global-scale ecometric and species distribution modeling. Such models are trained on present-day data and applied to the geologic past, or to future scenarios of climatic and environmental change. Model training requires integrated global datasets, describing species' occurrence and environment via consistent observational units. The Eco-ISEA3H database incorporates data from 17 sources, and includes 3,033 variables. The database is built on the Icosahedral Snyder Equal Area (ISEA) aperture 3 hexagonal (3H) discrete global grid system (DGGS), which partitions the Earth's surface into equal-area hexagonal cells. Source data were incorporated at six nested ISEA3H resolutions, using scripts developed and made available here. We demonstrate the utility of the database in a case study analyzing the bioclimatic envelopes of ten large, widely distributed mammalian species.Peer reviewe

ChargeUp! Data Swap: Using data from battery swapping e-motorcycles in Nairobi to assess impacts and plan infrastructure

The dearth of available data on e-motorcycle usage in African cities is a significant challenge in impact studies of e-motorcycle deployment. The ChargeUp! project aimed to fill this research gap using operational data from e-motorcycles and battery swap stations in Nairobi to perform modelling and analysis to determine several key outputs. This project included the analysis of: e-motorcycle trips; battery swapping demand; battery charging energy consumption; swap battery charging related emissions for a high renewables and high fossil energy mix scenarios; charging related electricity costs for different tariff scenarios; the effect of a co-ordinated charging scenario on emissions and tariffs; optimal battery ratios and required numbers of swap stations; and a methodology to determine optimal regions for battery swap stations based on trip data