Efficient Calculation of Distance Transform on Discrete Global Grid Systems and Its Application in Automatic Soil Sampling Site Selection

Geospatial data analysis often requires the computing of a distance transform (DT) for a given vector feature. For instance, in wildfire management, it is helpful to find the distance of all points in an area from the wildfire’s boundary. Computing a distance transform on traditional Geographic Information Systems (GIS) is usually adopted from image processing methods, albeit prone to distortion resulting from flat maps. Discrete Global Grid Systems (DGGS) are relatively new low-distortion globe-based GIS that discretize the Earth into highly regular cells using multiresolution grids. In this thesis, we introduce an efficient DT algorithm for DGGS. Our novel algorithm heavily exploits the hierarchy of a DGGS and its mathematical properties and applies to many different DGGSs. We evaluate our method by comparing its distortion with the DT methods used in traditional GIS and its speed with the application of general 3D mesh DT algorithms on the DGGS grid. We demonstrate that our method is efficient and has lower distortion. To evaluate our DT algorithm further, we have used a real-world case study of selecting soil test points within agricultural fields. Multiple criteria including the distance of soil test points to different features should be considered to select representative points in a field. We show that DT can help to automate the process of selecting test points, by allowing us to efficiently calculate objectives for a representative test point. DT also allows for efficient calculation of buffers from certain features such as farm headlands and underground pipelines, to avoid certain regions when selecting the test points

From Accessibility and Exposure to Engagement: A Multi-scalar Approach to Measuring Environmental Determinants of Children’s Health Using Geographic Information Systems

A growing body of research suggests that increasing the accessibility to health-related environmental features and increasing exposure to and engagement in outdoor environments leads to positive benefits for the overall health and well-being of children. Additionally, research over the last twenty-five years has documented a decline in the time children spend outdoors. Outdoor activity in children is associated with increased levels of physical fitness, and cognitive well-being. Despite acknowledging this connection, problems occur for researchers when attempting to identify the child’s location and to measure whether a child has made use of an accessible health-related facility, or where, when and for how long a child spends time outdoors. The purpose of this thesis is to measure children’s accessibility to, exposure to, and engagement with health-promoting features of their environment. The research on the environment-health link aims to meet two objectives: 1) to quantify the magnitude of positional discrepancies and accessibility misclassification that result from using several commonly-used address proxies; and 2) to examine how individual-level, household-level, and neighbourhood-level factors are associated with the quantity of time children spend outdoors. This will be achieved by employing the use of GPS tracking to objectively quantify the time spent outdoors using a novel machine learning algorithm, and by applying a hexagonal grid to extract built environment measures. This study aims to identify the impact of positional discrepancies when measuring accessibility by examining misclassification of address proxies to several health-related facilities throughout the City of London and Middlesex County, Ontario, Canada. Positional errors are quantified by multiple neighbourhood types. Findings indicate that the shorter the threshold distance used to measure accessibility between subject population and health-related facility, the higher the proportion of misclassified addresses. Using address proxies based on large aggregated units, such as centroids of census tracts or dissemination areas, can result in vast positional discrepancies, and therefore should be avoided in spatial epidemiologic research. To reduce the misclassification, and positional errors, the use of individual portable passive GPS receivers were employed to objectively track the spatial patterns, and quantify the time spent outdoors of children (aged 7 to 13 years) in London, Ontario across multiple neighbourhood types. On the whole, children spent most of their outdoor time during school hours (recess time) and the non-school time outdoors in areas immediately surrounding their home. From these findings, policymakers, educators, and parents can support children’s health by making greater efforts to promote outdoor activities for improved health and quality of life in children. This thesis aims to advance our understanding of the environment and health-link and suggests practical steps for more well-informed decision making by combining novel classification and mapping techniques

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