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    Random generation of realistic spatial data for use in classroom assessments

    Gorry, Patrick (2022) Random generation of realistic spatial data for use in classroom assessments. Masters thesis, National University of Ireland Maynooth.

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    We describe the design, development, implementation and usage of a software-based tool called RADIAN (RAnDom spatIal dAta geNerator) to generate realistic spatial datasets for use within teaching and learning environments who require spatial datasets. RADIAN provides configurable functionality for users to generate spatial datasets containing geometric points with associated parameters/attributes which can then be easily imported into a PostgreSQL PostGIS database or visualised using a GIS or equivalent software. Much work has been carried out in areas such as statistics, machine learning and software testing on how to generate realistic datasets for testing hypotheses, machine learning model training and validation, algorithmic analysis, and rigorous software testing on real-world data. However, less work has been published on the generation of realistic spatial data. This thesis contributes to this body of work. We outline the theoretical approach we have used in RADIAN to generate random geometric points within a given spatial extent (polygon). We demonstrate the effectiveness of RADIAN with a suite of example scenarios of the spatial data generated. We believe RADIAN will be very useful to both teachers and spatial analysts requiring realistic randomly generated for spatial analysis purposes. The software code is available as open-source software via GitHub. The thesis concludes with some suggestions for further research and development work which are possible from the research and development of RADIAN.

    Item Type: Thesis (Masters)
    Keywords: Spatial data; random data generation; classroom assessment; synthetic spatial data; GIS; RADIAN;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 16871
    Depositing User: IR eTheses
    Date Deposited: 19 Jan 2023 14:11
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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