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Spatial catchment areas using fuzzy lattice data structures
Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613474997141504 |
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| access_status_str | Open Access |
| author2 | Fabris-Rotelli, Inger Nicolette |
| author_browse | Fabris-Rotelli, Inger Nicolette |
| author_facet | Fabris-Rotelli, Inger Nicolette |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/100687 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:43.949Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/100687 Spatial catchment areas using fuzzy lattice data structures Fabris-Rotelli, Inger Nicolette dklerkm@gmail.com De Klerk, Michelle UCTD Sustainable Development Goals (SDGs) Semi-supervised Attribute augmented graph Label propagation Spatially disjoint Catchment areas Fuzzy lattice data Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2024. This thesis presents a comprehensive framework for defining and optimising service catchment areas through innovative approaches, addressing accessibility and resource allocation challenges, particularly in low-resource settings. The first methodology introduces fuzzy lattice catchment areas, using a semi-supervised, probabilistic approach to create overlapping service zones. By enabling communities to access multiple points of interest (POIs) within their range and incorporating drive-time thresholds, this approach ensures a more equitable distribution of demand and supply, minimising spatial imbalances. Building on this, the second methodology extends the fuzzy lattice framework by integrating attribute based connections, combining structural and contextual attributes to more accurately capture spatial dynamics. This dual consideration allows for a refined propagation of demand across networks, addressing limitations in traditional connectivity only models. The final methodology applies attribute based spatial segmentation, creating tailored macro-regions that align with local environmental and socio-economic factors. By leveraging probabilistic clustering, it optimises service placements and identifies both spatially accessible and disjoint regions. Collectively, these approaches advance the field of spatial planning by offering flexible, data driven solutions that adapt to regional characteristics, enhancing service accessibility and equitable resource distribution. The applications demonstrate significant potential across healthcare, urban planning, and beyond, providing a robust foundation for addressing evolving accessibility challenges. South African Medical Research Council (SAMRC) University of Pretoria (UP) National Research Foundation (NRF) Statistics PhD (Mathematical Statistics) Unrestricted Faculty of Natural and Agricultural Sciences SDG-09: Industry, innovation and infrastructure SDG-10: Reduced inequalities SDG-11: Sustainable cities and communities 2025-02-11T10:38:20Z 2025-02-11T10:38:20Z 2025-05 2024-11 Thesis * A2025 http://hdl.handle.net/2263/100687 https://doi.org/10.25403/UPresearchdata.28380461 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Semi-supervised Attribute augmented graph Label propagation Spatially disjoint Catchment areas Fuzzy lattice data Spatial catchment areas using fuzzy lattice data structures |
| title | Spatial catchment areas using fuzzy lattice data structures |
| title_full | Spatial catchment areas using fuzzy lattice data structures |
| title_fullStr | Spatial catchment areas using fuzzy lattice data structures |
| title_full_unstemmed | Spatial catchment areas using fuzzy lattice data structures |
| title_short | Spatial catchment areas using fuzzy lattice data structures |
| title_sort | spatial catchment areas using fuzzy lattice data structures |
| topic | UCTD Sustainable Development Goals (SDGs) Semi-supervised Attribute augmented graph Label propagation Spatially disjoint Catchment areas Fuzzy lattice data |
| url | http://hdl.handle.net/2263/100687 https://doi.org/10.25403/UPresearchdata.28380461 |