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Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library
Dissertation (MSc (Geoinformatics))--University of Pretoria, 2023.
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University of Pretoria
2024
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| _version_ | 1867613501974904833 |
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| access_status_str | Open Access |
| author2 | Rautenbach, Victoria-Justine |
| author_browse | Rautenbach, Victoria-Justine |
| author_facet | Rautenbach, Victoria-Justine |
| 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 | Dissertation (MSc (Geoinformatics))--University of Pretoria, 2023. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/94636 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:09.691Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| 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:2263/94636 Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library Rautenbach, Victoria-Justine u17198021@tuks.co.za Fabris-Rotelli, Inger Nicolette De Kock, Nicholas UCTD Python library ESDA Spatial statistics Raster datasets Spatial autocorrelation Sustainable development goals (SDGs) SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-09 SDG-11: Sustainable cities and communities Natural and agricultural sciences theses SDG-11 Dissertation (MSc (Geoinformatics))--University of Pretoria, 2023. autoESDA is a Python library developed with the aim of automating the Exploratory Spatial Data Analysis (ESDA) process. This is done by generating a HTML report made up of various ESDA graphs and statistics calculated according to the input dataset, requiring no other inputs from the user. ESDA (local spatial autocorrelation specifically) in Python has been a challenge for raster datasets, with software support lagging behind alternative platforms such as R. This dissertation documents the improvements made to the original library. These improvements include the support for raster datasets, an updated architectural design, and other minor, cosmetic improvements. The performance of the updated version of autoESDA is evaluated by investigating how its processing time varies according to vector and raster datasets that differ in size and complexity. These results are then discussed as a measure of how well the library has achieved its goal of automating the ESDA process. Finally, a roadmap for further improvements to the library is discussed. Geography, Geoinformatics and Meteorology MSc (Geoinformatics) Unrestricted Faculty of Natural and Agricultural Sciences 2024-02-15T09:23:43Z 2024-02-15T09:23:43Z 2024-04-01 2023-11-20 Dissertation * A2024 http://hdl.handle.net/2263/94636 https://doi.org/10.25403/UPresearchdata.25224725 © 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 Python library ESDA Spatial statistics Raster datasets Spatial autocorrelation Sustainable development goals (SDGs) SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-09 SDG-11: Sustainable cities and communities Natural and agricultural sciences theses SDG-11 Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library |
| title | Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library |
| title_full | Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library |
| title_fullStr | Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library |
| title_full_unstemmed | Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library |
| title_short | Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library |
| title_sort | automating exploratory spatial data analysis esda for vector and raster data development and evaluation of the autoesda python library |
| topic | UCTD Python library ESDA Spatial statistics Raster datasets Spatial autocorrelation Sustainable development goals (SDGs) SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-09 SDG-11: Sustainable cities and communities Natural and agricultural sciences theses SDG-11 |
| url | http://hdl.handle.net/2263/94636 https://doi.org/10.25403/UPresearchdata.25224725 |