Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library

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dc.contributor.advisor Rautenbach, Victoria-Justine
dc.contributor.coadvisor Fabris-Rotelli, Inger Nicolette
dc.contributor.postgraduate De Kock, Nicholas
dc.date.accessioned 2024-02-15T09:23:43Z
dc.date.available 2024-02-15T09:23:43Z
dc.date.created 2024-04-01
dc.date.issued 2023-11-20
dc.description Dissertation (MSc (Geoinformatics))--University of Pretoria, 2023. en_US
dc.description.abstract 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. en_US
dc.description.availability Unrestricted en_US
dc.description.degree MSc (Geoinformatics) en_US
dc.description.department Geography, Geoinformatics and Meteorology en_US
dc.description.faculty Faculty of Natural and Agricultural Sciences en_US
dc.identifier.citation * en_US
dc.identifier.doi https://doi.org/10.25403/UPresearchdata.25224725 en_US
dc.identifier.other A2024 en_US
dc.identifier.uri http://hdl.handle.net/2263/94636
dc.publisher University of Pretoria
dc.rights © 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.
dc.subject UCTD en_US
dc.subject Python library en_US
dc.subject ESDA en_US
dc.subject Spatial statistics en_US
dc.subject Raster datasets en_US
dc.subject Spatial autocorrelation en_US
dc.subject.other Sustainable development goals (SDGs)
dc.subject.other SDG-09: Industry, innovation and infrastructure
dc.subject.other Natural and agricultural sciences theses SDG-09
dc.subject.other SDG-11: Sustainable cities and communities
dc.subject.other Natural and agricultural sciences theses SDG-11
dc.title Automating exploratory spatial data analysis (ESDA) for vector and raster data : development and evaluation of the autoESDA Python library en_US
dc.type Dissertation en_US


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