A generic similarity test for spatial data

dc.contributor.advisorFabris-Rotelli, Inger Nicolette
dc.contributor.emailu15013121@tuks.co.zaen_ZA
dc.contributor.postgraduateKirsten, René
dc.date.accessioned2021-02-03T08:07:27Z
dc.date.available2021-02-03T08:07:27Z
dc.date.created2021-05-05
dc.date.issued2020
dc.descriptionDissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020.en_ZA
dc.description.abstractTwo spatial data sets are considered to be similar if they originate from the same stochastic process in terms of their spatial structure. Many tests have been developed over recent years to test the similarity of certain types of spatial data, such as spatial point patterns, geostatistical data and images. This research develops a similarity test able to handle various types of spatial data, for example images (modelled spatially), point patterns, marked point patterns, geostatistical data and lattice patterns. The test consists of three steps. The first step creates a pixel image representation of each spatial data set considered. In the second step a local similarity map is created from the two pixel image representations from step one. The local similarity map is obtained by either using the well-known similarity measure for images called the Structural SIMilarity Index (SSIM) when having continuous pixel values or a direct comparison in the case of discrete pixel values. The calculation of the final similarity measure is done in the third step of the test. This calculation is based on the S-index of Andresen's spatial point pattern test. The S-index is calculated as the proportion of similar spatial units in the domain where s_i is used as a binary indicator of similarity. In the case of discrete pixel values, s_i are still used as a binary input whereas in the case of continuous pixel values the resulting SSIM values are used as a non-binary s_i input. The proposed spatial similarity test is tested with a simulation study where the simulations are designed to have comparisons that are either 80% or 90% identical. With the simulation study it is concluded that the test is not sensitive to the resolution of the pixel image. The application is done on property valuations in Johannesburg and Cape Town. The test is applied to the similarity of property prices in the same area over different years as well as testing the similarity of property prices between the different areas of properties.en_ZA
dc.description.availabilityUnrestricteden_ZA
dc.description.degreeMSc (Advanced Data Analytics)en_ZA
dc.description.departmentStatisticsen_ZA
dc.description.sponsorshipThe financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.en_ZA
dc.identifier.citation*en_ZA
dc.identifier.otherA2021en_ZA
dc.identifier.urihttp://hdl.handle.net/2263/78217
dc.language.isoenen_ZA
dc.publisherUniversity of Pretoria
dc.rights© 2019 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.subjectUCTDen_ZA
dc.subjectMathematical Statisticsen_ZA
dc.subjectSpatial Statisticsen_ZA
dc.titleA generic similarity test for spatial dataen_ZA
dc.typeDissertationen_ZA

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