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Covariate construction of nonconvex windows for spatial point pattern data
Mini Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2020
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2020
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| _version_ | 1867613578018684928 |
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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 | © 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. |
| description | Mini Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2020 |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/73813 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:22.209Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| 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:2263/73813 Covariate construction of nonconvex windows for spatial point pattern data Fabris-Rotelli, Inger Nicolette u14194237@tuks.co.za Kraamwinkel, Christine Mahloromela, Kabelo UCTD Spatial Statistics Mini Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2020 In the field of spatial statistics, window selection for point pattern data is a complex process. In some cases, the point pattern window is given a priori when a local phenomena is studied. In other cases, a researcher may choose this region using some objective means that reflects their view that the window may be representative of a larger region, or based on a probability sampling method. The common approaches used are the smallest rectangular bounding window and convex windows due to the obvious use of the Euclidean distance. The chosen window must however cover the true domain of the sampled point pattern data. Choosing a window too large results in estimation and inference in areas which are empty of observed data, but for which it has not been confirmed that observations could have occurred there. These holes in the domain could be regions where for some geographic (or other) reason the phenomena of interest does not occur. In this mini-dissertation a review of methods for spatial convex and nonconvex window estimation is provided, and an algorithm is proposed for selecting the point pattern domain without the restriction of convexity, allowing for a better fit to the true domain, and based on spatial covariate information. The effect of the window choice on spatial intensity estimates is illustrated by giving particular attention to the technique of smoothed kernel intensity estimation. The proposed algorithm is applied in the setting of rural villages in Tanzania's Mara province. As a spatial covariate, remotely sensed data based on the elevation of a point pattern is used in the form of a Digital Elevation Model (DEM) GTOPO30, specific to village house locations in this setting. Mathematical morphological operators are also used to extract physiographic features from the DEM and are included here as a preprocessing step in the spatial window domain modelling. STATOMET, DST/NRF SARChI Chair Statistics MSc (Mathematical Statistics) Unrestricted 2020-03-23T14:23:03Z 2020-03-23T14:23:03Z 2020-09 2020 Mini Dissertation Mahloromela, K 2020, Covariate construction of nonconvex windows for spatial point pattern data, MSc mini-dissertation, University of Pretoria, Pretoria, viewed 200321 http://hdl.handle.net/2263/73813 S2020 http://hdl.handle.net/2263/73813 en © 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. application/pdf University of Pretoria |
| spellingShingle | UCTD Spatial Statistics Covariate construction of nonconvex windows for spatial point pattern data |
| title | Covariate construction of nonconvex windows for spatial point pattern data |
| title_full | Covariate construction of nonconvex windows for spatial point pattern data |
| title_fullStr | Covariate construction of nonconvex windows for spatial point pattern data |
| title_full_unstemmed | Covariate construction of nonconvex windows for spatial point pattern data |
| title_short | Covariate construction of nonconvex windows for spatial point pattern data |
| title_sort | covariate construction of nonconvex windows for spatial point pattern data |
| topic | UCTD Spatial Statistics |
| url | http://hdl.handle.net/2263/73813 |