Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Contributions to spatial uncertainty modelling in GIS : small sample data
Includes bibliographical references.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Statistical Sciences
2016
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613278794940416 |
|---|---|
| access_status_str | Open Access |
| author | Guo, Danni |
| author2 | Thiart, Christien |
| author_browse | Guo, Danni Thiart, Christien |
| author_facet | Thiart, Christien Guo, Danni |
| author_sort | Guo, Danni |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/19031 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:35.758Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/19031 Contributions to spatial uncertainty modelling in GIS : small sample data Guo, Danni Thiart, Christien Statistical Science Includes bibliographical references. Environmental data is very costly and difficult to collect and are often vague (subjective) or imprecise in nature (e.g. hazard level of pollutants are classified as "harmful for human beings"). These realities in practise (fuzziness and small datasets) leads to uncertainty, which is addressed by my research objective: "To model spatial environmental data with .fuzzy uncertainty, and to explore the use of small sample data in spatial modelling predictions, within Geographic Information System (GIS)." The methodologies underlying the theoretical foundations for spatial modelling are examined, such as geostatistics, fuzzy mathematics Grey System Theory, and (V,·) Credibility Measure Theory. Fifteen papers including three journal papers were written in contribution to the developments of spatial fuzzy and grey uncertainty modelling, in which I have a contributed portion of 50 to 65%. The methods and theories have been merged together in these papers, and they are applied to two datasets, PM10 air pollution data and soil dioxin data. The papers can be classified into two broad categories: fuzzy spatial GIS modelling and grey spatial GIS modelling. In fuzzy spatial GIS modelling, the fuzzy uncertainty (Zadeh, 1965) in environmental data is addressed. The thesis developed a fuzzy membership grades kriging approach by converting fuzzy subsets spatial modelling into membership grade spatial modelling. As this method develops, the fuzzy membership grades kriging is put into the foundation of the credibility measure theory, and approached a full data-assimilated membership function in terms of maximum fuzzy entropy principle. The variable modelling method in dealing with fuzzy data is a unique contribution to the fuzzy spatial GIS modelling literature. In grey spatial GIS modelling, spatial predictions using small sample data is addressed. The thesis developed a Grey GIS modelling approach, and two-dimensional order-less spatially observations are converted into two one-dimensional ordered data sequences. The thesis papers also explored foundational problems within the grey differential equation models (Deng, 1985). It is discovered the coupling feature of grey differential equations together with the help of e-similarity measure, generalise the classical GM( 1,1) model into more classes of extended GM( 1,1) models, in order to fully assimilate with sample data information. The development of grey spatial GIS modelling is a creative contribution to handling small sample data. 2016-04-20T14:11:49Z 2016-04-20T14:11:49Z 2007 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/19031 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Statistical Science Guo, Danni Contributions to spatial uncertainty modelling in GIS : small sample data |
| thesis_degree_str | Doctoral |
| title | Contributions to spatial uncertainty modelling in GIS : small sample data |
| title_full | Contributions to spatial uncertainty modelling in GIS : small sample data |
| title_fullStr | Contributions to spatial uncertainty modelling in GIS : small sample data |
| title_full_unstemmed | Contributions to spatial uncertainty modelling in GIS : small sample data |
| title_short | Contributions to spatial uncertainty modelling in GIS : small sample data |
| title_sort | contributions to spatial uncertainty modelling in gis small sample data |
| topic | Statistical Science |
| url | http://hdl.handle.net/11427/19031 |
| work_keys_str_mv | AT guodanni contributionstospatialuncertaintymodellingingissmallsampledata |