Thesis (Ph.D. (Statistics)) -- University of Limpopo, 2021 / In spatial statistics, several methods have been developed to measure the extent
of local and global spatial dependence (clustering) in measured data across
areas in a region of research interest. These methods are now routinely implemented
in most Geographical Information Systems (GIS) and statistical computer packages.
However, spatial statistics for measuring joint spatial dependence of multiple
spatial measurement and outcome data have not been well developed. A naive
analysis would simply apply univariate spatial dependence methods to each
data separately. Though this is simple and straightforward, it ignores possible
relationships between multiple spatial data because they may be measuring
the same phenomena. Limited work has been done on extending the Moran’s
index, a commonly used and applied univariate measure of spatial clustering,
to bivariate Moran’s index in order to assess spatial dependence for two spatial
data. The overall aim of this PhD was to develop multivariate spatial clustering
methods for multiple spatial data, especially in the health sciences. Our proposed
multivariate spatial clustering statistic is based on the fundamental theory
regarding canonical correlations. We firstly reviewed and applied univariate
and bivariate Moran’s indexes to spatial analyses of multiple non-communicable
diseases and related risk factors in South Africa. Then we derived our proposed
multivariate spatial clustering method, which was evaluated by simulation
studies and applied to a spatial analysis of multiple non-communicable diseases
and related risk factors in South Africa. Simulation studies showed that our
proposed multivariate spatial statistic was able to identify correctly clusters of
areas with high risks as well as clusters with low risk.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ul/oai:ulspace.ul.ac.za:10386/3691 |
| Date | January 2021 |
| Creators | Darikwa, Timotheus Brian |
| Contributors | Manda, S., Lesaoana, M. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | xxi, 204 leaves |
| Relation |
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