Understanding spatial perspectives on the spread and incidence of a disease is invaluable for public health planning and intervention. Choropleth maps are commonly used to provide an abstraction of disease risk across geographic space. These maps are derived from aggregated population counts that are known to be affected by the small numbers problem. Kernel density estimation methods account for this problem by producing risk estimates that are based on aggregations of approximately equal population sizes. However, the process of aggregation often combines data from areas with non-uniform spatial and population characteristics. This thesis presents a new method to aggregate space in ways that are sensitive to their underlying risk factors. Such maps will enable better public health practice and intervention by enhancing our ability to understand the spatial processes that result in disparate health outcomes.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc699888 |
| Date | 12 1900 |
| Creators | Jones, Jesse Jack |
| Contributors | Tiwari, Chetan, Dong, Pinliang, Oppong, Joseph R. |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | v, 48 pages : color illustrations, color maps, Text |
| Rights | Public, Jones, Jesse Jack, Copyright, Copyright is held by the author, unless otherwise noted. All rights reserved. |
Page generated in 0.0028 seconds