Spatial Analysis of Notified Cryptosporidiosis Infections in Brisbane, Australia

Annals of Epidemiology, Volume 19, Issue 12, Pages 900-907 (December 2009)

Wenbiao Hu, Kerrie Mengersen, and Shilu Tong

“This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia.

“We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis.

“Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%.

“There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.”

Mapping Data Shape Community Responses To Childhood Obesity

Health Affairs, 29, no. 3 (2010): 498-502

William M. Sage, Matthew Balthazar, Steven Kelder, Susan Millea, Stephen Pont, and Mohan Rao

Geographic information system (GIS) mapping can help communities visualize the health of their neighborhoods and identify opportunities for improvement. In Austin, Texas, Children’s Optimal Health, a nonprofit association, used GIS to map the prevalence of obesity among middle school children and to identify contributory factors. The maps indicated that obesity is a problem in all Austin middle schools. Two neighborhoods outside downtown Austin have particularly high concentrations of overweight and obese students. Maps also showed that the neighborhoods have different proportions of fast-food outlets, grocery stores selling fresh produce, green recreation space, and students failing cardiovascular testing. The mapping exercise spurred community groups to propose obesity interventions tailored to each neighborhood.”

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Insight into an Island Radiation: The Tarentola Geckos of the Cape Verde Archipelago

Journal of Biogeography, February 2010

Raquel Vasconcelos, Salvador Carranza, and D. James Harris

“Aim: To reassess the relationships between Tarentola geckos from the Cape Verde Islands by including specimens from all islands in the range. To determine the variation within forms by sequencing over 400 specimens, thereby allowing the discovery of cryptic forms and resolving some of the issues raised previously. This extensive sampling was also used to shed light on distributions and to explain genetic diversity by comparing the ages and ecological and geological features of the islands (size, elevation and habitat diversity).

“Location: The Cape Verde Islands: an oceanic archipelago belonging to the Macaronesian biogeographic region, located around 500 km off Senegal.

“Methods: A total of 405 new specimens of Tarentola geckos were collected from nine islands with very different geological histories, topography, climate and habitats. Mitochondrial cytochrome b (cyt b) gene and 12S rRNA partial sequences were obtained and analysed using phylogenetic methods and networks to determine molecular diversity, demographic features and phylogeographic patterns.

“Results: The phylogenetic relationships between all known forms of Cape Verdean Tarentola specimens were estimated for the first time, the relationships between new forms were assessed and previously hypothesized relationships were re-examined. Despite the large sample size, low intraspecific diversity was found using a 303-bp cyt b fragment. Star-like haplotype networks and statistical tests suggest the past occurrence of a rapid demographic and geographical expansion over most of the islands. Genetic variability is positively correlated with size, elevation and habitat diversity of the islands, but is not linearly related to the age of the islands. Biogeographical patterns have, in general, high concordance with phylogenetic breaks and with the three eco-geographical island groups. Volcanism and habitat diversity, both tightly linked with island ontogeny, as postulated by the general dynamic model of oceanic island biogeography, as well as present and historical size of the islands appear to be the main factors explaining the genetic diversity of this group.

“Main conclusions: The Tarentola radiation was clarified and is clearly associated with the geological and ecological features of the islands. Two factors may account for the low intraspecific variation: (1) recent volcanic activity and high ecological stress, and (2) poor habitat diversity within some islands. More studies are needed to align taxonomy with phylogenetic relationships, whereas GIS modelling may help to predict precise species distributions.”

Managing Uncertainty when Aggregating from Pixels to Objects: Habitats, Context-sensitive Mapping and Possibility Theory

International Journal of Remote Sensing, Volume 31, Issue 4 April 2010 , pages 1061 – 1068

Alexis Comber; Katie Medcalf; Richard Lucas; Peter Bunting; Alan Brown; Daniel Clewley; Johanna Breyer; Steve Keyworth

“Object-oriented remote sensing software provides the user with flexibility in the way that remotely sensed data are classified through segmentation routines and user-specified fuzzy rules. This paper explores the classification and uncertainty issues associated with aggregating detailed ‘sub-objects’ to spatially coarser ‘super-objects’ in object-oriented classifications. We show possibility theory to be an appropriate formalism for managing the uncertainty commonly associated with moving from ‘pixels to parcels’ in remote sensing. A worked example with habitats demonstrates how possibility theory and its associated necessity function provide measures of certainty and uncertainty and support alternative realizations of the same remotely sensed data that are increasingly required to support different applications.”