Spatial Analysis and Privation Index to Identify Urban Areas with a High Risk of Lymphatic Filariasis

Tropical Medicine & International Health, 2011 Mar 14

Bonfim C, Alves A, Costa TR, Alencar F, Pedroza D, Portugal JL, and Medeiros Z.

“Objective: To evaluate composite living conditions as indicators of urban areas with a higher risk of filariasis transmission.

“Methods: This was an ecological study in the municipality of Jaboatão dos Guararapes, in Brazil. The analysis units were census tracts. The study was divided into three phases. First, data gathered during an epidemiological investigation were analysed. Secondly, living condition indicators were drawn up and the relationship between these indicators and microfilaremia prevalence rates was analysed. Thirdly, positive cases were georeferenced with a view to identifying spatial concentration using kernel intensity estimates. Two composite living condition indicators were calculated: a socio-environmental risk index (in the form of scores) and a social deprivation index (through principal-component factor analysis).

“Results: Of 23,673 individuals examined, 1.4% had microfilaremia. According to the two indicators, greater prevalence was found in the high-risk strata, and this association was confirmed by the kernel intensity estimates.

“Conclusions: Classification of census tracts into risk strata showed the relevance of socio-economic factors and environmental conditions in identifying priority areas in urban spaces for interventions by the surveillance services and in planning filariasis control. Spatial analysis also proved to be an important tool for building up a territorially based surveillance system. These indicators, used in association with spatial analysis, are an instrument to be used by the Global Programme to Eliminate Lymphatic Filariasis.”

Visual Comparison of Moving Window Kriging Models

GeoViz: Linking Geovisualization with Spatial Analysis and Modeling, 10-11 March 2011, Hamburg, Germany

Urška Demšar and Paul Harris

“Kriging is a spatial prediction method, which can predict at any location and return a measure of prediction confidence (the kriging standard error). There exist many variations of kriging, some of which can be complex, especially those that allow many of its parameters to vary spatially. To calibrate such a kriging model and to be able to interpret its results can therefore be quite daunting. We suggest that visual analytics can help with this task. In particular, we focus on the Moving Window Kriging model and three robust variants and use Star Icons to evaluate model performance and to investigate the appropriateness of the criterion used when choosing a robust model fit.”