Spatial Analysis of Land Cover Determinants of Malaria Incidence in the Ashanti Region, Ghana

PLoS ONE 6(3): e17905. 2011.

Anne Caroline Krefis, Norbert Georg Schwarz, Bernard Nkrumah, Samuel Acquah, Wibke Loag, Jens Oldeland, Nimako Sarpong, Yaw Adu-Sarkodie, Ulrich Ranft, and Jürgen May

“Malaria belongs to the infectious diseases with the highest morbidity and mortality worldwide. As a vector-borne disease malaria distribution is strongly influenced by environmental factors. The aim of this study was to investigate the association between malaria risk and different land cover classes by using high-resolution multispectral Ikonos images and Poisson regression analyses. The association of malaria incidence with land cover around 12 villages in the Ashanti Region, Ghana, was assessed in 1,988 children <15 years of age. The median malaria incidence was 85.7 per 1,000 inhabitants and year (range 28.4–272.7). Swampy areas and banana/plantain production in the proximity of villages were strong predictors of a high malaria incidence. An increase of 10% of swampy area coverage in the 2 km radius around a village led to a 43% higher incidence (relative risk [RR] = 1.43, p<0.001). Each 10% increase of area with banana/plantain production around a village tripled the risk for malaria (RR = 3.25, p<0.001). An increase in forested area of 10% was associated with a 47% decrease of malaria incidence (RR = 0.53, p = 0.029).

“Distinct cultivation in the proximity of homesteads was associated with childhood malaria in a rural area in Ghana. The analyses demonstrate the usefulness of satellite images for the prediction of malaria endemicity. Thus, planning and monitoring of malaria control measures should be assisted by models based on geographic information systems.”

Development of Open Source Functionality for the Analysis and Visualization of Remotely Sensed Time Series

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

Connie A. Blok, Ulanbek D. Turdukulov, Raul Zurita-Milla, Bas Retsios, and Martin Schouwenburg

“The GEONETCast data dissemination system delivers low cost environmental data to users worldwide. Long time series of images from various satellites can be obtained to study dynamic phenomena. To explore the dynamics, displaying the images as animation with few controls is a common practice. However, some problems need to be addressed. We present how the current approach to animate time series of satellite images can be improved by pre-processing (pixel co-registration and resampling data from satellites with different spatio-temporal resolution), by analytical functionality (detecting features of interests and feature tracking) and by enriching the visualization environment with more interactive functions. But even then, animations still lead to information overload. We discuss our attempts to reduce this problem, and describe the resulting software component that is fully dedicated to visual exploration and analysis of dynamic phenomena and will be added to the open source ILWIS software.”