Gis and Remote Sensing Integraterd Malria Risk Mapping in Dembia Woreda, Northern Ethiopia,
No Thumbnail Available
Date
2007-07
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa Universty
Abstract
Every year, malaria continues to claim over a million lives around the globe. Attempts
have been made to control the disease by eliminating the parasite. However, un
known spatial distribution of mosquito known to cause malaria, eradication of the
parasite remains a daunting task. As a result, increased efforts and resources have
been channeled towards finding ways of minimizing the disease. There for this
investigation is aimed to contribute the concept and methods of the innovative
development and application of GIS and RS regarding malaria prevalence in Dembia
Woreda. The input data are based on the geospatial factors including climatic aspects,
social aspects and Topographic aspects from primary & secondary data. After words
The malaria hazard analysis was computed using multi criteria evaluation (MCE). To
run MCE, the selected environmental factors such as topographic factors (elevation, ,
and flow distance to stream, land use/ land cover and aquatic bodies were developed
and weighted. Then weighted overlay technique was computed in ArcGIS9.1 Model
Builder to generate malaria hazard map. For vulnerability analysis, health station
location in Spatial Analyst/ module was used to generate factor maps. For element at
risk, land use land cover map was used to generate element at risk map. To generate
malaria risk map of the woreda, land use land cover map which is the element at risk
in the woreda, the vulnerability map and the hazard map were overlaid using weighted
overlay analysis technique in ArcGIS9.1 environment. The final out put based on this
approach is a malaria risk map, which is classified in to 3 classes including, High-risk
area, moderate risk area and low risk area. This help to plan valuable measures to be
taken in early warning, monitor, control and prevent malaria epidemic
Description
Keywords
Gis and Remote Sensing