GIS and Remote Sensing Based Malaria Risk Assessment and Mapping in Raya Kobo Woreda, North Wollo, Ethiopia
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Date
2019-02
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Addis Ababa University
Abstract
Malaria is one of the world’s serious and complex public health problems and it remains one of the greatest killers of human beings in developing countries. Due to its tropical location and other environmental factors, part of Ethiopia is favorable for mosquito breeding and malaria transmission.
The purpose of this study was to generate malaria risk map of Raya Kobo woreda using GIS & RS techniques. The environmental factors considered for the analysis are temperature, rainfall, elevation, slope, soil, land use land cover, distance to streams and distance from health stations. The analysis was computed using multi criteria evaluation (MCE). Weights were assigned for these parameters by pair wise comparison method and weighted overlay was used in Arc GIS spatial analyst tools to produce the final malaria risk map of the study area. To generate malaria hazard map, elevation, slope, temperature, rainfall, soil and distance to streams factors are weighted and then weighted overlay technique was computed. For vulnerability analysis, health station location in Spatial Analyst was used to generate factor maps; and 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 ArcGIS.
The final risk map indicates that from the total of the study area 12.13 % is mapped as very high, 17.28 % as high, 26.71% as moderate, 25.91 % as low and 17.97%as very low malaria risk level. In other words, according to the result of the findings large area of the study area (29.41%) is located in very high and high risk area of malaria .This study also attempts to map malaria risk level of kebeles in the study area, to make malaria control and eradication program of the Woreda time and cost efficient.
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Keywords
GIS, Malaria, Pair wise comparison, Remote Sensing, Risk map, weighted overlay