GIS and remote sensing for malaria risk mapping, Ethiopia

研究成果: ジャーナルへの寄稿Conference article

3 引用 (Scopus)

抄録

Integrating malaria data into a decision support system (DSS) using Geographic Information System (GIS) and remote sensing tool can provide timely information and decision makers get prepared to make better and faster decisions which can reduce the damage and minimize the loss caused. This paper attempted to asses and produce maps of malaria prone areas including the most important natural factors. The input data were based on the geospatial factors including climatic, social and Topographic aspects from secondary data. The objective of study is to prepare malaria hazard, Vulnerability, and element at risk map which give the final output, malaria risk map. The malaria hazard analyses were computed using multi criteria evaluation (MCE) using environmental factors such as topographic factors (elevation, slope and flow distance to stream), land use/land cover and Breeding site were developed and weighted, then weighted overlay technique were computed in ArcGIS software to generate malaria hazard map. The resulting malaria hazard map depicts that 19.2%, 30.8%, 25.1%, 16.6% and 8.3% of the District were subjected to very high, high, moderate, low and very low malaria hazard areas respectively. For vulnerability analysis, health station location and speed constant in Spatial Analyst module were used to generate factor maps. For element at risk, land use land cover map were used to generate element at risk map. Finally malaria risk map of the District was generated. Land use land cover map which is the element at risk in the District, the vulnerability map and the hazard map were overlaid. The final output based on this approach is a malaria risk map, which is classified into 5 classes which is Very High-risk area, High-risk area, Moderate risk area, Low risk area and Very low risk area. The risk map produced from the overlay analysis showed that 20.5%, 11.6%, 23.8%, 34.1% and 26.4% of the District were subjected to very high, high, moderate, low and very low malaria risk respectively. This help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria epidemics.

元の言語英語
ページ(範囲)155-161
ページ数7
ジャーナルInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
40
発行部数8
DOI
出版物ステータス出版済み - 1 1 2014
外部発表Yes
イベントISPRS Technical Commission VIII Mid-Term Symposium 2014 - Hyderabad, インド
継続期間: 12 9 201412 12 2014

Fingerprint

malaria
Ethiopia
Geographic information systems
Remote sensing
information system
remote sensing
Hazards
hazard
Land use
district
land cover
vulnerability
land use
geographic information system
breeding site
decision support system
Decision support systems
environmental factors
decision maker
damages

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

これを引用

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title = "GIS and remote sensing for malaria risk mapping, Ethiopia",
abstract = "Integrating malaria data into a decision support system (DSS) using Geographic Information System (GIS) and remote sensing tool can provide timely information and decision makers get prepared to make better and faster decisions which can reduce the damage and minimize the loss caused. This paper attempted to asses and produce maps of malaria prone areas including the most important natural factors. The input data were based on the geospatial factors including climatic, social and Topographic aspects from secondary data. The objective of study is to prepare malaria hazard, Vulnerability, and element at risk map which give the final output, malaria risk map. The malaria hazard analyses were computed using multi criteria evaluation (MCE) using environmental factors such as topographic factors (elevation, slope and flow distance to stream), land use/land cover and Breeding site were developed and weighted, then weighted overlay technique were computed in ArcGIS software to generate malaria hazard map. The resulting malaria hazard map depicts that 19.2{\%}, 30.8{\%}, 25.1{\%}, 16.6{\%} and 8.3{\%} of the District were subjected to very high, high, moderate, low and very low malaria hazard areas respectively. For vulnerability analysis, health station location and speed constant in Spatial Analyst module were used to generate factor maps. For element at risk, land use land cover map were used to generate element at risk map. Finally malaria risk map of the District was generated. Land use land cover map which is the element at risk in the District, the vulnerability map and the hazard map were overlaid. The final output based on this approach is a malaria risk map, which is classified into 5 classes which is Very High-risk area, High-risk area, Moderate risk area, Low risk area and Very low risk area. The risk map produced from the overlay analysis showed that 20.5{\%}, 11.6{\%}, 23.8{\%}, 34.1{\%} and 26.4{\%} of the District were subjected to very high, high, moderate, low and very low malaria risk respectively. This help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria epidemics.",
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