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Targeting Surra interventions in Mindanao using Remote Sensing, Geographic Information System and Global Positioning System Jennifer A. Alforque Agriculturist II Department of Agriculture Regional Field Unit No. XI Davao City
[object Object],[object Object],[object Object],[object Object],Rationale
MUSCA POST RADDL - RFU XIII P 39,600 RADDL - RFU X P 40,800 RADDL – ARMM P 33,000 RADDL - RFU IX P 40,800 Mindanao RADDL – RFU XII P 33,000 RADDL-RFU XI P 43,200 Allocation for drugs and supplies per region: (2006) Total: P230,400
[object Object],[object Object],[object Object],[object Object],[object Object],Objectives
[object Object],[object Object],[object Object],[object Object],Required Datasets Minimum Themes ,[object Object],[object Object],[object Object]
Criteria for analysis ,[object Object],[object Object]
Flowchart of GIS analysis and modeling for identifying potential high and low Surra risk areas ADMINISTRATIVE  BOUNDARIES CLIMATE DEMOGRAPHY TOPOGRAPHY RAINFALL  POPULATION  DENSITY  DEM  MUNICIPAL  BOUNDARIES reclass interpolate RAINFALL PATTERNS  ELEVATION intersect POTENTIAL SURRA HIGH-RISK AREAS IDENTIFIED Decision-makers
Rainfall Patterns 1  = low rainfall distribution [low risk] 10  = high rainfall distribution [high risk)
Rainfall patterns
Elevation 1  = high elevation (low risk) = not suitable for flies 10  = low elevation (high risk) = suitable for flies
Potential Surra Risk Areas in Caraga Region  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Analysis: the stronger rainfall distribution with the lower elevation area, the risk is high
High Risk Population Total Positive Cases
Results and Discussions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions and Recommendations  ,[object Object],[object Object]
Acknowledgements Dr. David Bourn Dr. Jose Molina Dr. Neil Bantayan Prof. Jun Tiburan Co-participants Resource persons
Daghang salamat sa pagpaminaw! [Thank you for listening!]

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Targeting Surra Interventions In Mindanao Using Remote Sensing, Geographic Information System And Global Positioning System

  • 1. Targeting Surra interventions in Mindanao using Remote Sensing, Geographic Information System and Global Positioning System Jennifer A. Alforque Agriculturist II Department of Agriculture Regional Field Unit No. XI Davao City
  • 2.
  • 3. MUSCA POST RADDL - RFU XIII P 39,600 RADDL - RFU X P 40,800 RADDL – ARMM P 33,000 RADDL - RFU IX P 40,800 Mindanao RADDL – RFU XII P 33,000 RADDL-RFU XI P 43,200 Allocation for drugs and supplies per region: (2006) Total: P230,400
  • 4.
  • 5.
  • 6.
  • 7. Flowchart of GIS analysis and modeling for identifying potential high and low Surra risk areas ADMINISTRATIVE BOUNDARIES CLIMATE DEMOGRAPHY TOPOGRAPHY RAINFALL POPULATION DENSITY DEM MUNICIPAL BOUNDARIES reclass interpolate RAINFALL PATTERNS ELEVATION intersect POTENTIAL SURRA HIGH-RISK AREAS IDENTIFIED Decision-makers
  • 8. Rainfall Patterns 1 = low rainfall distribution [low risk] 10 = high rainfall distribution [high risk)
  • 10. Elevation 1 = high elevation (low risk) = not suitable for flies 10 = low elevation (high risk) = suitable for flies
  • 11.
  • 12. High Risk Population Total Positive Cases
  • 13.
  • 14.
  • 15. Acknowledgements Dr. David Bourn Dr. Jose Molina Dr. Neil Bantayan Prof. Jun Tiburan Co-participants Resource persons
  • 16. Daghang salamat sa pagpaminaw! [Thank you for listening!]