GIS and Statistical Analysis for WASH Programmes Course

The aim of any water sanitation and hygiene (WASH) programme is to promote good personal and environmental hygiene in order to protect health. Integrating GIS in WASH programmes helps in implementing target based interventions as it will help to build a model of the complex environment in a much simpler and easier way for you to understand the environment and make proper and informed decisions. Continuing developments in free open source GIS software for both desktop and web application is opening up great possibilities for capturing and processing geographical data, and then publishing maps via the internet. The ability to manage information on water and sanitation services and then overlay it with other spatial features has wide-ranging benefits for project planning and design, monitoring and evaluation, advocacy and accountability.

Integrating GIS in WASH programmes is the way forward, toward achievement of the post 2015 Development agenda aimed at achieving global access to water and sanitation.

WHO SHOULD APPLY?

Program Manager, Monitoring and Evaluation Experts, Civil Engineers, Environmentalist, Public health practitioners, epidemiologists, exposure scientists, quantitative health geographers, risk assessors, students.

DURATION

5 Days

REQUIREMENTS

No prior knowledge of GIS is required. Participants will be working on their own laptop with Quantum GIS (QGIS)

COURSE OBJECTIVES

By completing this course, participants will be able to:-

• Evaluate the spatial data requirements in Water Sanitation and Hygiene
• Use GIS and RS as a tool for monitoring WASH projects Activities
• Assess spatial data availability
• Understand the importance of Spatial Data Infrastructure (SDI) in WASH programs
• Use participatory GIS (PGIS) at community level
• Carry out water and sanitation modeling using GIS
• Collect data using Mobile data gathering tools
• Carry out basic data analysis using statistical software’s
• Design and implement their own GIS projects that integrate remote sensing data, GPS-based field information in a proper geospatial framework

Others Details

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