Training Course on Application of GIS Analysis in WASH (Water Sanitation and Hygiene) Programs.

Program Brief:

In an era where data drives decision-making, the integration of Geographic Information Systems (GIS) and Remote Sensing technologies is transforming the landscape of Water, Sanitation, and Hygiene (WASH) programs. These tools empower organizations to visualize, analyze, and interpret complex geospatial data, facilitating informed decisions that can enhance community health and environmental sustainability.

The Application of GIS Analysis in WASH Programs Course is designed for professionals eager to leverage these cutting-edge technologies to tackle pressing global challenges in water and sanitation. Throughout this 10-day course, participants will embark on a comprehensive journey that bridges theory and practice, equipping them with the essential skills to integrate GIS and Remote Sensing into their WASH initiatives effectively.

By enrolling in this course, participants will not only enhance their technical skills but also gain the confidence to implement GIS and Remote Sensing technologies in their WASH programs effectively. The hands-on approach, combined with real-world applications and collaborative learning, ensures that participants emerge as competent professionals ready to drive meaningful change in their communities. Join us in this transformative learning experience and become a catalyst for improving water, sanitation, and hygiene worldwide.

Course Objectives

By the end of this course, participants will:

  • Utilize GIS and Remote Sensing as Strategic Tools: Develop a strong understanding of how these technologies can monitor and enhance WASH project activities.
  • Assess Spatial Data Availability: Learn to evaluate and source spatial data critical for WASH program success.
  • Implement Participatory GIS (PGIS): Engage communities through participatory mapping techniques to ensure local needs are met.
  • Understand Spatial Data Infrastructure (SDI): Grasp the importance of SDI in creating an effective framework for WASH programs.
  • Design and Implement GIS Projects: Create and execute tailored GIS projects that incorporate remote sensing data and GPS-based information.
  • Conduct Water and Sanitation Modeling: Use GIS to model water systems and sanitation services for better planning and management.
  • Master Mobile Data Gathering Technology: Collect and manage data efficiently using mobile technology tailored for WASH applications.
  • Perform Basic Data Analysis: Gain proficiency in using statistical software to analyze both qualitative and quantitative data relevant to WASH initiatives.

Recommended For:

This course is ideal for a diverse range of professionals working in the WASH sector, including:

  • Environmental scientists and researchers
  • WASH project managers and coordinators
  • GIS and remote sensing specialists
  • Policy makers and development practitioners
  • Community engagement officers and local government representatives

Duration

5 Days

Physical Training Schedule

Start & End Date

Location

Fee (USD)

Register

Aug 3- Aug 7, 2026

Nairobi

1,500

Aug 24- Aug 28, 2026

Pretoria

4,950

Sep 7- Sept 11, 2026

Nairobi

1,500

Sep 21- Sept 25, 2026

Mombasa

1,850

Oct 5- Oct 9, 2026

Nairobi

1,500

Oct 5- Oct 9, 2026

Kigali

2,950

Nov 2- Nov 6, 2026

Nairobi

1,500

Nov 23- Nov 27, 2026

Dar es Salam

2,950

Dec 7- Dec 11, 2026

Nairobi

1,500

Jan 5- Jan 9, 2027

Kigali

2,950

Jan 26- Jan 30, 2027

Mombasa

1,850

Feb 2- Feb 6, 2027

Nairobi

1,500

Feb 2- Feb 6, 2027

Pretoria

4,950

Mar 2- Mar 6, 2027

Nairobi

1,500

Mar 23- Mar 27, 2027

Dar es Salaam

2,950

Apr 6- Apr 10, 2027

Nairobi

1,500

Apr 20- Apr 24, 2027

Nairobi

1,500

May 4- May 8, 2027

Mombasa

1,850

May 25- May 29, 2027

Nairobi

1,500

Jun 1- Jun 5, 2027

Kigali

2,950

Jun 22- Jun 26, 2027

Nairobi

1,500

Jul 6- Jul 10, 2027

Dar es Salaam

2,950

Online Training Schedule

Start & End Date

Fee (USD)

Register

July 27 – July 31, 2026

800

Aug 3 – Aug 7, 2026

800

Aug 24 – Aug 28, 2026

800

Sept 7 – Sept 11, 2026

800

Sept 21– Sept 25, 2026

800

Oct 5 – Oct 9, 2026

800

Oct 26 – Oct 30, 2026

800

Nov 9 – Nov 13, 2026

800

Dec 7 – Dec 11, 2026

800

Jan 5- Jan 9, 2027

800

Jan 26- Jan 30, 2027

800

Feb 2- Feb 6, 2027

800

Feb 23- Feb 27, 2027

800

Mar 2- Mar 6, 2027

800

Mar 23- Mar 27, 2027

800

Apr 6- Apr 10, 2027

800

Apr 20- Apr 24, 2027

800

May 4- May 8, 2027

800

Jun 1- Jun 5, 2027

800

Jun 22- Jun 26, 2027

800

Jul 6- Jul 10, 2027

800

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