GIS Data Analysis for WASH Programmes Training Course

Duration 10 days 16 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends WASH Programme Officers, GIS Officers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-WS-028

Before you join: Participants should understand basic WASH programme concepts such as water points, sanitation facilities, hygiene indicators, household surveys and monitoring data.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

WASH programmes depend on reliable evidence about who has services, where systems fail, which populations face the greatest barriers, and how conditions change after an intervention. Spreadsheet totals alone can conceal major differences between wards, settlements, camps and neighbourhoods. GIS analysis enables teams to combine facility inventories, household surveys, population data, hazards, access routes and monitoring records to target resources, verify results and explain priorities to programme managers, government counterparts and donors.

This course develops the practical GIS and data-analysis skills needed for water, sanitation and hygiene programming. Participants work with WASH data models, coordinate systems, mobile survey data, quality checks, geoprocessing, service coverage mapping, travel-time analysis, water-point functionality, sanitation service chains, hygiene indicators, population denominators and outbreak data. The course uses recognised WASH concepts including JMP service ladders, SDG 6 indicators, water-point functionality categories and institutional WASH monitoring definitions. Participants learn to assess data quality, select appropriate spatial methods, interpret uncertainty and produce maps that support defensible operational decisions.

Instructor-led sessions combine short technical briefings with guided case work drawn from rural water supply, urban sanitation, refugee settlements, school WASH, health-care facilities and cholera response. Participants use QGIS and mobile-data collection workflows to build a structured analysis from the first week onward. They leave with a reusable WASH GIS analysis package: a documented dataset, mapped service indicators, a prioritisation model, map layouts, a technical note and an update plan. The final capstone requires each participant to present a complete spatial analysis and defend its recommendations.

The course suits WASH practitioners, M&E staff, GIS officers, public-health analysts and programme managers who already work with programme data and need stronger spatial evidence for planning, monitoring and response.

What you will be able to do

  • Design a WASH GIS analysis brief linking decisions, indicators, datasets and outputs
  • Build structured WASH spatial datasets using data dictionaries, identifiers and metadata
  • Clean and validate survey, facility and geographic data using documented quality rules
  • Produce service coverage and equity maps using appropriate denominators and classifications
  • Calculate travel time, facility catchments and access gaps using network and cost-distance methods
  • Analyse water-point functionality, sanitation service chains and hygiene indicator patterns
  • Interpret population, hazard and outbreak data for WASH prioritisation decisions
  • Present a documented GIS analysis with maps, limitations and operational recommendations

Benefits of attending

For you

  • Ability to convert WASH monitoring data into maps for programme decisions
  • Practical confidence checking coordinate, survey and facility-data quality
  • Reusable methods for identifying underserved populations and failing water points
  • Stronger technical input to donor reports, government coordination and response planning
  • A documented GIS portfolio piece demonstrating WASH analytical capability

For your organisation

  • Better targeting of water, sanitation and hygiene investments by geographic need
  • Clearer evidence of service coverage, functionality and equity gaps
  • More reliable WASH datasets through documented validation and metadata procedures
  • Faster identification of access constraints, hazards and potential outbreak hotspots
  • Consistent maps and indicators for management, donor and coordination reporting

Course modules

16 modules over 10 days

Participants can frame a WASH decision as a spatial analysis brief with clear indicators and outputs.

  • Programme decision questions translated into spatial analysis requirements and indicators
  • Administrative boundaries, settlements, facilities and households as WASH spatial units
  • Results chains link activities, outputs, outcomes and mapped indicators
  • JMP service ladders and SDG 6.1.1, 6.2.1 definitions
  • Spatial equity questions disaggregated by sex, disability, wealth and location
  • Analysis briefs specify users, decisions, scale, update cycles and outputs

Participants can structure WASH datasets using consistent entities, attributes, identifiers and metadata.

  • Entity relationships connect water points, households, facilities, surveys and interventions
  • Attribute domains standardise facility type, functionality, ownership and service level
  • Unique identifiers preserve longitudinal records through repairs, replacement and repeat visits
  • Data dictionaries define fields, formats, units, sources and collection dates
  • ISO 19115 metadata elements document lineage, accuracy, constraints and custodians
  • Version control records edits, approvals, publication dates and responsible analysts

Participants can select coordinate systems and identify common location and geometry errors.

  • Geographic and projected coordinate reference systems selected for intended measurements
  • EPSG codes identify datum, projection, units and coordinate transformation parameters
  • GPS accuracy, precision and dilution of precision assessed before field deployment
  • Coordinate transformations prevent offset errors between national grids and WGS 84
  • Point, line and polygon geometry chosen from WASH asset behaviour
  • Topology rules detect gaps, overlaps, duplicates and invalid facility locations

Participants can specify mobile survey forms and field protocols for spatial WASH data collection.

  • KoboToolbox forms use skip logic, constraints, repeat groups and choice lists
  • ODK Collect captures coordinates, photographs, timestamps and enumerator identifiers
  • Survey questions align with JMP drinking-water, sanitation and hygiene indicators
  • Consent scripts explain purpose, access controls, retention and respondent rights
  • Enumerator protocols handle displaced populations, informal settlements and sensitive facilities
  • Offline synchronisation plans account for power, connectivity and device security

Participants can clean WASH datasets and document quality decisions for audit and reuse.

  • Range checks flag impossible pump depths, household sizes and collection times
  • Duplicate detection combines identifiers, coordinates, names and collection timestamps
  • Missing-value codes distinguish unknown, not applicable, refused and not observed
  • Outlier review uses histograms, box plots and location-based plausibility checks
  • Cleaning logs record rule, record count, decision, analyst and date
  • Quality scores report positional, attribute, temporal and completeness dimensions

Participants can use geoprocessing methods to connect WASH data with administrative, hazard and population layers.

  • Geocoding matches facility names and addresses against verified settlement gazetteers
  • Spatial joins attach administrative units, catchments and hazard zones
  • Buffers represent walking-distance thresholds, protection zones and service catchments
  • Intersect and dissolve operations summarise interventions within reporting boundaries
  • Nearest-neighbour analysis identifies isolated facilities and duplicate service locations
  • Processing models document inputs, parameters, outputs and repeatable workflows

Participants can map water-service coverage and identify geographic inequities using suitable denominators.

  • Service-area maps distinguish basic, limited and no-service drinking-water categories
  • Choropleth rates use appropriate denominators, classification methods and neutral colours
  • Graduated symbols communicate facility capacity, functionality and reporting confidence
  • Coverage gaps calculated against population, households and agreed service standards
  • Small-area estimates identify underserved wards masked by district averages
  • Map layouts include scale, north arrow, source, date and uncertainty notes

Participants can model access to WASH services using network, terrain and barrier data.

  • Network analysis calculates travel time using roads, paths, slopes and barriers
  • Cost-distance surfaces model walking access where transport networks are incomplete
  • Travel-time thresholds compare water access against programme service commitments
  • Seasonal flooding, insecurity and checkpoints incorporated into access assumptions
  • Facility catchments reveal overlap, exclusion and pressure on shared sources
  • Scenario comparisons test boreholes, rehabilitation sites and water-trucking routes

Participants can analyse water-point performance, repair histories and water-quality risks geographically.

  • Water-point inventories classify source type, management model and functionality status
  • Functionality rates separate operational, partially functional, nonfunctional and abandoned assets
  • Failure patterns mapped by pump type, installer, age and spare-parts access
  • Repair histories calculate downtime, repeat failure and maintenance response intervals
  • Water-quality results link microbial, chemical and sanitary inspection records
  • Risk maps prioritise inspections using hazard exposure, users and failure severity

Participants can analyse sanitation service chains and hygiene conditions while protecting respondent privacy.

  • Sanitation facility data classify containment, emptying, disposal and shared-use arrangements
  • FSM service chains map excreta flows from household containment to treatment
  • Hygiene observations analyse handwashing facilities, water availability and soap presence
  • School and health-care facility indicators follow JMP institutional monitoring definitions
  • Density mapping identifies neighbourhoods requiring communal sanitation or desludging services
  • Privacy-preserving aggregation protects household sanitation and hygiene response data

Participants can select defensible denominators and interpret WASH estimates by population group.

  • Population rasters, census tables and household listings establish coverage denominators
  • Dasymetric mapping reallocates population using building footprints and land-cover evidence
  • Cluster sampling frames use enumeration areas, settlements and displacement sites
  • Sample weights correct unequal selection probabilities and nonresponse patterns
  • Confidence intervals distinguish sampled estimates from complete facility inventories
  • Disaggregation tables expose gaps by gender, age, disability and settlement type

Participants can use raster and environmental data to assess WASH-related hazards and site suitability.

  • Satellite imagery supports settlement expansion, surface-water extent and flood exposure
  • Raster classification distinguishes built-up areas, vegetation, bare ground and water
  • NDVI and rainfall anomalies inform drought monitoring and water-source stress
  • Digital elevation models derive slope, flow direction and drainage accumulation
  • Multi-criteria suitability analysis weights groundwater potential, access, demand and hazards
  • Validation compares remote-sensing outputs with field observations and local knowledge

Participants can map WASH-relevant disease patterns while communicating uncertainty and confidentiality limits.

  • Case locations, onset dates and laboratory status structure outbreak datasets
  • Kernel density estimation identifies cholera case concentrations and possible clusters
  • Space-time analysis compares transmission patterns before and after WASH actions
  • Attack rates use population denominators and clearly defined exposure areas
  • Water-source proximity analysis tests plausible environmental transmission pathways
  • Situation maps communicate uncertainty, confidentiality limits and operational priorities

Participants can produce map-based WASH monitoring products for technical and nontechnical decision-makers.

  • Indicator specifications define numerator, denominator, disaggregation, frequency and data owner
  • Baseline and endline datasets align geographic units before measuring change
  • Time-series charts distinguish seasonal variation, reporting gaps and programme effects
  • QGIS layouts produce print-ready maps for donor reports and coordination meetings
  • Dashboard wireframes connect key indicators, filters, maps and decision questions
  • Data stories pair maps, charts and concise recommendations for nontechnical audiences

Participants can plan secure, reviewable and repeatable WASH GIS workflows for programme use.

  • Analysis plans sequence data acquisition, cleaning, review, publication and archive activities
  • Data protection impact assessments identify risks to respondents and sensitive sites
  • Role-based access controls limit editing, downloading and sharing permissions
  • Humanitarian Data Exchange publication checks remove identifiers and assess disclosure risk
  • Peer review protocols test assumptions, methods, cartography and interpretation
  • Update calendars assign owners, triggers, quality checks and dissemination products

Participants can build, review and present a complete WASH GIS analysis that recommends priority interventions.

  • Capstone brief defines WASH decision, users, geographic scope and success criteria
  • Integrated dataset combines survey, facility, population, administrative and hazard layers
  • Documented workflow applies cleaning, spatial analysis, indicator calculation and validation
  • Decision map presents coverage, access, functionality and priority intervention areas
  • Technical note records assumptions, limitations, metadata and data-protection controls
  • Peer review tests analytical claims, visual clarity and operational recommendations
  • Presentation defends prioritisation findings to programme managers and coordination partners

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. WASH GIS analysis brief template
  2. WASH spatial data dictionary
  3. Data quality assurance checklist
  4. Service coverage and accessibility map set
  5. WASH intervention prioritisation model
  6. GIS workflow and update plan

Who should attend

  • WASH Programme Officers — need evidence to target services and monitor implementation
  • GIS Officers — need WASH-specific methods, indicators and analytical workflows
  • Monitoring and Evaluation Specialists — need mapped indicators and defensible denominators
  • Public Health Analysts — need spatial evidence for hygiene risks and outbreak response
  • Water Utility Planning Staff — need to identify service gaps, asset failures and priority areas
  • Humanitarian Information Management Officers — need to combine WASH, population and hazard data

Skills and tools covered

WASH spatial analysisGIS data managementService coverage mappingAccessibility modellingWater-point analysisSpatial data quality

Tools and standards

QGIS, KoboToolbox, ODK Collect, JMP service ladders

How the course runs

Training methodology

The course combines short instructor briefings with guided analysis of realistic WASH datasets. Participants examine rural water-point inventories, household surveys, sanitation records, population data, flood exposure and outbreak reports. Group work focuses on selecting indicators, checking assumptions, interpreting maps and agreeing priority areas. Simulated programme decisions require participants to compare intervention options using coverage, access and functionality evidence. Individual application planning helps participants adapt data models, quality checks and reporting products to their own programme responsibilities.

Requirements and prerequisites

Participants should understand basic WASH programme concepts such as water points, sanitation facilities, hygiene indicators, household surveys and monitoring data. They should be comfortable working with spreadsheets, tables, filters and simple charts. Prior experience using QGIS, ArcGIS or another GIS package is helpful but not required; the course introduces core GIS operations before progressing to advanced analysis. Participants do not need programming, statistical modelling, remote-sensing or database-administration experience. Familiarity with their organisation's WASH datasets will help participants apply the methods directly.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants should be comfortable with spreadsheets and basic WASH monitoring concepts. Previous QGIS or ArcGIS experience is useful but not required, because the course establishes core GIS operations before advanced analysis.

Participants should bring a laptop capable of running QGIS and, where permitted, sample WASH datasets or reporting questions from their work. Training datasets are provided for all core activities.

The course is designed for WASH programme staff, GIS officers, M&E specialists, public-health analysts and humanitarian information managers. It is particularly relevant for staff who must justify geographic priorities using programme evidence.

General GIS courses teach mapping tools without focusing on WASH service definitions or programme decisions. This course applies GIS methods to water-point functionality, JMP indicators, sanitation service chains, hygiene data, access and outbreak risks.

Participants can use the templates and workflows to clean facility inventories, map service gaps, assess travel time and prepare decision maps. The capstone structure can be adapted to an active programme, subject to data-sharing rules.

Each day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The first week establishes data, GIS and mapping methods; the second week develops WASH analysis, reporting, governance and the final capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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