GIS Mapping for WASH Programmes Training Course

Participants can select, document and check coordinate reference systems for WASH mapping tasks.

• Geographic coordinates, datums and ellipsoids distinguished for field and satellite data
• EPSG codes used to document coordinate reference systems consistently
• UTM zones selected for local engineering and service-area measurements
• On-the-fly projection checked against known control locations
• Reprojection methods chosen to preserve area, distance or direction
• Coordinate transformations documented within project metadata records

Participants can structure a geodatabase that links WASH assets, inspections, households and monitoring records.

• Geodatabases structured with feature classes, tables, domains and relationship classes
• Unique identifiers designed for water points, latrines and household records
• Attribute schemas aligned with JMP indicators and programme monitoring questions
• Controlled vocabularies and coded value domains reduce entry variation
• One-to-many relationships connect facilities, inspections, repairs and water-quality tests
• Data dictionaries specify field definitions, formats, sources and update responsibilities

Participants can prepare location-enabled mobile forms and field protocols for WASH data collection.

• Mobile survey forms configured with geopoints, constraints, skip logic and repeats
• KoboToolbox form design captures water-point functionality and sanitation conditions
• GNSS accuracy, averaging and satellite geometry assessed during field collection
• Offline basemaps and survey packages prepared for low-connectivity locations
• Enumerator protocols address consent, safeguarding and respondent confidentiality
• Photo, barcode and timestamp fields support asset verification and audit trails

Participants can identify, correct and document positional, attribute and topology errors in WASH datasets.

• Range, completeness and logical-consistency checks identify suspect survey records
• Duplicate features detected using identifiers, proximity thresholds and attribute matching
• Positional accuracy assessed against imagery, control points and stated tolerances
• Topology rules flag gaps, overlaps, dangles and invalid geometries
• Field verification priorities ranked by consequence, uncertainty and accessibility
• QA logs record corrections, exceptions, reviewer decisions and approval status

Participants can produce clear, accurate base maps and operational WASH map layouts.

• Administrative boundaries, settlements, roads and hydrography assembled from authoritative sources
• Raster and vector layers clipped, filtered and symbolised for WASH planning maps
• Scale-dependent visibility and generalisation prevent misleading map detail
• Colour, classification and labels selected for legible operational map products
• North arrows, scale bars, sources and dates meet map-layout requirements
• Print and digital layouts configured for field teams and management briefings

Participants can map WASH assets, service status and maintenance requirements using structured asset data.

• Water-point inventories mapped by source type, functionality and management arrangement
• Sanitation facilities classified by technology, sharing, containment and emptying route
• Facility status categories documented with inspection dates and evidence sources
• Proportional symbols and graduated colours compare service assets without distortion
• Asset condition maps identify repair backlogs and preventive-maintenance priorities
• Spatial joins summarise WASH assets within wards, catchments and service zones

Participants can estimate WASH access and rank service gaps using distance, networks and population data.

• Euclidean buffers estimate proximity to water points, toilets and health facilities
• Network analysis calculates walking distance and travel time along usable routes
• Population rasters and household counts produce coverage denominators
• Thiessen polygons test nominal service areas where network data are unavailable
• Two-step floating catchment areas compare demand, capacity and access
• Coverage gaps ranked using distance, functionality, population and seasonal constraints

Participants can use terrain and hydrological data to screen WASH infrastructure and contamination risks.

• Watershed boundaries derived from digital elevation models and flow-direction grids
• Source protection zones mapped using catchments, setbacks and contamination pathways
• Drainage networks traced to assess flood exposure around sanitation infrastructure
• Terrain slope and wetness indices identify difficult construction locations
• Groundwater vulnerability screening combines geology, depth and land-use evidence
• Hydrological assumptions recorded before using terrain outputs for decisions

Participants can interpret satellite imagery for settlement, waterbody, flood and environmental screening.

• Satellite sensor resolution compared for settlement, waterbody and land-cover mapping
• Sentinel-2 spectral bands interpreted for vegetation, bare soil and surface water
• Cloud masks, composites and acquisition dates managed for seasonal comparability
• Normalized Difference Water Index detects open water and flood extent
• Land Surface Temperature supports screening of heat-related water-demand pressures
• Image interpretation distinguishes likely features from confirmed programme evidence

Participants can classify land cover, assess accuracy and identify changes relevant to WASH service planning.

• Training samples selected using field observations, imagery and reference datasets
• Supervised classification separates built-up land, cropland, water and vegetation
• Random Forest parameters tuned and outputs checked for implausible patterns
• Confusion matrices report producer accuracy, user accuracy and overall accuracy
• Change detection quantifies settlement expansion and surface-water variation
• Classification limits communicated before targeting communities or infrastructure investments

Participants can build transparent spatial priority models for WASH investment and response planning.

• Multi-criteria evaluation combines exposure, service deficits, hazard and vulnerability
• Indicator normalisation puts mixed units onto transparent comparable scales
• Weighted overlay documents stakeholder weights, evidence sources and sensitivity tests
• Vulnerability indices mapped without concealing uncertainty or minority settlements
• Hot Spot Analysis identifies statistically significant clusters of poor access
• Priority lists distinguish screening results from final programme decisions

Participants can monitor WASH programme performance over time using spatially referenced indicators.

• Baseline, midline and endline datasets matched using stable asset identifiers
• Time-enabled layers show functionality, coverage and repair trends
• Before-and-after maps separate programme change from seasonal variation
• Disaggregated indicators reveal disparities by location, settlement type and population group
• Automated joins refresh monitoring tables from validated field submissions
• Indicator definitions preserve comparability across reporting periods and partners

Participants can prepare map-based reporting products for operational and management decisions.

• Operational dashboards combine maps, filters, charts and exception lists
• Target users determine refresh cycles, alert thresholds and access permissions
• Web map pop-ups present inspection history, photos and repair status
• Summary statistics checked against source tables before publication
• Management briefings pair spatial findings with decisions, owners and deadlines
• Export formats selected for donor reports, field packs and coordination meetings

Participants can apply governance, security and metadata controls to WASH spatial information.

• Data governance roles assign stewardship, custody, approval and publication authority
• Data minimisation limits collection of personal and sensitive household attributes
• Access controls separate public maps from restricted protection information
• Metadata follows ISO 19115 elements for lineage, quality and constraints
• Version control and change logs protect authoritative programme datasets
• Data-sharing agreements specify purpose, retention, security and recipient responsibilities

Participants can build, review and present a complete WASH spatial analysis package supporting a defined programme decision.

• Problem statement defines decisions, users, geography, indicators and delivery timetable
• Project geodatabase integrates validated field, administrative, population and imagery data
• Spatial analyses justify coverage gaps, priorities and proposed interventions
• Map series and dashboard communicate findings, uncertainty and recommended actions
• Peer review tests methods, visual clarity, assumptions and decision relevance
• Presentation defends data choices, analytical limits and implementation next steps