GIS for Development Planning Training Course

Participants can select appropriate data models for planning datasets.

• Vector feature classes for points, lines, polygons and administrative boundaries
• Raster grids representing imagery, elevation, population surfaces and hazard intensity
• Attribute tables linked through unique identifiers and relational database keys
• Feature datasets organised by theme, geography, source and update cycle
• Geodatabase design for land, infrastructure, population and environmental layers
• File formats including GeoPackage, GeoJSON, Shapefile, GeoTIFF and CSV

Participants can prepare spatial layers that align accurately in a common reference system.

• Geographic coordinate systems using latitude, longitude and datum definitions
• Projected coordinate systems selected for distance, area and regional mapping
• EPSG codes used to document coordinate reference system choices
• On-the-fly projection distinguished from permanent dataset reprojection operations
• Datum transformation parameters checked before combining datasets from different sources
• Spatial alignment verified using roads, boundaries, landmarks and imagery

Participants can create a fit-for-purpose inventory of planning data sources.

• Administrative boundary sources assessed for authority, scale, version and currency
• Population datasets compared across census, surveys, registers and gridded estimates
• OpenStreetMap features reviewed for road, facility and settlement coverage
• Satellite imagery selected by spatial resolution, revisit frequency and cloud cover
• Field data collection specifications defined for facilities, assets and observations
• Data inventory records capture owner, licence, format, geography and refresh dates

Participants can evaluate spatial datasets and document their appropriate use.

• ISO 19115 metadata elements recorded for source, lineage, quality and restrictions
• Positional accuracy assessed against control points, imagery and authoritative layers
• Attribute completeness checked using null values, domains and logical rules
• Topology rules identify gaps, overlaps, duplicates and invalid polygon boundaries
• Data quality statements distinguish observed values, estimates and modelled outputs
• Access controls classify public, internal, restricted and personally sensitive datasets

Participants can clean, transform and combine datasets for spatial analysis.

• Field calculations standardise names, codes, dates, units and categorical values
• Joins and relates connect census tables, service records and boundary layers
• Clip, dissolve and merge operations create planning-area analysis datasets
• Geocoding converts facility addresses into point features with match scores
• Spatial joins summarise points, lines and raster values within administrative areas
• Processing logs document source files, parameters, outputs and quality checks

Participants can organise a reusable geodatabase for a planning project.

• Feature naming conventions distinguish source, theme, year, scale and status
• Attribute domains control permitted values for facility type and condition
• Unique identifiers preserve links between field records, assets and monitoring systems
• Layer groups structure baseline conditions, constraints, demand and proposed interventions
• Versioned datasets separate raw source data from analysis-ready working layers
• Project folders store data, scripts, layouts, exports and documentation consistently

Participants can identify patterns, concentrations and geographic disparities in planning data.

• Thematic mapping compares rates, counts, densities and percentages correctly
• Choropleth classification methods include quantile, equal interval and natural breaks
• Kernel density estimation identifies concentrations of incidents, demand or facilities
• Spatial aggregation summarises service indicators by ward, district and catchment
• Rate calculations use appropriate population, household or area denominators
• Outlier checks distinguish data errors from meaningful geographic variation

Participants can measure geographic access to facilities and services.

• Buffer analysis identifies populations within defined facility service distances
• Euclidean distance surfaces estimate straight-line access where networks are unavailable
• Nearest-neighbour analysis identifies underserved settlements and duplicate facility coverage
• Catchment areas compare travel thresholds against population demand estimates
• Overlay methods identify schools, clinics or water points near hazards
• Access assumptions documented for distance thresholds, barriers and transport modes

Participants can analyse routes, travel times and service areas using network data.

• Road network topology connects intersections, segments, restrictions and travel costs
• Service-area analysis maps reachable populations within specified travel-time thresholds
• Shortest-path analysis compares routes for emergency response and field operations
• Origin-destination cost matrices measure access between settlements and facilities
• Impedance values incorporate road class, surface condition, slope and seasonality
• Network results validated against local travel knowledge and observed journey times

Participants can combine spatial layers to identify development opportunities and constraints.

• Intersect operations identify parcels affected by zoning, hazards and protected areas
• Union analysis combines land-use, tenure and environmental constraint polygons
• Erase operations exclude restricted land from potential development areas
• Raster overlay integrates continuous surfaces such as slope, rainfall and elevation
• Constraint registers distinguish legal exclusions, technical limitations and negotiable factors
• Overlay outputs tested for scale mismatch, boundary uncertainty and outdated source data

Participants can build transparent site-selection and land-suitability models.

• Multi-Criteria Decision Analysis structures objectives, criteria, weights and alternatives
• Boolean suitability models apply mandatory exclusion and inclusion rules
• Weighted linear combination combines standardised criterion scores into suitability indices
• Pairwise comparison supports stakeholder weighting using the Analytic Hierarchy Process
• Sensitivity analysis tests whether weight changes alter priority locations
• Suitability maps document assumptions, thresholds, weights and decision limitations

Participants can select and interpret earth observation data for planning questions.

• Electromagnetic spectrum bands support vegetation, water, soil and built-up analysis
• Spatial, spectral, temporal and radiometric resolution guide imagery selection
• Sentinel-2 and Landsat imagery compared for planning monitoring applications
• Cloud masking and image compositing prepare usable optical satellite scenes
• False-colour composites reveal vegetation condition, water bodies and urban growth
• Ground truth observations support image interpretation and classification validation

Participants can produce and assess land-cover and land-change evidence.

• Training samples represent land-cover classes, seasonal conditions and spectral variation
• Supervised classification assigns pixels using labelled reference sample data
• Normalized Difference Vegetation Index measures vegetation greenness and change
• Post-classification comparison identifies transitions between two classified land-cover maps
• Confusion matrices report overall accuracy, producer accuracy and user accuracy
• Change maps distinguish genuine land conversion from seasonal or classification effects

Participants can prepare spatial evidence for disaster risk reduction planning.

• Risk framework combines hazard, exposure, vulnerability and coping capacity indicators
• Flood exposure maps intersect inundation zones with settlements and critical facilities
• Terrain derivatives calculate slope, aspect, flow direction and drainage accumulation
• Vulnerability indices combine housing, poverty, age and disability-related indicators
• Scenario maps compare probable impacts under defined event magnitude assumptions
• Risk communication maps avoid false precision and disclose uncertainty clearly

Participants can apply GIS evidence to land-use allocation and environmental screening.

• Existing land-use maps distinguish residential, commercial, agricultural and conservation areas
• Zoning compliance analysis compares proposed development footprints against planning controls
• Environmental sensitivity screening identifies wetlands, buffers, habitats and steep slopes
• Fragmentation metrics assess patch size, connectivity and edge effects
• Land-use conflict maps identify competing claims, incompatible uses and encroachment
• Scenario comparison maps show consequences of alternative spatial development patterns

Participants can design GIS-supported monitoring indicators for development programmes.

• Results frameworks link spatial indicators to outputs, outcomes and target populations
• Small-area indicators reveal geographic disparities hidden by national averages
• Baseline and follow-up maps compare coverage, access and condition changes
• SDG indicator disaggregation uses geography, sex, age and disability dimensions
• Dashboard specifications define indicators, refresh schedules, thresholds and map interactions
• Monitoring protocols record data sources, calculation rules and validation responsibilities

Participants can create clear maps for technical review and executive decisions.

• Visual hierarchy uses size, colour, contrast and placement to prioritise information
• Colour schemes distinguish sequential, diverging and categorical data appropriately
• Map layouts include title, legend, scale, north arrow, sources and date
• Classification choices matched to analytical purpose and audience interpretation needs
• Annotation identifies priority sites, findings, limitations and recommended actions
• Accessible map design considers colour vision, text size and print reproduction

Participants can plan an implementable GIS workstream for a development planning function.

• Problem statements define decision users, geographic scope, outputs and review criteria
• Work breakdown structures assign data, analysis, review and publication responsibilities
• Data governance plans specify ownership, update cycles, quality controls and access
• Standard operating procedures document repeatable mapping and analysis workflows
• Procurement specifications define software, data, hardware, support and training requirements
• Implementation roadmaps sequence quick wins, capability development and system improvements

Participants can build, review and present a complete GIS-based planning evidence pack for a defined development decision.

• Define a planning decision, target geography, stakeholders and evidence requirements
• Prepare documented datasets using coordinate checks, cleaning and metadata records
• Apply suitable proximity, overlay, network or suitability analysis methods
• Produce decision maps, indicator tables, assumptions and data limitation statements
• Peer review tests analytical logic, map clarity and recommendation traceability
• Present findings, priority locations, implementation actions and monitoring measures