GIS for Public Policy Training Course

Participants can assess geographic data sources and document their fitness for a policy purpose.

• Vector, raster, tabular and API data structures compared for policy use
• Feature classes, attributes, geometry types and coordinate reference systems
• Open Government Data portals assessed for authority, coverage and update cycles
• Data dictionaries record field definitions, units, codes and permitted values
• ISO 19115 metadata elements documented for discoverability and reuse
• Data provenance recorded from acquisition through transformation and publication

Participants can prepare and validate geographic datasets for reliable policy analysis.

• Coordinate reference systems selected using area, purpose and measurement requirements
• Projected and geographic coordinate systems distinguished through practical measurement consequences
• Datum transformations checked before combining national, municipal and satellite datasets
• Geocoding addresses with match scores, exceptions and privacy controls
• Geometry repair identifies duplicates, gaps, overlaps and invalid polygons
• Quality assurance log captures source, date, transformation and unresolved limitations

Participants can apply privacy, ethical and equity safeguards to geographic policy evidence.

• Data protection impact assessments identify risks in location-based personal data
• Anonymisation, aggregation and suppression methods protect sensitive point records
• Indigenous data sovereignty and community consent considered in map publication
• Equity review tests differential disclosure risks for vulnerable populations
• Licensing terms distinguish open, restricted, proprietary and confidential datasets
• Ethical map design avoids stigmatizing labels and misleading visual emphasis

Participants can build organised GIS projects and perform core data transformation operations.

• QGIS project structure organises layers, styles, paths and processing outputs
• Attribute joins connect programme records to administrative geographies
• Spatial joins assign facilities, households and incidents to reporting areas
• Field Calculator creates derived indicators with documented expressions
• Selection queries isolate eligible populations using transparent policy rules
• Processing models automate repeatable preparation steps and preserve audit trails

Participants can produce maps that accurately communicate policy patterns and limitations.

• Choropleth classification compares natural breaks, quantiles, equal intervals and rates
• Rates, ratios and denominators prevent misleading comparisons between unequal populations
• Graduated symbols, proportional symbols and dot density match policy questions
• Colour schemes meet accessibility requirements and communicate ordered values clearly
• Map layouts establish hierarchy through title, legend, scale and source notes
• Small-area uncertainty communicated using suppression, confidence intervals and annotations

Participants can measure geographic access to services and identify underserved populations.

• Network analysis measures travel time to schools, clinics and emergency services
• Service areas define populations reachable within policy-relevant time thresholds
• Origin-destination matrices compare access across wards, districts and demographic groups
• Location-allocation models test facility sites against demand and capacity
• Barriers, turn restrictions and mode assumptions documented for defensible results
• Accessibility indicators disaggregated by income, disability, age and gender

Participants can combine geographic criteria to target interventions and assess coverage.

• Buffer analysis estimates populations exposed to hazards, services or regulations
• Overlay operations identify intersections among need, eligibility and service coverage
• Distance decay functions model declining influence from facilities or hazards
• Weighted overlay combines criteria using explicit policy weights and thresholds
• Suitability maps distinguish hard constraints, preferences and excluded land
• Scenario assumptions recorded for review by policy and technical stakeholders

Participants can test whether policy indicators show meaningful geographic clustering.

• Spatial autocorrelation tests clustering using Global Moran's I statistics
• Local Indicators of Spatial Association identify significant high-high and low-low areas
• Kernel density estimation reveals incident concentrations without administrative boundary bias
• Hot Spot Analysis applies Getis-Ord Gi* with documented distance bands
• Cluster maps distinguish statistical significance from operational priority
• Multiple testing and small-number effects qualified in policy interpretation

Participants can create comparable population-based indicators across changing geographic boundaries.

• Population denominators selected from censuses, registers and gridded estimates
• Area-weighted interpolation estimates indicators across changed administrative boundaries
• Dasymetric mapping reallocates population using land cover and building data
• Boundary harmonisation preserves time-series comparability after ward reorganisation
• Missingness, undercount and temporal mismatch assessed before distributional analysis
• Equity baselines compare outcomes by deprivation, ethnicity, sex and disability

Participants can use raster data to measure environmental conditions and policy exposure.

• Raster resolution, extent and cell alignment set before environmental analysis
• Remote-sensing indices support land cover, heat and vegetation policy indicators
• Zonal statistics summarise raster exposure within neighbourhood and administrative polygons
• Terrain derivatives identify flood pathways, slope constraints and drainage catchments
• Interpolation methods compare inverse distance weighting, kriging and spline assumptions
• Raster calculator builds reproducible composite exposure and suitability indicators

Participants can interpret spatial relationships while distinguishing association from causal inference.

• Geographically weighted regression explores spatially varying relationships between policy variables
• Ordinary least squares diagnostics test residual autocorrelation and model specification
• Spatial lag and spatial error models address dependent observations
• Confounding, selection bias and reverse causality limited in observational mapping
• Difference-in-differences designs incorporate treated and comparison geographies over time
• Results tables separate association, uncertainty, assumptions and causal claims

Participants can design geographic indicators and evaluation approaches for policy interventions.

• Indicator reference sheets define numerator, denominator, frequency, owner and disaggregation
• Baseline maps establish pre-intervention conditions for target communities
• Before-and-after comparisons account for seasonal variation and boundary changes
• Control areas selected using matched characteristics and intervention timing
• Outcome harvesting locates unintended effects reported by communities and implementers
• Monitoring dashboards connect spatial indicators to programme review cycles

Participants can create transparent suitability analyses for planning and investment decisions.

• Multi-criteria decision analysis structures objectives, criteria, weights and alternatives
• Analytic Hierarchy Process derives pairwise weights and consistency ratios
• Constraint maps exclude protected land, hazards and incompatible zoning
• Suitability scenarios test weight changes, thresholds and policy priorities
• Participatory mapping records local knowledge, disputed boundaries and proposed sites
• Decision audit trails document evidence, trade-offs and approval rationale

Participants can analyse spatial risk to support adaptation, preparedness and recovery decisions.

• Hazard, exposure and vulnerability layers construct location-specific risk profiles
• Flood, heat and drought indicators identify priority adaptation areas
• Evacuation routes tested against road closures, capacity and travel time
• Critical infrastructure dependency maps identify cascading service disruption risks
• Early-warning coverage assessed against communication access and population vulnerability
• Recovery prioritisation combines damage assessments, deprivation and service restoration needs

Participants can apply GIS evidence to land, housing and capital investment decisions.

• Parcel, zoning and land-use layers reconcile planning policy conflicts
• Informal settlement mapping supports upgrading without exposing residents to harm
• Land administration records linked to service plans and infrastructure corridors
• Change detection identifies conversion, encroachment and development-control breaches
• Right-of-way analysis identifies acquisition impacts and resettlement risks
• Spatial evidence informs transparent capital investment sequencing decisions

Participants can map incidents and cases while applying appropriate privacy and interpretation controls.

• Incident geocoding separates reported location, occurrence location and reporting address
• Temporal hotspot analysis compares seasonal, weekly and hourly patterns
• Repeat-victimisation analysis identifies locations requiring preventive service responses
• Privacy-preserving aggregation protects complainants while retaining operational utility
• Problem-oriented policing scans clusters without inferring individual risk
• Public-health surveillance maps cases, catchments and intervention coverage

Participants can present spatial findings clearly to policy leaders and nontechnical stakeholders.

• Policy briefs frame spatial findings around decisions, options and consequences
• Story maps combine annotated maps, charts and evidence for nontechnical audiences
• Uncertainty statements specify data limits, confidence and decision implications
• Static, interactive and print products selected by user needs
• Web Map Service and Web Feature Service support controlled publication
• Accessibility checks address colour contrast, alt text and readable labels

Participants can define governance arrangements for sustainable policy GIS services.

• GIS operating model assigns stewardship, custodianship, approval and publication responsibilities
• Data-sharing agreements specify purpose, access, retention, security and accountability
• Standards register aligns coordinate systems, identifiers, metadata and update schedules
• Version control and change logs preserve reproducible policy evidence
• Procurement requirements define interoperability, ownership, support and exit provisions
• Implementation roadmap sequences people, data, technology, governance and funding

Participants can build, review and present a complete GIS-based policy analysis with defensible recommendations.

• Policy question, theory of change and decision audience defined
• Data inventory, metadata record and privacy assessment completed
• Reproducible workflow prepares, joins and validates spatial evidence
• Analytical method produces accessibility, equity, risk or suitability findings
• Decision map and policy brief communicate options, uncertainty and recommendations
• Peer review tests assumptions, ethics, reproducibility and decision usefulness
• Presentation defends evidence, trade-offs and implementation actions