Population and Demographic GIS Analytics Training Course

Participants can select and document geographic reference systems suitable for demographic analysis.

• Coordinate reference systems, datums and projected coordinate system selection
• Administrative hierarchies, statistical geographies and service catchment boundaries
• Boundary changes tracked through crosswalks, concordances and effective dates
• Stable geographic identifiers for joining census and administrative records
• Area calculations performed in suitable equal-area projected coordinate systems
• Geographic unit register documenting names, codes, versions and sources

Participants can assess population data sources for fitness, coverage and comparability.

• Census short-form, long-form and post-enumeration survey data characteristics
• Household surveys: sampling frames, weights, confidence intervals and geographic limits
• Administrative records from civil registration, education, health and utilities
• Population registers and their completeness, timeliness and residency definitions
• Source appraisal using coverage, currency, granularity and collection methodology
• Reference dates aligned before comparing population counts and demographic indicators

Participants can create a documented inventory of demographic GIS data assets.

• ISO 19115 metadata elements for geographic datasets and services
• Data inventory fields covering custodian, licence, scale, currency and restrictions
• Lineage records identifying transformations, assumptions and source versions
• Open data portals assessed for download formats and update schedules
• Licence conditions checked before redistributing demographic maps and extracts
• Naming conventions for raw, processed, analytical and publication datasets

Participants can prepare demographic tables and spatial layers for validated integration.

• Field profiling identifies missing values, duplicates, outliers and invalid codes
• Geographic identifiers standardised through padding, case rules and code lookups
• Attribute joins validated with matched records, unmatched records and duplicates
• Spatial joins distinguish containment, intersection and nearest-feature assignment methods
• Population totals reconciled after aggregation, disaggregation and boundary clipping
• Data transformation log records inputs, operations, outputs and validation results

Participants can evaluate geocoded address data for population and service analysis.

• Address standardisation separates house numbers, streets, localities and postal codes
• Geocoding match types, scores and candidate selection rules
• Positional accuracy assessed against reference imagery, parcels and street networks
• Unmatched addresses investigated using exception tables and manual review
• Point aggregation protects confidentiality while retaining useful geographic detail
• Geocoded households assigned to enumeration areas and service zones

Participants can calculate demographic measures using correct numerators, denominators and reference populations.

• Counts, proportions, percentages, ratios and rates distinguished correctly
• Crude rates calculated using population exposure and specified periods
• Age-specific rates reveal patterns concealed by aggregate population measures
• Direct age standardisation using a stated standard population
• Dependency ratios derived from working-age, child and older populations
• Rate suppression thresholds applied to unstable small-number estimates

Participants can design demographic maps that support valid area comparison.

• Choropleth maps use normalised values rather than raw counts
• Proportional symbols communicate totals alongside rates and proportions
• Dot-density maps represent estimated population using documented dot values
• Bivariate maps compare two demographic variables without visual overload
• Map layout includes scale, source, date, projection and classification notes
• Colour schemes selected for ordered data, categories and accessibility

Participants can choose map classification and generalisation methods appropriate to intended decisions.

• Natural breaks, quantiles, equal intervals and standard deviation classes
• Classification sensitivity tested against alternative break methods and ranges
• Data generalisation matches map scale, audience needs and disclosure requirements
• Uncertainty statements distinguish measurement error, model error and temporal uncertainty
• Missing-data patterns mapped before interpreting apparent demographic gaps
• Confidence intervals displayed where survey estimates support reliable comparison

Participants can use gridded population data and dasymetric methods to refine population distribution estimates.

• Raster population grids compared by resolution, reference year and modelling approach
• WorldPop, LandScan and GHSL population products assessed for intended use
• Dasymetric mapping redistributes counts using settlement, land-cover and building masks
• Pycnophylactic interpolation preserves totals while smoothing zonal population surfaces
• Raster zonal statistics estimate population within operational catchments
• Grid-cell estimates reported with resolution limits and model caveats

Participants can incorporate remotely sensed settlement evidence into population analysis.

• Optical imagery distinguishes built-up land, vegetation, water and bare ground
• Sentinel-2 spectral bands support settlement and land-cover interpretation
• Built-up indices identify impervious surfaces with stated classification limitations
• Building footprints used as ancillary evidence for population redistribution
• Image date and cloud cover assessed before comparing settlement extent
• Ground-reference samples validate land-cover classes and settlement boundaries

Participants can select defensible methods for estimating demographic values in incomplete geographies.

• Areal weighting transfers counts between incompatible source and target zones
• Dasymetric weighting improves interpolation using ancillary land-use information
• Regression-based small-area estimation combines surveys with auxiliary variables
• Bayesian smoothing stabilises rates for sparsely populated small areas
• Interpolation assumptions stated before publishing derived population estimates
• Validation compares estimates against independent counts and known totals

Participants can measure population access to facilities using network-based GIS methods.

• Network datasets model roads, travel restrictions, speeds and turn costs
• Service areas calculate populations within stated travel-time thresholds
• Origin-destination matrices measure travel times between residents and facilities
• Two-step floating catchment area method compares supply and demand
• Facility capacity incorporated through beds, staff, counters or service hours
• Accessibility results segmented by age, disability, income or settlement type

Participants can identify and interpret non-random demographic spatial patterns.

• Spatial weights matrices define neighbourhoods through contiguity or distance
• Global Moran's I tests overall spatial autocorrelation in demographic indicators
• Local Moran's I identifies high-high, low-low and spatial outliers
• Getis-Ord Gi-star maps statistically significant clusters of high values
• Multiple testing and small-area instability considered during hotspot interpretation
• Cluster findings checked against data quality, boundaries and contextual knowledge

Participants can map spatial inequality using transparent demographic and socioeconomic measures.

• Dissimilarity index measures uneven distribution between two population groups
• Location quotient identifies local concentration relative to reference populations
• Deprivation indices combine validated indicators using documented weighting rules
• Composite indicators tested for collinearity, directionality and missing values
• Rank-based maps distinguish relative deprivation from absolute population need
• Inequality findings reported without stigmatising communities or protected groups

Participants can analyse demographic change while controlling for boundary and measurement differences.

• Intercensal growth rates calculated using consistent geographic boundaries
• Change maps distinguish absolute change, percentage change and annualised growth
• Boundary harmonisation uses concordance tables before temporal comparison
• Cohort components separate fertility, mortality and migration contributions
• Migration flows mapped using origin-destination matrices and directional symbols
• Time-series anomalies checked against censuses, registration changes and shocks

Participants can develop population-based indicators for vulnerability and priority-setting decisions.

• Population at risk calculated within hazard zones and exposure footprints
• Vulnerability indicators combine demographic sensitivity, access and coping capacity
• Disease rates mapped using appropriate population denominators and reporting periods
• Displacement datasets assessed for registration bias, mobility and duplication
• Priority areas ranked using transparent criteria, weights and decision thresholds
• Humanitarian maps separate operational need estimates from verified population counts

Participants can apply privacy and ethical controls to demographic GIS outputs.

• Personally identifiable information identified within location and demographic attributes
• Aggregation, masking and suppression reduce disclosure risk in published outputs
• K-anonymity considerations applied when releasing small-area population tables
• Data minimisation limits collection and retention to stated analytical purposes
• Access controls assign permissions for raw, processed and published datasets
• Ethical review considers harm, exclusion, surveillance and community representation

Participants can document, quality-check and communicate demographic GIS findings for decision-makers.

• Workflow diagrams show inputs, transformations, outputs, checks and ownership
• Quality-control checklists test totals, joins, projections, rates and legends
• Version control principles preserve reproducible maps, scripts and data documentation
• Decision briefs connect findings, uncertainty, implications and recommended actions
• Map critiques assess visual hierarchy, comparability and interpretation risk
• Publication packages include metadata, methods notes and source citations

Participants can build, review and present a complete demographic GIS analysis for a defined planning decision.

• Problem statement defines decision owner, population, geography and timeframe
• Data inventory records sources, licences, limitations and processing requirements
• Indicator model specifies measures, denominators, classifications and thresholds
• Spatial analysis combines population distribution, service access and priority areas
• Map series communicates findings, uncertainty and recommended action locations
• Peer review tests assumptions, calculations, cartography and decision relevance
• Final presentation defends methods, findings, limitations and implementation recommendations