Healthcare GIS Applications Training Course

Participants can select appropriate coordinate systems and geographic units for health analysis.

• Geographic and projected coordinate systems compared for health mapping
• Datum selection and transformation errors affecting facility locations
• Administrative boundaries, service areas and enumeration areas distinguished
• Modifiable areal unit problem applied to district health indicators
• Population denominators matched to reporting periods and boundary versions
• Geographic identifiers reconciled across facility, census and programme datasets

Participants can create a relational data model for a healthcare GIS project.

• Feature classes structured for facilities, patients, services and boundaries
• Unique identifiers linking encounter records to geographic reference data
• Relational joins and one-to-many relationships checked before mapping
• Data dictionaries specify fields, definitions, sources and update cycles
• FHIR location resources related to facilities and service delivery points
• Metadata records capture lineage, scale, currency and usage restrictions

Participants can prepare health and population data for reliable spatial analysis.

• Missing values, duplicates and invalid codes profiled using quality rules
• Facility registers reconciled against master facility lists and coordinates
• Date fields standardised for monthly, seasonal and outbreak analyses
• Denominator data aligned with numerator definitions and reporting geography
• Outliers investigated through source checks, field ranges and record history
• Quality-control logs document corrections, exclusions and unresolved data issues

Participants can geocode, validate and document location data for health records.

• Address parsing separates house numbers, streets, settlements and postcodes
• Batch geocoding converts patient addresses into point coordinate records
• Match scores reviewed using thresholds, exception queues and manual checks
• Gazetteers resolve alternate settlement names and local spelling variations
• Positional accuracy classified by rooftop, street, locality and district matches
• Geocoding audit trails retain source address, method, score and reviewer

Participants can apply privacy safeguards when mapping sensitive health information.

• Minimum necessary data principle applied to spatial health datasets
• Small-number suppression prevents disclosure in maps and summary tables
• Spatial aggregation protects patient confidentiality at appropriate reporting levels
• Jittering and masking assessed against analytical accuracy requirements
• Role-based access controls separate identifiable and de-identified datasets
• Data-sharing agreements specify permitted uses, retention and disclosure procedures

Participants can design clear health maps for technical and executive audiences.

• Map purpose determines extent, scale, projection and information hierarchy
• Choropleth classes selected using quantiles, natural breaks and policy thresholds
• Rates and counts distinguished to prevent misleading disease burden maps
• Colour schemes chosen for sequential, diverging and categorical health variables
• Proportional symbols communicate facility capacity, caseload and resource quantities
• Legends, annotations and source notes support accurate map interpretation

Participants can calculate and map health indicators with appropriate denominators.

• Crude rates calculated from events, population denominators and time periods
• Age standardisation distinguishes population structure from health risk differences
• Standardised mortality ratios compared against reference population expectations
• Confidence intervals displayed when comparing small-area health indicators
• Empirical Bayes smoothing stabilises rates from small population counts
• Indicator specifications document numerator, denominator, exclusions and interpretation

Participants can identify and interpret spatial disease patterns using appropriate analytical methods.

• Point pattern maps distinguish event locations from aggregated case counts
• Kernel density estimation reveals concentrations without administrative boundary dependence
• Global Moran's I tests overall spatial autocorrelation in health rates
• Local Indicators of Spatial Association identify high-high and low-low areas
• Getis-Ord Gi* locates statistically significant hot and cold spots
• Cluster findings interpreted with surveillance bias, denominators and uncertainty

Participants can analyse changing health patterns across place and time.

• Time-enabled layers display case progression by reporting week and location
• Seasonal decomposition separates recurring patterns from unusual health events
• Space-time cubes organise observations by location, interval and measure
• Emerging hotspot analysis identifies persistent, intensifying and new clusters
• Outbreak thresholds compared with baseline incidence and reporting completeness
• Time-series maps communicate sequence without implying unsupported causation

Participants can model travel time and routes to healthcare services.

• Road networks prepared with travel speeds, restrictions and directional rules
• Service areas calculate populations reachable within defined travel-time thresholds
• Origin-destination cost matrices compare communities with available facilities
• Shortest-path analysis identifies referral routes and transport bottlenecks
• Travel-time assumptions tested for season, transport mode and road condition
• Network results validated against local travel reports and operational knowledge

Participants can assess service catchments, coverage gaps and referral relationships.

• Thiessen polygons estimate nearest-facility catchments under simplified assumptions
• Observed patient origins reveal actual catchments and bypass behaviour
• Two-step floating catchment area methods measure supply-demand accessibility
• Coverage gaps mapped against population, vulnerability and service eligibility
• Referral flows visualised between primary, secondary and tertiary facilities
• Catchment findings translated into outreach, transport and staffing options

Participants can evaluate candidate service locations using spatial planning criteria.

• Location-allocation models assign demand points to proposed service facilities
• Weighted overlay combines population need, access, hazards and land constraints
• Suitability criteria scored using documented weights and decision rules
• Exclusion zones identify unsuitable sites near hazards or protected areas
• Scenario comparisons test mobile clinics, new facilities and expanded capacity
• Sensitivity analysis examines how weighting changes site recommendations

Participants can use remote-sensing layers to support environmental health analysis.

• Satellite imagery sources compared by spatial, temporal and spectral resolution
• Land surface temperature derived from thermal imagery for heat-risk mapping
• Normalized Difference Vegetation Index supports vector habitat and exposure studies
• Land-cover classification identifies settlement, water, vegetation and impervious surfaces
• Raster reclassification converts continuous environmental measures into risk categories
• Remote-sensing limitations documented through cloud cover, resolution and validation

Participants can combine raster data to model environmental and service-related health exposures.

• Raster alignment checks cell size, extent, projection and resampling method
• Euclidean distance surfaces estimate proximity to hazards and health services
• Cost-distance modelling represents terrain, land cover and travel resistance
• Zonal statistics summarise exposure values within communities and service areas
• Multi-criteria raster models combine weighted environmental health risk factors
• Exposure results checked against field observations and epidemiological evidence

Participants can identify geographic inequities and prioritise populations for intervention.

• Social vulnerability indicators combined with health needs and service access
• Disaggregated analysis compares outcomes by sex, age, disability and income
• Equity gap maps identify populations facing multiple access disadvantages
• Priority indices rank areas using transparent need and feasibility criteria
• Bivariate mapping shows relationships between deprivation and health outcomes
• Interpretation avoids stigmatising communities or inferring individual characteristics

Participants can specify web maps and dashboards for health operations and reporting.

• Web map layers configured with scale ranges, pop-ups and field aliases
• Dashboard indicators connect maps, charts, filters and operational targets
• Refresh schedules match reporting frequency, data validation and user needs
• User roles define viewing, editing, exporting and administrative permissions
• Mobile data collection workflows capture facilities, stockouts and field observations
• Operational dashboards distinguish monitoring signals from confirmed analytical findings

Participants can present spatial findings with appropriate evidence, limitations and recommendations.

• Analytical narratives connect geographic evidence to service-planning decisions
• Map briefs state question, method, findings, limitations and recommended action
• Uncertainty communicated through confidence intervals, caveats and sensitivity results
• Executive presentations prioritise decisions, options, costs and implementation implications
• Technical appendices document sources, processing steps and analytical parameters
• Peer review checks interpretation, cartography, privacy and reproducibility

Participants can plan a controlled healthcare GIS project from request through maintenance.

• Project charters define sponsor, users, decisions, scope and success measures
• Data governance assigns stewardship, ownership, access approval and update responsibilities
• Workflow diagrams sequence acquisition, preparation, analysis, review and publication
• Version control procedures preserve approved data, maps and analytical outputs
• Risk registers address privacy, bias, data gaps and operational misuse
• Implementation roadmaps schedule pilots, validation, rollout and performance review

Participants can build, review and present a complete healthcare GIS analysis package addressing a service-planning or public-health decision.

• Project brief defines decision question, stakeholders, geography and success criteria
• Prepared geodatabase combines health, population, facility and contextual datasets
• Spatial methods selected for access, demand, risk or coverage question
• Map series presents indicators, patterns, catchments and priority locations
• Peer review tests assumptions, privacy safeguards, reproducibility and decision relevance
• Presentation recommends actions, implementation steps, limitations and monitoring measures