GIS Mapping for Business Analysis Training Course

Participants can assess, prepare and document business location data for analysis.

• Geocoding customer addresses with match scores, aliases, and exception handling
• Business identifiers joined to geography using documented matching keys
• Data dictionaries record field definitions, provenance, refresh dates, and owners
• Completeness, validity, uniqueness, consistency, and timeliness quality dimensions
• Duplicate detection uses standardisation, fuzzy matching, and survivorship rules
• Privacy classification separates personal data, sensitive locations, and public records

Participants can create a structured GIS project and prepare common spatial data formats.

• Import tabular, vector, raster, and web-service datasets into a project
• Set projected coordinate systems appropriate to distance and area calculations
• Transform coordinates while documenting datum shifts and transformation accuracy
• Clip, dissolve, merge, append, and repair vector features
• Attribute queries apply Boolean logic, null handling, and coded domains
• Geodatabases organise feature classes, relationships, domains, and metadata

Participants can produce clear, accurate maps that support business interpretation and action.

• Visual variables encode business measures through size, colour, value, and texture
• Choropleth classification compares quantile, equal interval, natural breaks, and custom thresholds
• Proportional symbols and dot density maps represent counts responsibly
• Legends, labels, scale bars, and north arrows support interpretation
• Map layouts apply visual hierarchy, annotation rules, and accessible colour choices
• Export specifications control resolution, formats, attribution, and print dimensions

Participants can assemble a documented spatial dataset for a defined business question.

• Geocode branch, customer, competitor, supplier, and incident location records
• Build trade-area polygons from drive time, radius, and administrative boundaries
• Create derived fields for sales density, penetration, and service coverage
• Join census, demographic, and economic indicators to target geographies
• Document assumptions, sources, transformations, and refresh responsibilities in metadata
• Version project data and preserve reproducible processing steps

Participants can use core vector analysis methods to measure proximity, coverage and relationships.

• Select features by location using intersect, within, contains, and nearest relationships
• Buffer facilities using distance bands, variable distances, and exclusion zones
• Overlay layers through intersect, union, identity, erase, and spatial join
• Summarise customers, revenue, and incidents within defined geographic units
• Calculate proximity measures between demand points, competitors, and facilities
• Validate spatial outputs against source records and known locations

Participants can quantify market opportunity, penetration and competitive position spatially.

• Define market potential using population, income, expenditure, and customer propensity
• Calculate market penetration from customer counts and eligible households
• Compare branch performance using sales per capita and indexed benchmarks
• Apply Huff gravity model inputs to competitive retail attraction
• Identify white-space opportunities through demand, supply, and access gaps
• Rank territories with weighted criteria and sensitivity checks

Participants can segment geographic markets and design balanced operational territories.

• Segment customers using geographic, demographic, behavioural, and value attributes
• Profile catchments by household composition, spending patterns, and mobility
• Create sales territories using contiguity, workload, potential, and travel constraints
• Measure territory balance with variance, compactness, and workload indicators
• Assign accounts using nearest facility, rules-based allocation, and exceptions
• Map service inequities by customer type, travel burden, and coverage

Participants can compare candidate locations using transparent multi-criteria analysis.

• Specify site-selection criteria, thresholds, weights, and disqualifying constraints
• Screen candidate locations with weighted overlay and Boolean suitability rules
• Estimate cannibalisation using existing catchments, overlap, and sales transfer assumptions
• Compare sites through demand potential, accessibility, cost, and competitive exposure
• Conduct sensitivity analysis on weights, thresholds, and uncertain inputs
• Prepare recommendation maps with evidence, caveats, and decision triggers

Participants can measure access and assess facility configurations using network-based methods.

• Construct network datasets from roads, restrictions, turn costs, and speeds
• Calculate drive-time isochrones for branches, clinics, warehouses, and customers
• Run origin-destination cost matrices between demand, facilities, and depots
• Locate facilities using p-median, maximum coverage, and minimax objectives
• Sequence delivery stops with vehicle routing constraints and service windows
• Assess accessibility using travel time, transport modes, and impedance assumptions

Participants can apply raster methods to continuous surfaces, suitability and event concentration.

• Interpret raster resolution, extent, cell values, and resampling methods
• Derive terrain indicators from digital elevation models and slope surfaces
• Reclassify land cover and continuous surfaces for suitability modelling
• Apply map algebra to combine constraints, scores, and environmental factors
• Use kernel density estimation for incidents, transactions, and event concentrations
• Compare raster outputs against ground conditions and independent reference data

Participants can derive and evaluate earth observation indicators for business analysis.

• Satellite imagery bands, spatial resolution, revisit frequency, and spectral signatures
• Calculate NDVI and built-up indices from multispectral imagery
• Supervised classification separates land-cover classes using labelled training samples
• Accuracy assessment reports confusion matrices, producer accuracy, and user accuracy
• Change detection identifies development, vegetation loss, and flood extent
• Integrate earth observation indicators with branch planning and risk models

Participants can test spatial patterns and interpret geographic variation in business outcomes.

• Spatial autocorrelation measured with Moran's I and Local Indicators of Spatial Association
• Hot spot analysis identifies statistically significant clusters using Getis-Ord Gi*
• Regression diagnostics test residual spatial dependence, multicollinearity, and heteroscedasticity
• Ordinary least squares relates sales outcomes to demographic predictors
• Geographically weighted regression examines local variation in predictor relationships
• Interpret significance, effect size, uncertainty, and model limitations responsibly

Participants can identify geographic risk concentrations and compare disruption scenarios.

• Map hazard exposure, asset vulnerability, and operational criticality
• Flood, wildfire, crime, and supply disruption indicators inform risk screening
• Build risk surfaces from probability, impact, and control effectiveness scores
• Prioritise inspections using spatial risk scores and resource constraints
• Scenario maps compare disruption impacts under alternative assumptions
• Communicate uncertainty without overstating precision or causal claims

Participants can publish governed map products that support managerial decisions.

• Design executive map products around decisions, actions, and audience needs
• Configure web maps with scale-dependent visibility, pop-ups, and filters
• Build dashboards combining key performance indicators, maps, charts, and selectors
• Apply data refresh schedules, access permissions, and publication governance
• Optimise layers, symbology, and queries for responsive web performance
• Write map narratives that distinguish evidence, interpretation, and recommendation

Participants can specify repeatable GIS workflows for production business analysis.

• Model repeatable workflows with documented inputs, parameters, outputs, and checks
• Use ModelBuilder or graphical processing models for batch geoprocessing
• Write Python scripts for file handling, spatial joins, and exports
• Query spatial databases using SQL, spatial indexes, and geometry functions
• Schedule refresh processes with logging, error handling, and version control
• Apply reproducibility principles to shared business intelligence workflows

Participants can apply governance controls to location data and spatial recommendations.

• Define data stewardship roles, quality thresholds, retention, and approval workflows
• Assess location-data privacy risks using purpose limitation and minimisation principles
• Record source licences, attribution requirements, and permitted reuse conditions
• Test analytical fairness across neighbourhoods, customer groups, and protected characteristics
• Audit model assumptions, parameter choices, and decision consequences
• Establish review gates before publishing operational or public-facing maps

Participants can build, review and present a complete GIS business analysis with a defensible recommendation.

• Frame a business decision, stakeholder audience, and measurable success criteria
• Prepare and document location data using defensible quality controls
• Apply spatial analysis methods appropriate to stated decision questions
• Produce decision maps, indicators, and a concise recommendation
• Review findings through peer challenge, sensitivity checks, and caveat testing
• Present methods, results, limitations, and implementation actions to decision-makers