ArcGIS Spatial Data Analysis Training Course

Participants can design an analysis-ready geodatabase and verify spatial reference and data quality.

• File geodatabase structure, feature datasets and domain-controlled attributes
• Coordinate systems distinguished from geographic and projected coordinate reference systems
• Projection choice based on distance, area, direction and jurisdiction
• Metadata reviewed for lineage, currency, resolution and usage constraints
• Topology rules identify gaps, overlaps, dangles and duplicate features
• Attribute validation uses domains, subtypes, null checks and coded values

Participants can prepare spatial and tabular data for reliable geoprocessing and analysis.

• Field mapping aligns source attributes with analytical requirements
• Calculate Field derives rates, ratios, categories and standardised measures
• Join Field and Add Join connect tabular records correctly
• Dissolve, Multipart To Singlepart and Merge standardise feature geometry
• Select By Attributes isolates valid records and excludes erroneous values
• Data dictionaries document field definitions, units, sources and transformations

Participants can geocode address records, assess match quality and prepare results for analysis.

• Address locators and geocoding services matched to data jurisdiction
• Standardise address fields before batch geocoding source records
• Match scores, status codes and candidate addresses reviewed systematically
• Rematch Addresses resolves unmatched and low-confidence geocoding results
• Geocoded points compared against reference boundaries and aerial imagery
• Positional uncertainty documented before using addresses in decision analysis

Participants can measure proximity and create defensible buffer-based analyses for location questions.

• Buffer distances selected from policy thresholds, travel assumptions or risk criteria
• Multiple Ring Buffer compares graduated proximity zones around facilities
• Near and Generate Near Table calculate nearest-feature relationships
• Euclidean Distance surfaces represent straight-line access and exposure
• Dissolved buffers prevent double-counting overlapping service catchments
• Distance outputs checked against projection units and source accuracy

Participants can combine spatial layers using appropriate overlay operations and interpret resulting attributes.

• Intersect retains shared geometry and attributes from overlapping features
• Union combines polygon extents for land-use and risk analysis
• Identity transfers selected polygon attributes to input feature geometry
• Spatial Join summarises point, line and polygon relationships
• Summarise Within aggregates counts, rates and values by boundaries
• Overlay outputs checked for sliver polygons and attribute duplication

Participants can derive terrain variables from elevation data and assess their analytical uses.

• Digital elevation models evaluated for resolution, vertical datum and voids
• Hillshade visualises terrain form using illumination azimuth and altitude
• Slope calculates rate of elevation change for each raster cell
• Aspect identifies terrain orientation for exposure and solar analysis
• Contour creates elevation lines using defined interval and index values
• Viewshed estimates visible areas from observation points and targets

Participants can prepare raster datasets and classify cell values for spatial decision analysis.

• Raster properties inspected for cell size, extent, bands and pixel type
• Clip Raster and Extract By Mask align analysis extent
• Resample methods selected for categorical and continuous raster values
• Reclassify converts raw values into defined analytical suitability classes
• Raster Calculator applies conditional expressions and arithmetic operations
• NoData handling prevents false values in derived raster outputs

Participants can construct transparent raster suitability models using weighted criteria and constraints.

• Suitability criteria derived from stated operational or planning requirements
• Standardisation converts disparate measurement scales into comparable scores
• Weighted Overlay applies documented criterion weights and class values
• Weighted Sum retains continuous values for sensitivity testing
• Boolean constraints exclude prohibited, unsafe or unavailable locations
• Sensitivity analysis tests effects of alternative weights and thresholds

Participants can interpret imagery, calculate spectral indices and assess land-cover change.

• Multispectral band combinations reveal vegetation, water, soil and urban surfaces
• Spectral signatures compared across land-cover classes and acquisition dates
• Normalized Difference Vegetation Index estimates vegetation greenness from imagery
• Supervised classification uses training samples and defined class schemes
• Confusion matrices assess classification accuracy using reference observations
• Change detection compares classified rasters and reports transition areas

Participants can assess point distributions and distinguish apparent clusters from meaningful spatial patterns.

• Point density maps show concentration using kernel density estimation
• Aggregation effects assessed through grid size and boundary selection
• Average Nearest Neighbor tests clustering against random point distribution
• Ripley’s K examines spatial pattern across multiple distance bands
• Incident rates standardised using population, exposure or asset denominators
• Point patterns interpreted with reporting bias and collection-process constraints

Participants can test spatial autocorrelation and identify statistically significant clusters and outliers.

• Spatial weights matrices define neighbour relationships and distance bands
• Global Moran’s I tests overall spatial autocorrelation patterns
• Local Moran’s I identifies clusters and spatial outliers
• Getis-Ord Gi* identifies statistically significant hot and cold spots
• False discovery rate correction manages multiple testing effects
• Cluster maps interpreted alongside data quality and operational context

Participants can evaluate spatial regression outputs and identify when ordinary regression assumptions fail.

• Ordinary least squares model specifies dependent and explanatory variables
• Multicollinearity assessed using variance inflation factor diagnostics
• Residual maps reveal spatial structure unexplained by regression models
• Koenker statistic tests non-stationarity and heteroskedasticity concerns
• Spatial lag and spatial error model concepts compared
• Regression findings reported without confusing association and causation

Participants can analyse travel-based accessibility and evaluate network-constrained service coverage.

• Network datasets represent roads, restrictions, turns and travel costs
• Service Area calculates travel-time catchments around facilities
• Closest Facility identifies shortest routes between incidents and resources
• Origin-Destination Cost Matrix compares access across many locations
• Location-Allocation selects facility sites using demand and capacity
• Travel-time assumptions documented for peak, off-peak and emergency conditions

Participants can compare alternatives using documented criteria, weights and spatial decision rules.

• Decision criteria separated into constraints, factors and evaluation objectives
• Analytic Hierarchy Process derives relative weights through pairwise comparison
• Criteria weights checked for stakeholder agreement and decision ownership
• Scenario models compare baseline, conservative and growth assumptions
• Candidate locations ranked using suitability, access and risk measures
• Decision records capture evidence, trade-offs and approval rationale

Participants can build, parameterise and document ModelBuilder workflows for repeatable spatial analysis.

• ModelBuilder diagrams sequence tools, variables, inputs and derived outputs
• Parameters expose reusable inputs for users and project scenarios
• Iterators process multiple datasets, categories and geographic areas
• Preconditions control tool execution and prevent invalid workflow sequences
• Model validation identifies broken paths, environment settings and errors
• Exported Python scripts support inspection and future automation planning

Participants can apply quality assurance controls and document reproducible spatial analysis workflows.

• Analysis plans define inputs, methods, parameters, outputs and acceptance criteria
• Geoprocessing history records tool settings and execution messages
• Environment settings control extent, snap raster, cell size and mask
• Independent checks compare results against known locations and totals
• Metadata captures sources, transformations, limitations and responsible analysts
• Versioned project folders separate source data, working data and outputs

Participants can create analytical map products and written findings suited to decision-makers.

• Map purpose determines projection, scale, classification and visual hierarchy
• Sequential, diverging and categorical colour schemes match data semantics
• Classification methods compared using natural breaks, quantiles and equal intervals
• Layout elements include legend, scale bar, sources, date and caveats
• Charts and summary tables support maps without duplicating information
• Findings briefs distinguish observed patterns, interpretations and recommendations

Participants can prepare analysis outputs for controlled sharing through ArcGIS Online and project packages.

• Project packages preserve maps, data, toolboxes and supporting documentation
• Web layers configured with appropriate fields, symbology and refresh settings
• ArcGIS Online groups organise access for teams and stakeholders
• Sharing permissions protect sensitive locations, attributes and analytical outputs
• Web maps designed for inspection, filtering and stakeholder discussion
• Publication checklists verify credits, metadata, privacy and version status

Participants can build, review and present a complete ArcGIS spatial analysis project addressing a defined business or public-service decision.

• Problem statement defines decision owner, question, scope and success criteria
• Analysis-ready geodatabase integrates validated vector, raster and tabular inputs
• Selected methods combine proximity, overlay, raster or statistical analysis
• ModelBuilder workflow documents repeatable processing steps and parameters
• Quality review tests assumptions, outputs, uncertainty and data limitations
• Decision-ready map series presents findings, options and recommendations