ArcGIS Pro Spatial Analysis and Mapping Training Course

Participants can import, convert and document common spatial and tabular data sources.

• Data import from CSV, Excel, shapefile, GeoJSON and file geodatabases
• XY Table To Point conversion with explicit coordinate fields
• Feature Class To Feature Class for controlled schema conversion
• Joining tabular records using stable identifiers and cardinality checks
• Appending datasets after field mapping, domain review and duplicate screening
• Data source documentation using item descriptions, credits and usage constraints

Participants can select, verify and document spatial reference systems for analytical requirements.

• Geographic versus projected coordinate systems and their measurement consequences
• Datum transformations selected from source metadata and area-of-use guidance
• Project tool outputs created without overwriting authoritative source datasets
• Define Projection used only to correct missing spatial-reference labels
• Distortion assessment for distance, area, direction and scale requirements
• Coordinate-system choices recorded in project metadata and map documentation

Participants can validate attributes and geometry before conducting spatial analysis.

• Field types, aliases, domains and subtypes for controlled attribution
• Calculate Field expressions using Arcade and Python parser syntax
• Select By Attributes queries using SQL operators and null handling
• Identify duplicate geometries, null values and invalid coded-value entries
• Topology rules for overlaps, gaps, dangles and boundary containment
• Edit sessions, snapping, attribute rules and error inspector workflows

Participants can investigate patterns, records and spatial relationships before formal analysis.

• Definition queries isolate valid records without altering source feature classes
• Select By Location applies containment, intersection and distance relationships
• Summarize Within aggregates points, lines or polygons by reporting zones
• Frequency tables expose repeated values, gaps and candidate duplicate records
• Chart views compare distributions, trends and category totals before mapping
• Bookmarks, map notes and selection sets preserve investigation context

Participants can design readable, accurate and publication-ready thematic maps.

• Visual variables matched to nominal, ordinal, interval and ratio data
• Graduated symbols classified with natural breaks, quantiles and equal intervals
• Symbology hierarchy, transparency and scale ranges for readable thematic maps
• Label classes, Maplex rules and annotation for dense features
• Layout composition using visual hierarchy, legends, grids and marginalia
• Export settings for PDF, PNG, accessibility and print production

Participants can apply vector geoprocessing methods to location and proximity questions.

• Buffer, Multiple Ring Buffer and variable-distance proximity analysis
• Clip, Erase, Intersect and Union selected by analytical question
• Spatial Join configured with match options and summary statistics
• Near and Generate Near Table for closest-feature measurements
• Dissolve and Multipart To Singlepart for aggregation and disaggregation
• Overlay outputs checked for sliver polygons and boundary artefacts

Participants can test and communicate statistically significant spatial patterns.

• Spatial weights matrices defined by contiguity, distance bands and k-nearest neighbors
• Global Moran's I interpreted for clustered, dispersed or random patterns
• Hot Spot Analysis using Getis-Ord Gi* with false-discovery-rate correction
• Cluster and Outlier Analysis using Anselin Local Moran's I
• Incremental Spatial Autocorrelation identifies meaningful distance bands before hotspot testing
• Statistical results communicated with rates, denominators, confidence and caveats

Participants can prepare, analyse and assess raster data for spatial modelling.

• Raster properties: cell size, extent, bands, bit depth and NoData
• Extract By Mask, Clip Raster and resampling method selection
• Raster Calculator expressions using Map Algebra operators and conditional logic
• Reclassify continuous values into documented suitability or risk classes
• Zonal Statistics As Table summarising raster values by polygons
• Cell-size and resampling effects assessed before reporting raster results

Participants can construct transparent multicriteria models for site and priority decisions.

• Problem framing converts policy criteria into measurable spatial factors
• Constraint masks distinguish excluded areas from ranked candidate locations
• Weighted Overlay combines standardised criteria using transparent stakeholder weights
• Weighted Sum preserves continuous factor values for multicriteria scoring
• Sensitivity analysis tests alternative weights, thresholds and classification schemes
• Suitability outputs documented with assumptions, exclusions and decision limits

Participants can derive terrain and drainage information from elevation data.

• DEM sources evaluated for vertical accuracy, resolution and acquisition date
• Fill, Flow Direction and Flow Accumulation prepare hydrological surfaces
• Slope, Aspect and Hillshade derived for terrain interpretation
• Watershed and Snap Pour Point delineate drainage catchments
• Contour generation controlled by interval, smoothing and elevation units
• Terrain outputs checked against imagery, survey points and known channels

Participants can use imagery and three-dimensional views to support spatial interpretation.

• Multispectral band combinations distinguish vegetation, water, built surfaces and bare soil
• Normalized Difference Vegetation Index calculated from near-infrared and red bands
• Supervised classification uses training samples, spectral signatures and accuracy assessment
• Point cloud and multipatch layers visualised in local and global scenes
• Extrude, visibility and line-of-sight tools support three-dimensional communication
• Imagery results validated against reference data, dates and seasonal conditions

Participants can model travel, service coverage and facility allocation using network methods.

• Network dataset components: edges, junctions, turns, restrictions and costs
• Service Area analysis models travel-time coverage from facilities
• Closest Facility routes incidents to appropriate available services
• Origin-Destination Cost Matrix compares accessibility between demand and supply
• Location-Allocation identifies facility sites under specified demand and capacity assumptions
• Network assumptions documented for mode, turn penalties and time dependence

Participants can geocode, review and analyse address data with clear quality controls.

• Locator roles, reference data and match settings for geocoding
• Geocode Addresses outputs reviewed with match status and score thresholds
• Rematch Addresses resolves candidates through source verification and edit tracking
• Reverse geocoding supports incident records, inspections and field observations
• Point aggregation, density surfaces and rate normalisation distinguish concentrations from risk
• Geocoding quality reports record unmatched, tied and low-confidence addresses

Participants can generate and evaluate interpolated surfaces from sampled point data.

• Semivariograms describe spatial dependence, range, sill and nugget effects
• Ordinary kriging estimates continuous surfaces from sampled point measurements
• Empirical Bayesian Kriging selected for automated local semivariogram modelling
• Cross-validation compares prediction errors, standardised residuals and uncertainty
• Prediction standard error maps communicate confidence alongside estimated values
• Interpolation assumptions checked against sampling design and physical processes

Participants can identify changes, recurring events and emerging patterns through time.

• Time-enabled layers configured with instants, intervals, time zones and display formats
• Temporal filtering isolates events within defined reporting periods and recurrence windows
• Space Time Pattern Mining Cube aggregates observations by location and time
• Emerging Hot Spot Analysis identifies new, persistent and sporadic clusters
• Trend charts distinguish seasonal variation from sustained operational change
• Temporal outputs documented with aggregation periods, missing data and reporting cutoffs

Participants can build parameterised ModelBuilder workflows for repeatable spatial processing.

• ModelBuilder variables, parameters and environments create repeatable geoprocessing workflows
• Iterators process multiple feature classes, rasters or administrative units
• Preconditions and inline variable substitution control model execution order
• Model documentation records inputs, outputs, assumptions and version information
• Python window commands export geoprocessing history into ArcPy scripts
• Error messages, logging and intermediate data management support troubleshooting

Participants can create basic ArcPy scripts for repeatable data-processing tasks.

• ArcPy script structure imports modules, defines paths and executes tools
• arcpy.management functions automate conversion, projection and attribute processing
• SearchCursor and UpdateCursor iterate records with explicit field lists
• Try-except blocks capture geoprocessing errors and write useful messages
• Script parameters expose inputs, outputs and optional processing choices
• Version-controlled scripts separate reusable code from machine-specific paths

Participants can package, publish and review GIS outputs for controlled stakeholder use.

• ISO 19115 metadata elements record lineage, quality, constraints and contacts
• Layer files, map packages and project packages preserve reusable configurations
• Web layers published to ArcGIS Online with item details and sharing groups
• Feature service editing settings control ownership, attachments and editor tracking
• Map review checklist tests symbology, scale, credits, accessibility and claims
• Data governance decisions separate authoritative sources, working copies and published outputs

Participants can build, review and present a complete ArcGIS Pro decision-support analysis for a named stakeholder.

• Client brief translated into analytical question, criteria, outputs and acceptance tests
• Source data audited, projected, documented and prepared within a file geodatabase
• Vector, raster or network methods selected against stated decision requirements
• Analysis model built with traceable parameters, assumptions and quality checks
• Decision map and technical notes produced for a named stakeholder
• Peer review addresses method validity, cartographic clarity and implementation actions
• Presentation defends findings, uncertainty and recommended next analytical steps