Spatial Analysis with Python and ArcGIS Training Course

Participants can organise vector, tabular and geodatabase data for reliable analysis.

• Vector, raster, table and multidimensional data models compared
• Feature classes, feature datasets and relationship classes within file geodatabases
• Attribute domains and subtypes used to control data values
• Unique identifiers, joins and relates designed for traceable records
• Field mapping and calculated fields applied during data integration
• Metadata elements recording source, date, lineage and usage constraints

Participants can select coordinate systems and assess data suitability before analysis.

• Geographic and projected coordinate systems distinguished through measurement consequences
• Datum transformations selected when combining datasets from different reference frames
• Projection choice matched to distance, area and directional analysis
• Topology rules identify gaps, overlaps, dangles and duplicate features
• Positional accuracy, attribute completeness and temporal currency assessed systematically
• Spatial resolution and minimum mapping unit considered before interpretation

Participants can write and run readable Python code for GIS data tasks.

• Python variables, strings, lists and dictionaries used for GIS parameters
• Conditional statements and loops control repeated data processing tasks
• Functions package reusable geoprocessing logic with clear input arguments
• File paths managed with pathlib for portable project scripts
• Exceptions and logging record failures, warnings and processing status
• Jupyter notebooks document code, outputs and analytical commentary

Participants can automate ArcGIS geoprocessing tasks using ArcPy scripts.

• ArcPy environment settings control workspaces, extents and processing outputs
• Tool parameters passed through arcpy management and analysis modules
• Describe objects inspect dataset properties before processing begins
• Cursors read, update and create attribute records efficiently
• Batch processing loops apply tools across multiple feature classes
• Script tools expose validated parameters within ArcGIS Pro toolboxes

Participants can use GeoPandas to inspect, transform and analyse vector datasets.

• GeoDataFrames read GeoJSON, shapefile and geopackage spatial datasets
• Pandas operations clean attributes, parse dates and handle missing values
• Spatial joins connect features using intersects, contains and nearest predicates
• Coordinate reference systems assigned, checked and transformed with GeoPandas
• Dissolve, explode and overlay operations create new analytical geographies
• GeoPackage outputs preserve geometry and attributes for ArcGIS use

Participants can measure distance, service reach and spatial accessibility using appropriate methods.

• Euclidean buffers model straight-line influence zones around selected features
• Multiple-ring buffers compare graduated access thresholds and service standards
• Near and Generate Near Table tools calculate feature proximity
• Thiessen polygons allocate nearest-facility catchments from point locations
• Distance decay assumptions tested when interpreting access and exposure
• Barrier features incorporated before reporting proximity-based conclusions

Participants can combine geographic layers to identify intersections, exclusions and allocations.

• Clip, erase and intersect operations define eligible and excluded areas
• Union and identity overlays retain attributes from overlapping polygon layers
• Spatial joins aggregate counts, sums and means by geography
• Dissolve operations create reporting zones from shared attribute values
• Areal weighting estimates values across incompatible administrative boundaries
• Overlay outputs checked for sliver polygons and attribute duplication

Participants can prepare raster datasets and derive terrain-based analytical surfaces.

• Raster cell size, extent, bands and bit depth evaluated
• Mosaic datasets organise imagery and elevation sources for analysis
• Slope, aspect and hillshade derived from digital elevation models
• Raster calculator expressions combine conditional and continuous surface criteria
• Reclassification converts raw cell values into analytical categories
• Zonal statistics summarise raster values within reporting polygons

Participants can derive classified land-cover and change outputs from imagery.

• Spectral bands and reflectance values interpreted for land-cover discrimination
• Normalized Difference Vegetation Index calculated from near-infrared and red bands
• Training samples designed for supervised image classification workflows
• Random Forest classification parameters selected and accuracy assessed
• Confusion matrices report producer, user and overall accuracy
• Post-classification comparison identifies land-cover transitions between observation dates

Participants can test whether mapped patterns show meaningful spatial clustering or dispersion.

• Spatial weights matrices define neighbour relationships for statistical testing
• Global Moran's I tests overall spatial autocorrelation in values
• Local Moran's I identifies high-high, low-low and outlier clusters
• Getis-Ord Gi star detects statistically significant hotspot areas
• Kernel density estimation converts incident points into intensity surfaces
• Multiple testing and modifiable areal unit effects qualify results

Participants can generate and assess interpolated surfaces from sampled observations.

• Inverse distance weighting estimates values from nearby observations
• Ordinary kriging models spatial autocorrelation through semivariograms
• Trend analysis identifies broad directional patterns before interpolation
• Cross-validation compares predicted values against observed sample locations
• Interpolation barriers and anisotropy considered for physical processes
• Prediction uncertainty mapped alongside interpolated value surfaces

Participants can model travel routes, service areas and location allocation on networks.

• Network datasets represent streets, travel modes, restrictions and costs
• Shortest path analysis calculates efficient routes between origins and destinations
• Service areas model reachable locations within time or distance thresholds
• Origin-destination cost matrices compare access across many locations
• Location-allocation models identify facilities serving demand under stated criteria
• Travel-time assumptions documented for defensible accessibility findings

Participants can build transparent weighted suitability models for site-selection decisions.

• Decision criteria translated into measurable spatial factors and constraints
• Boolean exclusion masks remove prohibited or infeasible candidate areas
• Factor rasters standardised using common suitability scoring scales
• Weighted overlay combines criteria using documented relative importance
• Analytic Hierarchy Process supports pairwise weighting of decision criteria
• Sensitivity analysis tests how weights affect priority locations

Participants can analyse geographic events and changes across time periods.

• Date fields standardised for time-enabled layers and temporal filtering
• Space-time cubes aggregate incidents by location and time interval
• Emerging hotspot analysis classifies persistent, new and diminishing patterns
• Time-series charts compare seasonal trends across administrative areas
• Before-and-after spatial comparisons separate change from data artefacts
• Temporal aggregation choices documented to avoid misleading trend claims

Participants can use elevation and three-dimensional data to assess visibility and form.

• Scene layers display buildings, terrain and point-cloud derived features
• Digital surface and terrain models selected for visibility questions
• Viewshed analysis identifies visible areas from specified observer locations
• Line-of-sight tools test obstruction along critical sight lines
• Extrusion and elevation settings create interpretable three-dimensional scenes
• Vertical datum and height assumptions recorded for reliable outputs

Participants can publish governed spatial outputs for stakeholder access and reuse.

• Hosted feature layers configured with fields, domains and editing controls
• Web maps combine operational layers, symbology, pop-ups and bookmarks
• ArcGIS Online sharing settings manage public, group and organisational access
• Dashboards present indicators, maps, selectors and operational summaries
• Item descriptions document purpose, sources, limitations and update frequency
• Publishing checks confirm performance, permissions and display quality

Participants can communicate spatial findings clearly without overstating analytical certainty.

• Visual hierarchy directs attention through symbols, labels and layout structure
• Classification methods compared using natural breaks, quantiles and equal intervals
• Colour schemes selected for sequential, diverging and categorical data
• Scale bars, north arrows and source notes support map interpretation
• Uncertainty and limitations communicated through legends and report text
• Executive summaries connect spatial evidence with recommended actions

Participants can document, test and govern spatial analysis workflows for reuse.

• Data lineage records inputs, transformations, outputs and responsible analysts
• Python code reviews assess readability, parameters and error handling
• Quality assurance checklists verify geometry, attributes and analytical outputs
• Version control principles manage script revisions and shared project files
• Named output conventions support retrieval, audit and scheduled updates
• Data governance controls address access, retention and sensitive locations

Participants can build, review and present a complete Python-supported spatial analysis for a defined decision.

• Problem statement defines decision users, criteria, constraints and success measures
• Data inventory assesses source fitness, projection, quality and permissions
• Python workflow automates preparation, analysis and output generation steps
• Spatial model combines proximity, overlay, raster or network methods
• Quality review tests assumptions, intermediate outputs and analytical limitations
• Decision map and technical briefing present findings, uncertainty and recommendations