GIS Workflow Automation Training Course

Participants can document a GIS workflow before building automation components.

• SIPOC diagrams used to define suppliers, inputs, processes and customers
• Swimlane process maps separating analyst, system and reviewer responsibilities
• Decision rules documented for valid inputs, exceptions and escalation routes
• Data lineage recorded from source acquisition through final published outputs
• Workflow specifications written with parameters, assumptions and acceptance criteria
• Version identifiers assigned to datasets, models, scripts and map outputs

Participants can prepare consistent spatial data inputs for automated processing.

• Vector geometry types selected for points, lines, polygons and multipart features
• Raster properties checked for extent, resolution, bands and NoData values
• Coordinate reference systems compared using datum, projection and unit requirements
• GeoPackage, GeoJSON, Shapefile and File Geodatabase format constraints assessed
• Attribute schemas standardised through field names, types, domains and null rules
• File naming conventions applied to source, intermediate and final datasets

Participants can run and configure repeatable geoprocessing tasks in QGIS.

• QGIS Processing Toolbox organised by native, GDAL, GRASS and SAGA providers
• Algorithm parameters configured for clip, dissolve, buffer and spatial join
• Temporary outputs replaced with controlled paths and descriptive filenames
• Processing history reviewed to identify reproducible tool settings
• Batch Processing used for repeated operations across multiple input datasets
• Model inputs exposed through parameters, defaults and descriptive help text

Participants can construct parameterised visual workflows using ArcGIS Pro ModelBuilder.

• ModelBuilder elements connected through variables, tools, connectors and preconditions
• Iterators configured for feature classes, rasters, files and workspace contents
• Inline variable substitution applied to names, paths and output expressions
• Model parameters published for user-selected datasets and processing options
• Intermediate data marked for deletion after dependent tools complete
• Model validation used to identify broken paths and invalid parameters

Participants can write and run basic Python code for GIS processing tasks.

• Python variables, lists, dictionaries and functions used for GIS task logic
• Conditional statements control processing choices based on data characteristics
• For loops process multiple files, layers and attribute values
• Pathlib constructs portable file paths across project directory structures
• Virtual environments separate project dependencies from system Python installations
• Script comments explain assumptions, parameters and expected output products

Participants can structure Python geoprocessing scripts using ArcPy patterns.

• ArcPy environment settings control workspace, overwrite behavior and coordinate systems
• Tool parameters passed using named variables and explicit output paths
• SearchCursor and UpdateCursor process attribute records efficiently
• Feature layers created for selections, definitions and temporary analysis subsets
• GetParameterAsText supports reusable script tools with user inputs
• Messages, warnings and errors returned through structured script reporting

Participants can select appropriate Python libraries for common open-source GIS tasks.

• GeoPandas reads, filters, joins and writes vector spatial datasets
• Rasterio accesses raster metadata, bands, windows and output files
• Shapely performs geometry creation, validation and spatial predicate operations
• Pandas supports tabular cleaning, grouping and attribute transformation tasks
• Library selection considers performance, file formats and deployment constraints
• Requirements files record package versions for reproducible script execution

Participants can use GDAL commands for repeatable raster and vector conversion tasks.

• gdal_translate converts raster formats, subsets extents and changes data types
• gdalwarp reprojects rasters using defined resampling methods and target grids
• gdalbuildvrt creates virtual mosaics without duplicating source raster files
• ogr2ogr converts vector formats, filters attributes and reprojects geometries
• Command-line arguments stored in batch files for repeatable processing sequences
• Exit codes checked to identify failed commands and incomplete outputs

Participants can automate common raster preparation and analysis sequences.

• Raster preprocessing sequence defined for projection, clipping, resampling and mosaicking
• Nearest neighbour, bilinear and cubic resampling matched to data purpose
• Band calculations automated for indices, thresholds and classified outputs
• Cloud masks and NoData rules applied before raster analysis operations
• Tiled processing strategies reduce memory pressure on large imagery datasets
• Raster statistics generated for quality review and analytical reporting

Participants can automate vector cleaning, enrichment and transformation processes.

• Geometry repair identifies self-intersections, duplicates and invalid polygon rings
• Spatial joins enrich asset records using administrative and service boundaries
• Field calculations apply conditional expressions, dates and coded value rules
• Dissolve operations aggregate features using defined grouping attributes
• Duplicate detection combines geometry comparison with key attribute matching
• Schema mapping aligns incoming datasets with target feature class structures

Participants can embed data quality checks within automated GIS workflows.

• Completeness checks compare received records against expected source counts
• Validity rules test geometry, coordinate systems, attribute domains and dates
• Topological rules identify gaps, overlaps, dangles and unintended intersections
• Positional checks compare feature locations against reference layers and tolerances
• Exception tables capture failed records, reasons and required corrective actions
• Quality thresholds determine whether workflows proceed, pause or reject inputs

Participants can make automated GIS processes easier to diagnose and recover.

• Try-except blocks capture anticipated failures without obscuring root causes
• Structured logs record timestamps, inputs, tool settings and output locations
• Error messages distinguish invalid data, missing files and permission failures
• Checkpoint files allow restart from completed stages after process interruption
• Retry rules defined for temporary network, service and file-lock failures
• Recovery procedures document rollback actions and responsible escalation contacts

Participants can generate consistent map and report outputs from standard templates.

• Map templates standardise symbology, scale bars, legends and layout elements
• Dynamic text inserts dates, locations, statistics and data source references
• Map series configuration produces location-specific pages from index features
• Layer visibility controlled through scale ranges, definitions and map themes
• Export settings manage PDF resolution, image formats and file naming
• Summary tables and charts generated from processed spatial attribute data

Participants can plan recurring GIS automation runs with controlled dependencies.

• Batch jobs grouped by trigger, frequency, dependencies and expected duration
• Windows Task Scheduler configured for scheduled Python script execution
• Cron expressions define recurring runs on Linux-based processing servers
• Dependency checks confirm source availability before downstream processes begin
• Notification rules send completion, warning and failure messages to owners
• Run calendars account for data delivery windows and reporting deadlines

Participants can test automated GIS workflows and improve processing reliability.

• Test cases cover valid inputs, invalid inputs, edge cases and volumes
• Expected outputs compared using counts, extents, attributes and visual inspection
• Unit tests isolate reusable functions from full workflow dependencies
• Benchmark timings identify slow operations, storage limits and memory constraints
• Profiling results guide indexing, filtering and intermediate data improvements
• Acceptance tests confirm workflow outputs meet documented user requirements

Participants can prepare GIS automation workflows for controlled operational use.

• Readme files describe installation, inputs, outputs, dependencies and execution steps
• Standard operating procedures assign ownership, approvals and support responsibilities
• Source control concepts manage script revisions, releases and change records
• Access controls protect credentials, sensitive datasets and published output folders
• Data retention rules define archival periods for inputs, logs and products
• Change impact assessments evaluate effects of schema, software and policy updates

Participants can evaluate an automation proposal against technical and operational criteria.

• Requirements traceability links business needs to workflow functions and outputs
• Architecture diagrams show data stores, tools, users and transfer points
• Build-versus-buy decisions compare licensing, maintenance and capability requirements
• Risk registers assign probability, impact, controls and accountable owners
• Implementation roadmaps sequence pilot, testing, rollout and support activities
• Peer reviews assess clarity, maintainability, controls and user suitability

Participants can build, review and present a complete automated GIS workflow with documented controls and outputs.

• Process specification defines business trigger, inputs, transformations and final outputs
• Parameterised model or Python script automates the selected GIS workflow
• Validation rules test geometry, attributes, coordinate systems and processing completeness
• Error handling records failures, exceptions, recovery actions and responsible owners
• Map or dataset outputs produced using controlled naming and delivery rules
• Technical documentation records dependencies, execution steps, assumptions and support procedures
• Peer review assesses usability, reproducibility, data controls and output quality