Automating Complex Gis Workflows And Geoprocessing Tasks

Understanding Workflow Automation

Defining workflows and geoprocessing in GIS

A geographic information system (GIS) workflow is a sequence of geoprocessing tools and models that automate spatial analysis and data management tasks. Geoprocessing refers to the manipulation and analysis of geographic data to create new output datasets. Manual geoprocessing using the GIS graphical user interface is laborious. Automating complex, multi-step geoprocessing workflows improves productivity and standardizes processes.

Benefits of automating workflows

Automating GIS workflows provides many benefits over manual data processing:

Saves time – Automation allows faster processing of large datasets.
Improves consistency – Standardized, repeatable processes minimize human errors.
Facilitates updating – Workflows can rapidly integrate new inputs and recreate outputs.
Enables advanced analysis – Complex multi-tool analyses can be automated.
Allows reuse – Automated workflows may be shared and reused.

Challenges of manual workflows

Manually executing GIS geoprocessing tools and tasks has several drawbacks:

Time-consuming – Analysts waste time on repetitive dataset processing.
Prone to errors – Mistakes easily creep in during manual operations.
Hard to reproduce – Documenting all steps for reuse is difficult.
Difficult to update – New outputs require completely re-executing workflows.
Limitations in complexity – Building advanced multi-tool processes manually is impractical.

These inefficiencies and limitations motivate the shift towards automating workflows.

Tools for Building Automated Workflows

Using ModelBuilder for visual workflow design

ArcGIS ModelBuilder provides a visual programming environment to create automated workflows. Analysts drag-and-drop tools into a model diagram and define inputs/outputs through a graphical user interface. Behind the scenes, model logic is saved as Python script. Benefits of ModelBuilder include:

Rapid visual workflow creation without coding
Encapsulates a sequence of geoprocessing tools and tasks
Sets model parameters and logical design through UI
Packages workflows for sharing and reuse

Programming workflows with Python

Python scripting gives the most flexible way to automate workflows. Python code directly accesses ArcGIS geoprocessing tools and functions to iterate through datasets. Benefits include:

Full control over workflow logic with code
Integration with other Python libraries
Advanced script logic like conditional tests
Custom functions to encapsulate reuse

Example Python script for geocoding addresses

This script iterates through a table of addresses, geocodes each record using the ArcPy geocoding toolset, and writes the output geometries to a feature class with source address attributes joined:

import arcpy
address_table = r"C:\address.csv" 
output_feature_class = r"C:\geocoded_addresses.shp"
address_locator = r"C:\locator.loc" 

# Geocode addresses and write output to feature class
arcpy.GeocodeAddresses_geocoding(address_table, address_locator)
arcpy.JoinField_management(output_feature_class,"ResultID", address_table,"ResultID”)

Key Components of Automated Workflows

Integrating tools and models

Automated workflows integrate:

Multiple geoprocessing tools – Spatial analysis, overlay operations, data conversions
Custom script tools and models – Encapsulates custom logic and parameterization
Iterators – Process multiple datasets like feature classes in a workspace

These components are orchestrated into a coordinated automated process.

Incorporating iteration and conditional logic

Automated workflows incorporate:

Iteration using Python loops to process multiple datasets
Conditional if/else logic to handle branches in a workflow
Error handling through try/except blocks
Parameter substitution to customize each iteration

This scripting logic handles variability and errors.

Handling errors gracefully

Automated workflows anticipate and handle failures through:

Try/except blocks to catch errors
Error logging to text files or databases
Notifications to technical staff
Retry logic or alternate paths

Robust error handling prevents workflow failures.

Real-World Applications

Automating analysis of new geospatial datasets

Automated workflows rapidly integrate and analyze new geospatial data layers without manual processing. For example:

Overlay census demographic data with zip code boundaries
Intersect new pipeline routes with environmental constraints
Union updated parcel cadastre over flood hazard zones

Automation enables quick turnaround ingestion and processing.

Streamlining production of periodic maps and reports

Automated cartography and reporting workflows eliminate repetitive manual efforts each update cycle by programmatically:

Select updated datasets as workflow inputs
Construct layered PDF maps with templates
Populate report tables and generate charts
Export outputs to file shares

Scheduling workflows optimizes periodic products.

Enabling reuse of standardized processes

Well-constructed workflows encapsulate complex analysis tasks for reuse such as:

Specialized suitability models
Advanced overlay index calculations
Statistical simulations
Machine learning predictions

Standard tools and Python functions lack this packaged functionality that facilitates reuse across the organization.

Testing and Deploying Automated Workflows

Strategies for testing automated workflows

Rigorously testing automated workflows is essential before deployment including:

Unit testing – Isolate and validate each component
Integration testing – Assess workflow end-to-end
Edge case testing – Exercise boundary conditions
Failure testing – Simulate exceptions and errors

Test input datasets and expected outputs should cover real-world scenarios.

Sharing workflows with other GIS users

To maximize reuse across an organization:

Save Python scripts in shared repositories
Publish ModelBuilder workflows as tools and services
Package workflows as automatic scripts
Promote templates, documentation, and use cases of workflows

Training enables users to apply standardized workflows for common tasks.

Considerations for integration into business systems

To migrate workflows from prototypes to enterprise IT production systems:

Assess computational infrastructure needed
Build in schedulers or trigger automation from data updates
Set expectations for workflow life cycle and deprecations
Instrument reporting metrics on workflow executions
Establish monitoring, alerting rules, and support escalations

Workflows designed for server integration follow IT system disciplines.