Automating Gis Workflows With Python And Modelbuilder

Automating Workflows for Efficiency

Benefits of Automation in GIS

Automating repetitive GIS tasks and workflows provides multiple advantages for GIS analysts and organizations. Key benefits include improved efficiency, consistency, scalability, and integration. By coding workflows instead of performing manual work, GIS professionals can complete more high-value analysis rather than mundane data tasks. Automated processes also execute identically each time, minimizing human error and variability in outputs. Scripts and models easily scale to process more data or run faster with better infrastructure. Finally, automation facilitates connecting GIS to other business systems through APIs and web services.

Overview of ModelBuilder

ModelBuilder is ArcGIS’s visual programming language for building automated workflows without coding. Analysts graphically link data sources, geoprocessing tools, and outputs, creating models that run each process automatically. ModelBuilder utilizes ArcGIS’s rich tool libraries for data conversion, analysis, and mapping. Models simplify repeat tasks, chain complex operations, produce consistent results, and integrate GIS into other systems. Users set parameters to control model execution, share them through toolboxes, and run them across folders of data.

Building Workflows with ModelBuilder

To automate processes in ModelBuilder, users drag tools onto the canvas and connect them by their inputs and outputs. Each tool runs sequentially, passing data between them. Logical operators like Iterate and conditional tools add flow control and branching logic. Parameters expose tools’ configurable options in the model interface. ModelBuilder handles data types between tools and offers history, validation, and editing functions. Models run natively in ArcMap or ArcGIS Pro or publish as services, scripts, or tools. Best practices include planning data flow thoroughly, designing for reuse, using relative path names, limiting iterations, and testing rigorously before finalizing workflows.

Python Scripting for Customization

Why Use Python for GIS Automation?

Python excels as a scripting language for GIS automation due to its code simplicity, extensive specialized libraries, open ecosystem, and full customization. Python syntax uses easy-to-read keywords accessible for beginners compared to complex codes like JavaScript. Mature Python GIS packages like ArcPy, GeoPandas, Shapely, Fiona, and PyQGIS provide specialized geoprocessing tools. Python and its libraries are also open source and benefit from strong community support. Most importantly, Python allows crafting original scripts tailored to unique needs, not possible within GUI modelers alone. Users can integrate advanced algorithms, employ complex logic, and build custom interfaces.

Python Modules for GIS

Many Python libraries enable GIS automation tasks. Key packages include ArcPy for managing ArcGIS geodatabases, GeoPandas for working with geospatial data structures, Shapely for geometry operations, Fiona for vector data access, Rasterio for rasters, PyQGIS to control QGIS, Geopy for geocoding, Whitebox for advanced analysis, and Matplotlib for maps and graphics. These tools process, analyze, convert, map and share geographic information within Python workflows. Developers can write scripts federating modules to execute robust geospatial data pipelines, analytics, and reporting fully unattended.

Creating Custom Tools with Python

GIS analysts can build original Python scripts implementing specialized logic tailored to their needs. Functions automate data preparation like coordinate transformations, custom field calculations, dynamic segmentation, automated metadata updates, and other data management tasks not available in off-the-shelf tools. Spatial analysis scripts execute targeted algorithms and models using vector overlays, proximity tools, terrain modeling, pathfinding, 3D visualization, machine learning classifiers, and more for engineering, logistics, emergency response, and business planning. Python GUI scripts also create user-friendly dashboards, custom forms, macro recording, and automated map atlases.

Integrating Python with ModelBuilder

Accessing ModelBuilder from Python

Python provides multiple techniques to invoke, control and customize ModelBuilder workflows from scripts. At runtime, Python uses ArcGIS’s arcpy.gp module to directly call models by name, passing in parameters. Python iteratively runs models over datasets using loops. Logic controls model execution paths based on conditional expressions. Scripts dynamically modify model parameters for flexible behavior. Complete models export as Python scripts callable from larger automation tasks. This achieves the best of both worlds: ModelBuilder’s visual workflows for standard tasks, wrapped in Python’s higher order logic.

Importing Python Scripts into ModelBuilder

To utilize Python automation directly inside ModelBuilder, users add a Script tool referencing their .py file, exposing its parameters. ModelBuilder feeds features to the script tool when executed, returning output datasets. This allows complex Python data processing unavailable in native ModelBuilder tools. Some additional benefits include easier migration of existing scripts into models, tool reusability, independent testing/updates, and distributed processing for specific functions. ModelBuilder handles passing data between other processes.

Best Practices for Integration

Maintaining clean interfaces between ModelBuilder and Python enables easier integration. Scripts avoid hard-coding data paths relied upon by models by using placeholder variables. Model script tools pass source parameters outward to enable reuse. Documentation guides model authors on script tools’ expectations and outputs. Testing scripts independently verifies functionality before integrating. If models call many external scripts, Python controlling logic keeps the model simple and focused only on chaining primary steps. Reusing the same script tools modularly produces consistent, supportable automation.

Example Automated Workflows

Automated Geocoding Workflow

A common GIS workflow automates geocoding address lists by street or postal codes and generates maps automatically from the results. The geocoding script handles string parsing, conflating alternate street names, querying locational databases, applying match criteria, reporting non-matches, and standardizing output data structure with x/y values. Separate scripts spatially join aggregated demographic data to geocoded points and render layered thematic maps parameterized for paper size. Scheduling tasks connect these script tools into a reproducible reports automating the entire geocoding to mapped deliverables workflow.

Automated Map Production Workflow

Producing professional print maps requires automating multi-step styling, export, and reporting around GIS datasets. ModelBuilder iteratively processes feature layers by applying cartographic representations for points, lines and polygons including appropriate symbology, nested scale dependencies, and label formatting. A script tool handles dynamic text, legend, and scale bar placement customized to layout dimensions. Final scripts export outputs at the right DPI and dimensions for printing and bundle pages together with formatted cover pages and tables of contents for release.

Automated Spatial Analysis Workflow

Many GIS studies require repeatable sequences of geospatial analysis tools to prepare, process, model, and summarize research data. Workflows first preprocess datasets such as DEM terrain scans requiring smoothing, sun angle shading, edge conditioning, and water flattening transforms before analysis. Core logic for suitability modeling, least cost pathway, viewshed delineation, spatial interpolation and others automated in Python script tools. Parameters control re-running for alternative scenarios. Final tools handle stitching raster mosaics, converting formats for delivery, calculating statistics, exporting charts, and publishing services to share outputs interactively.