Applying Python Fundamentals To Arcpy For Optimal Workflows

Leveraging Python in ArcGIS for Efficient Workflows

ArcGIS provides advanced geoprocessing and geospatial analysis capabilities out-of-the-box. However, mastering the Python programming language unlocks the full potential of ArcGIS through the ArcPy site package. ArcPy enables automating complex workflows, ensuring repeatable processes, and building custom tools and applications.

This article provides actionable insights into:

Core concepts for leveraging Python and ArcPy
Efficient workflows for spatial data analysis and mapping
Best practices for coding and multiprocessing

Follow the techniques outlined below to boost productivity, enhance quality, and create innovative solutions with ArcGIS and Python.

Table of Contents

Understanding the ArcPy Module

ArcPy serves as the essential gateway enabling Python to harness the geoanalytic capabilities of ArcGIS. Key features include:

Accessing and manipulating ArcGIS geospatial datasets like vectors, rasters, and tables
Executing geoprocessing tools and combining them into models/scripts
Automating workflows for analysis, data conversion, and map production
Handling licenses for ArcGIS extensions and managing background processing

Overview of ArcPy Capabilities and Integration with ArcGIS

The 300+ classes and 2000+ functions within ArcPy provide full access to ArcGIS functionalities through an intuitive Python interface. Users can integrate ArcPy scripts seamlessly with ArcGIS Pro and ArcMap to perform automated workflows.

Key capabilities include:

Geoprocessing: Execute tools, build models, automate workflows
Data Access: Query layers and tables, update attribute values
Mapping: Create dynamic print-quality cartography and map books
Sharing: Publish web maps, feature layers, geoprocessing services

Key Classes and Functions for Geoprocessing Workflows

Core ArcPy classes for automating analysis workflows:

arcpy.mp: Multiprocessing and parallel execution
arcpy.sa: Raster analysis and image processing
arcpy.da: Data access and cursor operations
arcpy.mapping: Map document manipulation and analysis

Helper functions:

Env: Set environment variables like workspace and extensions
Describe: Retrieve metadata for data like coordinate system
List*: List available datasets, fields, tools, etc.
Get*: Get object properties like spatial reference

Reading and Writing Geo-Spatial Data

Efficiently accessing spatial data in Python enables automating workflows across feature classes, raster datasets, tables, topologies, networks, and more. Key concepts include:

Accessing Feature Classes, Rasters, Tables with ArcPy

Use arcpy.da cursors for fetching records or updating values
Read rasters as NumPy arrays for analysis using arcpy.RasterToNumPyArray()
Reference data by full path or within a arcpy.mp.ArcGISProject()

For example:

“`python
fc = r”C:\Project\Data\StudyArea.gdb\ Landcover”

with arcpy.da.SearchCursor(fc, [“TYPE”, “SHAPE@”]) as cursor:
for row in cursor:
print(“Feature type: ” + row[0])
print(row[1].area) # Print geometry property
“`

Creating New Data and Exporting Outputs

Construct geospatial datasets like rasters and feature classes using arcpy.management tools
Set appropriate environments like output coordinate system and cell size
Use arcpy.Copy_management() or arcpy.conversion tools to export outputs

Example of exporting model results:

“`python
model_output = r”C:\Project\Results\vulnerability.gdb\risk_scores”

output_shp = r”C:\Project\Maps\risk_zones.shp”

arcpy.env.outputCoordinateSystem = model_output

arcpy.CopyFeatures_management(model_output, output_shp)
“`

Handling Different Data Formats like Shapefiles, Geodatabases

Use filename and path to reference shapefile (.shp) datasets
Access geodatabase tables and feature classes directly by name without extension
Set workspace to parse relative paths within a database

Example reading shapefile and writing to file geodatabase:

“`python
arcpy.env.workspace = “C:/Project/Data/Boundary.gdb”

in_shp = r”C:\Project\Data\study_area.shp”

out_fc = “project_area”

arcpy.CopyFeatures_management(in_shp, out_fc)
“`

Automating Analysis and Geoprocessing

Python scripting streamlines executing tools repeatedly across features, constructing workflows, and sharing processing logic. This enables efficient and reproducible analysis.

Iterating Tools Over Features for Batch Processing

Use arcpy.da cursors to iterate through rows in attribute tables
Alternatively, list and iterate over feature class directly
Check for and skip invalid inputs within loop logic

Example batch slope calculation:

“`python
input_dem = r”C:\Project\DEM\terrain.tif”

watersheds = “C:\Project\Data\Watersheds.gdb\Basins”

with arcpy.da.UpdateCursor(watersheds, [“Slope”]) as cursor:
for row in cursor:
basin = row[0]
out_slope = Slope(input_dem, basin)
row[0] = out_slope
cursor.updateRow(row)
“`

Constructing Analysis Model Workflows

Build sequences of geoprocessing tools into arcpy.ModelBuilder() workflows
Iterate model execution over feature classes using arcpy.ApplyGeoprocessingModelToFeatuers()
Share logic as script tools and geoprocessing packages

Example model workflow:

“`python
model = arcpy.ModelBuilder(“C:/Project/Models/Risk.gmdx”)

# Add geoprocessing tools to model
slope = model.tool(“Slope”)
aspect = model.tool(“Aspect”)
relief = model.tool(“Ruggedness”)

# Wire tool inputs / outputs
aspect.input = slope.output
relief.inputs[0] = aspect.output

arcpy.ApplyGeoprocessingModelToFeatures(relief, watersheds)
“`

Handling Errors and Providing User Feedback

Robust error handling ensures continuous workflows and useful diagnostics:

Wrap tool executions in try / except blocks to catch errors
Manage transaction blocks for consistent outputs on failures
Use arcpy.AddMessage() to update processing status
Raise custom exceptions on invalid conditions

Example:

“`python
try:
out_slope = Slope(input_dem, basin_feature)
cursor.updateRow(row)
arcpy.AddMessage(“Calculated slope for watershed ” + basin)

except ExecuteError:
arcpy.AddError(“Slope tool failed for feature” + basin)
“`

Streamlining Map Production

Automating map creation eliminates tedious manual workflows while enforcing cartographic standards.

Automated Cartography with Dynamic Text, Scale Bars

Programmatically control text elements with arcpy.mapping.TextElement()
Generate scale bars and north arrows using arcpy.mapping.AddScalebar()
Create layout elements like titles and logo graphics

Example dynamic title text:

“`python
title_text = arcpy.mapping.TextElement()
title_text.text = “Wildfire Risk Zones”
title_text.name = arcpy.ValidateTableName(“title”)

arcpy.mapping.AddText(“C:/Project/Templates/Layout.mxd”, title_text)
“`

Creating Multi-page Map Books and Atlases

Build Data Driven Pages using index feature class
Customize template with map series elements like page numbers
Export PDF map books using arcpy.mapping module

Example exporting water basin atlas:

“`python
atlas = arcpy.mp.ArcGISProject(“C:/Project/atlas.aprx”)

atlas.listMaps()[0].dataDrivenPages.refresh()

pdf_atlas = r”C:/Project/Output/WatershedAtlas.pdf”

arcpy.mapping.ExportToPDF(atlas, pdf_atlas)
“`

Exporting Maps to Various Formats like PDF and Image

Common encodings:

PDF – Redistributable, maintains geospatial referencing
JPG – Good for online sharing and applications
TIFF – Large format print and archiving

Example exporting wildlife habitat map to georeferenced TIFF:

“`python
habitat_map = atlas.listMaps(“Habitats”)[0]

output_tiff = r”C:\Project\Output\wildlife_habitats_2022.tif”

arcpy.mapping.ExportToTIFF(habitat_map, output_tiff)
“`

Best Practices

Follow these key recommendations for performant, maintainable scripts:

Efficient Coding Workflows to Reduce Processing Time

Minimize reading / writing to disk by storing data in memory
Use arcpy.env.extent and arcpy.env.mask to limit processing bounds
Encode logic into toolboxes and Python packages for reusability
Take advantage of multiprocessing for independent tasks

Managing Background Processing and Multiprocessing

Robust distributed execution patterns:

Wrap long-running tasks using arcpy.Async() to enable background geoprocessing
Set arcpy.env.parallelProcessingFactor to higher number to use more CPU cores
Ensure proper locks to prevent I/O race conditions

Handling Licenses for ArcGIS Extensions

Check extension availability using arcpy.CheckExtension() and arcpy.CheckOutExtension()
Wrap extension use in start / stop checkpoint blocks
Consider ArcGIS licensing restrictions when exporting toolchains