Accelerating Field-Level Analysis In Arcgis Through Scripting
The Problem of Slow Analysis in ArcGIS
Performing analysis on large datasets in ArcGIS can often be a slow and cumbersome process. At the field level, analysts frequently work with high-resolution imagery, lidar data, and large vector datasets that tax the processing capabilities of ArcGIS Desktop. Tasks like spatial overlays, raster processing, and geoprocessing operations can take hours to run for data intensive workflows.
This presents a major pain point for many GIS analysts who require quick turnaround times for their analysis outputs. Waiting hours or days for key results can set back projects and delay critical decisions for field operations. There is a clear need for methods to radically accelerate ArcGIS performance for field-level analysis tasks.
Understanding the Analysis Bottleneck
To understand where the analysis bottleneck occurs, we must first examine the ArcGIS architecture. Data processing occurs in sequential steps, passing data through the layers of the system including data access, analysis execution, processing, and drawing. At each stage, the underlying compute and memory resources are pushed to their limits.
Analysis functions themselves also represent performance drag. Many tools rely on inefficient processes for subsetting data, calculating statistics, and updating values. These core operations leverage legacy libraries that fail to take advantage of modern hardware advances.
Finally, data transfer between memory and storage contributes to slow speeds. Reading and writing large datasets has significant overhead, introducing latency before analysis logic ever runs.
Optimizing Data Inputs and Environments
The first technique to accelerate analysis is optimizing data inputs and environments. It is important to only load and process the minimal data required for the analysis. We can subset datasets by location, attributes, or features to limit reading and processing requirements.
Analysis environments can be configured to run at maximum capacity. Increasing parallel processing on multi-core machines speeds computation. Server job scaling allows large problems to leverage grid resources. And database analytics improve throughput by taking advantage of database architectures.
Parallel Processing with ArcPy Cursors
Python scripting with ArcPy enables multi-threaded analysis workflows that maximize parallelism. Fine-grained cursors iterate over data in chunks that get farmed out concurrently to separate Python processes.
By partitioning heavy geoprocessing into smaller segment units of work, scripts achieve near-linear speedup based on the number of available cores. Parallel cursor processing can improve runtimes by 10x or greater depending on data volumes.
Scripting Tools for Faster Processing
Python toolboxes contain process-specific scripts designed explicitly for fast computation. Purpose-built analysis functions streamline key operations like raster algebra, spatial joining, point density, and proximity analysis.
These tools directly access data backends through extensions and APIs while minimizing overhead. Specialized libraries like Numpy, Numba, Apache Spark, and Arcade enable just-in-time compilation and vectorization that significantly accelerates math operations.
Automating Batch Analysis Workflows
Batch analysis automation is key to improving efficiency for repetitive workflows. The ability to chain together sequences of geoprocessing functions in self-contained scripts eliminates manual process delays.
Parallel batch processing on servers runs independent scripts simultaneously on separate data slices to exploit distributed resources. As each script finishes, automation collects outputs and moves them directly into the next analytical step.
Best Practices for Writing Fast Scripts
While scripting presents many performance advantages, realizing speedup requires following guidelines:
- Load only necessary data into memory
- Pre-allocate outputs to fixed sizes
- Use explicit data types to reduce coercion
- Minimize access to slow storage resources
- Consolidate geoprocessing chains into functions
- Apply buffered reading and writing of datasets
Well-structured scripts that take heed of these best practices avoid major performance pitfalls that hinder analysis runtimes.
Example Scripts for Common Analysis Tasks
Accelerated analogues to traditional ArcGIS analysis are embodied in specialized scripts:
- Fast Overlay Analysis – Performs topology corrections to enable rapid spatial intersections
- Fast Raster Calculator – Hardware-optimized raster algebra on array-based math
- Fast Point Density – Multi-threaded kernel density estimation
- Fast Table Analytics – In-memory column calculations and statistics
These scripts encapsulate performance best practices for common GIS analysis use cases that are frequent bottlenecks.
Testing and Benchmarking Performance Gains
Quantifying script acceleration requires controlled tests against standard ArcGIS processing. Benchmarking analysis runtimes across a range of data volumes plots speedup gained.
Profiling toolbox functions identifies optimization targets. Lagging sections of code reveal opportunities to inject faster algorithms and parallel logic.
Iteratively revising scripts based on benchmark-driven insights leads to orders of magnitude in improved runtimes. This fact-based methodology produces the highest performance outcomes.
Achieving Real-Time Analysis Speeds
By combining data reduction, parallelization, purpose-built tools, and automation, analysis that used to take hours or days can run in minutes or seconds. This enables interactive speeds where outputs reflect live changes.
Script integration with real-time data dashboards, sensor networks, and augmented reality interfaces opens new possibilities for instant field analysis. Going from static reporting to dynamic analytics is transformative for many geospatial use cases.
