Handling Large Join Operations In Arcgis For Dominant Area Analysis

The Problem of Slow Performance with Big Data

As geographic information systems (GIS) continue to grow in their use and applications, the size and complexity of spatial datasets have increased exponentially. This poses significant challenges when attempting to join large feature classes and tables in ArcGIS for conducting spatial analysis. When joining millions of records between datasets based on location or attribute relationships, processing times can slow to a crawl.

Excessively long join operations can severely hamper productivity and delay critical decision-making that relies on up-to-date GIS analysis. Domains such as urban planning, logistics, public health, and emergency response rely heavily on the ability to rapidly relate tabular data to feature layers based on spatial and non-spatial attributes.

Some of the most common spatial analysis tasks such as determining service areas based on drive times (known as dominant area analysis), performing site suitability analysis by relating demographic data to regions, aggregating data for statistical analysis, and mapping trends with temporal data all require joining large tabular datasets to area features. Slow join speeds can cause these operations to take hours or even fail entirely.

Table of Contents

By best utilizing ArcGIS tools and functionality while following database management and analysis best practices, GIS analysts can dramatically improve join performance to efficiently work with big data. This allows them to conduct spatial analysis at scale, unlocking deeper insights to make critical decisions even when working with massive feature classes, imagery, and tabular sources.

Strategies to Optimize Join Operations

There are a number of best practices GIS analysts should keep in mind to ensure fast join performance across ArcGIS Desktop and ArcGIS Pro when working with large datasets:

Using Data Management Best Practices to Streamline Geodatabases

Efficiently structuring data in a geodatabase is key for enabling responsive join performance. Some key guidelines include:

Store datasets locally rather than using network or cloud drives
Group feature classes, raster data, and tables into datasets for better organization and query speed
Utilize registered tables to efficiently manage tabular attributes related to spatial features
Model spatial relationships between features to reduce on-the-fly feature comparisons
Design attribute schema with performant data types, limiting unneeded precision to save storage and memory space

Leveraging Spatial Indexes for Faster Feature Matching

Creating spatial indexes significantly accelerates feature selection and table joins by adding a background indexing framework. This quickly narrows down candidate features based on location when joining tabular data, dramatically cutting down processing time. After spatial indexing, performance boosts of over 100x faster have been demonstrated with joins, selections, and geoprocessing.

Partitioning Data into Tiles for Focused Analysis

For extremely large feature classes encompassing entire countries or regions, another optimization strategy is partitioning into tiles. This breaks down analysis into more manageable chunks that can load into memory and join more efficiently. By spatially defining tiles such as groups of counties or states, data volumes and complexity is reduced for each piece of the overall analysis workflow.

Performing Dominant Area Analysis

Dominant area analysis revolves around defining areas encompassed within drive time service areas from point locations. Key facilities such as stores, distribution centers, and hospitals utilize dominant areas for planning locations and logistics to best serve surrounding populations. By joining demographic and other attribute data to the generated drive time polygons, analysts can deeply profile defined service areas and customer bases.

Defining Dominant Areas based on Drive Time Polygons

The ArcGIS Network Analyst extension enables efficient generation of drive time service area polygons centered on points of interest. After digitizing road network feature classes with accurate speed limits, travel time zones outward from specified locations are built with just a few clicks. The millions of vertices comprising drive time polygons utilize spatial indexing for responsive display and analysis.

Joining Demographic Data to Dominant Areas for Site Analysis

Analysts typically need to augment the generated dominant area polygons with additional attributes to perform site suitability modeling and other forms of spatial analysis. Census block group layers containing 100+ field tables with income levels, age breakdowns, housing statistics and other demographic factors are extremely useful to associate with service areas.

Joining such dense attribute tables can grind performance to a halt without care. Rather than attempting to join full regional census tables, first use a definition query to extract a subset representing blocks groups falling within each dominant area. This focused demographic extract prevents bottlenecks when appending tables.

Best Practices for Efficient Data Aggregation

In many cases, analysts need to calculate summarized statistics across all block groups within a dominant area such as average household income or total number of seniors. Executing attribute aggregation using tools such as Summary Statistics or Tabulate Intersection on extremely large join outputs can fail. By first running Divide, separate service areas into smaller tiles before re-joining tables and finally merging for statistics.

Optimizing ArcGIS Geoprocessing Tools

In addition to data preparation strategies, configuring ArcGIS tool settings for maximum performance is critical when working with big data. There are also opportunities to customize tool logic for large datasets using Python scripting.

Configuring Analysis Tool Settings for Large Data

Most ArcGIS geoprocessing tools expose parameters allowing analysts to restrict computation extent, directly leverage spatial indexing, and specify level of precision. Setting processing extent using a polygon feature class rather than calculating across entire feature classes removes unnecessary overhead.

Tools can also be set to use space time composite indexing tied directly into spatial indexing frameworks for accelerates analysis response. Output grid cell size and tool precision should be tested for any flexibility to increase speed without sacrificing required accuracy.

Using Python Scripting to Override Default Tool Behaviors

For ultimate control when conducting spatial analysis against massive datasets, GIS analysts can script custom tools using the ArcPy module for Python. This allows overriding potentially slow default assumptions and processes in standard tools along with creating custom data preparation logic before analysis.

Common optimizations including directly querying a subset of features using a rectangular envelope, introducing multithreading for parallel processing, implementing dynamic tile schemes to analyze area batches, and handling intermediate data spill to disk instead of memory.

Monitoring and Improving Join Performance

After establishing an optimized framework for managing data and configuring tools, GIS analysts must still continually track join speeds to identify bottlenecks as workflows scale up. There is no substitute for quantitatively monitoring performance with timings and benchmarks.

Benchmarking Join Times for Comparison

Running test joins between sample feature classes and tabular data, recording time elapsed provides a baseline measurement indicating overall geospatial database performance. Testing various table and feature class sizes shows range of responsiveness available. Comparing with past benchmarks after database changes spots speed regressions needing attention.

Identifying Performance Bottlenecks with Diagnostic Tools

Slow joins can result from network, database, software, or hardware constraints across complex GIS technology stacks. Windows Performance Monitor with appropriate counters exposes low-level OS, memory, disk, network, and database usage statistics during join operations. Spikes reveal overloaded components struggling to keep up.

Upgrading Hardware Resources Strategically for Cost-Effective Scalability

Analyzing diagnostic telemetry and continuing to push benchmarks informs effective upgrades providing the most significant responsiveness boost per budget. Balancing CPU cores, RAM capacity, solid state storage for caching, and maximum network throughput facilitates efficiently handling large joins.

Conclusion

By following GIS data management practices focused on simplifying storage schema design, maintaining indexed data structures, spatially partitioning analysis, and optimizing tool execution configuration, ArcGIS analysts can achieve performant join operations orders of magnitude faster. Monitoring benchmarks then spotlights paths for further optimizations and scale.

Rather than accepting slow spatial analysis limited by temporary join speeds, proactive analysts can take control over performance for responsively extracting deep insights from big data.