GeoAnalytics Engine brings GeoAnalytics tools from ArcGIS Server and Pro into your Spark infrastructure on premises and in the cloud. The GeoAnalytics Engine also includes over 100 spatial type functions.
Most of the tools in GeoAnalytics Server and Desktop are available within the GeoAnalytics Engine tools module. Use
PySpark to import the tools module from the GeoAnalytics Engine package. For example:
import geoanalytics.toolsWithin GeoAnalytics Engine, some tools such as Append Data, Create Buffers, Dissolve Boundaries, and Merge Layers run using Spark SQL functions instead of dedicated tools like in GeoAnalytics Server and GeoAnalytics Desktop. The comparison table below provides links to example workflows that replicate the functionality of these GeoAnalytics tools using Spark SQL.
GeoAnalytics tool comparison by product
GeoAnalytics tools are available in three products:
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GeoAnalytics Engine tools and spatial type functions provide distributed processing across your existing Spark infrastructure.
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GeoAnalytics Server tools provide distributed processing across multiple server cores and machines with ArcGIS Enterprise.
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GeoAnalytics Desktop tools provide parallel processing across multiple cores on your laptop or desktop running ArcGIS Pro.
The following comparison table gives a high-level overview of the tools available in each of the GeoAnalytics products.
| GeoAnalytics Server | GeoAnalytics Desktop | GeoAnalytics Engine | |
|---|---|---|---|
| Aggregate Points | |||
| Append Data | 1 | ||
| Build Multi-Variable Grid | |||
| Calculate Density | |||
| Calculate Field | |||
| Calculate Motion Statistics | |||
| Clip Layer | |||
| Copy to Data Store | 2 | ||
| Create Buffers | 3 | ||
| Create Routes | |||
| Create Service Areas | |||
| Create Space Time Cube | |||
| Describe Dataset | 2 | ||
| Detect Incidents | |||
| Dissolve Boundaries | 4 | ||
| Enrich from Multi-Variable Grid | |||
| Find Closest Facilities | |||
| Find Dwell Locations | |||
| Find Hot Spots | |||
| Find Point Clusters | |||
| Find Similar Locations | |||
| Forest-based Classification and Regression | 5 | ||
| Generalized Linear Regression | 5 | ||
| Generate OD Matrix | |||
| Geocode Locations from Table | |||
| Geographically Weighted Regression | |||
| Group By Proximity | |||
| Join Features | |||
| Merge Layers | 6 | ||
| Nearest Neighbors | |||
| Overlay Layers | |||
| Reconstruct Tracks | |||
| Reverse Geocode | |||
| Run Python Script | |||
| Snap Tracks | |||
| Summarize Attributes | 2 | ||
| Summarize Center and Dispersion | 7 | ||
| Summarize Within | |||
| Trace Proximity Events | |||
| Big Data Connection tools |
- 1. Use pyspark.sql.DataFrame.unionByName.
- 2. Use Spark SQL
- 3. Use ST_Buffer and ST_Aggr_Union
- 4. Use ST_Aggr_Union and Spark SQL
- 5. Use Spark MLlib
- 6. Use pyspark.sql.DataFrame.unionByName
- 7. For calculating Ellipse and Mean Center use ST_AggrStdDevEllipse and ST_AggrMeanCenter. Central feature and Median center are not supported.