"},{"ID":323652,"user_firstname":"Corinne","user_lastname":"Walker","nickname":"cwalker","user_nicename":"cwalker","display_name":"Corinne Walker","user_email":"cwalker@esri.com","user_url":"","user_registered":"2022-12-06 16:07:24","user_description":"Corinne is a Product Marketing Manager on Esri\u2019s Spatial Analytics & Data Science team. She has a background in marketing and business analytics with experience working in the technology and geospatial industries.","user_avatar":"
"}],"short_description":"Discover the latest enhancements and capabilities in ArcGIS GeoAnalytics Engine, Esri's big data analytics engine for parallelized processing.","flexible_content":[{"acf_fc_layout":"content","content":"ArcGIS GeoAnalytics Engine<\/a> brings powerful spatiotemporal analysis and data science capabilities to your big data, in your cloud environments and data warehouses.\u00a0 Analysts and data scientists can leverage a comprehensive library of spatial functions, tools, and utilities that enable solving spatial intelligence questions at scale.\u00a0 This week we are excited to announce the latest release of GeoAnalytics Engine which includes a variety of new features and capabilities.<\/p>\n This release delivers many new analysis and indexing enhancements.\u00a0 The new Snap Tracks<\/a> tool supports taking a set of location track data and generating a copy that adjusts the data to align with network lines (usually streets).\u00a0 The snapping is based on considerations such as the observation\u2019s distance to the line and the previous and future observations.\u00a0 It can be used to improve visualization of track data as well as other downstream analytics such as calculating more accurate route and movement behavior patterns.<\/p>\n <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1837242,"id":1837242,"title":"snap_comparison","filename":"snap_comparison.gif","filesize":896082,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-engine\/analytics\/whats-new-in-arcgis-geoanalytics-engine-1-1\/snap_comparison","alt":"Animated image of GPS track data being snapped to align with a street network","author":"6481","description":"","caption":"Example of GPS track-based data snapped to road network","name":"snap_comparison","status":"inherit","uploaded_to":1837222,"date":"2023-02-09 21:08:54","modified":"2023-02-09 21:09:37","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1047,"height":705,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison.gif","medium-width":388,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison.gif","medium_large-width":768,"medium_large-height":517,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison.gif","large-width":1047,"large-height":705,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison.gif","1536x1536-width":1047,"1536x1536-height":705,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison.gif","2048x2048-width":1047,"2048x2048-height":705,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison-691x465.gif","card_image-width":691,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/snap_comparison.gif","wide_image-width":1047,"wide_image-height":705}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Another new tool, Nearest Neighbors<\/a>, identifies a given number of nearest spatial records to each of a set of input spatial records. This is a useful enrichment capability that can be used to support questions such as determining candidate facilities to satisfy demand (in commercial and logistics workflows), or – categorizing the most common competitors for retail locations, as well as summarizing their average distance.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1837262,"id":1837262,"title":"nearestneighbors_geoanalytics","filename":"nearestneighbors_geoanalytics.png","filesize":10121,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-engine\/analytics\/whats-new-in-arcgis-geoanalytics-engine-1-1\/nearestneighbors_geoanalytics","alt":"Graphic sketch of how nearest neighbor analysis works with point-based data","author":"6481","description":"","caption":"Illustration of Nearest Neighbor functionality","name":"nearestneighbors_geoanalytics","status":"inherit","uploaded_to":1837222,"date":"2023-02-09 21:10:52","modified":"2023-02-09 21:11:24","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":665,"height":278,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","medium-width":464,"medium-height":194,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","medium_large-width":665,"medium_large-height":278,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","large-width":665,"large-height":278,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","1536x1536-width":665,"1536x1536-height":278,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","2048x2048-width":665,"2048x2048-height":278,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","card_image-width":665,"card_image-height":278,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/nearestneighbors_geoanalytics.png","wide_image-width":665,"wide_image-height":278}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Users also have several new spatial functions available to them in this release.\u00a0 Most notably, there are new functions ST_H3Bin<\/a>\/H3Bins<\/a> and ST_BinId<\/a> that enable indexing data by the H3 bin specification<\/a>, which is a hierarchical geospatial indexing system commonly used for easily summarizing point data and showing high level patterns. \u00a0Other new functions, ST_ClosestPoint<\/a> and ST_ShortestLine<\/a> (and their geodesic equivalents), continue to expand the set of proximity utilities in GeoAnalytics Engine, and the new ST_Densify<\/a>\/GeodesicDensify<\/a> functions enable adding vertices and shortening segment length on complex features, which can support workflows around aligning datasets that come from different sources.<\/p>\n Format support has been broadened in this release to include GeoJSON<\/a> for both data ingress and egress, further enabling users to work with common sources of data.\u00a0 Additionally, data can now be read in and written out in the GeoParquet<\/a> format.\u00a0 GeoParquet<\/a> is a format gaining popularity across a variety of industries, and support in GeoAnalytics Engine broadens the ability for users to work with data directly, in the common forms used in their data lakes and data stores.<\/p>\n New, out-of-the-box capabilities have also been added to support saving output data to feature services<\/a> hosted in ArcGIS Enterprise or Online.\u00a0 Using the GeoAnalytics Engine library in a script or a notebook, users can now save output data to a new layer, overwrite an existing layer, or append to a layer, all using the Spark DataFrameWriter.\u00a0 These streamlined options for storing output data to feature layers make it easier to visualize analysis results and share information products for strategic decision making.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1838222,"id":1838222,"title":"write_featsvc_code","filename":"write_featsvc_code.png","filesize":21766,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-engine\/analytics\/whats-new-in-arcgis-geoanalytics-engine-1-1\/write_featsvc_code","alt":"Screenshot of Python code that shows using GeoAnalytics Engine to write a Spark DataFrame to a feature service","author":"6481","description":"","caption":"Example of writing data to an output feature service","name":"write_featsvc_code","status":"inherit","uploaded_to":1837222,"date":"2023-02-10 22:10:19","modified":"2023-02-10 22:11:02","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":939,"height":280,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code.png","medium-width":464,"medium-height":138,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code.png","medium_large-width":768,"medium_large-height":229,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code.png","large-width":939,"large-height":280,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code.png","1536x1536-width":939,"1536x1536-height":280,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code.png","2048x2048-width":939,"2048x2048-height":280,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code-826x246.png","card_image-width":826,"card_image-height":246,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/write_featsvc_code.png","wide_image-width":939,"wide_image-height":280}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Other significant enhancements in this release include improvements for coordinate systems and transformations<\/a>, including broad support for WKT identifiers across all spatial functions that accept a spatial reference as input.\u00a0 Additionally, there are new utilities to allow a user to look up the list of supported spatial references and transformations, from within the scripting environment, making it quicker to identify and select the one they need to work with.<\/p>\n Visualizing intermediate and output data via the lightweight plotting method<\/a> now has support for displaying ArcGIS basemaps, making it easier to understand the geographic context for your data.\u00a0 The basemaps supported include the Light Gray Canvas, Dark Gray Canvas, Streets, and OpenStreetMap.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1837292,"id":1837292,"title":"plot4","filename":"plot4.png","filesize":48383,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-engine\/analytics\/whats-new-in-arcgis-geoanalytics-engine-1-1\/plot4","alt":"Image of a web map with multiple layers and the ArcGIS light gray canvas basemap","author":"6481","description":"","caption":"Example of intermediate data visualized against the ArcGIS Light Gray Canvas basemap","name":"plot4","status":"inherit","uploaded_to":1837222,"date":"2023-02-09 21:24:37","modified":"2023-02-09 21:25:16","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":995,"height":555,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4.png","medium-width":464,"medium-height":259,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4.png","medium_large-width":768,"medium_large-height":428,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4.png","large-width":995,"large-height":555,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4.png","1536x1536-width":995,"1536x1536-height":555,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4.png","2048x2048-width":995,"2048x2048-height":555,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4-826x461.png","card_image-width":826,"card_image-height":461,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/plot4.png","wide_image-width":995,"wide_image-height":555}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Lastly, the GeoAnalytics Engine 1.1 release also adds support for Spark 3.3.1 and the latest runtimes for Databricks and AWS EMR.<\/p>\n <\/p>\n For more information on the latest features, check out the product release notes<\/a> or stop by the GeoAnalytics Engine Community Site<\/a> for resources on getting started, tips and tricks with various tools, and much more!<\/p>\n"}],"related_articles":[{"ID":1789392,"post_author":"323652","post_date":"2022-12-08 16:05:57","post_date_gmt":"2022-12-09 00:05:57","post_content":"","post_title":"ArcGIS GeoAnalytics Engine in Databricks: Scalable Geospatial Analysis in a Data Science Workflow","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"arcgis-geoanalytics-engine-in-databricks-scalable-geospatial-analysis-in-a-data-science-workflow","to_ping":"","pinged":"","post_modified":"2022-12-15 10:21:36","post_modified_gmt":"2022-12-15 18:21:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1789392","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/q12019card.png","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/03\/GeoAnalytics-Banner.jpg"},"yoast_head":"\nNew spatial tools and functions<\/h3>\n
New and improved support for common data formats<\/h3>\n
Visualization and data transformation<\/h3>\n