{"id":602442,"date":"2020-02-18T02:39:03","date_gmt":"2020-02-18T10:39:03","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=602442"},"modified":"2020-02-25T09:29:01","modified_gmt":"2020-02-25T17:29:01","slug":"track-heat-mapping-with-geoanalytics-desktop-tools","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools","title":{"rendered":"Track Heat Mapping with GeoAnalytics Desktop tools"},"author":7431,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,22941,23351],"tags":[42181,25351,35561,24321,23391],"industry":[],"product":[36561],"class_list":["post-602442","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-mapping","category-transportation","tag-arcgis-pro","tag-big-data","tag-geoanalytics","tag-geoprocessing","tag-spatial-analytics","product-arcgis-pro"],"acf":{"short_description":"Create a track heat map in ArcGIS Pro using parallel analytics with GeoAnalytics Desktop tools.","flexible_content":[{"acf_fc_layout":"content","content":"

Quite often analysts are tasked with uncovering the patterns of where objects travel. For example, this could be showing patterns in vehicle movement, animal behavior, or sensor data. This blog outlines a simple workflow using GeoAnalytics Desktop tools<\/a> to create a track heat map in three quick steps: create lines of movement from GPS points, aggregate lines into bins, and symbolize your results.<\/p>\n"},{"acf_fc_layout":"content","content":"

Unsure whether you have track data? Tracks are points collected over time with a unique identifier for each object being observed. Examples of a unique identifier are the FlightIDs for airplanes or the name of a hurricane. <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":664062,"id":664062,"title":"WhatAreTracks_gif","filename":"WhatAreTracks_gif-1.gif","filesize":81173,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/whataretracks_gif-2","alt":"GIF depicting an example of a track dataset","author":"7431","description":"","caption":"GIF depicting an example of a track dataset","name":"whataretracks_gif-2","status":"inherit","uploaded_to":602442,"date":"2019-11-21 22:18:44","modified":"2019-11-21 22:21:58","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":992,"height":650,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1.gif","medium-width":398,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1.gif","medium_large-width":768,"medium_large-height":503,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1.gif","large-width":992,"large-height":650,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1.gif","1536x1536-width":992,"1536x1536-height":650,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1.gif","2048x2048-width":992,"2048x2048-height":650,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1-710x465.gif","card_image-width":710,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/WhatAreTracks_gif-1.gif","wide_image-width":992,"wide_image-height":650}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

In this workflow we analyze tracks composed of 50 million GPS points representing ship movement near Hawaii. Each GPS point has a field for the time, location, and a ship ID as well as some other attributes like status, heading, and vessel type. Using ArcGIS Pro, let\u2019s visualize the input tracks as they travel near Hawaii:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":659302,"id":659302,"title":"Raw input - yellow","filename":"Raw-input-yellow-1.png","filesize":157122,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/raw-input-yellow-2","alt":"50 million GPS locations of AIS ship data","author":"7431","description":"","caption":"50 million GPS locations of AIS ship data used as input for analysis","name":"raw-input-yellow-2","status":"inherit","uploaded_to":602442,"date":"2019-11-18 23:46:52","modified":"2019-11-18 23:47:57","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":1461,"height":845,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1.png","medium-width":451,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1.png","medium_large-width":768,"medium_large-height":444,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1.png","large-width":1461,"large-height":845,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1.png","1536x1536-width":1461,"1536x1536-height":845,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1.png","2048x2048-width":1461,"2048x2048-height":845,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1-804x465.png","card_image-width":804,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Raw-input-yellow-1.png","wide_image-width":1461,"wide_image-height":845}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

As you can see, other than noting that ships aren\u2019t traveling on land, there aren\u2019t many noticeable patterns. To uncover patterns in a large dataset like this, it\u2019s common to aggregate all the raw points into bins (squares or hexagons) to show where the most observations were made. However, binning the points won\u2019t reveal the travel patterns of sequential points (tracks); instead it will just analyze where the GPS points were collected. Because we\u2019re interested in the pathways of ships, let\u2019s use a more suitable workflow for track analysis than point binning.<\/p>\n"},{"acf_fc_layout":"content","content":"

Step 1: Create tracks from time-enabled points<\/h2>\n"},{"acf_fc_layout":"content","content":"

To create the track features (lines) we want to visualize as a heat map, use the GeoAnalytics tool Reconstruct Tracks<\/a>. This powerful tool summarizes points collected over time into lines representing unique track events for each object.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":735232,"id":735232,"title":"trackexample","filename":"trackexample.png","filesize":34031,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/trackexample","alt":"Reconstruct Tracks result","author":"7431","description":"Example result of Reconstruct Tracks using GeoAnalytics Desktop tools","caption":"Example result of Reconstruct Tracks using GeoAnalytics Desktop tools","name":"trackexample","status":"inherit","uploaded_to":602442,"date":"2020-02-13 17:27:44","modified":"2020-02-13 17:28:32","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":563,"height":267,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","medium-width":464,"medium-height":220,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","medium_large-width":563,"medium_large-height":267,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","large-width":563,"large-height":267,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","1536x1536-width":563,"1536x1536-height":267,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","2048x2048-width":563,"2048x2048-height":267,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","card_image-width":563,"card_image-height":267,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/trackexample.png","wide_image-width":563,"wide_image-height":267}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

For this workflow, we use the field VesselName<\/code> to identify unique ships. Additionally, we choose to split tracks when there\u2019s a time lapse of 6 Hours<\/code> or more between points. For example, here are the input parameters:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":659322,"id":659322,"title":"UI - Reconstruct Tracks","filename":"UI-Reconstruct-Tracks.png","filesize":12162,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/ui-reconstruct-tracks-3","alt":"Parameters used in heat map analysis for the Reconstruct Tracks GeoAnalytics Desktop tool","author":"7431","description":"","caption":"Parameters used in heat map analysis for the Reconstruct Tracks GeoAnalytics Desktop tool","name":"ui-reconstruct-tracks-3","status":"inherit","uploaded_to":602442,"date":"2019-11-18 23:49:17","modified":"2019-11-18 23:49:36","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":452,"height":821,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks.png","medium-width":144,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks.png","medium_large-width":452,"medium_large-height":821,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks.png","large-width":452,"large-height":821,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks.png","1536x1536-width":452,"1536x1536-height":821,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks.png","2048x2048-width":452,"2048x2048-height":821,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks-256x465.png","card_image-width":256,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Reconstruct-Tracks.png","wide_image-width":452,"wide_image-height":821}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

The result contains trips (blue lines in the map below) for each vessel. <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":659332,"id":659332,"title":"Tracks - blue","filename":"Tracks-blue-1.png","filesize":167077,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/tracks-blue-3","alt":"Reconstruct Tracks result showing individual trips for each tracked vessel","author":"7431","description":"","caption":"Reconstruct Tracks result showing individual trips for each tracked vessel","name":"tracks-blue-3","status":"inherit","uploaded_to":602442,"date":"2019-11-18 23:49:48","modified":"2019-11-18 23:50:13","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":1477,"height":861,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1.png","medium-width":448,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1.png","medium_large-width":768,"medium_large-height":448,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1.png","large-width":1477,"large-height":861,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1.png","1536x1536-width":1477,"1536x1536-height":861,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1.png","2048x2048-width":1477,"2048x2048-height":861,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1-798x465.png","card_image-width":798,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/Tracks-blue-1.png","wide_image-width":1477,"wide_image-height":861}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Step 2: Aggregate track features into hexagon bins<\/h2>\n"},{"acf_fc_layout":"content","content":"

Next, we create the heat map of the tracks. Since we\u2019re interested in a detailed heat map, we use a grid distance of 1 mile<\/code>. We use the GeoAnalytics Desktop tool Summarize Within \u2013 which can summarize points, lines or polygons into polygons or bins. Here is an example of the input parameters:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":659342,"id":659342,"title":"UI - Summarize Within","filename":"UI-Summarize-Within.png","filesize":13725,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/ui-summarize-within-2","alt":"Summarize Within GeoAnalytics Desktop tool parameters","author":"7431","description":"","caption":"Parameters used in heat map analysis for the Summarize Within GeoAnalytics Desktop tool","name":"ui-summarize-within-2","status":"inherit","uploaded_to":602442,"date":"2019-11-18 23:50:24","modified":"2019-11-18 23:50:55","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":449,"height":820,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within.png","medium-width":143,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within.png","medium_large-width":449,"medium_large-height":820,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within.png","large-width":449,"large-height":820,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within.png","1536x1536-width":449,"1536x1536-height":820,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within.png","2048x2048-width":449,"2048x2048-height":820,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within-255x465.png","card_image-width":255,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Summarize-Within.png","wide_image-width":449,"wide_image-height":820}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Step 3: Symbolize your results<\/h2>\n"},{"acf_fc_layout":"content","content":"

The result of Summarize Within is a hexagonal grid with a COUNT<\/code> field to tell us how many ships traveled through each grid cell. To symbolize the result as a heat map and make the high-count locations standout, use Unclassed Colors<\/strong> and a Multipart Color Scheme<\/strong> on the COUNT<\/code> field. Here is what the symbology parameters look like:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":659352,"id":659352,"title":"UI - Symbology","filename":"UI-Symbology.png","filesize":10336,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/ui-symbology-2","alt":"Parameters used for symbolizing a heat map","author":"7431","description":"","caption":"Heat map symbology applied to the Summarize Within result","name":"ui-symbology-2","status":"inherit","uploaded_to":602442,"date":"2019-11-18 23:51:06","modified":"2019-11-18 23:51:27","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":392,"height":842,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology.png","medium-width":122,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology.png","medium_large-width":392,"medium_large-height":842,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology.png","large-width":392,"large-height":842,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology.png","1536x1536-width":392,"1536x1536-height":842,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology.png","2048x2048-width":392,"2048x2048-height":842,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology-216x465.png","card_image-width":216,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/09\/UI-Symbology.png","wide_image-width":392,"wide_image-height":842}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

The output of the Summarize Within tool is a heat map representation of trends within your track dataset. Contrast the results of Reconstruct Tracks (blue lines) and Summarize Within (heat map) to visualize the impact this heat map workflow has in understanding track patterns. The heat map uncovers trends in ship traffic volume that would have otherwise been hidden without summarizing the large datasets using GeoAnalytics. We use the Swipe effect tool<\/a> in ArcGIS Pro to really illustrate the improvements:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":602502,"id":602502,"title":"Track Heat Map GeoAnalytics Desktop","filename":"20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","filesize":1100105,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/track-heat-mapping-with-geoanalytics-desktop-tools\/20190909001952-a0f55f7ad2-gif-2-mp4-com","alt":"Heat map compared with individual tracks using the Swipe effect in ArcGIS Pro","author":"7431","description":"Reconstruct Tracks output lines and Summarize Within heat map using Swipe effect in ArcGIS Pro","caption":"Heat map compared with individual tracks using the Swipe effect in ArcGIS Pro","name":"20190909001952-a0f55f7ad2-gif-2-mp4-com","status":"inherit","uploaded_to":602442,"date":"2019-09-08 21:53:31","modified":"2019-09-09 00:50:18","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":600,"height":346,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","medium-width":453,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","medium_large-width":600,"medium_large-height":346,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","large-width":600,"large-height":346,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","1536x1536-width":600,"1536x1536-height":346,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","2048x2048-width":600,"2048x2048-height":346,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","card_image-width":600,"card_image-height":346,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2022\/10\/20190909001952-a0f55f7ad2.gif-2-mp4.com_.gif","wide_image-width":600,"wide_image-height":346}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Remember how this workflow begins with 50 million points? Well, on my laptop I was able to run Reconstruct Tracks analysis in just under 5 minutes and Summarize Within in only 2 minutes! GeoAnalytics Desktop tools allow us to complete jobs that may have previously (before parallel analysis with GeoAnalytics) not completed or took multiple hours using analysis tools.<\/p>\n"},{"acf_fc_layout":"content","content":"

Are you currently working with track datasets? Comment below and tell us about the problems you are solving and the workflows you use to do it.<\/p>\n"},{"acf_fc_layout":"content","content":"

If you\u2019re interested in completing this workflow, check out a sample script (and sample data) written in arcpy here.<\/a><\/p>\n"},{"acf_fc_layout":"content","content":"

Laptop specs : 16 GB of RAM, 4 cores.<\/p>\n"}],"authors":[{"ID":7431,"user_firstname":"Bethany","user_lastname":"Scott","nickname":"Bethany Scott","user_nicename":"bethanyscott","display_name":"Bethany Scott","user_email":"BScott@esri.com","user_url":"","user_registered":"2018-03-21 18:21:18","user_description":"Bethany (she\/her) is a Product Engineer on the Data Pipelines team and the GeoAnalytics team. 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