{"id":903271,"date":"2020-07-13T09:25:42","date_gmt":"2020-07-13T16:25:42","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=903271"},"modified":"2021-05-28T07:16:20","modified_gmt":"2021-05-28T14:16:20","slug":"spatiotemporal-methods-with-arcgis-pro-intelligence-part-2-understanding-movement-data-and-analysis","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/pro-intelligence\/analytics\/spatiotemporal-methods-with-arcgis-pro-intelligence-part-2-understanding-movement-data-and-analysis","title":{"rendered":"Spatiotemporal Methods with ArcGIS Pro Intelligence – Part 2: Understanding Movement Data and Analysis"},"author":48671,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,24641,24371],"tags":[709191,25351,23551,23391,23571],"industry":[],"product":[758642],"class_list":["post-903271","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-defense","category-public-safety","tag-arcgis-pro-intelligence","tag-big-data","tag-location-analytics","tag-spatial-analytics","tag-whats-new","product-pro-intelligence"],"acf":{"short_description":"Devices are generating tons of movement data. Analyze this unique type of data with the Movement Tools inside ArcGIS Pro Intelligence.","flexible_content":[{"acf_fc_layout":"content","content":"

Today we are generating tons of data.\u00a0<\/span><\/span>Everything<\/span><\/span> from the <\/span><\/span>watches on your wrist to the car you drive can track our locations at regular intervals and store it locally or in the cloud. <\/span><\/span>Often<\/span><\/span>\u00a0this data is\u00a0<\/span><\/span>simple<\/span><\/span> and lightweight but can be high volume<\/span><\/span>\u00a0making analysis using traditional GIS techniques challenging.\u00a0<\/span><\/span>When talking about this highly accurate, point-in-time collection of data, we are referring to what is termed as <\/span><\/span>\u201c<\/span><\/span>m<\/span><\/span>ovement\u00a0<\/span><\/span>d<\/span><\/span>ata<\/span><\/span>\u201d<\/span><\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":903291,"id":903291,"title":"84E033E2-5149-4D51-BF72-1BB449019793","filename":"84E033E2-5149-4D51-BF72-1BB449019793.png","filesize":635811,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/pro-intelligence\/analytics\/spatiotemporal-methods-with-arcgis-pro-intelligence-part-2-understanding-movement-data-and-analysis\/84e033e2-5149-4d51-bf72-1bb449019793","alt":"","author":"48671","description":"","caption":"","name":"84e033e2-5149-4d51-bf72-1bb449019793","status":"inherit","uploaded_to":903271,"date":"2020-06-26 14:42:16","modified":"2020-06-26 14:42:16","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":1324,"height":785,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793.png","medium-width":440,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793.png","medium_large-width":768,"medium_large-height":455,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793.png","large-width":1324,"large-height":785,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793.png","1536x1536-width":1324,"1536x1536-height":785,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793.png","2048x2048-width":1324,"2048x2048-height":785,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793-784x465.png","card_image-width":784,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/06\/84E033E2-5149-4D51-BF72-1BB449019793.png","wide_image-width":1324,"wide_image-height":785}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

What is Movement Data?<\/strong><\/p>\n

Movement data is defined as anything that consists of simple point features, with a date-time and some sort of unique identifier for the point assigning it to a track or device.\u00a0<\/span>Often<\/span>\u00a0movement data can be organized into point tracks, which are collections of movement data from the same device<\/span>.<\/span>\u00a0When thinking of movement data, think of data that you would see coming from a GPS.\u00a0<\/span>This data can come in a variety of source formats, from things like Comma-Separated Value (CSV) files, GPS Exchange File (GPX), shapefiles,\u00a0<\/span>Keyhole Markup Language (<\/span>KML<\/span>) file<\/span>s, or a myriad of Internet of Things data endpoints. Normally, these files can contain from a few thousand to millions of records per file.\u00a0 This data can come from cell phone applications like Tracker for ArcGIS, <\/span>mobile location data,\u00a0<\/span>smart watch applications, to things like ankle monitors for criminals and parolees.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

How can we best analyze this rich source of data?<\/span>\u00a0<\/span><\/p>\n

Introducing the Movement Toolse<\/span><\/b>t<\/span><\/b>\u00a0<\/span><\/p>\n

The Movement Tools\u00a0<\/span>are<\/span>\u00a0new in ArcGIS Pro Intelligence 2.6<\/span>.<\/span>\u00a0<\/span>The<\/span>se tools can be found in the Analysis Tool\u00a0<\/span>g<\/span>allery, under Movement Tools and are<\/span>\u00a0a series of tools designed to specifically process and analyze movement data.\u00a0 This toolset introduces three tools that will help with these challenging tasks.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

Compare Areas<\/span><\/b>\u00a0– This tool allows you to compare movement data against known areas of interest stored in a polygon feature layer.\u00a0 This polygon feature can either be time-aware or can be just a simple polygon feature class.\u00a0<\/span>\u00a0<\/span><\/p>\n

Find\u00a0Cotravelers<\/span><\/b>\u00a0– Allows you to find and identify unique identifiers that are moving through space and time together.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

Find Meeting Locations<\/span><\/b>\u00a0– This tool allows for the identification of areas where multiple unique identifiers are congregating near each other in space and time.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u00a0<\/span><\/p>\n

Getting started with the Movement Toolset<\/span><\/b>\u00a0<\/span><\/p>\n

When you are ready to start analyzing your movement data<\/span>\u00a0a few steps need to occur prior to running the tools.\u00a0 First<\/span>,\u00a0<\/span>you<\/span>\u00a0need to import the data.\u00a0 If the data is in a shapefile, you\u00a0<\/span>can<\/span>add<\/span>\u00a0that to your map and\u00a0<\/span>set your time field.\u00a0 Otherwise, if the data is stored in a CSV, KML, GPX\u00a0<\/span>or any other type of file, you will need to\u00a0<\/span>convert the file with<\/span>\u00a0one of\u00a0<\/span>the following tools:<\/span>\u00a0<\/span><\/p>\n