{"id":1213102,"date":"2021-05-21T10:37:17","date_gmt":"2021-05-21T17:37:17","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1213102"},"modified":"2021-05-21T10:44:51","modified_gmt":"2021-05-21T17:44:51","slug":"detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering","title":{"rendered":"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering"},"author":71281,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[42181,25581],"industry":[],"product":[36561],"class_list":["post-1213102","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-arcgis-pro","tag-spatial-statistics","product-arcgis-pro"],"acf":{"related_articles":"","card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/cover_small.jpg","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/cover-2.jpg","authors":[{"ID":71281,"user_firstname":"Cheng-Chia","user_lastname":"Huang","nickname":"Cheng-Chia Huang","user_nicename":"cheng-chia_huang","display_name":"Cheng-Chia Huang","user_email":"Cheng-Chia_Huang@esri.com","user_url":"","user_registered":"2020-07-24 16:17:41","user_description":"Cheng-Chia Huang is a Sr. Product Engineer in Spatial Statistics Team at esri. With GIS and Geography background, she enjoys solving geographical problems using spatial data science techniques.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/profile-465x465.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"short_description":"In ArcGIS Pro 2.8, we enhance the density-based clustering tool with the capability to take time into account! ","flexible_content":[{"acf_fc_layout":"content","content":"<p>In ArcGIS Pro 2.8, we\u2019ve enhanced the <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/spatial-statistics\/densitybasedclustering.htm\"><strong>Density-based Clustering tool<\/strong><\/a>. The Density-based Clustering tool under the <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/spatial-statistics\/an-overview-of-the-spatial-statistics-toolbox.htm\">Spatial Statistics toolbox<\/a> (<a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/spatial-statistics\/an-overview-of-the-mapping-clusters-toolset.htm\">Mapping Clusters toolset<\/a>) helps us to explore the spatial pattern in point data and finding clusters and noises. The strength of this tool is that it is able to detect point clusters with arbitrary shapes and it does not require a predetermined number of clusters.<\/p>\n<p>However, the previous version of the Density-based Clustering tool does not consider time information, which has become increasingly valuable in modern spatial-temporal data collection. To respond to this problem, the Density-based Clustering tool now has the capability to incorporate the time dimension with either DBSCAN or OPTICS methods. In other words, this tool can now detect spatial-temporal clusters and tackle problems such as:<\/p>\n<ul>\n<li>identifying where and when taxi pick-up demands emerge and help connect drivers with potential customers;<\/li>\n<li>identifying where and when the wildfires happen frequently to facilitate future fire hazard response planning;<\/li>\n<li>or even, detecting the movement path of animals or vessels without a corresponding identifier (ID).<\/li>\n<\/ul>\n<p>Furthermore, we provide several ways to visualize spatial-temporal clusters for you to better interpret the clustering results. In this blog article, I will demonstrate how the enhanced Density-based Clustering tool answers the three questions above.<\/p>\n"},{"acf_fc_layout":"content","content":"<h2>Data used in this blog article<\/h2>\n"},{"acf_fc_layout":"content","content":"<ol>\n<li><span data-contrast=\"auto\">The yellow and green taxi trip records can be downloaded from The New York City Taxi and Limousine Commission (TLC) website.\u00a0<\/span><a href=\"https:\/\/www1.nyc.gov\/site\/tlc\/about\/tlc-trip-record-data.page\"><span data-contrast=\"none\">https:\/\/www1.nyc.gov\/site\/tlc\/about\/tlc-trip-record-data.page<\/span><\/a><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"%1.\" data-font=\"Calibri, Calibri_MSFontService, sans-serif\" data-listid=\"7\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">The historical wildfire data can be accessed at\u00a0<\/span><span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">United State<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\">\u00a0Department of Agriculture (USDA) website.\u00a0<\/span><a href=\"https:\/\/data.fs.usda.gov\/geodata\/edw\/datasets.php?xmlKeyword=MTBS\"><span data-contrast=\"none\">https:\/\/data.fs.usda.gov\/geodata\/edw\/datasets.php?xmlKeyword=MTBS<\/span><\/a><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"%1.\" data-font=\"Calibri, Calibri_MSFontService, sans-serif\" data-listid=\"7\" data-aria-posinset=\"2\" data-aria-level=\"1\">The AIS Vessel Tracks 2018 is provided by the <a href=\"https:\/\/www.bts.gov\/maps\">U.S. Department of Transportation Bureau of Transportation Statistics<\/a>, and it can be accessed at <a href=\"https:\/\/marinecadastre.gov\/data\">https:\/\/marinecadastre.gov\/data<\/a><\/li>\n<\/ol>\n"},{"acf_fc_layout":"content","content":"<h3><span class=\"TextRun SCXW189927874 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW189927874 BCX0\">1.\u00a0<\/span><\/span><span class=\"TextRun SCXW189927874 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW189927874 BCX0\">Where and when do more people take\u00a0<\/span><\/span><span class=\"TextRun SCXW189927874 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW189927874 BCX0\">a\u00a0<\/span><\/span><span class=\"TextRun SCXW189927874 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW189927874 BCX0\">taxi in New York City<\/span><\/span><span class=\"TextRun SCXW189927874 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW189927874 BCX0\">?\u00a0<\/span><\/span><span class=\"EOP SCXW189927874 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">I<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">dentifying spatial-temporal clusters\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">of taxi\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">pick-up points can\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">be useful in\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">traffic management plan<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">ning or connecting drivers with potential customers.\u00a0\u00a0<\/span><\/span><em><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">F<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">igure 1<\/span><\/span><\/em><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">shows<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">\u00a0the yellow\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">cab\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">taxi pick-up points in New York City on Saturday, Jan 16<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun Superscript SCXW66011976 BCX0\" data-fontsize=\"11\">th<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">,\u00a0<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2 SCXW66011976 BCX0\">2016<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">\u00a0and it is very unlikely that one can identify the spatial-temporal concentration of demand by looking at the map.\u00a0 The enhanced\u00a0<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">Density-based Clustering tool<\/span><\/span><span class=\"TextRun SCXW66011976 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW66011976 BCX0\">\u00a0serves as a powerful tool for this problem.\u00a0<\/span><\/span><span class=\"EOP SCXW66011976 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225352,"id":1225352,"title":"Taxi_NYC","filename":"Taxi_NYC.png","filesize":468327,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/taxi_nyc-2","alt":"","author":"71281","description":"","caption":"","name":"taxi_nyc-2","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:10:43","modified":"2021-05-07 21:10:43","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":1359,"height":1068,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC.png","medium-width":332,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC.png","medium_large-width":768,"medium_large-height":604,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC.png","large-width":1359,"large-height":1068,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC.png","1536x1536-width":1359,"1536x1536-height":1068,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC.png","2048x2048-width":1359,"2048x2048-height":1068,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC-592x465.png","card_image-width":592,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/Taxi_NYC.png","wide_image-width":1359,"wide_image-height":1068}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>Assuming that all clusters have similar densities, I choose <em>Defined distance (DBSCAN)<\/em> as the <em>Clustering Method<\/em>. Also, I provide 200 as <em>the Minimum Features per Cluster<\/em>, 200 Meters as the <em>Search Distance<\/em> and 10 Minutes as the <em>Search Time Interval<\/em>. The parameters of <em>Minimum Features per Cluster<\/em> and <em>Search Distance<\/em> together define the minimum spatial density of the returned clustering result. Similarly, the <em>Minimum Features per Cluster<\/em> and <em>Search Time Interval<\/em> together define the minimum temporal density of the returned clustering result. The selection of these parameter values is set only for this example and should be adjusted for different dataset\/questions based on your domain knowledge. (<em>Figure 2<\/em>)<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225392,"id":1225392,"title":"F2","filename":"F2-1.png","filesize":42168,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f2-3","alt":"","author":"71281","description":"","caption":"","name":"f2-3","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:14:48","modified":"2021-05-07 21:14:48","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":539,"height":670,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1.png","medium-width":210,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1.png","medium_large-width":539,"medium_large-height":670,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1.png","large-width":539,"large-height":670,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1.png","1536x1536-width":539,"1536x1536-height":670,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1.png","2048x2048-width":539,"2048x2048-height":670,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1-374x465.png","card_image-width":374,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F2-1.png","wide_image-width":539,"wide_image-height":670}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>The tool generates clusters of points and noise. <em>Figure 3<\/em> is the clustering result after excluding noise points. You might notice that there are many overlaying clusters (The clusters in the yellow box). Let\u2019s zoom in to those clusters (<em>Figure 4<\/em>). These clusters are located around the Madison Square Garden, which sits above one of the main railroad stations in New York City (Pennsylvania Station). This could indicate that many people come into the city by train and then continue the travel to other places by taxi. Also, the various colors in <em>Figure 4<\/em> tell us that there are multiple taxi pick-up clusters at different times of the day. This temporal detail is available because the tool incorporates time attributes using the provided <em>Time Field<\/em> and <em>Search Time Interval<\/em>. Taking time into account is important for taxi pick-up data. It differentiates when taking a taxi is in great demand around the Madison Square Garden, which is not able to be achieved if we used Density-based Clustering without time.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225412,"id":1225412,"title":"F3","filename":"F3.png","filesize":480255,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f3-3","alt":"","author":"71281","description":"","caption":"","name":"f3-3","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:17:17","modified":"2021-05-07 21:17:17","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":1115,"height":1003,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3.png","medium-width":290,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3.png","medium_large-width":768,"medium_large-height":691,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3.png","large-width":1115,"large-height":1003,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3.png","1536x1536-width":1115,"1536x1536-height":1003,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3.png","2048x2048-width":1115,"2048x2048-height":1003,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3-517x465.png","card_image-width":517,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F3.png","wide_image-width":1115,"wide_image-height":1003}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":1225432,"id":1225432,"title":"F4","filename":"F4.png","filesize":370425,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f4","alt":"","author":"71281","description":"","caption":"","name":"f4","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:19:28","modified":"2021-05-07 21:19:28","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":1184,"height":1003,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4.png","medium-width":308,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4.png","medium_large-width":768,"medium_large-height":651,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4.png","large-width":1184,"large-height":1003,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4.png","1536x1536-width":1184,"1536x1536-height":1003,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4.png","2048x2048-width":1184,"2048x2048-height":1003,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4-549x465.png","card_image-width":549,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F4.png","wide_image-width":1184,"wide_image-height":1003}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">In Arc<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">GIS<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">\u00a0Pro 2.8,\u00a0<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">we provide three ways for you to explore these spatial-temporal clusters. First,\u00a0<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">the tool returns the\u00a0<\/span><\/span><em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">Start Time<\/span><\/span><\/em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">,\u00a0<\/span><\/span><em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">End Time<\/span><\/span><\/em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">, and\u00a0<\/span><\/span><em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">Mean Time<\/span><\/span><\/em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">\u00a0of each cluster in the attribute table. Therefore, we can set the output layer\u00a0<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">to be\u00a0<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">time<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">&#8211;<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">enabled by choosing the\u00a0<\/span><\/span><em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">Mean Time<\/span><\/span><\/em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">\u00a0as the time field (<\/span><\/span><em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">Figure\u00a0<\/span><\/span><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">5<\/span><\/span><\/em><span class=\"TextRun SCXW123831305 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW123831305 BCX0\">).\u00a0\u00a0<\/span><\/span><span class=\"EOP SCXW123831305 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225532,"id":1225532,"title":"F5","filename":"F5-1.png","filesize":58288,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f5-2","alt":"","author":"71281","description":"","caption":"","name":"f5-2","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:31:50","modified":"2021-05-07 21:31:50","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":1082,"height":696,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1.png","medium-width":406,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1.png","medium_large-width":768,"medium_large-height":494,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1.png","large-width":1082,"large-height":696,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1.png","1536x1536-width":1082,"1536x1536-height":696,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1.png","2048x2048-width":1082,"2048x2048-height":696,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1-723x465.png","card_image-width":723,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F5-1.png","wide_image-width":1082,"wide_image-height":696}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">After the output layer becomes time<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">&#8211;<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">enabled, a\u00a0<\/span><\/span><a class=\"Hyperlink SCXW27729226 BCX0\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/help\/mapping\/time\/visualize-temporal-data-using-the-time-slider.htm\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW27729226 BCX0\" data-ccp-charstyle=\"Hyperlink\">T<\/span><\/span><span class=\"TextRun Underlined SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW27729226 BCX0\" data-ccp-charstyle=\"Hyperlink\">ime\u00a0<\/span><\/span><span class=\"TextRun Underlined SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW27729226 BCX0\" data-ccp-charstyle=\"Hyperlink\">S<\/span><\/span><span class=\"TextRun Underlined SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW27729226 BCX0\" data-ccp-charstyle=\"Hyperlink\">lider<\/span><\/span><\/a><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">should<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">\u00a0appear at the top of the map (<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">Figure\u00a0<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">6<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">)<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">.<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">Then<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">\u00a0we can click play\u00a0<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">to\u00a0<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">see the clusters popping up at different time<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">s<\/span><\/span><span class=\"TextRun SCXW27729226 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW27729226 BCX0\">.\u00a0<\/span><\/span><span class=\"EOP SCXW27729226 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225572,"id":1225572,"title":"F6","filename":"F6.png","filesize":681310,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f6","alt":"","author":"71281","description":"","caption":"","name":"f6","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:38:31","modified":"2021-05-07 21:38:31","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":1395,"height":1076,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6.png","medium-width":338,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6.png","medium_large-width":768,"medium_large-height":592,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6.png","large-width":1395,"large-height":1076,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6.png","1536x1536-width":1395,"1536x1536-height":1076,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6.png","2048x2048-width":1395,"2048x2048-height":1076,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6-603x465.png","card_image-width":603,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F6.png","wide_image-width":1395,"wide_image-height":1076}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">The<\/span><\/span><em><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">Time Span per Cluster<\/span><\/span><\/em><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0chart\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">that\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">com<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">es\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">with the output layer is another way to explore\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">the<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0clustering result.\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">Each line\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">in\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">the chart represen<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">ts<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0a cluster from its start time to its end time. For example, there is a taxi pi<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">c<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">k<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">&#8211;<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">up cluster near<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0the<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0Lower-East\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">Side<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0between midnight\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">and\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">4 AM (<\/span><\/span><em><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">Figure 7<\/span><\/span><\/em><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">).<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0 In\u00a0<\/span><\/span><em><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">Figure 8<\/span><\/span><\/em><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">, by selecting clusters around\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">Madison Square Garden<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">, we see\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">clusters start to appear around 11 AM and end around 10 PM.\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">This\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">means people frequently take\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">a\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">taxi during this period<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">.\u00a0 However, there is\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">a<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0noticeable\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">gap between 4 PM to 5 PM<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">that<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0need<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">s<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">\u00a0more investigation<\/span><\/span><span class=\"TextRun SCXW205150848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW205150848 BCX0\">.\u00a0<\/span><\/span><span class=\"EOP SCXW205150848 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225592,"id":1225592,"title":"F7","filename":"F7.png","filesize":292221,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f7","alt":"","author":"71281","description":"","caption":"","name":"f7","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:39:47","modified":"2021-05-07 21:39:47","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":1503,"height":1082,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7.png","medium-width":363,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7.png","medium_large-width":768,"medium_large-height":553,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7.png","large-width":1500,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7.png","1536x1536-width":1503,"1536x1536-height":1082,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7.png","2048x2048-width":1503,"2048x2048-height":1082,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7-646x465.png","card_image-width":646,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F7-1500x1080.png","wide_image-width":1500,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":1225762,"id":1225762,"title":"F83","filename":"F83.jpg","filesize":145952,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f83","alt":"","author":"71281","description":"","caption":"","name":"f83","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 22:10:07","modified":"2021-05-07 22:10:07","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1485,"height":1084,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83.jpg","medium-width":358,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83.jpg","medium_large-width":768,"medium_large-height":561,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83.jpg","large-width":1480,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83.jpg","1536x1536-width":1485,"1536x1536-height":1084,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83.jpg","2048x2048-width":1485,"2048x2048-height":1084,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83-637x465.jpg","card_image-width":637,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F83-1480x1080.jpg","wide_image-width":1480,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">Apart from the chart, w<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">e can also visualize the clustering result<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">s<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0in a 3D scene.\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">When adding\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">the output layer\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">to<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0a\u00a0<\/span><\/span><a class=\"Hyperlink SCXW231260259 BCX0\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/help\/mapping\/map-authoring\/scenes.htm\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW231260259 BCX0\" data-ccp-charstyle=\"Hyperlink\">Local Scene<\/span><\/span><\/a><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">,\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">choose\u00a0<\/span><\/span><em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">Elevation<\/span><\/span><\/em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0and\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">select<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0<\/span><\/span><em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">Time Exaggeration<\/span><\/span><\/em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0as the feature elevation (<\/span><\/span><em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">Figure<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a09<\/span><\/span><\/em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">).\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">The <em>Time Exaggeration<\/em> field is a field the tool creates for the purpose of 3D mapping of the results. Earlier times have lower values, and later times have higher values.\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2 SCXW231260259 BCX0\">So<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0when this field is used as an elevation source,<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">taxi pick-up<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0points closer to the ground are earlier in the day and higher points are later in the day<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">(<\/span><\/span><em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">Figure<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">\u00a0<\/span><\/span><\/em><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\"><em>10<\/em>)<\/span><\/span><span class=\"TextRun SCXW231260259 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW231260259 BCX0\">.<\/span><\/span><span class=\"EOP SCXW231260259 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225712,"id":1225712,"title":"F9","filename":"F9-2.png","filesize":47370,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f9-3","alt":"","author":"71281","description":"","caption":"","name":"f9-3","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 22:07:31","modified":"2021-05-07 22:07:31","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":1064,"height":711,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2.png","medium-width":391,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2.png","medium_large-width":768,"medium_large-height":513,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2.png","large-width":1064,"large-height":711,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2.png","1536x1536-width":1064,"1536x1536-height":711,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2.png","2048x2048-width":1064,"2048x2048-height":711,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2-696x465.png","card_image-width":696,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F9-2.png","wide_image-width":1064,"wide_image-height":711}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":1213322,"id":1213322,"title":"figure10","filename":"figure10.jpg","filesize":60591,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/figure10","alt":"","author":"71281","description":"","caption":"","name":"figure10","status":"inherit","uploaded_to":1213102,"date":"2021-04-23 23:29:33","modified":"2021-04-23 23:29:33","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":806,"height":634,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10.jpg","medium-width":332,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10.jpg","medium_large-width":768,"medium_large-height":604,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10.jpg","large-width":806,"large-height":634,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10.jpg","1536x1536-width":806,"1536x1536-height":634,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10.jpg","2048x2048-width":806,"2048x2048-height":634,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10-591x465.jpg","card_image-width":591,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure10.jpg","wide_image-width":806,"wide_image-height":634}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h3>2.Where and when do wildfires happen more frequently in the US? How can we identify areas with denser fire clusters to facilitate future fire hazard response planning?<\/h3>\n"},{"acf_fc_layout":"content","content":"<p>Different from DBSCAN, the other method &#8211; OPTICS &#8211; constructs a density-based clustering structure that is able to <span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">identify clusters with <\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">different densities, and easy for us to explore the density pattern of our data. <\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">Here we have all the<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">wildfire incidents in 2001 across the\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">contiguous United States<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0(<\/span><\/span><em><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">Figure 11<\/span><\/span><\/em><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">)<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">.\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0Picking\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">OPTICS<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0as the\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">Clustering Method<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">,<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">I\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">set the\u00a0<\/span><\/span><em><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">Minimum\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">F<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">eatures per Cluster<\/span><\/span><\/em><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">to 10<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">,\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">and\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">select\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">500 mile<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">s<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0as the<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0<em>Search Distance<\/em><\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">(<\/span><\/span><em><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">Figure 1<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">2<\/span><\/span><\/em><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">)<\/span><\/span><span class=\"TextRun SCXW87498304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87498304 BCX0\">. Unlike DBSCAN, \u00a0Search Distance does not impact the result much and only helps us to reduce the processing time. I also set Search Time Interval to 1 month. By providing a smaller Search Time Interval, we will find clusters with higher temporal density.\u00a0<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1213362,"id":1213362,"title":"wildfire_2001","filename":"wildfire_2001.png","filesize":474083,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/wildfire_2001","alt":"","author":"71281","description":"","caption":"","name":"wildfire_2001","status":"inherit","uploaded_to":1213102,"date":"2021-04-23 23:42:23","modified":"2021-04-23 23:42:23","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":1348,"height":779,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001.png","medium-width":452,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001.png","medium_large-width":768,"medium_large-height":444,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001.png","large-width":1348,"large-height":779,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001.png","1536x1536-width":1348,"1536x1536-height":779,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001.png","2048x2048-width":1348,"2048x2048-height":779,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001-805x465.png","card_image-width":805,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_2001.png","wide_image-width":1348,"wide_image-height":779}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":1225462,"id":1225462,"title":"F12","filename":"F12.png","filesize":43122,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f12","alt":"","author":"71281","description":"","caption":"","name":"f12","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:22:35","modified":"2021-05-07 21:22:35","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":581,"height":737,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12.png","medium-width":206,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12.png","medium_large-width":581,"medium_large-height":737,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12.png","large-width":581,"large-height":737,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12.png","1536x1536-width":581,"1536x1536-height":737,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12.png","2048x2048-width":581,"2048x2048-height":737,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12-367x465.png","card_image-width":367,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F12.png","wide_image-width":581,"wide_image-height":737}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">The tool identifies\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">3 spatial-temporal wildfire clusters<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0that are different in densities (<\/span><\/span><em><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">Figure 13<\/span><\/span><\/em><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">)<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">.<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0Cluster 1 (light blue) in\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">the\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">W<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">est region has the lowe<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">st<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0density<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">and Cluster 2 (dark blue) in the\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">E<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">ast part has the highest density.\u00a0\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">The\u00a0<\/span><\/span><em><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">Time Span Per Cluster<\/span><\/span><\/em><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0chart also tells us the\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">temporal characteristics\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">of these three clusters.\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">W<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">ildfire incidents<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0in the\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">W<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">est<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0(light blue)\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">happen\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">frequently from mid-April to mid-October.<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0Whereas\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">most wildfire incidents\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">surrounding the\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">Appalachian Mountains<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">\u00a0happen during\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">f<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">all and\u00a0<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">w<\/span><\/span><span class=\"TextRun SCXW71144397 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW71144397 BCX0\">inter.<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1213402,"id":1213402,"title":"wildfire_OPTICS_result1","filename":"wildfire_OPTICS_result1.png","filesize":345822,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/wildfire_optics_result1","alt":"","author":"71281","description":"","caption":"","name":"wildfire_optics_result1","status":"inherit","uploaded_to":1213102,"date":"2021-04-23 23:44:56","modified":"2021-04-23 23:44:56","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":1235,"height":963,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1.png","medium-width":335,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1.png","medium_large-width":768,"medium_large-height":599,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1.png","large-width":1235,"large-height":963,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1.png","1536x1536-width":1235,"1536x1536-height":963,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1.png","2048x2048-width":1235,"2048x2048-height":963,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1-596x465.png","card_image-width":596,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result1.png","wide_image-width":1235,"wide_image-height":963}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">Looking closer at<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">the\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">Appalachian Mountain<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">area<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">(<\/span><\/span><em><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">Figure 14<\/span><\/span><\/em><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">)<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">, you might notice some points belong<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">ing<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0to<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0Cluster 3 (green color)\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">appear to be<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0in\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">C<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">luster 2 (dark blue color).\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">This is because the enhanced tool\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun AdvancedProofingIssueV2 SCXW192733323 BCX0\">takes into account<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0the temporal aspect of the wildfire incidents.\u00a0 For example, w<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">ildfire\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">incident #<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">354 is identified as<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0a member of<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">Cluster 3 (green color)<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0but not\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">Cluster 2 (dark blue color)<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0because it\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">happened\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">in\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">s<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">pring (<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">April 10<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"SpellingErrorSuperscript Superscript ContextualSpellingAndGrammarErrorV2 SCXW192733323 BCX0\" data-fontsize=\"12\">th<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"SpellingErrorSuperscript Superscript ContextualSpellingAndGrammarErrorV2 SCXW192733323 BCX0\" data-fontsize=\"12\">\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2 SCXW192733323 BCX0\">)<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">but not\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">f<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">all\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">or\u00a0<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">w<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">inter<\/span><\/span><span class=\"TextRun SCXW192733323 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW192733323 BCX0\">.\u00a0<\/span><\/span><span class=\"EOP SCXW192733323 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1225472,"id":1225472,"title":"F14","filename":"F14.png","filesize":214626,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/f14","alt":"","author":"71281","description":"","caption":"","name":"f14","status":"inherit","uploaded_to":1213102,"date":"2021-05-07 21:23:34","modified":"2021-05-07 21:23:34","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":1086,"height":831,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14.png","medium-width":341,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14.png","medium_large-width":768,"medium_large-height":588,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14.png","large-width":1086,"large-height":831,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14.png","1536x1536-width":1086,"1536x1536-height":831,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14.png","2048x2048-width":1086,"2048x2048-height":831,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14-608x465.png","card_image-width":608,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/F14.png","wide_image-width":1086,"wide_image-height":831}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">The OPTICS method outputs another chart called the\u00a0<\/span><i><span data-contrast=\"auto\">Reachability Chart<\/span><\/i><span data-contrast=\"auto\">, which helps us to\u00a0<\/span><span data-contrast=\"auto\">identify the clusters and their densities<\/span><span data-contrast=\"auto\">.\u00a0 In the chart,\u00a0<\/span><i><span data-contrast=\"auto\">Reachability Distance<\/span><\/i><span data-contrast=\"auto\">\u00a0indicates how close points are from others<\/span><span data-contrast=\"auto\">.\u00a0<\/span><span data-contrast=\"auto\">\u00a0T<\/span><span data-contrast=\"auto\">he deeper the valley<\/span><span data-contrast=\"auto\">\u00a0(l<\/span><span data-contrast=\"auto\">ower value in the reachability distance<\/span><span data-contrast=\"auto\">)<\/span><span data-contrast=\"auto\">, the denser the cluster is.<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">For example, i<\/span><span data-contrast=\"auto\">n\u00a0<\/span><i><span data-contrast=\"auto\">Figure 15<\/span><\/i><span data-contrast=\"auto\">,\u00a0<\/span><span data-contrast=\"auto\">Cluster 2 (dark blue color)<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">has\u00a0<\/span><span data-contrast=\"auto\">the highest density\u00a0<\/span><span data-contrast=\"auto\">among the three clusters and thus it has\u00a0<\/span><span data-contrast=\"auto\">the deepest valley<\/span><span data-contrast=\"auto\">\u00a0in the\u00a0<\/span><i><span data-contrast=\"auto\">Reachability Chart<\/span><\/i><span data-contrast=\"auto\">.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We can also use t<\/span><span data-contrast=\"auto\">he<\/span><i><span data-contrast=\"auto\">\u00a0<\/span><\/i><i><span data-contrast=\"auto\">Reachability Chart<\/span><\/i><i><span data-contrast=\"auto\">\u00a0<\/span><\/i><span data-contrast=\"auto\">to dynamically explore details of the clusters. When s<\/span><span data-contrast=\"auto\">electing the\u00a0<\/span><span data-contrast=\"auto\">low-valley points\u00a0<\/span><span data-contrast=\"auto\">of Cluster 1 (light blue)\u00a0<\/span><span data-contrast=\"auto\">in<\/span><span data-contrast=\"auto\">\u00a0the\u00a0<\/span><i><span data-contrast=\"auto\">Reachability Chart<\/span><\/i><span data-contrast=\"auto\">,\u00a0<\/span><span data-contrast=\"auto\">those points<\/span><span data-contrast=\"auto\">\u00a0that are most dense in the cluster are interactively shown\u00a0<\/span><span data-contrast=\"auto\">on the map\u00a0<\/span><span data-contrast=\"auto\">(<\/span><i><span data-contrast=\"auto\">Figure 15<\/span><\/i><span data-contrast=\"auto\">)<\/span><span data-contrast=\"auto\">.\u00a0<\/span><span data-contrast=\"auto\">\u00a0Another way to further identify details in the clusters<\/span><span data-contrast=\"auto\">\u00a0is<\/span><span data-contrast=\"auto\">\u00a0by a<\/span><span data-contrast=\"auto\">djusting the\u00a0<\/span><i><span data-contrast=\"auto\">Sensitivity<\/span><\/i><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">value<\/span><span data-contrast=\"auto\">.\u00a0<\/span><span data-contrast=\"auto\">\u00a0In\u00a0<\/span><i><span data-contrast=\"auto\">Figure 16<\/span><\/i><span data-contrast=\"auto\">, the original Cluster 1\u00a0<\/span><span data-contrast=\"auto\">is now\u00a0<\/span><span data-contrast=\"auto\">divided into three denser clusters<\/span><span data-contrast=\"auto\">\u00a0after I increase<\/span><span data-contrast=\"auto\">d<\/span><span data-contrast=\"auto\">\u00a0the\u00a0<\/span><i><span data-contrast=\"auto\">Sensitivity<\/span><\/i><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">value.\u00a0 Please refer to the\u00a0<\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/spatial-statistics\/how-density-based-clustering-works.htm\"><span data-contrast=\"none\">documentation<\/span><\/a><span data-contrast=\"auto\">\u00a0for more details about r<\/span><i><span data-contrast=\"auto\">eachability<\/span><\/i><i><span data-contrast=\"auto\">\u00a0distance and s<\/span><\/i><i><span data-contrast=\"auto\">ensitivity<\/span><\/i><i><span data-contrast=\"auto\">.<\/span><\/i><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1213422,"id":1213422,"title":"wildfire_OPTICS_result3","filename":"wildfire_OPTICS_result3.png","filesize":272549,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/wildfire_optics_result3","alt":"","author":"71281","description":"","caption":"","name":"wildfire_optics_result3","status":"inherit","uploaded_to":1213102,"date":"2021-04-23 23:47:41","modified":"2021-04-23 23:47:41","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":1385,"height":1068,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3.png","medium-width":338,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3.png","medium_large-width":768,"medium_large-height":592,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3.png","large-width":1385,"large-height":1068,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3.png","1536x1536-width":1385,"1536x1536-height":1068,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3.png","2048x2048-width":1385,"2048x2048-height":1068,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3-603x465.png","card_image-width":603,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result3.png","wide_image-width":1385,"wide_image-height":1068}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":1213432,"id":1213432,"title":"wildfire_OPTICS_result4","filename":"wildfire_OPTICS_result4.png","filesize":276003,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/wildfire_optics_result4","alt":"","author":"71281","description":"","caption":"","name":"wildfire_optics_result4","status":"inherit","uploaded_to":1213102,"date":"2021-04-23 23:48:26","modified":"2021-04-23 23:48:26","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":1393,"height":1083,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4.png","medium-width":336,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4.png","medium_large-width":768,"medium_large-height":597,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4.png","large-width":1389,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4.png","1536x1536-width":1393,"1536x1536-height":1083,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4.png","2048x2048-width":1393,"2048x2048-height":1083,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4-598x465.png","card_image-width":598,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/wildfire_OPTICS_result4-1389x1080.png","wide_image-width":1389,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">In summary,\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">using\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">the\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">OPTICS clustering method with\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">a\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">search time interval, we can identify the\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">regions that\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">have been\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">frequently impacted\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">by wildfires<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">.<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">\u00a0The findings can be useful in\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">allocat<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">ing\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">resource<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">s<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">\u00a0for\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">the\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">recovery effort<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">\u00a0and\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">adjust<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">ing<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">\u00a0strategies for\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">future\u00a0<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">fire hazard response<\/span><\/span><span class=\"TextRun SCXW265146875 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW265146875 BCX0\">.\u00a0<\/span><\/span><span class=\"EOP SCXW265146875 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"<h3><span class=\"TextRun SCXW234301538 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW234301538 BCX0\">3.<\/span><\/span><span class=\"TextRun SCXW234301538 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW234301538 BCX0\">How can we detect the vessel paths?<\/span><\/span><span class=\"EOP SCXW234301538 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">Since\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">OPTICS\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun AdvancedProofingIssueV2 SCXW183356906 BCX0\">is able to<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">detect clusters with different densit<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">ies,<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">it can also be used to identify the movement of objects even if they are traveling at different speeds.\u00a0\u00a0<\/span><\/span><em><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">Figure 17<\/span><\/span><\/em><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">is\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">a\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">ferry route data<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">set\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">where<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0each point was\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">collected every 5 minutes<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">.\u00a0 Without knowing which points belong to the same vessel, using OPTICS with a 5-minute\u00a0<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">Search Time Interval<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">, this tool c<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">aptur<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">es<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0the path of vessels<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0with normal and abnormal<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0routes correctly identified<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0(<\/span><\/span><em><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">Figure 18<\/span><\/span><\/em><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">)<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">.<\/span><\/span><span class=\"TextRun SCXW183356906 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW183356906 BCX0\">\u00a0\u00a0<\/span><\/span><span class=\"EOP SCXW183356906 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1213442,"id":1213442,"title":"figure17","filename":"figure17.jpg","filesize":88787,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/figure17","alt":"","author":"71281","description":"","caption":"","name":"figure17","status":"inherit","uploaded_to":1213102,"date":"2021-04-23 23:55:42","modified":"2021-04-23 23:55:42","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":840,"height":642,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17.jpg","medium-width":341,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17.jpg","medium_large-width":768,"medium_large-height":587,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17.jpg","large-width":840,"large-height":642,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17.jpg","1536x1536-width":840,"1536x1536-height":642,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17.jpg","2048x2048-width":840,"2048x2048-height":642,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17-608x465.jpg","card_image-width":608,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure17.jpg","wide_image-width":840,"wide_image-height":642}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":1213462,"id":1213462,"title":"figure18","filename":"figure18.jpg","filesize":98573,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\/figure18","alt":"","author":"71281","description":"","caption":"","name":"figure18","status":"inherit","uploaded_to":1213102,"date":"2021-04-24 00:04:03","modified":"2021-04-24 00:04:03","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":855,"height":652,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18.jpg","medium-width":342,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18.jpg","medium_large-width":768,"medium_large-height":586,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18.jpg","large-width":855,"large-height":652,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18.jpg","1536x1536-width":855,"1536x1536-height":652,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18.jpg","2048x2048-width":855,"2048x2048-height":652,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18-610x465.jpg","card_image-width":610,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/figure18.jpg","wide_image-width":855,"wide_image-height":652}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2><span class=\"TextRun SCXW35774529 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW35774529 BCX0\">Conclusion<\/span><\/span><span class=\"EOP SCXW35774529 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">In\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">ArcGIS Pro<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a02.8,\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">the enhanced\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">D<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">ensity-based\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">C<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">lustering tool<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0based on ST-DBSCAN <span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0(<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">Reference<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\"> 1<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">) <\/span><\/span>and ST-OPTICS <span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0(<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">Reference<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\"> 2<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">)<\/span><\/span> methods<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">incorporates time into the algorithm<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">.<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0I<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">t\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">now\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">has\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">the\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">capability<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0to identify spatial-temporal clusters\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">and<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">can\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">even\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">capture\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">movement from point data.\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">This\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">blog\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">article\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">demonstrates\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">how to interpret the chart output<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">s<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0and how to visualize the clusters\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">with<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0a time-enabled layer\u00a0<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun CommentStart SCXW180550236 BCX0\">and<\/span><\/span><span class=\"TextRun SCXW180550236 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW180550236 BCX0\">\u00a0a 3D scene.\u00a0<\/span><\/span><\/p>\n<p><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">Moreover,\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">several\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">studies that enhance D<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">ensity<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">-based Cluster<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">ing<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">with Time also can be applied <\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">to line data\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">and to<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">identify<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\"> clusters<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0of<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0movement<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">s. T-OPTICS<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0(<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">Reference<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">3)<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">, for\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">example, is<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0an algorithm based o<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">n OPTICS that can group trajec<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">tories\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun AdvancedProofingIssueV2 SCXW138880553 BCX0\">and also<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0return\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">reachability<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\"> charts to help understand the cluster<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0move<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">ment<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0patterns.\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">For example, T-OPTICS can be applied to historical hurricane trajectories and identify the common trajector<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">ies.\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">TRACLUS<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0(Reference4)<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0is another\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">popular one for grouping sub-trajectories wit<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">h similar behavior a<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">nd find the\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">representative trajectory of each\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2 SCXW138880553 BCX0\">clusters<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">.\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">Instead of clustering the\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">hurricane trajectories as a whole and identify the common trajectories, <\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">TRACLUS\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">detects similar patterns of sub-trajectories. Thus,<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">\u00a0it will identify\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">many\u00a0<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">hurricanes turn in th<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">e same direction when they passed a certain terrain<\/span><\/span><span class=\"TextRun SCXW138880553 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138880553 BCX0\">.<\/span><\/span><\/p>\n"},{"acf_fc_layout":"content","content":"<h2><strong>References<\/strong><\/h2>\n"},{"acf_fc_layout":"content","content":"<ol>\n<li>Birant, D. &amp; Kut, A. (2007, January). &#8220;ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data.&#8221; In Data &amp; Knowledge Engineering (Vol 60, No. 1, pp. 208-221).<\/li>\n<li>Agrawal, K. P., Garg, S., Sharma, S., &amp; Patel, P. (2016, November). &#8220;Development and validation of OPTICS based spatio-temporal clustering technique.&#8221; In Information Sciences (Vol 369, pp. 388-401).<\/li>\n<li>M Nanni, D Pedreschi (2006). \u201cTime-focused clustering of trajectories of moving objects.\u201d Journal of Intelligent Information Systems (Vol 27, No. 3, pp. 267-289)<\/li>\n<li>JG Lee, J Han, KY Whang (2007). \u201cTrajectory clustering: a partition-and-group framework\u201d In Proceedings of the ACM SIGMOD Conference on Management of Data. ACM, 593&#8211;604.<\/li>\n<\/ol>\n"}]},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\" \/>\n<meta property=\"og:site_name\" content=\"ArcGIS Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/esrigis\/\" \/>\n<meta property=\"article:modified_time\" content=\"2021-05-21T17:44:51+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@ESRI\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":[\"Article\",\"BlogPosting\"],\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\"},\"author\":{\"name\":\"Cheng-Chia Huang\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/f3cf31d56644d3d60ffece23a6dbb360\"},\"headline\":\"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering\",\"datePublished\":\"2021-05-21T17:37:17+00:00\",\"dateModified\":\"2021-05-21T17:44:51+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\"},\"wordCount\":10,\"publisher\":{\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#organization\"},\"keywords\":[\"ArcGIS Pro\",\"spatial statistics\"],\"articleSection\":[\"Analytics\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\",\"url\":\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\",\"name\":\"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering\",\"isPartOf\":{\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#website\"},\"datePublished\":\"2021-05-21T17:37:17+00:00\",\"dateModified\":\"2021-05-21T17:44:51+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.esri.com\/arcgis-blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#website\",\"url\":\"https:\/\/www.esri.com\/arcgis-blog\/\",\"name\":\"ArcGIS Blog\",\"description\":\"Get insider info from Esri product teams\",\"publisher\":{\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.esri.com\/arcgis-blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#organization\",\"name\":\"Esri\",\"url\":\"https:\/\/www.esri.com\/arcgis-blog\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/Esri.png\",\"contentUrl\":\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/Esri.png\",\"width\":400,\"height\":400,\"caption\":\"Esri\"},\"image\":{\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/esrigis\/\",\"https:\/\/x.com\/ESRI\",\"https:\/\/www.linkedin.com\/company\/5311\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/f3cf31d56644d3d60ffece23a6dbb360\",\"name\":\"Cheng-Chia Huang\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/profile-465x465.jpg\",\"contentUrl\":\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/profile-465x465.jpg\",\"caption\":\"Cheng-Chia Huang\"},\"description\":\"Cheng-Chia Huang is a Sr. Product Engineer in Spatial Statistics Team at esri. With GIS and Geography background, she enjoys solving geographical problems using spatial data science techniques.\",\"sameAs\":[\"www.linkedin.com\/in\/cheng-chia-karie-huang\"],\"url\":\"https:\/\/www.esri.com\/arcgis-blog\/author\/cheng-chia_huang\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering","og_locale":"en_US","og_type":"article","og_title":"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering","og_url":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering","og_site_name":"ArcGIS Blog","article_publisher":"https:\/\/www.facebook.com\/esrigis\/","article_modified_time":"2021-05-21T17:44:51+00:00","twitter_card":"summary_large_image","twitter_site":"@ESRI","schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":["Article","BlogPosting"],"@id":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering#article","isPartOf":{"@id":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering"},"author":{"name":"Cheng-Chia Huang","@id":"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/f3cf31d56644d3d60ffece23a6dbb360"},"headline":"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering","datePublished":"2021-05-21T17:37:17+00:00","dateModified":"2021-05-21T17:44:51+00:00","mainEntityOfPage":{"@id":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering"},"wordCount":10,"publisher":{"@id":"https:\/\/www.esri.com\/arcgis-blog\/#organization"},"keywords":["ArcGIS Pro","spatial statistics"],"articleSection":["Analytics"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering","url":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering","name":"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering","isPartOf":{"@id":"https:\/\/www.esri.com\/arcgis-blog\/#website"},"datePublished":"2021-05-21T17:37:17+00:00","dateModified":"2021-05-21T17:44:51+00:00","breadcrumb":{"@id":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/detect-spatial-temporal-point-clusters-by-incorporating-time-into-density-based-clustering#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.esri.com\/arcgis-blog\/"},{"@type":"ListItem","position":2,"name":"Detect Spatial-Temporal Point Clusters by Incorporating Time into Density-based Clustering"}]},{"@type":"WebSite","@id":"https:\/\/www.esri.com\/arcgis-blog\/#website","url":"https:\/\/www.esri.com\/arcgis-blog\/","name":"ArcGIS Blog","description":"Get insider info from Esri product teams","publisher":{"@id":"https:\/\/www.esri.com\/arcgis-blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.esri.com\/arcgis-blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.esri.com\/arcgis-blog\/#organization","name":"Esri","url":"https:\/\/www.esri.com\/arcgis-blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/logo\/image\/","url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/Esri.png","contentUrl":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/Esri.png","width":400,"height":400,"caption":"Esri"},"image":{"@id":"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/esrigis\/","https:\/\/x.com\/ESRI","https:\/\/www.linkedin.com\/company\/5311\/"]},{"@type":"Person","@id":"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/f3cf31d56644d3d60ffece23a6dbb360","name":"Cheng-Chia Huang","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/image\/","url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/profile-465x465.jpg","contentUrl":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/profile-465x465.jpg","caption":"Cheng-Chia Huang"},"description":"Cheng-Chia Huang is a Sr. Product Engineer in Spatial Statistics Team at esri. With GIS and Geography background, she enjoys solving geographical problems using spatial data science techniques.","sameAs":["www.linkedin.com\/in\/cheng-chia-karie-huang"],"url":"https:\/\/www.esri.com\/arcgis-blog\/author\/cheng-chia_huang"}]}},"text_date":"May 21, 2021","author_name":"Cheng-Chia Huang","author_page":"https:\/\/www.esri.com\/arcgis-blog\/author\/cheng-chia_huang","custom_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/05\/cover-2.jpg","primary_product":"ArcGIS Pro","tag_data":[{"term_id":42181,"name":"ArcGIS Pro","slug":"arcgis-pro","term_group":0,"term_taxonomy_id":42181,"taxonomy":"post_tag","description":"","parent":0,"count":323,"filter":"raw"},{"term_id":25581,"name":"spatial statistics","slug":"spatial-statistics","term_group":0,"term_taxonomy_id":25581,"taxonomy":"post_tag","description":"","parent":0,"count":128,"filter":"raw"}],"category_data":[{"term_id":23341,"name":"Analytics","slug":"analytics","term_group":0,"term_taxonomy_id":23341,"taxonomy":"category","description":"","parent":0,"count":1329,"filter":"raw"}],"product_data":[{"term_id":36561,"name":"ArcGIS Pro","slug":"arcgis-pro","term_group":0,"term_taxonomy_id":36561,"taxonomy":"product","description":"","parent":0,"count":2037,"filter":"raw"}],"primary_product_link":"https:\/\/www.esri.com\/arcgis-blog\/?s=#&products=arcgis-pro","_links":{"self":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog\/1213102","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/users\/71281"}],"replies":[{"embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/comments?post=1213102"}],"version-history":[{"count":0,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog\/1213102\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/media?parent=1213102"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/categories?post=1213102"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/tags?post=1213102"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/industry?post=1213102"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/product?post=1213102"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}