{"id":380092,"date":"2019-03-01T15:55:43","date_gmt":"2019-03-01T23:55:43","guid":{"rendered":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=380092"},"modified":"2020-02-21T12:21:19","modified_gmt":"2020-02-21T20:21:19","slug":"detecting-water-utility-leaks-with-geoanalytics-server","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server","title":{"rendered":"Detecting Water Utility Leaks with GeoAnalytics Server"},"author":8022,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,23051],"tags":[25781,340332,35561,34031,342212],"industry":[],"product":[36941],"class_list":["post-380092","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-water","tag-3d","tag-data-analysis","tag-geoanalytics","tag-hot-spots","tag-water-utility","product-geoanalytics-server"],"acf":{"short_description":"See how GeoAnalytics tools can help detect leaks in a water utility's infrastructure.","flexible_content":[{"acf_fc_layout":"content","content":"<p>Increasingly, organizations are finding a need to be able to process, analyze, and understand larger amounts of data faster \u2013 whether it&#8217;s customer data, sensor reads, moving assets, competitive information \u2013 the list goes on. A more precise understanding of your data often yields better business decisions and improved operations.<\/p>\n<p>In this blog, we will delve into a data analysis workflow for a water utility. Specifically, we will look at a utility&#8217;s water network to identify when and where leaks occurred and use that information to figure out what is causing leaks in certain areas.<\/p>\n<p>Every water supply network experiences leaks, but the cause of leaks can be numerous and be difficult to identify. Contributing factors range from older infrastructure and maintenance gaps to pressure changes and even soils that are corrosive to the water mains buried within them. Beyond water loss, leaks can be the source of customer dissatisfaction, financial loss, and even further infrastructure issues and erosion. Because of these repercussions, understanding where leaks have occurred is not enough. To effectively mitigate future issues, it is important to also understand when leaks happened and what caused them.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":421552,"id":421552,"title":"leak","filename":"leak.jpg","filesize":255177,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/leak","alt":"image of pipe burst","author":"8022","description":"","caption":"","name":"leak","status":"inherit","uploaded_to":380092,"date":"2019-01-25 22:14:25","modified":"2019-01-25 22:17:46","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":1280,"height":853,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak-150x150.jpg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak-300x200.jpg","medium-width":300,"medium-height":200,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak-768x512.jpg","medium_large-width":768,"medium_large-height":512,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak-1024x682.jpg","large-width":1024,"large-height":682,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak.jpg","1536x1536-width":1280,"1536x1536-height":853,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak.jpg","2048x2048-width":1280,"2048x2048-height":853,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak-698x465.jpg","card_image-width":698,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/leak.jpg","wide_image-width":1280,"wide_image-height":853}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>For this analysis, we will be using\u00a0<a href=\"https:\/\/www.esri.com\/en-us\/arcgis\/products\/arcgis-geoanalytics-server\">GeoAnalytics Server<\/a>, a big data analysis capability of ArcGIS Enterprise that uses distributed computing to speed up typical processing time so you can complete your analyses faster. GeoAnalytics Server comes with a <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/big-data-analytics\/an-overview-of-the-big-data-analytics-toolbox.htm\">collection of tools<\/a> to help you aggregate, summarize and find patterns within your larger datasets.<\/p>\n<p>The dataset we will be analyzing is 14 years of work orders and service requests from the White House Utility District in Tennessee. It is a good midsized dataset that will lend itself well to GeoAnalytics, and be\u00a0especially performant when we do additional analysis joining the data with other large datasets. This dataset is also comparable to the datasets other utilities would commonly be working with at various sizes.<\/p>\n<p>Let&#8217;s take a look at the analysis. Our raw data of service requests looks a little something like this:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":447612,"id":447612,"title":"AllLeaks","filename":"AllLeaks.png","filesize":453117,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/allleaks","alt":"All leaks displayed on a map","author":"8022","description":"","caption":"","name":"allleaks","status":"inherit","uploaded_to":380092,"date":"2019-02-27 18:08:13","modified":"2019-02-27 18:08:39","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":938,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks.png","medium-width":375,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks.png","medium_large-width":768,"medium_large-height":534,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks.png","large-width":1348,"large-height":938,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks.png","1536x1536-width":1348,"1536x1536-height":938,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks.png","2048x2048-width":1348,"2048x2048-height":938,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks-668x465.png","card_image-width":668,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/AllLeaks.png","wide_image-width":1348,"wide_image-height":938}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p style=\"text-align: left\">Here we have a lot of points &#8211; many overlapping &#8211; that extend back in time. Each point has valuable information on when the leak was reported and even the duration between when the work order was opened and when it was closed.<\/p>\n<p><strong>Workflow #1<\/strong><\/p>\n<p>The first analysis we want to do is take all of these work orders, aggregate them into bins, and visualize them in 3D across space and time. Sounds complicated? It\u2019s not! We\u2019ll use the GeoAnalytics tool <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/big-data-analytics\/create-space-time-cube.htm\">Create Space Time Cube<\/a> to do this. We\u2019ll use ArcGIS Pro as our client, while the analysis will run on our GeoAnalytics Server.<\/p>\n<p>First, we\u2019ll take the layer of service requests and add the following into our Create Space Time Cube tool parameters:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":427432,"id":427432,"title":"toolUI_CreateSpaceTimeCube","filename":"toolUI_CreateSpaceTimeCube.png","filesize":23036,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/toolui_createspacetimecube","alt":"Tool parameters in ArcGIS Pro","author":"8022","description":"","caption":"","name":"toolui_createspacetimecube","status":"inherit","uploaded_to":380092,"date":"2019-02-01 17:02:43","modified":"2019-02-01 17:02:57","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":361,"height":526,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube-206x300.png","medium-width":179,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube.png","medium_large-width":361,"medium_large-height":526,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube.png","large-width":361,"large-height":526,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube.png","1536x1536-width":361,"1536x1536-height":526,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube.png","2048x2048-width":361,"2048x2048-height":526,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube-319x465.png","card_image-width":319,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/toolUI_CreateSpaceTimeCube.png","wide_image-width":361,"wide_image-height":526}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>Here we are telling the tool:<\/p>\n<ul>\n<li>What we want to name our results (Output Name)<\/li>\n<li>The size of square bins we will be aggregating into (Distance Interval)<\/li>\n<li>The time span that we want to cover (Time Interval)<\/li>\n<li>When our data starts (Time Interval Alignment and Reference Time)<\/li>\n<\/ul>\n<p>We could also include any summary statistics we want to calculate for each space time bin. In this case, we\u2019ll just leave the summary fields blank so that we are just calculating the count of points within each bin. However, if we wanted, we could fill empty bins with zeros or the average value of neighbors, or other algorithm and we could calculate statistics for that bin such as the average number of work orders or max\/min. For us, this dataset is on the smaller side, so we&#8217;ll just calculate the number of work orders for now. If you are interested in more details on the inputs of this tool, check out the Create Space Time Cube documentation under <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/big-data-analytics\/create-space-time-cube.htm#GUID-55674DAC-B8F6-45AA-AD8A-9B0ADC02C405\">syntax<\/a>.<\/p>\n<p>So, what these parameters mean is that we will be sizing each aggregation bin at 3,000 feet by 3,000 feet, and splitting the bins up into 6 month chunks all the way back from April 2004 to present. Since our data is historical, being able to use time in our analysis is critical.<\/p>\n<p>This Create Space Time Cube analysis will take just about 30 seconds to run. Once the tool completes, we will take the resulting netCDF file and use the ArcGIS Pro geoprocessing tool <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/space-time-pattern-mining\/visualizecube3d.htm\">Visualize Space Time Cube in 3D<\/a> to visualize our results. The result will look something like an army of square bins:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":432822,"id":432822,"title":"cubes8","filename":"cubes8.png","filesize":142795,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/cubes8","alt":"A map with many cubes","author":"8022","description":"","caption":"","name":"cubes8","status":"inherit","uploaded_to":380092,"date":"2019-02-07 22:07:51","modified":"2019-02-07 22:08:05","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":788,"height":688,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8-300x262.png","medium-width":299,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8-768x671.png","medium_large-width":768,"medium_large-height":671,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8.png","large-width":788,"large-height":688,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8.png","1536x1536-width":788,"1536x1536-height":688,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8.png","2048x2048-width":788,"2048x2048-height":688,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8-533x465.png","card_image-width":533,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/cubes8.png","wide_image-width":788,"wide_image-height":688}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>This is an interesting visual, but it doesn\u2019t tell us much yet. Let\u2019s refine the results by filtering these cubes for those that have 4 or more reported leaks in that 6 month time period. This weeds out areas where there weren&#8217;t as many work orders to focus on those where there were.<\/p>\n<p>We can do this by using a definition query. You\u2019ll see how, after the definition query is applied, the data is pared down even further, but still maintains its position in space and time across our district:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":432812,"id":432812,"title":"query10","filename":"query10.png","filesize":340398,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/query10","alt":"A map with 3D cubes and a query","author":"8022","description":"","caption":"","name":"query10","status":"inherit","uploaded_to":380092,"date":"2019-02-07 22:07:28","modified":"2019-02-07 22:07:43","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":1075,"height":883,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10-300x246.png","medium-width":300,"medium-height":246,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10-768x631.png","medium_large-width":768,"medium_large-height":631,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10-1024x841.png","large-width":1024,"large-height":841,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10.png","1536x1536-width":1075,"1536x1536-height":883,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10.png","2048x2048-width":1075,"2048x2048-height":883,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10-566x465.png","card_image-width":566,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/query10.png","wide_image-width":1075,"wide_image-height":883}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>The color of each bin represents the number of leaks. The darker the color blue, the more leaks that have been reported. This data is dynamic in ArcGIS Pro, so by filtering our data we are able to see that there are certain locations where leaks have been more prevalent \u2013 specifically around the city of White House and Nashville. If we click on a bin, we are also able to see how many leaks were reported in that area in that 6 month time period. For this bin, there were 10 leaks from this time period from December 2013 &#8211; April 2014:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":432802,"id":432802,"title":"selection12","filename":"selection12.png","filesize":319696,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/selection12","alt":"A map with 3D cubes and a selection","author":"8022","description":"","caption":"","name":"selection12","status":"inherit","uploaded_to":380092,"date":"2019-02-07 22:07:06","modified":"2019-02-07 22:07:20","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":929,"height":883,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12-300x285.png","medium-width":275,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12-768x730.png","medium_large-width":768,"medium_large-height":730,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12.png","large-width":929,"large-height":883,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12.png","1536x1536-width":929,"1536x1536-height":883,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12.png","2048x2048-width":929,"2048x2048-height":883,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12-489x465.png","card_image-width":489,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/selection12.png","wide_image-width":929,"wide_image-height":883}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>From this analysis, we can understand where and when leaks in the district have been reported. We could even share the data as a web layer to ArcGIS Enterprise for browser-based visualization and data exploration. Below is a screenshot of that &#8211; you can see how the data is rendered in the Enterprise portal. These results are now easily shareable through the web with other stakeholders who can use the results to drill down on areas in the district that need attention.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":449952,"id":449952,"title":"Cubes2","filename":"Cubes2.png","filesize":307009,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/cubes2","alt":"","author":"8022","description":"","caption":"","name":"cubes2","status":"inherit","uploaded_to":380092,"date":"2019-03-01 22:58:08","modified":"2019-03-01 22:58:17","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":1917,"height":815,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2.png","medium-width":464,"medium-height":197,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2.png","medium_large-width":768,"medium_large-height":327,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2.png","large-width":1917,"large-height":815,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2.png","1536x1536-width":1536,"1536x1536-height":653,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2.png","2048x2048-width":1917,"2048x2048-height":815,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2-826x351.png","card_image-width":826,"card_image-height":351,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Cubes2.png","wide_image-width":1917,"wide_image-height":815}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>So, we&#8217;ve done part of the analysis. But, we may have more questions. Are there areas where there have been a growing or diminishing number of leaks? What is the trend of these leaks? Seeing these common patterns could help us mitigate future events by gaining a deeper understanding of these trends.<\/p>\n<p><strong>Workflow #2<\/strong><\/p>\n<p>To answer these questions, we can run the <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/space-time-pattern-mining\/emerginghotspots.htm\">Emerging Hot Spots tool<\/a> in ArcGIS Pro. Though this isn\u2019t a GeoAnalytics tool, we have used GeoAnalytics to run the Create Space Time Cube analysis that will be fed into Emerging Hot Spots.<\/p>\n<p>Emerging Hot Spots is a great way to see which areas have had leaks persistently reported throughout time, which areas have seen an increasing number of leaks, which areas have had relatively few leaks, and more. It\u2019s a good way to take the guesswork out of analyzing the Create Space Time Cube results by factoring in changes over time as part of the analysis without having to manually mine through visual patterns.\u00a0Below you can see the results of that analysis. (This also exemplifies how you can use ArcGIS Pro tools with GeoAnalytics tools. A common workflow is to use GeoAnalytics to pare down big data or aggregate it in order to use it in other analyses.)<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":432902,"id":432902,"title":"Hotspot14","filename":"Hotspot14.png","filesize":433495,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/hotspot14","alt":"Map with hot spots","author":"8022","description":"","caption":"","name":"hotspot14","status":"inherit","uploaded_to":380092,"date":"2019-02-07 22:33:45","modified":"2019-02-07 22:34:01","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":1074,"height":883,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14-300x247.png","medium-width":300,"medium-height":247,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14-768x631.png","medium_large-width":768,"medium_large-height":631,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14-1024x842.png","large-width":1024,"large-height":842,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14.png","1536x1536-width":1074,"1536x1536-height":883,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14.png","2048x2048-width":1074,"2048x2048-height":883,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14-566x465.png","card_image-width":566,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Hotspot14.png","wide_image-width":1074,"wide_image-height":883}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>Now that we have run the Emerging Hot Spots analysis, we can see that there are is a significant area of \u2018Intensifying Hot Spots\u2019 in the middle of our district (light red squares in the middle.) This is interesting to us because the area around that hot spot is new development and infrastructure. This analysis indicates to us this is an area we should have some technicians visit and monitor as it seems to be a hot spot that is experiencing more leaks than before.<\/p>\n"},{"acf_fc_layout":"content","content":"<p><strong>Workflow #3<\/strong><\/p>\n<p>Let\u2019s do one last analysis on our data. We have another dataset that contains information on water mains and laterals. This dataset has a lot of descriptive information on the pipes that transport water such as the material, diameter, installation date as well as soil corrosivity. As mentioned, soil corrosivity has been found to have a relationship to leakages in the network as it can erode water infrastructure.<\/p>\n<p>Our next question to answer using GeoAnalytics is: have there been leaks close to pipes that are highly corrosive? For this analysis, we\u2019ll use a GeoAnalytics tool called <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/big-data-analytics\/join-features.htm\">Join Features<\/a>, which will again run in ArcGIS Pro (our client) using our GeoAnalytics Server.<\/p>\n<p>We\u2019ll enter the following into our tool parameters:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":432792,"id":432792,"title":"joinfeatures16","filename":"joinfeatures16.png","filesize":8937,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/joinfeatures16","alt":"The join feature tool's parameters","author":"8022","description":"","caption":"","name":"joinfeatures16","status":"inherit","uploaded_to":380092,"date":"2019-02-07 22:06:39","modified":"2019-02-07 22:06:54","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":359,"height":637,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16-169x300.png","medium-width":147,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16.png","medium_large-width":359,"medium_large-height":637,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16.png","large-width":359,"large-height":637,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16.png","1536x1536-width":359,"1536x1536-height":637,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16.png","2048x2048-width":359,"2048x2048-height":637,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16-262x465.png","card_image-width":262,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/joinfeatures16.png","wide_image-width":359,"wide_image-height":637}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>We are selecting our dataset of raw leaks as the target layer and the mains as the layer that will be joined (based on spatial proximity) to the leaks. We are also specifying that we want to join the main data to every leak that is within 500 feet of that section of the main.<\/p>\n<p>By joining these two datasets, our result layer will contain both the leaks within 500 feet of a main as well as the details of the mains (including the corrosivity information). Only the leaks within 500 feet of a main will be returned, which helps us filter down our data even more to find leaks that may have been caused by these soil conditions.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":432872,"id":432872,"title":"Join18","filename":"Join18.png","filesize":332542,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/geoanalytics-server\/analytics\/detecting-water-utility-leaks-with-geoanalytics-server\/join18","alt":"A map showing leak and water main proximity","author":"8022","description":"","caption":"","name":"join18","status":"inherit","uploaded_to":380092,"date":"2019-02-07 22:29:27","modified":"2019-02-07 22:29:51","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":1075,"height":883,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18-150x150.png","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18-300x246.png","medium-width":300,"medium-height":246,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18-768x631.png","medium_large-width":768,"medium_large-height":631,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18-1024x841.png","large-width":1024,"large-height":841,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18.png","1536x1536-width":1075,"1536x1536-height":883,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18.png","2048x2048-width":1075,"2048x2048-height":883,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18-566x465.png","card_image-width":566,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/Join18.png","wide_image-width":1075,"wide_image-height":883}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>By clicking on this leak, we see that it is near a main in corrosive soil since the leaks and main have been joined together into one dataset. From here, we can then start to understand where there are other leaks on water mains buried in corrosive soils, and potentially target our main replacement schedules around these affected areas, as well as the areas we identified in our space time cube and hot spot analysis.<\/p>\n<p>This is just one example of how GeoAnalytics Server can process a larger amount of complex data very quickly in multiple ways. It also shows how you can use GeoAnalytics as a complement to your ArcGIS Pro analysis tools, as well as ArcGIS Enterprise by creating web layers out of your results that you can then use in maps and applications.<\/p>\n<p>GeoAnalytics provides many other algorithms you can use based on your workflows, including upcoming regression and prediction tools, clustering, aggregation, and helpful data management tools.<\/p>\n<p>For other analysis examples and list of tools you can in ArcGIS Pro using GeoAnalytics Server, visit <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/big-data-analytics\/an-overview-of-the-big-data-analytics-toolbox.htm\">An overview of the big data analytics toolbox<\/a> web help.<\/p>\n<p>To see an analysis of how GeoAnalytics Server can mine through sensor measurements, watch the demo video\u00a0<a href=\"https:\/\/www.youtube.com\/watch?v=Pk3a4E7jfpI\">Detecting Ozone Measurements with GeoAnalytics Server<\/a>.<\/p>\n<p>And, for more information on the White House Utility District, visit this <a href=\"https:\/\/www.esri.com\/videos\/watch?videoid=4378&amp;isLegacy=true\">Esri Case Study: White House Utility District<\/a>.<\/p>\n<p>If you have any questions on this blog or GeoAnalytics Server, feel free to reach out to GeoAnalytics@esri.com.<\/p>\n<p>&#8211; Hilary and Sarah (+ special thanks to Matt Kennedy, Bethany Scott and Derek Lorbiecki)<\/p>\n"}],"authors":[{"ID":8022,"user_firstname":"Hilary","user_lastname":"Curtis","nickname":"HC","user_nicename":"hcurtis","display_name":"Hilary Curtis","user_email":"HCurtis@esri.com","user_url":"","user_registered":"2018-05-23 23:31:52","user_description":"Hilary is a Product Manager for ArcGIS Enterprise and loves urban ecology, biking and her dog Ada.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/12\/1057e88680157c0-150x150.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"},{"ID":8132,"user_firstname":"Sarah","user_lastname":"Ambrose","nickname":"SAmbrose","user_nicename":"sambrose","display_name":"Sarah Ambrose","user_email":"SAmbrose@esri.com","user_url":"","user_registered":"2018-06-27 23:43:49","user_description":"Sarah is a product engineer on the GeoAnalytics team.","user_avatar":"<img alt='' src='https:\/\/secure.gravatar.com\/avatar\/cc3c93b37cdb8ae378855c43454bf9b9e5417c055237013c1916fb279fe87ffb?s=96&#038;d=blank&#038;r=g' srcset='https:\/\/secure.gravatar.com\/avatar\/cc3c93b37cdb8ae378855c43454bf9b9e5417c055237013c1916fb279fe87ffb?s=192&#038;d=blank&#038;r=g 2x' class='avatar avatar-96 photo' height='96' width='96' loading='lazy' decoding='async'\/>"}],"related_articles":[{"ID":386402,"post_author":"8022","post_date":"2018-12-19 11:46:13","post_date_gmt":"2018-12-19 19:46:13","post_content":"","post_title":"GeoAnalytics Server Analysis Demo - Ozone Detection","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"geoanalytics-server-analysis-demo-ozone-detection","to_ping":"","pinged":"","post_modified":"2020-02-21 12:22:14","post_modified_gmt":"2020-02-21 20:22:14","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=386402","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":410232,"post_author":"8022","post_date":"2019-01-16 17:17:09","post_date_gmt":"2019-01-17 01:17:09","post_content":"","post_title":"Following the flow of data in GeoAnalytics Server","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"following-the-flow-of-data-in-geoanalytics-server","to_ping":"","pinged":"","post_modified":"2020-02-21 12:22:08","post_modified_gmt":"2020-02-21 20:22:08","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=410232","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":270772,"post_author":"8132","post_date":"2018-07-26 06:00:40","post_date_gmt":"2018-07-26 13:00:40","post_content":"","post_title":"Visualize aggregated data in ArcGIS GeoAnalytics Server 10.6.1","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"data-aggregation-using-geoanalytics-server","to_ping":"","pinged":"","post_modified":"2019-11-01 14:04:51","post_modified_gmt":"2019-11-01 21:04:51","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=270772","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":82061,"post_author":"7431","post_date":"2018-01-22 09:00:56","post_date_gmt":"2018-01-22 09:00:56","post_content":"","post_title":"What\u2019s new in ArcGIS Enterprise 10.6: Geocode Locations from Table in ArcGIS GeoAnalytics Server","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"whats-new-in-arcgis-enterprise-10-6-geocode-locations-from-table-in-arcgis-geoanalytics-server","to_ping":"","pinged":"","post_modified":"2024-01-25 14:01:50","post_modified_gmt":"2024-01-25 22:01:50","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/whats-new-in-arcgis-enterprise-10-6-geocode-locations-from-table-in-arcgis-geoanalytics-server\/","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":123471,"post_author":"7431","post_date":"2018-03-20 05:30:37","post_date_gmt":"2018-03-20 05:30:37","post_content":"This year's Esri Developer Summit was the biggest one yet. Crucial conference highlights\u00a0include:\u00a0the giant outdoor carnival full of games and food and fun, and\u00a0the opportunity to talk with\u00a0visitors\u00a0to\u00a0the\u00a0GeoAnalytics showcase.\u00a0Getting to hear your questions, comments and\u00a0use\u00a0cases\u00a0is always exciting and inspiring.\u00a0In case you weren't there, our team has\u00a0compiled the most frequently asked questions here in hopes of answering yours!\n\n<strong> \u201cWhat is ArcGIS GeoAnalytics Server?\u201d<\/strong>\n\n<a href=\"http:\/\/enterprise.arcgis.com\/en\/server\/latest\/get-started\/windows\/what-is-arcgis-geoanalytics-server-.htm\" target=\"_blank\">GeoAnalytics Server<\/a> is\u00a0a licensing role for one or more ArcGIS Server machines used for the analysis of large volumes of vector and tabular data. What\u2019s unique about these analysis servers is that they can distribute processing across multiple machines and cores. This means that when your datasets become too large to be processed on a single machine or core, GeoAnalytics can step in to speed up that process. Jobs that may have taken months, weeks, or days can take hours or minutes with GeoAnalytics.\n\n<strong>\"I see that the ArcGIS Enterprise portal's Map Viewer has both Standard Tools and GeoAnalytics Tools. What are the differences, and when would I use one or the other?\u201d<\/strong>\n\nStandard tools are available by default in ArcGIS Enterprise and perform feature analysis using your hosting server. These tools are useful when you are processing average-sized data. GeoAnalytics Tools, however, process data in parallel on your GeoAnalytics Server, and are specialized to process larger amounts of data more quickly. Both toolsets contain some similar tools, like Aggregate Points, Join Features, and Find Hot Spots, but GeoAnalytics\u2019 extra edge is that it provides tools to track trends, patterns and anomalies in both space and time.\n\n<a title=\"Compare standard and GeoAnalytics Tools\" href=\"https:\/\/enterprise.arcgis.com\/en\/portal\/latest\/use\/standard-and-big-data-feature-analysis-comparison.htm\" target=\"_blank\">Learn more about standard analysis and GeoAnalytics Tools<\/a>\n\n<strong>\u201cGeoAnalytics, GeoEvent, Insights, and analysis in ArcGIS Pro: How does it all work together?\"<\/strong>\n\nGeoAnalytics, <a title=\"GeoEvent\" href=\"http:\/\/www.esri.com\/arcgis\/products\/geoevent-server\" target=\"_blank\">GeoEvent Server<\/a>, and <a title=\"Insights\" href=\"https:\/\/www.esri.com\/en-us\/arcgis\/products\/insights-for-arcgis\/overview\" target=\"_blank\">Insights for ArcGIS<\/a> complement each other, but also work well in isolation. GeoEvent is used to ingest real-time stream data from sources like sensors and GPS measurements. GeoEvent exposes ingested data as feature layers, which can optionally be explored further in Insights or processed using GeoAnalytics Tools. Insights provides a data workbench experience for your data, allowing you to explore, iterate on and share your findings via charts, graphs and maps. Insights can take advantage of different analysis engines, so while you are using your Insights workbook, your analysis may be powered by Insights, standard analysis tools, or even GeoAnalytics Tools, depending on the configuration of ArcGIS Enterprise.\n\n<strong>\u201cI have a huge dataset, and it\u2019s not drawing quickly. I want to draw a zillion points, how do I do that?\u201d<\/strong>\n\nWell, you may not want to. Why? Because visualizing millions and trillions of features on their own isn\u2019t informative. Instead, consider using GeoAnalytics to visualize big data patterns by aggregating and summarizing trends. This allows you to explore and find patterns that would otherwise go unseen amid your many features. For example, below are millions of\u2026.can you tell? Exactly \u2014\u00a0it\u2019s hard to see anything with that many points. These are taxi pickup locations in New York City (Do not try this at home!)\n\n<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/03\/gax_devsummit.gif\"><img class=\"alignnone size-full wp-image-102575\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/03\/gax_devsummit.gif\" alt=\"NYC Taxi big data dataset aggregated into polygons\" width=\"600\" height=\"608\" \/><\/a>\n\n<strong>\u201cAm I able to automate GeoAnalytics workflows? Does it work with the Jupyter Notebooks I\u2019ve been hearing so much about?\u201d<\/strong>\n\nYes, and yes! The ArcGIS Python API can be used to automate and execute workflows. For those of you who are fans of Jupyter Notebooks, GeoAnalytics Tools can be executed there, too! Additionally, GeoAnalytics Tools can be used with Model Builder in ArcGIS Pro and in ArcPy. If REST is your preferred tool execution method (props to you!) then you can run the tools through the REST API.\n\n<strong>\u201cThat all sounds great. Now,\u00a0how do I install it? Is GeoAnalytics an extension or a separate install?\u201d<\/strong>\n\nGeoAnalytics is a server role within ArcGIS Enterprise, not an extension. It builds off of the ArcGIS Enterprise base deployment. First, install ArcGIS Server and license it for GeoAnalytics, then federate your GeoAnalytics site with your Enterprise portal. You\u2019ll need to also install and configure the ArcGIS Data Store (spatiotemporal big data), used as an output for your results. Once your environment is all set up, you can start crunching some data!\n\nHopefully this was useful in answering your questions. Which features are you excited about? Are you currently using GeoAnalytics? Let us know in the comments below!","post_title":"ArcGIS GeoAnalytics Server: DevSummit 2018 FAQs!","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"arcgis-geoanalytics-server-devsummit-2018-faqs","to_ping":"","pinged":"","post_modified":"2018-06-22 17:06:09","post_modified_gmt":"2018-06-22 17:06:09","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/arcgis-geoanalytics-server-devsummit-2018-faqs\/","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/03\/watercard.jpg","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/01\/water.jpg"},"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>Detecting Water Utility 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