{"id":1096581,"date":"2020-12-23T07:45:25","date_gmt":"2020-12-23T15:45:25","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1096581"},"modified":"2021-01-19T09:58:05","modified_gmt":"2021-01-19T17:58:05","slug":"whats-new-for-spatial-statistics-in-arcgis-pro-2-7","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/whats-new-for-spatial-statistics-in-arcgis-pro-2-7","title":{"rendered":"What\u2019s new for Spatial Statistics in ArcGIS Pro 2.7?"},"author":9862,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23771,23341],"tags":[24311,150382,25581],"industry":[],"product":[36561],"class_list":["post-1096581","blog","type-blog","status-publish","format-standard","hentry","category-3d-gis","category-analytics","tag-analysis","tag-arcgispro","tag-spatial-statistics","product-arcgis-pro"],"acf":{"short_description":"All the new tools and capabilities in ArcGIS Pro 2.7 for spatial statistics and data engineering.","flexible_content":[{"acf_fc_layout":"content","content":"<p>With the release of <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/get-started\/install-and-sign-in-to-arcgis-pro.htm\">ArcGIS Pro 2.7<\/a> on December 16th 2020, the Spatial Statistics team is excited to share with you the new capabilities we&#8217;ve added in ArcGIS Pro 2.7, ranging from out of the box Data Engineering tools to sophisticated statistical methods for analysis. Let\u2019s explore each of the new capabilities and tools in more detail!<\/p>\n"},{"acf_fc_layout":"content","content":"<h2>New Data Engineering Tools<\/h2>\n"},{"acf_fc_layout":"content","content":"<p>Data Engineering is an integral and often the most time-consuming part of an analysis. The following new tools available in ArcGIS Pro 2.7 can help make your data ready for subsequent analysis!<\/p>\n<ul>\n<li><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/spatial-statistics\/dimensionreduction.htm\"><strong>Dimension Reduction<\/strong><\/a>: This new tool in <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-utilities-toolset.htm\">Utilities toolset<\/a>) reduces the number of dimensions of a set of continuous variables into fewer components using Principal Component Analysis (PCA) or Reduced-Rank Linear Discriminant Analysis (LDA).<br \/>\nDimension reduction is commonly used to explore multivariate relationships between variables, reduce the computational cost of machine learning algorithms, and provide comparable (or better) results while consuming fewer computational resources.<\/li>\n<li><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/transform-field.htm\"><strong>Transform Field<\/strong><\/a>: This new tool in <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-data-management-toolbox.htm\">Data Management toolbox<\/a> (<a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-fields-toolset.htm\">Fields toolset<\/a>) transforms continuous values by applying mathematical functions (such log, square root, Box-Cox, multiplicative inverse, square, exponential, and inverse Box-Cox) and changes the shape of the distribution.<br \/>\nA transformation can be applied to reduce skewness in the distribution and make it follow a normal (Gaussian) distribution.<\/li>\n<li><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/standardizefield.htm\"><strong>Standardize Field<\/strong><\/a>: This new tool in <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-data-management-toolbox.htm\">Data Management toolbox<\/a> (<a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-fields-toolset.htm\">Fields toolset<\/a>) standardizes continuous values by converting them to values that follow a specified scale. Standardization methods include z-score, minimum-maximum, absolute maximum, and robust standardization.<\/li>\n<li><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/encode-field.htm\"><strong>Encode Field<\/strong><\/a>: This new tool in <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-data-management-toolbox.htm\">Data Management toolbox<\/a> (<a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-fields-toolset.htm\">Fields toolset<\/a>) converts categorical values (string, integer, or date) into multiple numerical fields, each representing a category. The encoded numerical fields can be used in most data science and statistical workflows, including regression models.<\/li>\n<li><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/reclassify-field.htm\"><strong>Reclassify Field<\/strong><\/a>: This new tool, also in <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-data-management-toolbox.htm\">Data Management toolbox<\/a> (<a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/an-overview-of-the-fields-toolset.htm\">Fields toolset<\/a>), reclassifies values in a numerical or text field into classes based on bounds defined manually or using a reclassification method.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"content","content":"<h2>New Spatial Statistics Tools<\/h2>\n"},{"acf_fc_layout":"content","content":"<ul>\n<li><a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/spatial-statistics\/spatial-outlier-detection.htm\"><strong>Spatial Outlier Detection<\/strong><\/a>\u2014This new tool in <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>) identifies spatial outliers in point features by calculating the local outlier factor (LOF) of each feature. The LOF is a measurement that describes how isolated a location is from its local neighbors.<\/li>\n<li><a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/spatial-statistics\/spatial-association-between-zones.htm\"><strong>Spatial Association Between Zones<\/strong><\/a>\u2014 This new tool in <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-modeling-spatial-relationships-toolset.htm\">Modeling Spatial Relationships toolset<\/a>) measures the degree of spatial association between two regionalizations of the same study area in which each regionalization is composed of a set of categories, called zones. For example, this tool can be used to measure the association between forest type and soil class of the same study area.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"image","image":{"ID":1097361,"id":1097361,"title":"Examples of high and low association between blue and orange zones are shown.","filename":"SABRE.jpg","filesize":20052,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/whats-new-for-spatial-statistics-in-arcgis-pro-2-7\/sabre","alt":"Examples of high and low association between blue and orange zones are shown.","author":"9862","description":"Examples of high and low association between blue and orange zones are shown.","caption":"Examples of high and low association between blue and orange zones are shown.","name":"sabre","status":"inherit","uploaded_to":1096581,"date":"2020-12-23 15:57:56","modified":"2020-12-23 17:16:12","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":597,"height":257,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","medium-width":464,"medium-height":200,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","medium_large-width":597,"medium_large-height":257,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","large-width":597,"large-height":257,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","1536x1536-width":597,"1536x1536-height":257,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","2048x2048-width":597,"2048x2048-height":257,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","card_image-width":597,"card_image-height":257,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SABRE.jpg","wide_image-width":597,"wide_image-height":257}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"sidebar","content":"<p>Learn more about how the tool <a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/spatial-statistics\/spatial-association-between-zones.htm\"><strong>Spatial Association Between Zones<\/strong><\/a> works with examples in <a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/spatial-association-between-zones-a-new-way-to-compare-two-maps\/\">this blog post<\/a>!<\/p>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Right","spotlight_image":false},{"acf_fc_layout":"content","content":"<ul>\n<li><a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/spatial-statistics\/neighborhood-summary-statistics.htm\"><strong>Neighborhood Summary Statistics<\/strong><\/a>\u2014 This new tool in <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-measuring-geographic-distributions-toolset.htm\">Measuring Geographic Distributions toolset<\/a>) calculates local summary statistics of one or more numeric fields of point or polygon features using neighborhoods. The local statistics include mean (average), median, standard deviation, interquartile range, skewness, and quantile imbalance.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"content","content":"<h2>New Space Time Pattern Mining Tools<\/h2>\n"},{"acf_fc_layout":"content","content":"<ul>\n<li><a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/space-time-pattern-mining\/curvefitforecast.htm\"><strong>Curve Fit Forecast<\/strong><\/a><strong>,\u00a0<a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/space-time-pattern-mining\/exponentialsmoothingforecast.htm\">Exponential Smoothing Forecast<\/a><\/strong>, and\u00a0<strong><a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/space-time-pattern-mining\/forestbasedforecast.htm\">Forest-based Forecast<\/a><\/strong>\u2014Three new parameters detect and identify\u00a0<a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/space-time-pattern-mining\/understanding-outliers-in-time-series-analysis.htm\">outliers in the time series<\/a>\u00a0at each location of a space-time cube. Identified outliers can be viewed using interactive pop-up charts on the output features.<\/li>\n<li>The\u00a0<strong><a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/space-time-pattern-mining\/visualizecube2d.htm\">Visualize Space Time Cube in 2D<\/a><\/strong>\u00a0and\u00a0<strong><a class=\"xref xref\" href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/2.7\/tool-reference\/space-time-pattern-mining\/visualizecube3d.htm\">Visualize Space Time Cube in 3D<\/a><\/strong>\u00a0tools include a new display theme to visualize and explore the time series outliers of a space-time cube.<\/li>\n<li>Updated outlier display theme in <strong><a href=\"https:\/\/angp.maps.arcgis.com\/home\/item.html?id=dea0df7731d242388cd29bcd2c24b986\">Space Time Cube Explorer Add-in<\/a><\/strong> for ArcGIS Pro 2.7.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"image","image":{"ID":1097481,"id":1097481,"title":"TimeSeriesOutlier","filename":"TimeSeriesOutlier.jpg","filesize":34694,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/whats-new-for-spatial-statistics-in-arcgis-pro-2-7\/timeseriesoutlier","alt":"Time Series Outlier Visualization","author":"9862","description":"Time Series Outlier Visualization in 3D with above fitted values show in purple and below fitted values shown in green.","caption":"Time Series Outlier Visualization in 3D with above fitted values show in purple and below fitted values shown in green.","name":"timeseriesoutlier","status":"inherit","uploaded_to":1096581,"date":"2020-12-23 17:04:50","modified":"2020-12-23 17:06:14","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":593,"height":343,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","medium-width":451,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","medium_large-width":593,"medium_large-height":343,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","large-width":593,"large-height":343,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","1536x1536-width":593,"1536x1536-height":343,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","2048x2048-width":593,"2048x2048-height":343,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","card_image-width":593,"card_image-height":343,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/TimeSeriesOutlier.jpg","wide_image-width":593,"wide_image-height":343}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>For a complete list of all the new capabilities in ArcGIS Pro 2.7, see <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/get-started\/whats-new-in-arcgis-pro.htm#GUID-4499EBA8-0C84-4B9A-98AD-F2689651297E\">What&#8217;s new in ArcGIS Pro 2.7<\/a>.<\/p>\n"},{"acf_fc_layout":"content","content":"<h2>Message from Spatial Statistics Team<\/h2>\n"},{"acf_fc_layout":"content","content":"<p>In 2020 so many things have changed; the way we work, interact and collaborate. However, this pandemic has also showed us that somethings never change like the dedication of the GIS community to make this world a better place and our team&#8217;s commitment to continue providing the tools our users need to be successful. We can&#8217;t wait to see how you leverage these new tools and capabilities in your analysis workflows.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1096871,"id":1096871,"title":"Spatial Stats team","filename":"SS_Team.png","filesize":800456,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/whats-new-for-spatial-statistics-in-arcgis-pro-2-7\/ss_team","alt":"Spatial Stats team picture","author":"9862","description":"Spatial Statistics Team: Alberto Nieto, Ankita Bakshi, Carlos Osorio-Murillo, Cheng-Chia Huang, Eric Krause, Hu Shao, Jenora D\u2019Acosta, Jie Liu, Lauren Bennett, Lynne Buie, Mark Janikas, Orhun Aydin, Ting-Hwan Lee, Xiaodan Zhou","caption":"Spatial Statistics Team: Alberto Nieto, Ankita Bakshi, Carlos Osorio-Murillo, Cheng-Chia Huang, Eric Krause, Hu Shao, Jenora D\u2019Acosta, Jie Liu, Lauren Bennett, Lynne Buie, Mark Janikas, Orhun Aydin, Ting-Hwan Lee, Xiaodan Zhou","name":"ss_team","status":"inherit","uploaded_to":1096581,"date":"2020-12-23 00:34:45","modified":"2020-12-23 00:57:07","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":822,"height":630,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team.png","medium-width":341,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team.png","medium_large-width":768,"medium_large-height":589,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team.png","large-width":822,"large-height":630,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team.png","1536x1536-width":822,"1536x1536-height":630,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team.png","2048x2048-width":822,"2048x2048-height":630,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team-607x465.png","card_image-width":607,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/SS_Team.png","wide_image-width":822,"wide_image-height":630}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/spatialstats.github.io\/"}],"authors":[{"ID":9862,"user_firstname":"Ankita","user_lastname":"Bakshi","nickname":"Ankita Bakshi","user_nicename":"abakshi","display_name":"Ankita Bakshi","user_email":"ABakshi@esri.com","user_url":"https:\/\/spatialstats.github.io\/","user_registered":"2019-08-13 18:45:44","user_description":"Ankita Bakshi is a Product Owner and a Senior Product Engineer on the Spatial Statistics Team at Esri. With a background in environmental engineering and computer science, she is passionate about solving social, economic, and environmental problems with Spatial Analysis and Data Science. In her role, Ankita enjoys researching, finding solutions to build software, and loves creating video and written content to make the software tools more approachable and applicable to real world challenges. Outside of work Ankita enjoys going on hikes and dancing to Bollywood music.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/12\/Ankita_Bakshi-e1577730248180-259x261.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"related_articles":[{"ID":949901,"post_author":"55021","post_date":"2020-07-28 14:21:32","post_date_gmt":"2020-07-28 21:21:32","post_content":"","post_title":"Time Series Forecasting 101 \u2013 Part 1. COVID-19 data preparation with ArcGIS Notebooks in ArcGIS Pro","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"time-series-forecasting-101-part-1-covid-19-data-preparation","to_ping":"","pinged":"","post_modified":"2020-07-30 20:53:06","post_modified_gmt":"2020-07-31 03:53:06","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=949901","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":950761,"post_author":"55021","post_date":"2020-07-28 14:22:51","post_date_gmt":"2020-07-28 21:22:51","post_content":"","post_title":"Time Series Forecasting 101 \u2013 Part 2. Forecast COVID-19 daily new confirmed cases with Exponential Smoothing Forecast and Forest-based Forecast","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"time-series-forecasting-101-part-2-forecast-covid-19-daily-new-confirmed-cases-with-exponential-smoothing-forecast-and-forest-based-forecast","to_ping":"","pinged":"","post_modified":"2020-07-31 13:56:46","post_modified_gmt":"2020-07-31 20:56:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=950761","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":951201,"post_author":"55021","post_date":"2020-07-28 14:23:05","post_date_gmt":"2020-07-28 21:23:05","post_content":"","post_title":"Time Series Forecasting 101 \u2013  Part 3. Forecast COVID-19 cumulative confirmed cases with Curve Fit Forecast and Evaluate Forecasts by Location","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"time-series-forecasting-101-part-3-forecast-covid-19-cumulative-confirmed-cases-with-curve-fit-forecast-and-evaluate-forecasts-by-location","to_ping":"","pinged":"","post_modified":"2020-07-31 13:54:24","post_modified_gmt":"2020-07-31 20:54:24","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=951201","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":945871,"post_author":"58381","post_date":"2020-07-28 14:24:22","post_date_gmt":"2020-07-28 21:24:22","post_content":"","post_title":"Time Series Forecasting 101 - Part 4. 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