{"id":2044982,"date":"2023-12-14T10:15:37","date_gmt":"2023-12-14T18:15:37","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=2044982"},"modified":"2024-06-11T05:27:52","modified_gmt":"2024-06-11T12:27:52","slug":"end-to-end-spatial-data-science-1-clustering-us-precipitation-regions","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-1-clustering-us-precipitation-regions","title":{"rendered":"End-to-end spatial data science 1: Clustering US Precipitation Regions"},"author":154341,"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],"tags":[760452,35661,24341,30241,759592],"industry":[],"product":[36841,36561],"class_list":["post-2044982","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-data-engineering","tag-machine-learning","tag-python","tag-r","tag-spatial-data-science","product-api-python","product-arcgis-pro"],"acf":{"authors":[{"ID":154341,"user_firstname":"Nicholas","user_lastname":"Giner","nickname":"Nick Giner","user_nicename":"nginer","display_name":"Nicholas Giner","user_email":"NGiner@esri.com","user_url":"","user_registered":"2021-01-07 14:31:25","user_description":"Nick Giner is a Product Manager for Spatial Analysis and Data Science.  Prior to joining Esri in 2014, he completed Bachelor\u2019s and PhD degrees in Geography from Penn State University and Clark University, respectively. In his spare time, he likes to play guitar, golf, cook, cut the grass, and read\/watch shows about history.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/01\/headshot-e1610030307989-213x200.jpeg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"short_description":"This is the first in a series of blogs that showcase an end-to-end spatial data science workflow for clustering US precipitation regions.","flexible_content":[{"acf_fc_layout":"content","content":"<h2>Introduction<\/h2>\n<p>This blog series is based on the recent research paper \u201cDelineating precipitation regions of the contiguous United States from cluster analyzed gridded data\u201d, from a 2021 <a href=\"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/24694452.2020.1828803\">issue<\/a> of the <em>Annals of the American Association of Geographers <\/em>(Marston and Ellis, 2021).\u00a0 The goal of the paper is to create a map of climate regions in the United States based on 30 years of historical precipitation data.\u00a0 My goal is to use a combination of Esri technology and open-source technology to reproduce the workflow and replicate the results.\u00a0 Throughout the following blogs, we\u2019ll perform the entire analysis using:<\/p>\n<ul>\n<li>ArcGIS Pro<\/li>\n<li>ArcGIS Notebooks<\/li>\n<li>ArcGIS API for Python<\/li>\n<li>R-ArcGIS Bridge<\/li>\n<li>Open-source Python<\/li>\n<li>Open-source R<\/li>\n<\/ul>\n<h2>The problem<\/h2>\n<p>Humans have created and used climate region maps to help understand climate geography and its impact on agriculture, navigation, weather and natural hazards, where we live, where we travel to, etc. since the time of the Ancient Greeks.\u00a0 Within the past 150 years, several climate region classifications have been proposed, each with variations in their input data, methodology, and purpose.\u00a0 For example, the extremely well-known <a href=\"https:\/\/en.wikipedia.org\/wiki\/K%C3%B6ppen_climate_classification\">K\u00f6ppen classification<\/a> divides Earth into climate regions based on precipitation and temperature, while the <a href=\"https:\/\/en.wikipedia.org\/wiki\/Thornthwaite_climate_classification\">Thornthwaite classification<\/a> outlines regions based on a location\u2019s precipitation and evapotranspiration characteristics.\u00a0 More recently, the National Centers for Environmental Information (NCEI) produced a 9-region climate map of the US based on county-level climate characteristics, which were spatially aggregated first to the US state level, and then to the region level.<\/p>\n<p>The many factors that influence climate\u2014temperature, precipitation, elevation, latitude, proximity to oceans\u2014however, do not adhere to geopolitical boundaries, so the NCEI map may not represent the true climate geography of the United States.\u00a0 For example, the Northeast, Southeast, and Ohio Valley are relatively homogeneous in mean annual precipitation, while there is much more intra-region heterogeneity in the Northwest and West.\u00a0 The range of average annual precipitation across the Northwest is several hundred millimeters!<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2197812,"id":2197812,"title":"precip_mean_map","filename":"precip_mean_map.jpg","filesize":276611,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-1-clustering-us-precipitation-regions\/precip_mean_map","alt":"","author":"154341","description":"","caption":"Map showing average  annual precipitation (mm) from 1981-2010 in the contiguous United States.  The 9-region NCEI map is overlaid for reference.","name":"precip_mean_map","status":"inherit","uploaded_to":2044982,"date":"2023-12-13 18:49:28","modified":"2023-12-13 21:33:35","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":1510,"height":1001,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map.jpg","medium-width":394,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map.jpg","medium_large-width":768,"medium_large-height":509,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map.jpg","large-width":1510,"large-height":1001,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map.jpg","1536x1536-width":1510,"1536x1536-height":1001,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map.jpg","2048x2048-width":1510,"2048x2048-height":1001,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map-701x465.jpg","card_image-width":701,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/precip_mean_map.jpg","wide_image-width":1510,"wide_image-height":1001}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>In the paper, the authors use a novel, data-driven approach to create a modern climate region map based on seasonal precipitation trends in the US for the 30-year period of 1981-2010.\u00a0 They calculate a total of 16 different precipitation variables (4 variables x 4 seasons) for each location in a gridded dataset of daily precipitation data, then use a series of machine learning techniques for dimensionality reduction and cluster analysis.\u00a0 The final clusters represent climate regions with similar seasonal precipitation signatures over the 30-year period.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2198022,"id":2198022,"title":"cluster_map_elim","filename":"cluster_map_elim.jpg","filesize":229968,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-1-clustering-us-precipitation-regions\/cluster_map_elim","alt":"","author":"154341","description":"","caption":"The final map of 13 precipitation regions resulting from the cluster analysis.","name":"cluster_map_elim","status":"inherit","uploaded_to":2044982,"date":"2023-12-13 19:56:53","modified":"2023-12-14 12:37:09","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":1510,"height":1005,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim.jpg","medium-width":392,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim.jpg","medium_large-width":768,"medium_large-height":511,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim.jpg","large-width":1510,"large-height":1005,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim.jpg","1536x1536-width":1510,"1536x1536-height":1005,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim.jpg","2048x2048-width":1510,"2048x2048-height":1005,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim-699x465.jpg","card_image-width":699,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_elim.jpg","wide_image-width":1510,"wide_image-height":1005}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2>What&#8217;s next<\/h2>\n<p>In this series of blog articles, I\u2019ll walk you step-by-step through my process of reproducing the analysis in this paper including the data ingest, preparation, engineering, and machine learning.\u00a0 Hopefully at the end you\u2019ll have not only learned a bit about climate geography in the US, but more importantly how to leverage open-source Python and R with ArcGIS Pro and ArcGIS Notebooks to complete an end-to-end spatial data science project.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2197092,"id":2197092,"title":"workflow_v4","filename":"workflow_v4.jpg","filesize":220423,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-1-clustering-us-precipitation-regions\/workflow_v4","alt":"","author":"154341","description":"","caption":"Spatial data science workflow performed in the blog series.","name":"workflow_v4","status":"inherit","uploaded_to":2044982,"date":"2023-12-13 14:08:10","modified":"2023-12-13 21:35:47","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":1881,"height":906,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4.jpg","medium-width":464,"medium-height":223,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4.jpg","medium_large-width":768,"medium_large-height":370,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4.jpg","large-width":1881,"large-height":906,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4-1536x740.jpg","1536x1536-width":1536,"1536x1536-height":740,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4.jpg","2048x2048-width":1881,"2048x2048-height":906,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4-826x398.jpg","card_image-width":826,"card_image-height":398,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/workflow_v4.jpg","wide_image-width":1881,"wide_image-height":906}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2>References (for the blog series)<\/h2>\n<p>Marston, M.L., Ellis, A.W., 2021. Delineating precipitation regions of the contiguous United States from cluster analyzed gridded data. <em>Annals of the American Association of Geographers<\/em>. 111(6), 1721-1739.<\/p>\n<p>Hair Jr., J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1998. <span style=\"text-decoration: underline\">Multivariate Data Analysis (5th Edition)<\/span>. Prentice-Hall, Inc., Upper Saddle River, NJ.<\/p>\n<p>O&#8217;Sullivan, D., Unwin, D.J., 2003. <span style=\"text-decoration: underline\">Geographic Information Analysis<\/span>. John Wiley &amp; Sons, Hoboken, NJ.<\/p>\n"},{"acf_fc_layout":"sidebar","content":"<h2 style=\"text-align: left\">Spatial data science with R, Python, and ArcGIS<\/h2>\n<p>Here are the links to all the articles of the series:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-1-clustering-us-precipitation-regions\/\">Part 1<\/a>. Clustering US Precipitation Regions<\/li>\n<li><a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-2-data-preparation-and-data-engineering-using-r\/\">Part 2<\/a>. Data preparation and data engineering using R<\/li>\n<li><a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-3-data-preparation-and-data-engineering-using-python\/\">Part 3<\/a>. Data preparation and data engineering using Python<\/li>\n<li><a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-4-data-preparation-using-spatial-analysis-and-automation-in-arcgis\/\" target=\"_blank\" rel=\"noopener\">Part 4<\/a>. Data preparation using spatial analysis and automation in ArcGIS<\/li>\n<li><a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/end-to-end-spatial-data-science-5-machine-learning-cluster-analysis-in-python-and-arcgis\">Part 5<\/a>. Machine Learning: Cluster analysis using Python and ArcGIS<\/li>\n<\/ul>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Center","spotlight_image":false}],"related_articles":"","card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/cluster_map_resized.jpg","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/AdobeStock_96810852_fixed.png"},"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>End-to-end spatial data science 1: Clustering US Precipitation Regions<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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