{"id":1152,"date":"2014-12-11T15:09:20","date_gmt":"2014-12-11T20:09:20","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1152"},"modified":"2026-02-18T11:28:01","modified_gmt":"2026-02-18T16:28:01","slug":"buck2014endmember","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2014\/12\/11\/buck2014endmember\/","title":{"rendered":"Endmember representation of human geography layers"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>This paper presents an endmember estimation and representation approach for human geography data cubes. Human-related factors that can be mapped for a geographic region include factors relating to population, age, religion, education, medical access and others. Given these hundreds (or even thousands) of factors mapped over a region, it is extremely difficult for an analyst to summarize and understand the interactions between all of these factors. In this paper, a method to provide a compact representation and visualization of hundreds of human geography layers is presented. These are large data cubes containing a range of human geographic information including some represented using fuzzy values. Results on a human geography data cube compiled for the state of Missouri, USA is presented.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"\/\/doi.org\/10.1109\/CIBD.2014.7011520\"><img decoding=\"async\" border=\"2\" alt=\"IEEE Link\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee.jpg\" height=\"50\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/buck2014endmember.pdf\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" height=\"50\"><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>A. Buck, A. Zare, J. Keller, and M. Popescu, \u201cEndmember representation of human geography layers,\u201d in IEEE Symp. Computational Intelligence in Big Data (CIBD), 2014. <\/code><\/pre>\n<pre><code>@InProceedings{buck2014endmember,\nTitle = {Endmember representation of human geography layers},\nAuthor = {Andrew Buck and Alina Zare and James Keller and Mihail Popescu},\nBooktitle = {IEEE Symp. Computational Intelligence in Big Data (CIBD)},\nYear = {2014},\nMonth = {Dec.},\nDoi = {10.1109\/CIBD.2014.7011520},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: This paper presents an endmember estimation and representation approach for human geography data cubes. Human-related factors that can be mapped for a geographic region include factors relating to population, age, religion, education, medical access and others. Given these hundreds (or even thousands) of factors mapped over a region, it is extremely difficult for an [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[273,681],"class_list":["post-1152","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-endmember","tag-sparsity-promotion"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1152","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=1152"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1152\/revisions"}],"predecessor-version":[{"id":14617,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1152\/revisions\/14617"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1152"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1152"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1152"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}