{"id":254,"date":"2018-01-16T21:16:53","date_gmt":"2018-01-17T04:16:53","guid":{"rendered":"https:\/\/blogs.ubc.ca\/carlygardner\/?p=254"},"modified":"2018-02-22T18:03:08","modified_gmt":"2018-02-23T01:03:08","slug":"lab-1-modelbuilder-exercise","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/carlygardner\/2018\/01\/16\/lab-1-modelbuilder-exercise\/","title":{"rendered":"Lab 1: Modelbuilder Exercise"},"content":{"rendered":"<p>In this lab, I obtained health data from the CDC (Centers for Disease Control and Prevention) to create a map showing heart disease rates by county in the southeastern United States. We used a spatial weights matrix, the Hot Spot Analysis tool and\u00a0 ModelBuilder.<!--more--><\/p>\n<p>After downloading the data I had to correct for a common issue where numbers in the file need to be treated like text. Then, in conducting the spatial analysis, I explicitly defined spatial relations amongst spatial objects by modelling spatial relationships with a specific spatial weights matrix. For this, I created a model using the Modelbuilder.<\/p>\n<p>Unfortunately, some errors occurred in our Hot Spot Analysis tool. For some reason each hot spot map was not added to the display. I circumnavigated this issue by generating each of the maps individually (but only chose two different years to generate, rather than producing all 17). If I generated enough, it would be worth building an animation model in order to see how heart disease rates change in this geographical area over time.<\/p>\n<p><a href=\"https:\/\/blogs.ubc.ca\/carlygardner\/2018\/01\/16\/lab-1-modelbuilder-exercise\/cdc_wonder_2000\/\" rel=\"attachment wp-att-273\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-273 size-full\" src=\"https:\/\/blogs.ubc.ca\/carlygardner\/files\/2018\/01\/CDC_Wonder_2000.jpg\" alt=\"\" width=\"1650\" height=\"1275\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this lab, I obtained health data from the CDC (Centers for Disease Control and Prevention) to create a map showing heart disease rates by county in the southeastern United States. We used a spatial weights matrix, the Hot Spot Analysis tool and\u00a0 ModelBuilder.<\/p>\n","protected":false},"author":35511,"featured_media":273,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1546727],"tags":[],"class_list":["post-254","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-geographic-information-science"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/posts\/254","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/users\/35511"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/comments?post=254"}],"version-history":[{"count":4,"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/posts\/254\/revisions"}],"predecessor-version":[{"id":295,"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/posts\/254\/revisions\/295"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/media\/273"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/media?parent=254"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/categories?post=254"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/carlygardner\/wp-json\/wp\/v2\/tags?post=254"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}