{"id":39857,"date":"2026-04-23T14:24:59","date_gmt":"2026-04-23T18:24:59","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=39857"},"modified":"2026-04-23T14:25:01","modified_gmt":"2026-04-23T18:25:01","slug":"tracking-tree-trends-observing-spatial-trends-in-global-forest-area-changes-from-1990-2020","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/maps\/tracking-tree-trends-observing-spatial-trends-in-global-forest-area-changes-from-1990-2020\/","title":{"rendered":"Tracking Tree Trends: Observing spatial trends in global forest area changes from 1990-2020"},"content":{"rendered":"\n

This lab report is a continuation of my work on visualizing changes in forest area from 1990-2020<\/a> where I looked at observing global trends before isolating the top-10 losses and gains by proportions of land area and total hectares. This report uses the same data set but represented the information in a choropleth map to further contextualize the data in a more spatially oriented format. I think it is important to view data in this format to supplement the idea of using this data to identify impacts of country-specific environmental policies, as often environmental impacts are borderless. By viewing data more spatially we can observe if any geographic location groups experience similar trends.\u00a0\u00a0<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

I chose to first show a map that displayed forest area as a proportion of each country\u2019s land area<\/strong>, so when viewed alongside the map showing percent change in forest area<\/strong> a user could visually compare if losses or gains were in any way correlated to the country\u2019s forest density. I chose to represent forest area proportion on a color scale from white to green. This was an intentional break from the typical red to green coloring since I did not want to associate low forest area as a \u201cnegative\u201d aspect given the various types of healthy ecosystems in existence that are not specifically forests. This color choice also clearly indicated countries with no forest area by shading them white, while the green color darkened as forest density increased. From this we can see that the darker shading, representing a larger percentage of forest area, appears mostly along the equator, which unfortunately correlates to the total change map\u2019s red shading, representing high loss of forest area. Since it is difficult to distinguish exact values from these maps I created 2 dot density maps for the top-10 highest and lowest values, where losses again tended towards the equator, while gains were more distributed, but were often found in countries that had lower forest proportions.<\/p>\n\n\n\n


Next I used small multiples<\/strong> to show changes over time to attempt to observe any major location shifts, however these maps showed that while the extremes may shift between countries over time the majority of countries either showed gradual addition or<\/em> loss over time. We can see that countries slightly north of the equator trade places for highest loss across the time periods. Further, the majority of countries in the southern hemisphere tend to show more forest loss, while countries in the northern hemisphere tend to show more recovery.<\/p>\n\n\n\n

<\/p>\n\n\n\n

REFLECTION<\/strong>:<\/p>\n\n\n\n

A key learning from my previous analysis was that the display of percent change over time was not intuitive (eg. 80% forest area change should be represented as 20% loss, and 110% forest area change should be shown as 10% gained). This means that my percent change data needed further cleaning to achieve the desired metrics. To edit my data I took the following steps:<\/p>\n\n\n\n

– to show the percent lost<\/strong> for all values below 100%: (100 – (date \/ previous date*100))\u00a0<\/p>\n\n\n\n

– to show no change<\/strong> for all values that equal exactly 100%: set to 0<\/p>\n\n\n\n

– to show percent<\/strong> increased<\/strong> for all values above 100%: ((date \/ previous date*100)-100)<\/p>\n\n\n\n

I chose a red-green diverging color spectrum to represent the most loss (red) to the most gain (green) with my center point as 0. This was a continuation of the red \/ green choices from my previous report. However, an issue I encountered with the color spectrum was an inability to set the spectrum to display only values that exactly equal 0 as white. I attempted to create a custom color spectrum (red-white-green), or use stepped colors, but due to the distribution of the data this often resulted in many of the countries with values close to 0 appearing in the same color group as the 0 values.\u00a0\u00a0<\/p>\n\n\n\n

The dot-density maps are an effective supplement to highlight top values since the color spectrum makes it difficult to identify exact measures, but given the size of the maps it is difficult to distinguish the shading of the individual dots, especially for losses where the data is more centralized, which resulted in 10 very similar looking dots. The map itself is helpful for identifying the top-10, but was not as effective as I had hoped in clearly ranking the values. \u00a0<\/p>\n\n\n\n

In all the legends that display color spectrum coding I would like to include a box plot to show the distribution of data. For example, the total area change color spectrum is set with a min value of -88, a middle color at 0, and a max value at 173, which then appears to show that most countries should be regaining forest area since 0-173 appears larger. However, by plotting the data distribution we can clearly show that the majority of data points do in fact fall below the 0 value.\u00a0This addition would feel especially valuable given that the size of some heavily forested countries (eg. Russia) can also lead a viewer to believe that the high presence of the color green shows a positive overall trend, despite the fact that both the total percentage of forest area and total hectares of forest area are still decreasing globally. <\/p>\n","protected":false},"excerpt":{"rendered":"

This lab report is a continuation of my work on visualizing changes in forest area from 1990-2020 where I looked at observing global trends before isolating the top-10 losses and gains by proportions of land area and total hectares. This report uses the same data set but represented the information in a choropleth map to…<\/p>\n","protected":false},"author":4632,"featured_media":39858,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":true,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[341],"tags":[],"coauthors":[1926],"class_list":["post-39857","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-maps"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2026\/04\/Screenshot-2026-04-23-at-11.50.01-AM.png?fit=1623%2C968&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-amR","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39857","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/users\/4632"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=39857"}],"version-history":[{"count":1,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39857\/revisions"}],"predecessor-version":[{"id":39859,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39857\/revisions\/39859"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/39858"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=39857"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=39857"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=39857"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=39857"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}