{"id":40174,"date":"2026-05-07T15:28:42","date_gmt":"2026-05-07T19:28:42","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=40174"},"modified":"2026-05-07T15:28:43","modified_gmt":"2026-05-07T19:28:43","slug":"tracking-tree-trends-identifying-forest-outcome-relationships-across-policy-peers-based-on-epi-scores","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/networks\/tracking-tree-trends-identifying-forest-outcome-relationships-across-policy-peers-based-on-epi-scores\/","title":{"rendered":"Tracking Tree Trends: Identifying forest outcome relationships across policy-peers based on EPI scores"},"content":{"rendered":"\n

This lab report intends to contextualize the observations from my prior reports about global forest trends. The previous reports aimed to identify global and country-specific trends in forest area changes between 1990-2020. This report uses the same data set, and was appended with the 2024 Environmental Performance Index Scores which \u201cranks 180 countries on climate change performance, environmental health, and ecosystem vitality. These indicators provide a gauge at a national scale of how close countries are to established environmental policy targets.\u201d I chose to include this measurement in my project as it aligned with my original goals of using environmental data to identify countries performing at each environmental extreme and identify best policy practices. Since policymakers frequently benchmark against countries with similar governance contexts and frameworks the goal of this visualization is to display policy neighborhoods alongside forest outcomes to highlight outliers whose policies might warrant analysis or replication based on environmental performance. <\/strong>However, it is important to note that the two data sets did not completely align, resulting in some countries that were present in the previous labs being absent in this work. These values are typically territories that are self-governing but still represented by another country\u2019s policy and I can assume were combined into that country\u2019s policy score.’\u00a0<\/p>\n\n\n\n

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

My goal was to view environmental policy commonality across countries using a network graph, and identify if there were any relationships between these policies and forest outcomes and what outliers would emerge. I chose the Fruchterman-Reingold layout for my network map because of how it applies edge weights to create proximity and distributes nodes to avoid clustering, creating a clean sphere (that looks slightly like a globe). This layout choice resulted in meaningful spatial positioning that clearly shows densely packed neighborhoods and more spare areas. From this we can see that EPI scores have a skewed distribution, with the more common scores typically in the middle-lower range. The edge connections also clearly create a spiral of data from the lowest to highest EPI scores, which helps contextualize where a node might sit on that distribution.\u00a0<\/p>\n\n\n\n

To allow for the analysis of forest outcomes alongside EPI scores I applied the coloring from my previous labs to the nodes (a red-green color scale) to indicate each country\u2019s percent-change in forest area. I then chose to scale nodes based on connection weight to show which countries had more policy peers as it helped constrain comparison groups. This provided more actionable insight to compare differences in forest outcomes between countries with similar scores and simplified the identification of forest outcome outliers within EPI peer groups. For example Sierra Leone (our most connected country) is a near neighbor of Kyrgyzstan but has vastly different forest outcomes, which would prompt investigation into what might differ in their individual policies despite their EPI similarity?\u00a0<\/p>\n\n\n\n

The graph shows that the countries with higher EPI scores are typically gaining forest area, however very few countries fall into this group and are loosely connected to the rest of the graph. However, as the connections between nodes increase so do the differences in forest outcomes. A low EPI score does not necessarily correlate with low forest outcomes – while red appears as a predominant color toward the middle and lower end of the graph\u2019s EPI scores there are frequent green nodes present which indicates that the relationship between EPI scores and forest outcomes does not persist across all the data.<\/p>\n\n\n\n

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

Evidently, this is a lot of information to pack into one visual so I went through many iterations of the graph\u2019s supporting material including the title, descriptive section, and the legend. I conducted user testing to assess how effectively insight could be uncovered through use of the graph. Through these sessions I realized that the success of the visualization relied on the reader correctly interpreting a lot of abstract and unfamiliar information. In my redesigns I did my best to contextualize the data to help avoid any misinterpretations: I placed a description of the EPI scores immediately below the title, and positioned the legend above the graph rather than to the side to encourage a top-down reading pattern that would provide context prior to the visual. The main goal of the visualization is about the policy peer groups so I added a lot of supplementary details to build user understanding, such as the node-size legend, explicitly identifying the highest and lowest peer values, and adding small multiples of the visualization highlighting how the identified peer policy connections looked within the map.<\/p>\n","protected":false},"excerpt":{"rendered":"

This lab report intends to contextualize the observations from my prior reports about global forest trends. The previous reports aimed to identify global and country-specific trends in forest area changes between 1990-2020. This report uses the same data set, and was appended with the 2024 Environmental Performance Index Scores which \u201cranks 180 countries on climate…<\/p>\n","protected":false},"author":4632,"featured_media":40175,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_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":"","jetpack_post_was_ever_published":false},"categories":[342],"tags":[],"coauthors":[1926],"class_list":["post-40174","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-networks"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2026\/05\/Final.png?fit=1996%2C1525&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-arY","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/40174","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=40174"}],"version-history":[{"count":2,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/40174\/revisions"}],"predecessor-version":[{"id":40179,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/40174\/revisions\/40179"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/40175"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=40174"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=40174"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=40174"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=40174"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}