{"id":13686,"date":"2019-04-18T12:38:58","date_gmt":"2019-04-18T16:38:58","guid":{"rendered":"http:\/\/studentwork.prattsi.org\/infovis\/?p=13686"},"modified":"2019-04-22T01:19:04","modified_gmt":"2019-04-22T05:19:04","slug":"world-trade-network-in-1994","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/world-trade-network-in-1994\/","title":{"rendered":"World Trade Network in 1994"},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n\n

The network is a more complicated concept compared to other data vis genres. On a network the meanings of edges. Since the required elements of data are particular. There are not many options for me to choose from. I used the 1994 World Trade data from CASOS Public Datase1ts. Through the visualization of this data, I want to discover the economic status of 80 countries in 1994 and find the most active trading country back in time.<\/p>\n\n\n\n

Design Examples<\/h2>\n\n\n\n
\"Circos
Fig. 1 Circos appears in September 2006 issue of Seed Magazine<\/a>. The image is part of Manuel Lima’s article Look Around You: A Visual Exploration of Complex Networks<\/a>. The image that shows the synteny between the mouse genome and human chromosome 1.
<\/figcaption><\/figure><\/div>\n\n\n\n

The first graph that inspired me is from Seed Magazine about the gene. The graph showed a focus on the top right part. The colors are very beautiful. By looking at this graph, I got really interested in making a circular network. This graph was created with Circos<\/a> which is a powerful DataViz program, particularly for chord diagrams. However, since it does not have a graphical interface which means every command needs to be typed in manually. Plus that it doesn’t support popular data file formats, makes it impossible to learn Circos in a short time.<\/p>\n\n\n\n

\"\"
Fig. 2 GENDER PORTRAYAL IN FILM By <\/a>Jill Marie Hackett<\/a> <\/figcaption><\/figure><\/div>\n\n\n\n

I found this example made by Jill Marie Hacket from the final projects on this blog. This beautiful graph drew my attention immediately when I saw it. The whole poster looks like a console from a Sci-Fi film. There are a few big labels shown on the poster which make it look clean. Also, there is almost zero useless ink on this poster that inspired me a lot on the visual design. I was surprised that the networks on the poster were done in Gephi.<\/p>\n\n\n\n

Material, Tools & Process<\/h2>\n\n\n\n

I used the World Trade data<\/a> from CASOS datasets. The original file is in XML format. First, I used OpenRefine<\/a> to clean the data. On the XML, there are the node table(countries) and the edge table(country to country network). Fortunately, the country names were included in the edges, so I only needed to export the edge table. Before exporting, I deleted unnecessary columns and renamed the source, target and weight columns to make them detectable by Gephi. <\/p>\n\n\n\n

Gephi<\/a> is a data viz tool used particularly for networks. Compared to other data vis tools such as Tableau and Carto, Gephi is not really user-friendly since it is still in its beta stage. I encountered some bugs while using. However, the tool is the only options I can use for free for network visualization projects, and the bugs are not fatal regarding my workflow. In Gephi, I imported the CSV file I generated with OpenRefine and selected undirected option. In the beginning, I used modularity to color and cluster the nodes and used weighted degree to resize them. <\/p>\n\n\n\n

For the network layout, I tried every preset layout such as Force Atlas and Yifan Hu, but none of them looked really good. I downloaded other layouts from Gephi plugins and finally decided to use Circle Pack layout which can group the nodes by modularity class accurately.<\/p>\n\n\n\n

I used Ubuntu for the label font and added a font stroke to make the text more readable.<\/p>\n\n\n\n

Result & Reflection<\/h2>\n\n\n\n
\"\"
Fig. 3 World Trade Datavis<\/figcaption><\/figure>\n\n\n\n

This is the final network. Through the network, the most active trading county is Slovenia which is weird. Also, the labels of some countries such as “Rep.” are wrong. Unfortunately, I didn’t realize the data is wrong until I finished the project. I can still see which countries are closely connected to each other. Countries in the same continent tend to show a stronger relationship. For example, Singapore and Malaysia are in the same color. For this network practice, I don’t plan to find the new data and redo the visualization. But I should definitely check the accuracy next time when I work on any data. <\/p>\n\n\n\n

I think the most difficult part of this project is to understand what the network means in particular scenarios, as well as what the clusters calculated by Gephi mean. I started to understand them by actually using Gephi. For the next step, I want to try making my own network data for the final project and use Illustrator the make the network look better.<\/p>\n","protected":false},"excerpt":{"rendered":"

Introduction The network is a more complicated concept compared to other data vis genres. On a network the meanings of edges. Since the required elements of data are particular. There are not many options for me to choose from. I used the 1994 World Trade data from CASOS Public Datase1ts. Through the visualization of this…<\/p>\n","protected":false},"author":594,"featured_media":13735,"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":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[149,342],"tags":[],"coauthors":[374],"class_list":["post-13686","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-labs","category-networks"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2019\/04\/world_trade.png?fit=1024%2C1024&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-3yK","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/13686","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\/594"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=13686"}],"version-history":[{"count":3,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/13686\/revisions"}],"predecessor-version":[{"id":14078,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/13686\/revisions\/14078"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/13735"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=13686"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=13686"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=13686"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=13686"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}