Marie Kondo told us to spark joy…but where does our used clothing really go?

Janelle Lee
Geo-visualization project, SA 8905, Fall 2020

Project Link: click here (use full screen mode for optimal viewing)

Background and Inspiration

In 2015, I lived in Nairobi, Kenya for eight months to participate in a work abroad internship program. On weekends, the other interns and I would explore the city—or, as they say in Nairobi, “go into town”. One of our usual excursions was to go to Toi Market, an open market beside Kibera, which is one of the largest slums in Africa. The market primarily sells clothing, shoes, and miscellaneous household items. I dug up my old travel blog documenting one of our weekend trips to Toi Market:

“The market is essentially a maze—any turn left or right takes you deeper into the layers of Toi and once inside, it’s difficult to find a way out unless you can retrace your steps. Although hot, sunny, and noisy on the outside, most of Toi is shaded and traffic is muffled by the metal-sheeted roofs and make-shift walls that divide each stall from the next. Piles of clothing extend as far as you can see and you begin to wonder where all of it came from. Many items still have their Value Village, Saver Thrift Store, or Salvation Army tags on them which gives a clue.”

I recall having a conversation with a local who told me that a clothing shipment came in every Wednesday—a seemingly infinite supply of second-hand clothing, much of which isn’t even climate appropriate. One of my most memorable images from the market is an endless wall of shelves filled with used Ugg boots (apologies for the blurry photo):

Used clothing doesn’t just stay in Nairobi. For about two weeks, I was working in Kisumu, a city in western Kenya about 450 km north-west of Nairobi. While walking around Kisumu one day, I saw a lady selling some shirts and jeans, of which included a bright red Tim Hortons t-shirt:

Seeing the volume and frequency of used clothing shipments into Kenya left an impression on how I view consumption and consumer waste, particularly in a time when minimalism and “sparking joy” by getting rid of things that we no longer use or wear have become lifestyle trends. To be fair, I haven’t read Marie Kondo’s book or watched her Netflix show, so perhaps the practices she advocates for are more nuanced and thoughtful about how and where people should get rid of their stuff. In any case, my goal for this geo-visualization project is to encourage us to be more aware and mindful of where our used clothing goes before we decide to donate it to “benefit others” (or before we even purchase new items in the first place).

Project Description

My geo-visualization includes two interactive maps and one graph:

Screenshot of my geo-visualization

The first map illustrates trade flow lines between countries—users can select an import and/or export country of interest to see where used clothing is shipped around the world. The other map is a choropleth map that shows whether a country is a net exporter or net importer of used clothing. Users can view the results for different years between 1995 and 2019.

The scatterplot compares each country’s GDP per capita and its net trade value of used clothing. A positive net trade value indicates that a country is a net exporter, while a negative trade value indicates that a country is a net importer. Users can press a play button to see how the scatterplot changes between 1995 and 2019. As a whole, the maps and scatterplot show a pattern in used clothing trade flows. Richer countries tend to export the most used clothing and poorer countries are the primary recipients of these shipments.


I used Tableau as the data visualization software for this project. My primary motivation for using Tableau was to learn how to use the software having no prior experience with it. I also knew that it was an effective tool for visualizing and interacting with data, and I wanted users to be hands-on with the data in my geo-visualization.

Data & Methods

The UN Comtrade Database provides international trade data for thousands of different commodities. One of these commodities is “Clothing; worn, and other worn articles”. Unfortunately, I was unable to find a more detailed description of this commodity, so I assumed that it referred to second-hand/used clothing or any kind. I retrieved the trade value (in USD) of used clothing exports between 1995 and 2019. The database also provides the weight (in kg) of used clothing shipments, but unfortunately most countries do not record their exports/imports in weight so most of this data was missing in the database (and therefore not useable).

Screenshot of the UN Comtrade Database

After collecting the data from the UN Comtrade Database, I added the data into ArcMap to create a shape file of trade flow lines between countries using the “XY to Line” tool (see screenshot below). I then added this shape file to a new worksheet in Tableau where I was able to adjust the width of each line based on the trade value of used clothing shipments between countries. This formed the basis for my first map.

Trade flow lines created in ArcMap

For my choropleth map, I first summarized the trade value data in Excel. More specifically, I calculated the following for each country: 1) the total value of used clothing exports, 2) the total value of used clothing imports, and 3) the net trade value (calculated by subtracting total imports from total exports). The net trade value data was added to Tableau and used as the variable for the choropleth map. A divergent colour scheme was applied to the map to differentiate between countries with a positive versus negative net trade value (i.e. net exporters and net importers, respectively). A filter was added to the map so users can view the results for different years between 1995 and 2019.

For the scatterplot, data on GDP per capita for each country were retrieved from the World Bank’s open data catalogue. The data were added to Tableau and a scatterplot was created using GDP per capita on the x-axis and net trade value of used clothing on the y-axis. Points on the scatterplot were made into proportional symbols to easily visualize differences in GDP per capita. An animation function was added to the scatterplot so that users can see how each country’s GDP and net trade value change over time. The United States, United Kingdom, Ghana, and Ukraine were labeled in the scatterplot to act as reference points in the graph. The US and UK are two of the top net exporters of used clothing in recent years while Ghana and Ukraine or two of the top net importers.

Geo-visualization Improvements Wish List

  • My initial idea for the trade flow map was to use a 3D model of the earth and animate the trade flow lines between countries. Users would be able to rotate the earth and the animated lines would more clearly and dynamically illustrate the direction of used clothing shipments (i.e. from exporter to importer).
  • The layout of the geo-visualization can be improved so that the balance between white space and text/visuals is more balanced when viewed on different devices. I had difficulty adjusting the layout in Tableau to be suitable for one device type without interfering with the layout on another device (e.g. smart phone versus a desktop). With the current layout, the geo-visualization elements appear much more spread out with a lot of white space in between.
  • When viewing the geo-visualization using the Tableau software on my computer, the playback speed of the scatterplot time lapse is fine; however, it is extremely slow when viewing it through the shareable link. I’d like to figure out how to resolve this so that the scatterplot animation doesn’t lag when others view it through the link.
  • For the countries that are labeled in the scatterplot (US, UK, Ghana, and Ukraine), I would like to add an outline their points so that they are easily identifiable. Currently, it’s difficult to tell which circle each label is referring to. I would also like to change the proportional symbols by reducing the number of classes for GDP per capita and increasing the size contrast between each class. Unfortunately, I wasn’t able to figure out how to customize the proportional symbols (e.g. choosing the number of classes).

Limitations & Future Work

  • One of the primary limitations of this geo-visualization is in the data. I only downloaded export trade value between countries, as opposed to both exports and imports. Export values are reported by the “reporting country” (i.e. the country that is exporting the commodity). The reporting country must also identify the “partner country” of that export (i.e. the country that is receiving the commodity). It was therefore assumed that the trade value of the imports received by the partner country is equal to the trade value of the exports reported by the reporting country. However, there are often mismatches between the trade value reported by the exporter and the importer because of differences in commodity valuation by different countries. The UN International Trade Statistics Knowledgebase explains this discrepancy here.
  • It would be interesting to supplement the geo-visualization with additional information on the total amount of second-hand clothing that each country produces (including items that get exported and those that stay within a country). This would give us a better sense of the proportion of clothing that ends up getting exported rather than staying in the domestic market.