Trade Cartograms at UseR! 2010

A bit of shameless self-promotion! I will be presenting my work on trade cartograms at UseR! 2010. I’ll update this with a link to the abstract when it is listed there.

Earlier this year I posted on the use of cartograms to visualize dyadic trade flows.

About UseR!

useR! 2010, the R user conference, will take place at the Gaithersburg, Maryland, USA campus of the National Institute of Standards and Technology (NIST) from 2010-07-21 to 2010-07-23. Pre-conference tutorials will take place on July 20.

The conference is organized by NIST and funded by the R Foundation for Statistical Computing.

Following the successful useR! 2004, useR! 2006, useR! 2007, useR! 2008, and useR! 2009, conferences, the conference is focused on:

1. R as the `lingua franca’ of data analysis and statistical computing,
2. providing a platform for R users to discuss and exchange ideas how R can be used to do statistical computations, data analysis, visualization and exciting applications in various fields,
3. giving an overview of the new features of the rapidly evolving R project.

As for the predecessor conferences, the program consists of two parts:

1. invited lectures discussing new R developments and exciting applications of R,
2. user-contributed presentations reflecting the wide range of fields in which R is used to analyze data.

A major goal of the useR! conference is to bring users from various fields together and provide a platform for discussion and exchange of ideas: both in the formal framework of presentations as well as in the informal part of the conference in Gaithersburg.

Prior to the conference, on 2010-07-20, there are tutorials offered at the conference site. Each tutorial has a length of 3 hours and takes place either in the morning or afternoon.

Visualizing Dyadic Trade Flows

I gave a talk at the Fletcher School today on my work on dyadic trade flows (slides).

In a nutshell, the talk argues that cartograms and dendrograms can give students and practitioners a better understanding of the patterns of trade among partner contries, both for teaching and for research. We have thousands of observations of dyadic relationships in panel datasets. Most often these datasets are presented as aggregates: total annual world trade, top exporters in world trade, top exporters, top exporters in an industry sector, top exporters to a political union (such as the EU), top exporters within a geographic area, etc. What these statistics ignore is the information in the dyadic trade flows: who trades with whom?

What I offer is a way to crunch down the total number of country dyads into manageable graphics that can appear on a single slide. We can look directly at the dyadic patterns of trade using hierarchic clustering (dendrograms). We can compare partner trade flows across countries and time periods using cartograms. The techniques are not new; what is new is the presentation of rich international trade datasets in relatively complete format that can be digested by inspection, rather than with complex and poorly understood statistical techniques. Complete annual sets of cartograms and dendrograms give scholars the power to explore the distribution of dyadic trade and discover hypotheses that are worth testing more carefully, either with quantitative or qualitative methods.

One of the reasons trade courses have focused so much on models, theorems, and policy of international trade is that it is hard to describe trade patterns in any meaningful and comparable terms. My slides suggest how to do exactly that: present changes to global trade patterns in a succinct, visual format that enables rich comparisons across time and space.

Baseline map for international comparisons

The next time you see a nice color-coded GIS map of a statistic, you should think twice about the way the data are being presented. Countries’ size on the map is determined by their land mass, not their population. Though the shapes are familiar, any geographic projection subtly adds influence to some countries and steals it from others. Greenland is the famous example from grade school. The map conditions us to ignore countries with high population density, even major powers such as India and China, and to assume that countries with low population density are more important.

This unfamiliar shape should be the basis for comparisons of human statistics: wealth, income, health, literacy, the things that matter most.

This cartogram by Worldmapper shows country size in proportion to population, not land mass.

Source: Worldmapper.org

Worldmapper

This is another great site for cartograms. The Worldmapper site has a wealth of world cartograms based on country statistics. For those of you that don’t remember, a cartogram begins with a base map, e.g., the Mercator projection of the earth. The cartogram distorts the map such that country boundaries remain contiguous, but the area of each country reflects the size of the statistic in question.

What this enables us to do is to make sensible comparisons of a huge variety of data at a glance. Whereas it would be difficult to absorb a table of statistics for every country in the world normalized by country size or by population, the world map gives a very rough, first cut in seconds.

Best political map of the election

Mark Newman’s election maps reinforce just how hard it is to come up with anything new. I was thinking to myself on election night that the maps we have are nearly useless for helping people understand how the election is going. Instead of geographic area, the sizes of the states ought to reflect their electoral weight. Of course, Newman has been doing this since 2004.

We can correct for this by making use of a cartogram, a map in which the sizes of states are rescaled according to their population. That is, states are drawn with size proportional not to their acreage but to the number of their inhabitants, states with more people appearing larger than states with fewer, regardless of their actual area on the ground. On such a map, for example, the state of Rhode Island, with its 1.1 million inhabitants, would appear about twice the size of Wyoming, which has half a million, even though Wyoming has 60 times the acreage of Rhode Island.

Mark Newman's 2008 Electiooral Vote Cartogram