cluelessresearch.com

Jos compains of my 1995 technique to create the animations (animated gifs), and the lack of interactivity. Perhaps this flash(y) version will be of greater appeal to him.

The whole 1982-2006 period is posted. If you pay attention, the map changes slightly to reflect changes in the distribution of seats across the nation. E.g. the creation of Tocantins following the 1988 Constitution (1990 map), or the increase in the number of deputies elected from São Paulo in 1994.

I’ve been collecting Brazilian electoral data for my dissertation for some time, and have always wondered about how to display the somewhat massive data available in an efficient manner. Take the best case scenario: 27 districts (states) x 4 elections (since 1994) x 7 largest parties=756 data points. This is a lot of numbers to look at in a table! Imagine using aggregate data at the municipality level: 5000 x 4 x 7! No, you do the math…

Maps, of course, is one way to display the data. The major problem then is that the widely varying population density in Brazil would produce a misleading map of the voting distribution across the country. That is the reason I am considering using cartograms, as discussed in the last post (link). Displayed below is a whole set of cartograms displaying data for the Câmara dos Deputados for the past four elections. The idea now is that areas in the cartogram should be proportional to the number of seats assigned to each district (which in Brazil are the states.) Given the high degree of malapportionment, the cartogram looks somewhat different from the one based on population or vote totals we presented previously. The time dimension is presented as a movie, so it is easy to follow the spatial distribution of seats for each party throughout the recent elections.

Now I only have to figure out how to put this in paper format…

The Brazilian electoral court (TSE - Tribunal Superior Eleitoral) has finally posted the 2006 elections results in a format suitable for researchers. This past week I got the data in shape for analysis in my dissertation and decided it was a good time to do some charts. As usual, the plots were done in R, this time using the maptools package.

The second round was a landslide in favour of the incumbent, Lula da Silva, from the PT (Workers’ Party). He got around 61% of the votes, while Geraldo Alckmin got 39%.

Perhaps more interesting is the spatial distribution of the votes. The individual units in the map are what the Brazilian Geographic and Statistical Institute (IBGE) calls “mesoregions”, but the original data is by municipality and electoral zone.

It is noticeable how the Northeast is overwhelmingly red, indicating Lula won there by extremely wide margins. On the other hand, margins were much thinner in the south, in the center-west and in São Paulo.

I’ve always been dissatisfied with maps like this, since it overrepresents areas such as the west of the country, where the population (and therefore vote) density is much lower than in the coast. Ditto for country areas versus the big cities. Yet, the geographical representation allows us to grasp the overall pattern and correlate with facts that we know. For example, the northeast is much poorer than the south, so we immediately recognize that Lula did worse in richer areas.

Cartograms are a way to “correct” the overrepresentation of low density areas. By correction, of course, we mean distorting, but that is the whole point of the procedure. For voting and other social science data, geographic distance is just as arbitrary. Gastner and Newman invented one method to produce cartograms that seem to work very well in practice (paper here.) The original software was written in C, but there is a java version by Frank Hardisty which I used, since it uses shapefiles as input and output. Click here to take a look at maps for the 2004 US election.

Most of the Brazilian west is dramatically shrunk, while the big cities (particularly São Paulo) are several times blown up. In fact, I find it particularly helpful in showing the votes in the big metropolitan areas, and comparing it to areas in the country side. Although interesting, I wonder if the cartogram is too distorted to be useful, and would be interested in hearing other opinions.

I created a page to keep track of the 2006 Presidential elections polls (with pretty graphs!) I plan to keep it updated on a weekly basis. Comment
on the plot and other suggestions welcome in this post.

files:

data (stata format)

code (R, using ggplot)