Category Archives: politics

Different kind of map. Map of road deaths

The UN has launched a ‘decade for action’ to tackle road traffic accidents, which kill more people around the world than malaria, and are the leading cause of death for young people – especially in developing countries.

Visualizing the most recent data on traffic deaths and injuries, from the 2009 Global status report on road safety by PCA was my interest. I’ve used a subset of countries where all of the data were available and make the “statistical map” less cluttered by small countries.

Map show countries (green squares) and statistics (red diamonds). The closer countries to each other on the map, the more similar they are in whatever parameters describing them. In this case those are # of deaths, % of each type of death, GNI, etc. The closer those parameters to group of countries the more significant they are (larger values) for that group. For example, Russia, Iran,  Chile and South Africa have largest # of death per capita and % of pedestrians killed (two red diamonds that are closest to this group of countries).

The resulting map (biplot of Principal components) speaks for itself. Majority of road death are pedestrians, with cyclist and bicyclists following behind in poor countries. Developed countries have more of vehicles and larger % of death in car accidents. Japan having largest fleet has small number of death in cars, quite interestingly.  Netherlands, not surprisingly  having so  much bicyclists  stands away from the rest of Europe and other developed countries with having larger % of death of bicyclists.

What data behind “change in trust in science” really show

Post by Razib Khan made me wanna look at the data behind the questionable change in trust in science from 1998 to 2008 a bit more.

The dilemma whether trust in science vs. religion was impacted by the “broadsides against religion” was approached by asking responders whether they agree with this statement: “We trust too much in science and not enough in religious faith.”  The responses were:

– Strongly agree
– Agree
– Not agree or disagree
– Disagree
– Strongly disagree

The data are right here:

Looking at these data, Razib made very reasonable conclusion “don’t see much difference”.

I could not pass an opportunity to apply principal component analysis to this table above.

The biplot below shows both responses and demographic categories.

Demographic responses in 1998 are shown in green and those in 2008 are in blue. Arrows  are connecting the same category of demographic between two different years. It is clearly that there is no change in total and majority of individual categories.

However, there are 3 peculiarities that caught my attention. There are three red arrows on the plot showing quite significant change. That’s why I love PCA – easy way to visualize data with multiple variables, and the data are still there for us to explore (some think that PCA is a black magic that eats all the data away) !

So back to original data now. Changes in those with “none” religious preferences and “liberal” political views are quite similar (such overlap between these groups is not suprising), in which big part of people who were uncertain (‘neither‘) have transitioned into a group of “disagree“. For “independent” class,  responses in all categories changed except those in “agree” group. (Interesting observation by itself, that indicates the fluid unpredictible character of independent voters?)   Big part of “strongly agreeing”  and “neither” is lost (from 14% to 5% and from 34% to 28% respectively) while  “disagreeing” % grew from 22% to 36%.

To sum up, careful analysis of data shows that in all three categories of responders with largest changes from 1998 to 2008,  the group supporting science grew (“disagree and “strongly disagree” categories of responses) . The major source of this growth seems to be from the pool of those with neutral opinion (‘neither“) except independent for which large % of those that “strongly agree” also switched to “disagree“.

So, I am confused.. People in conservative and religious groups that would be affected the way Robert Wright hypotheses did not show changes in the way they view trust in science vs religion. At the same time, more people  from liberal groups disagree with the statement indicating that they trust science more than before. How exactly this is a sign of weaking trust in science?

By the way, I find the statement  to be  pretty confusing way to ask such a straightforward question…

Data analysis for vaccine awareness week

I have combined the following data into one data matrix:
2009 Vaccination data table (subset of most often given vaccines reflecting the trend) ;
– American Human Development Index by State from American Human development Project;

Classification of Blue and Red states;

First, I have applied PCA to just vaccination rates data, where states partisan classification was used for classes.
No correlation between vaccination rates and partisan class was observed.

Second, Principal Component Analysis was applied to all data (vaccination rates and human development index) with autoscaling.

PC1 captures 39% of variance in the data and separates samples by those having high rank (cumulative index), high Education, Income and Health index from those having low Rank. Mostly blue states and some red states (AK, ND, NE, UT, KS) have highest rank. Vaccination rates do not contribute into PC1 (close to 0) indicating that there is no direct correlation between human development index and vaccination.

PC 2 separates states by vaccination rates. Those on top have higher vaccination rates that those on the bottom of a biplot. There is week correlation (captured in ~16% of variance in the data) between vaccination rates and education and income index and rank.

4 groups of states are classified by PCA:
1. Red states having quite good vaccination rates and very bed HD index.
2. Mostly blue states and some red states having best vaccination rates and best HD index.
3. Purple states having worst vaccination rates and worst HD index.
4. Mixed states – some blue, some red and Co with very bed vaccination rates but high Health Index.