Category: graphs

In case you haven’t heard, the North Carolina General Assembly has run amok.

It’s hard to believe that things could get worse: the last NCGA approved Ammendment One, which declared that straight marriage was the only recognized family. And they tried to outlaw accelerating sea level rise by declaring that straight lines were the only recognized graph.

And yet after the 2012 election, things turned upside down.

  • Senator Tom Tucker displayed amazing arrogance and unfamiliarity with his job description when he told a reporter: “I am the senator, you are the citizen. You need to be quiet” (Huffington Post)
  • A House resolution was proposed which would allow the establishment of a state religion, as well as incorporating prayer as a public institution (WRAL)
  • Another bill was proposed to criminalize womens’ nipples. (DTH)
  • The budget committee has considered making ends meet by closing NC’s public universities (a tactic known as, ‘eating your seed corn’) (N&O)
  • The Senate has passed a bill rolling back 40 years of environmental protections in order to make way for fracking, in defiance of the state Department of Environment’s recommendations. (McClatchyDC)

That’s just some of the more bizarre social experimentation going on; there’s been plenty of garden-variety attacks on voting rights, public education and social services for the poor.

The point of all this is, a lot of people are justifiably annoyed. So much so, that weekly protests at the state capitol have broken out, headed by the state NAACP and dubbed ‘Moral Mondays’. Peaceful protesters have been arrested by the score, then the hundred, for voicing their disgust with a runaway legislature.

Conservatives have fought back, and some have fought dirty. In one especially skeezy move, the right-wing Civitas Institute has published a public database of information on the protesters, including their photograph, and city of residence. It’s creepy, but now that it exists, it’s a window into what is happening on Moral Mondays.

The Civitas data record a total of 457 arrests. Of these, all but 8 gave their residence as in North Carolina. That is to say, 98% of the arrested are clearly locals. This data reinforces an earlier survey which found the same proportion in the protesters as a whole. This matters because some, including governor Pat McCrory have tried to dismiss the protests as the work of outside agitators.

Something disappointing about the Civitas effort is that the infographics provided are drab and at times completely inappropriate. (I mean, really?)

To show them how it’s done, let’s map out some information. Here are the absolute number of arrests, categorized by county and by city. Unfortunately, the city data which were available from the NC DOT did not have all of the cities in the arrest data, leading to 65 of 85 cities being represented in the map, explaining why some counties (eg, Cherokee) report arrests but contain no cities reporting arrest. This may introduce a bias in which smaller cities and towns are not represented when city-based data are used.


Fig 1a. Moral Monday arrests, binned by county.


Fig 1b. Moral Monday arrests, binned by city


Fig 1c. Composite map of 1a and 1b.

A few things seem to pop out: Arrests are geographically centered around the Triangle (Chapel Hill, Durham, and Raleigh), with other major centers around cities (eg, Charlotte, Wilmington, and Asheville). Comparing to other political maps (such as Amendment One or the 2012 presidential election), this pattern is not surprising, however, why it is happening is less clear.

Continue reading

Today in LabLulz, I’m going to walk through a recent preparation I did in my chemistry lab: increasing and measuring the concentration of hydrogen peroxide.

WARNING: This procedure involves heat and the end product is a powerful oxidizer. Don’t get burned and don’t get it on yourself – wear gloves, splash-resistant goggles, and an apron. I had a spill of ~15%, all over everything, including myself. It was okay, but only because I followed safety protocols. I didn’t have the apron though, and I had to get pantsless.

Hydrogen peroxide is an interesting substance; it’s formula is H2O2, meaning that it is composed of two hydrogen atoms bonded to two oxygen atoms.


Figure 1. Behold, the hydrogen peroxide molecule!

It is a powerful oxidizer, decaying into water and free oxygen. This is because the bond between the two oxygen atoms, called the peroxide bond, is unstable. Some substances which contain the peroxide bond are even explosive, like triacetonetriperoxide. Because it’s an explosive precursor, and somewhat dangerous on its own, concentrated hydrogen peroxide can be difficult to come by. The weak 3% solution found in drugstores is all that is available to DIYers, hobbyists, and other scientists outside of the mainstream chemical supply chain.

Fortunately, it is relatively trivial to increase the concentration from 3% to around 30%. There are several tutorials on the subject at YouTube (TheChemLife; zhmapper, nerdalert226) so I’m going to focus on measuring the concentration of the end product, a procedure which the videos tend to treat very qualitatively. I hope this tutorial will be informative and useful, even outside of punklabs; the process is easily generalized and density is important in many fields, including medicine and winemaking.

The concentrating procedure is pretty simple: pour about 500 mL of the 3% solution into a beaker and heat it, forcing the excess water to evaporate until there is a tenth as much liquid left (peroxide boils at 150 C, compared to 100 C for water.) There are only a couple of tricky points: the liquid must NOT boil, only steam – if it starts boiling, the peroxide will decay. Bits of dust and dirt will also cause disintegration, so the equipment must be kept very clean and free from scratches.

Okay, so after a few hours, I have about 50 mL of liquid. I drop a bit into a solution of corn starch and potassium iodide, and the mixture turns black, a positive test for oxidizers. I add a squirt to some sulfuric acid and copper wire, and the metal wire begins bubbling and the solution begins to turn blue with copper sulfate*. This reaction is faster and more vigorous than when I try it with the 3% solution, so I’ve clearly succeeded in increasing the concentration, but to what level? To answer that question, I’m going to measure the density of the solution. Continue reading

A while back, we started looking at a poorly thought-out article from the website C3Headlines. C3 is starting to make a name for itself as a goldmine of climate comedy- their claims have recently been addressed at Tamino and SkepticalScience.

We’re going to keep digging into C3‘s claim that carbon dioxide concentrations have been increasing linearly over the 20th century. They seemed to draw this claim by eyeballing the graph of CO2 concentrations and qualitatively describing them as linear, apparently using the inset in their first figure to compare linear, quadratic, and exponential trends. This is a faulty method: it’s an elementary fact of calculus that ANY smooth curve, when viewed appropriately, will appear linear. The point has already been made but it’s worthwhile to keep looking because there are some interesting graphical follies at play; examining them further might help us understand how and why graphs are misunderstood.

Figure 1: From C3Headlines’ article on “The Left/Liberal Bizarro Anti-Science Hyperbole”, which claims that CO2 concentrations are increasing linearly. Click to read it, if you dare…

C3‘s second graph in this article measures the change in atmospheric CO2 by calculating a month-to-month percentage change. It’s not entirely clear why they are using a percent change, rather than the standard practice of expressing rate of change as concentration change per year (like the source of their data uses). Whereas ppm/year is an absolute measure, each datum generated by the percentage-change method depends strongly upon the value of the previous month. As a measure of long-term rate of change, it is a bit questionable.

My primary concern, though, is with their use of monthly data in the first place. In my last article, we noted that, without explanation, C3 confined their focus to January CO2 concentrations. Were they consistent, they’d also look at January rates of change – of course, doing so might lead to unacceptable conclusions.

 Figure 2. Rates of CO2 accumulation have been calculated for the month of January, consistent with earlier investigation of January CO2 concentration. Over the period of observation, rates have increased at a significant (P~0.0005) acceleration of 0.11 ppm/year^2. Monthly rates throughout this article have been calculated by considering the change in CO2 between adjacent months, and assuming that a month is 1/12 of a year. Interpolated values of CO2 were used to avoid annoying data holes early in the record.

Instead, they look at the rate of change for every single month on record. Why do I find that problematic? Well, let’s look at the full record, with monthly resolution: Continue reading

I love graphs – my eyes quickly glaze over at a table of numeric data, but a graph, used correctly, can quickly and easily tell the whole story.

‘Used correctly’ is the key phrase – for all their power, graphs are infamously easy to bungle, and when used incorrectly they can misinform – or lie outright.

I’m going to look at an example that touches on a few graphical and statistical concepts near and dear to my heart, as well as carbon geochemistry.

Fig. 1: An image from C3Headlines; the 3 C's are "Climate, Conservative, Consumer". Oh, and the article is titled "The Left/Liberal Bizarro, Anti-Science Hyperbole Continues". It sure would be tragic if they made obvious n00b mistakes after using such language. Click for link!

Coming from an article on the website C3Headlines, this image claims that carbon dioxide concentrations have ‘Linear, Not Exponential Growth’. thereby ‘expos[ing] the lunacy of typical left/liberal/progressive/Democrat anti-science’, The author has reached this conclusion by graphing January CO2 levels* and fitting a linear trendline to them.

Already this is a warning sign – the comparisons the author makes are entirely qualitative, apparently  based up on eyeballing the graph. However, trend lines are created by a statistical process called a linear regression, which comes with a caveat: it will fit a trend line to ANY data given to it, linear or nonlinear. Fortunately, there are also ways of evaluating how good a trend line is. Continue reading


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