Sustainability Math A Quantitative Literacy and Mathematics Resource for Instructors

The distribution of occurrences of tornadoes by time of day is presented in the accompanying graph from NOAA’s Historical Records and Trends page for tornadoes, which is a good example of a skewed distribution.

Because most tornadoes are related to the strength of a thunderstorm, and thunderstorms normally gain most of their energy from solar heating and latent heat released by the condensation of water vapor, it is not surprising that most tornadoes occur in the afternoon and evening hours, with a minimum frequency around dawn (when temperatures are lowest and radiation deficits are highest). However, tornadoes have occurred at all hours of the day, and nighttime occurrences may give sleeping residents of a community little or no warning.

The page includes the same type of graph by region in the country. If you want to know the distribution of tornadoes by state, NOAA has you covered on their U.S. Tornado Climatology page where you will find a map for the average number of tornadoes by state.  You can download tornado data from NOAA’s Storm Events Database.

NOAA has an annual tornado report that contains the graph here.  The graphs suggests an increase.

In contrast to the previous four years, tornado activity across the U.S. during 2017 was above average. During January-September there were 1,262 confirmed tornadoes with 144 preliminary tornado reports still pending confirmation for October-December. This brings the preliminary tornado count to 1,406 with the final count expected to be slightly lower. The 1991-2010 annual average number of tornadoes for the U.S. is 1,253.

The page includes a map of the locations of tornadoes for 2017, a drop down menu for years dating back to 2006, and as monthly menu.  You can download tornado data from NOAA’s Storm Events Database.

NASA has you covered with their Global Temperature Anomalies from 1880 to 2017 video. We embedded the YouTube version here but there are different versions on their Global Temperature Anomalies from 1880 to 2017 page all of which can be downloaded. This is a product of NASA’s Scientific Visualization Studio.

You can find out with Our World in Data’s Sustainable Development Goals tracker.

In 2015 the world set a new sustainable development agenda, pledging within the United Nations (UN) to achieve 17 development goals by 2030: The Sustainable Development Goals (also known as The Global Goals). Ranging from eradicating poverty, to ensuring clean energy for all, to reaching sustainable levels of consumption, the array of targets across these goals were selected to drive our efforts in the 15 years up to 2030.

Our World in Data has data for all 17 goals on their SDG page.  For example, their Goal 5: Achieve gender equality and empower all women and girls page has 24 charts including the one posted here on unmet need for contraception. As is always the case with Our World in Data, each chart has easy access to the data and you can download their graphs.

We are within about a month of the peak of Arctic sea ice in its yearly cycle of freezing and thawing. At the moment, sea ice is at a record low (see chart) tracking close to 2017 and 2016, where as 2012 holds the record for the lowest extent of ice. NSID has an interactive real time chart (the last data point here is Feb 25) where you can select any and all years from 1979 to the present and download the graph. The data can be downloaded in an Excel spreadsheet from their Sea Ice Data and Analysis Tools page where they also have links to animations.  There are materials in both the Calculus Projects and Statistics Projects pages using this data.

Our World in Data has an interactive chart that compares income inequality with gini coefficients. For example the chart here has the United States, United Kingdom, France, Germany, Netherlands, and Japan (you can select other countries too). Of these six countries the U.S. has greater income inequality than the other five.  It has also grown considerably since the mid 1970s.  As always with Our World in Data, you can download the data set so it can be used in statistics courses. You can also download graphs, such as the one here.

Climate.gov has your answer with the article Winter so far has people out west asking, Where’s the snow?   (Feb 15, 2018) by Tom DiLiberto.

Farther south in Arizona, snows across the Rockies and in the Upper Colorado River Basin have been extremely low so far this year. Snow water equivalents—the amount of liquid water that would result  if  the snow melted in an instant—are between 0 and 30% of the median for this time of year for a broad region.  In fact, the “best” areas for snow this season lie along the Front Range in Colorado and are only just around normal.

Why does this matter?

For areas in the Upper Colorado River Water Basin along the southern Rockies which rely on snow melt for water resources later in the year, snow amounts this low bring fears. Particularly, is there going to be enough snowmelt to fill  Lakes Mead and Powell, which provide water to major cities like Tucson and Phoenix?

What is the cause? A second La Nina year in a row is part of the explanation, but (as their graph here shows)

As we continue to warm the planet due to emissions of greenhouse gases, mountain snowpack out west will likely continue to dwindle. Assuming we continue to increase global emissions of greenhouse gases (A2 scenario), the snow water equivalent of the snowpack in California by the end of the century will be 43% of what it was from 1971-2000. In Colorado, the snow water equivalent will be 26% less than that observed from 1971-2010.

A smaller and earlier-melting snowpack means less water to runoff into streams and tributaries in lower elevations. For places in the Sierra Nevada Mountains, Upper Colorado, and Upper Rio Grande River basins that rely heavily on a melting snowpack to provide the bulk of their annual runoff, climate change will have profound impacts on reservoir levels, water storage, and the people and ecosystems who rely on them.

There is enough quantitative information to use this article in a QL based course.

We can answer this question by using FRED. The accompanying graph was created with FRED’s graphing tool (see below for a quick tutorial on creating this graph), which creates an interactive graph that can be downloaded along with the data. The blue line represents total manufacturing jobs, which consistently decreases during a recession (gray bands). Manufacturing jobs peaked in 1979 at just below 20 million and now stand at about 12.5 million. The red line provides another perspective and represents the percent of manufacturing jobs relative to all employment.  In the 1940s manufacturing represented almost 40% of all employment. It has been decreasing ever since and today it is down to around 8.5%.

How to create the graph: Start by searching FRED for manufacturing employment. You should get this.  On the upper right click edit graph and then add line (second button on top). Search employment and click on All Employees: Total Nonfarm Payrolls.  Add the data series. Go to format (third button across the top)  and click right under y-axis position for LINE 2.  Now go to edit line 2 (first button across the top). Under customize data search manufacturing. Click All Employees: Manufacturing. In formula type b/a. Now click add next to All Employees: Manufacturing.  This does it. FRED offers a powerful tool.

You can read about the drought on the Climate.gov post Day Zero approaches in Cape Town (Feb 7, 2018) by Michon Scott:

The exact date it will arrive depends on the latest calculations; as of February 6, 2018, Day Zero was projected to occur on May 11, 2018. That’s the day the taps might well be turned off for the roughly 4 million residents of South Africa’s second-largest city. Cape Town officials have blamed multiple factors in the water shortage, but one of the principal culprits is poor precipitation, and the problem has persisted since 2015.

The post includes interesting maps, such as the one here, and a link to Cape Town’s  water dashboard. The U.S. is also looking at wide spread drought which you can learn more about at United States Drought Monitor.

In a recent article by Richard Florida, It’s Not the Food Deserts: It’s the Inequality, the case is made that food deserts aren’t the real problem.

Instead of within cities, the biggest geographic differences in the way Americans eat occur across regions. The map above plots the geography of healthy versus unhealthy eating across America’s 3,500-plus counties. Dark red indicates a lower health index based on grocery purchases, while light yellow represents a higher health index. While there is some variation within cities and metro areas, by far the biggest and most obvious differences are across broad regions of the country.

Ultimately, the fundamental difference in America’s food and nutrition has more to do with class than location. More than 90 percent of the difference in Americans’ nutritional inequality is the product of socioeconomic class, according to the study. And it’s not just that higher-income Americans have more money to spend on food. In fact, the cost of healthy food is not as prohibitively high as people tend to think. While healthy food costs a little bit more than unhealthy food, most of that is driven by the cost of fresh produce.

The article has useful graphs and summary statistics and can be used in  QL or statistics based course.