Tag Archives: data source

How well are we vaccinating?

Our World in Data now has a vaccinations as part of their Coronavirus Pandemic Data Explorer. As you can see the U.S. is doing relatively well. Now, Israel is doing much better than anyone and they aren’t on the graph because it makes it hard to see the rest of the countries selected here. Kevin Drum noted this is his post today The US is Doing OK on COVID-19 Vaccinations. He notes (referencing roughly the same graph here):

Why do I keep posting charts like this? Because we’ve spent way too much time on doom and gloom about how incompetently we’ve rolled out the COVID-19 vaccine. With the well-known exception of Israel, we’re doing as well or better than anyone else. If we’re incompetent, then the entire world is incompetent.

You can download the data from the Our World in Data page.

How hot was December 2020?

From NOAA’s Global Climate Report – December 2020:

The global land and ocean surface temperature for December 2020 was 0.78°C (1.40°F) above the 20th century average and the eighth highest departure from average for December in the 1880–2020 record. Compared to recent months, this value was the smallest monthly temperature departure during 2020 and the smallest monthly temperature departure since February 2018.

However, compared to all Decembers, this was the seventh highest December percentage since records began in 1951. Meanwhile, the most notable cooler-than-average conditions were present across parts of southern Asia, where temperatures were at least 2.0°C (3.6°F) below average. Other notable cooler-than-average conditions were present across the tropical Pacific Ocean, where La Niña was present during December 2020. However, no land or ocean areas had record-cold December temperatures.

The time series data is available in the box on the top center of the page under Temperature Anomalies Time Series.

How big are new single family homes?

The Census Bureau report New Single-Family Homes Sold Not as Large as They Used to Be by Philip Thompson (12/21/2020) notes:

The average square footage of new homes sold in the United States increased from 2,457 in 2010 to 2,724 in 2015 but dropped in 2019 to 2,518, according to the U.S. Census Bureau’s Characteristics of New Housing.

Note that the title is bit misleading based on the first sentence of the article. Homes are smaller than in 2015 but still larger than in 2010. Interestingly (also see graph)

Despite the decline in average square footage, the share of homes with four bedrooms or more that were sold increased from 41% in 2010 to 49% in 2019.

Now, note the switch to comparing to 2010 as the number of 4+ bedroom homes is down from 2015. Plenty to explore here for stats/QL class, for instance what is the relationship between home size and the number of bedrooms?

The link in the first quote brings you to a page with numerous xls files of data about homes. The article has four other graphs.

What book do I Recommend?

I’ve never done a book recommendation before and that changes today. If you are looking for a book that has about 75 excellent graphs and uses paleoclimatology data to connect changing climate as it impacts society during the time period of roughly 1200 to 1500, then I recommend Bruce M. S. Campbell’s book The Great Transition – Climate, Disease and Society in the Late-Medieval WorldThe book connects modern science along with data and graphs to tell the story of medieval Europe. I can certainly see this book being used in some form of interdisciplinary seminar or a data science course where student work to reproduce the graphs (of course, you can just read the book for fun). The book pointed me toward the Paleoclimatology Datasets posted at NOAA. The is a lot of data here and it takes some work to get what you might want, but it is a valuable resource.

What are the projections for northeast high school graduates?

The last post, What are the projections for high school graduates? provided national high school graduation rates from WICHE. I noted in that post that regional data and reports are available along with race and ethnicity. The graph here (for public high school only) is from the report for the northeast  (use the drop down menu to select northeast). There is a wealth of information and available data in the WICHE report.

How hot was November 2020?

From NOAA’s Global Climate Report – November 2020:

The combined global average temperature over the land and ocean surfaces for November 2020 was 0.97°C (1.75°F) above the 20th century average of 12.9°C (55.2°F). This was the second warmest November in the 141-year global record, behind the record warm November set in 2015 (+1.01°C / +1.82°F).

Some highlights:

According to NCEI’s regional analysis, Oceania had its warmest November on record, with a temperature departure from average of +2.06°C (+3.71°F). This value shattered the previous record of 1.85°C (3.33°F) by 0.21°C (0.38°F).

Australia had its warmest November in the nation’s 111-year record with a national mean temperature departure of +2.47°C (+4.45°F). This surpassed the now second highest November temperature set in 2014 by 0.40°C (0.72°F).

So far this year:

The January–November 2020 global temperature was the second highest on record at 1.00°C (1.80°F) above average and only 0.01°C (0.02°F) shy of tying the record set in 2016. According to NCEI’s Annual Rankings Outlook, there is a 54% chance of 2020 ending as the warmest year on record.

Time series data is available near the top of the page.

How are the top 0.1% doing?

The EPI article Wages for the top1% skyrocketed 160% since 1979 while the share of wages for the bottom 90% shrunk by Lawrence Mishel and Jori Kandra (12/1/2020) reports:

As Figure A shows, the top 1.0% of earners are now paid 160.3% more than they were in 1979. Even more impressive is that those in the top 0.1% had more than double that wage growth, up 345.2% since 1979 (Table 1). In contrast, wages for the bottom 90% grew only 26.0% in that time.

The top 0.1% go off the chart. There are two other tables of data nd the data for the chart copied here is available.

What is the Indo-Pacific warm pool?

The climate.gov article A warm pool in the Indo-Pacific Ocean has almost doubled in size, changing global rainfall patterns by Alison Stevens (12/3/2020) is your primer on the Indo-Pacific warm pool.

Due to human-caused climate change, our planet’s ocean has been heating up at a rate of 0.06 degrees C (0.11 degrees F) per decade over the past century. But this warming isn’t uniform. In fact, recent NOAA-funded research shows that a large pool of the ocean’s warmest waters,  stretching across the Indian and west Pacific Oceans, has grown warmer and almost doubled in size since 1900. This expanding warm pool not only impacts ocean life; according to the study, it is driving changes in the Madden Julian Oscillation (MJO), a key weather and climate pattern, and in regional rainfall around the globe.

Here is how it works:

The warming is uneven across the region, with greater warming in the west Pacific. This unevenness creates a temperature contrast that enhances cloud-forming winds, moisture, and energy over the region, and it draws in warm moist air from the Indian Ocean. The uneven expansion of the Warm Pool has warped the MJO—a global pattern of clouds, wind, rain and pressure, active in the winter, that starts over the Indian Ocean and travels eastward around the tropics in 30-60 days. Though the total lifespan of its global trek has stayed the same, the study finds that the MJO’s clouds and rain now spend 3-4 fewer days over the Indian Ocean and 5-6 more days over the Maritime Continent and west Pacific, fueled by heat and moisture where the warming is greater.

The first link in the quote above is to global ocean temperature data.

Where is state level covid data?

The 91-DIVOC page An interactive visualization of the exponential spread of COVID-19 has excellent visualizations of state level data along with world data. For example, the graph downloaded from their visualization here is the top 10 states for deaths per 100,000 (day 0 is 11/30/2020). You might find it surprising the the Dakotas are in the top 10. The visualizations allow the user to select numerous categories and highlight selected elements. Each visualization has a csv link to download the data.