Choropleth maps are a pretty useful kind of map that colors distinct areas of the map (e.g. states, counties or countries) to reflect different numerical or categorical values. It is useful to show differences across geographic regions. I’ve been making a bunch of these recently (stressed states, bitcoin electricity consumption, college admissions). One of the issues that can be problematic with these maps is that some regions can be very large but only have very few people. If the choropleth map is tracking a intensity value (like CO2 emissions per capita), a large area with a high color value might visually indicate that total emissions (emissions per capita x # of people) is also high. In the US this is reflected in states like Alaska, Montana and Wyoming, which are large but have very few people.
I decided to make a modified choropleth map (updated after learning that it’s called a cartogram) that scales the size of the states to be the proportional to the state’s population. States with larger populations show up as larger. This is equivalent to making each state have the same population density. Since New Jersey has the highest population density of any state in the US (1200 people/square mile), it stays the same size in this map and all the other states shrink, to reflect their lower population density. For example, California has a larger population than NJ (4.4x), but its physical size is about 20x larger. So California is shrunk to about 20% its original size to make its physical size 4.4x the size of NJ.
The states are also colored to show population as well (darker redder colors reflect larger population while yellow/beige reflects small populations).
Living in California, I decided to make another animation, this time with scaled to the density of California, so some states that are less dense will shrink, while others that are denser will grow. New Jersey grows quite a bit. Because many of the dense Northeast states grow a bit, I had to space them out (manually) so you could still see them otherwise they’d overlap too much.
Data Sources and Tools:
Fires are once again raging in California and air quality in one of the most populated metropolitan areas in the country (the San Francisco Bay Area) is quite poor. This map show current air quality in the Bay Area. For more information see the EPA’s Air Quality website.
EPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.
|Air Quality Index
Levels of Health Concern
|Good||0 to 50||Air quality is considered satisfactory, and air pollution poses little or no risk.|
|Moderate||51 to 100||Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.|
|Unhealthy for Sensitive Groups||101 to 150||Members of sensitive groups may experience health effects. The general public is not likely to be affected.|
|Unhealthy||151 to 200||Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.|
|Very Unhealthy||201 to 300||Health alert: everyone may experience more serious health effects.|
|Hazardous||301 to 500||Health warnings of emergency conditions. The entire population is more likely to be affected.|
For more information and additional maps see the EPA’s Air Quality website.
This visualization looks at the staggeringly high energy use of Bitcoin and puts it into context: comparing it to electricity usage of US states. Unfortunately for Bitcoin, high energy usage is an intended feature of the system, rather than an unintended consequence. This is because mining is an increasingly energy intensive process, based upon increasingly computationally intensive calculations that are performed on high powered computers and graphical processing units.
Currently, 28 out of 50 states plus the District of Columbia all have lower electricity consumption than estimated annual bitcoin electricity consumption (~73 TWh per year). These states are highlighted in variations of yellow. This is approximately equal to the average annual electricity usage across all US States. States with higher electricity consumption than bitcoin are highlighted in shades of red.
When dividing the total energy use (73 TWh) by the current number of transactions (93 million), we get an average energy consumption of 783 kWh per transaction. Click on the “Energy per Transaction” button to see this visualization. What’s crazy is that a transaction is simply a transfer of bitcoin between “wallets”, recording the transaction, and a validation of the process. There’s no good reason why verifying digital transactions should take this much energy, except that it was built into the fundamental process of validating and mining bitcoin. 783 kWh is larger than the monthly per capita electricity consumption in 10 US states. It could also drive you and your family over 2000 miles in an electric car (e.g. Tesla Model S).
I’m not expert enough in this area to know how much more energy consumption will rise into the future, but if crypto advocates’ predictions come true and bitcoin is used extensively, millions of transactions will occur per hour instead of per year and the price of bitcoin may rise much higher than it currently is. If the price rises, then miners will be willing to expend more energy to “mine” the more valuable bitcoin. Needless to say, this sounds like a very bad idea from an energy consumption and sustainability standpoint.
Data and Tools:
Long-term stress has been shown to be detrimental for your health. While it’s probably not possible to completely eliminate stress from people’s lives, there are many individual choices and decisions that can influence the amount of stress that people experience, including where they live, what job they have, their socio-economic conditions etc. . . One interesting bit of data analysis looks at an aggregate level to understand how stress differs from state to state depending on specific economic, demographic and other geographic factors.
Click on the buttons below the map to switch between the different categories.
The Congressional Budget Office (CBO) released a report that analyzed the impacts of the Senate health care bill and estimated that 22 million Americans would lose health care by 2026 (see previous post). 22 million amounts to almost 7% of the US population (about 1 in 15 Americans). I wondered how the impacts of these changes would be distributed across different states.