US coronavirus deaths have surpassed 300,000. Many of these deaths could have been avoided if swift action had been taken in February and March, as many other countries did. This graph shows an rough estimate of the number of US deaths that could have been avoided if the US had acted similar to other countries.
This graph takes the rate of coronavirus deaths by country (normalized to their population size) and imagines what would happen if the US had had that death rate, instead of its own. It then applies that reduction (or increase) in death rate to the total number of deaths that the US has experienced. The US death rate is about 600/million people in September 2020 and if a country has a death rate of 60/million people, then 90% of US deaths (about 180,000 people) could have been avoided if the US had matched their death rate. The government response to the pandemic is one of several important factors that determine the number of cases and deaths in a country. This means proper messaging about the need to wear masks and socially distance as well as providing payments to citizens and business to help them during the economic shutdown. Other important factors can include the overall health of the population, the population structure (i.e. age distribution of population), ease of controlling borders to prevent cases from entering the country, presence of universal or low-cost health care system, and relative wealth and education of the population.
The graph lets you compare the potential reduction in US deaths when looking at 30 different countries. You can choose those 30 countries based on total population, GDP or GDP per capita. These give somewhat different sets of countries to compare death rates, which is an indication of the effectiveness of the coronavirus response.
A valid criticism of this graph is that testing and data collection is very different in each of the countries shown and the comparisons are not always valid. This is definitely a problem with all coronavirus data but for the most part, the very large differences between death rates would still exist even if data collection were totally standardized. Some of the data from the poorest countries is less reliable, because they have less testing capabilities.
Source and Tools:
Data on coronavirus deaths by country is from covid19api.com and downloaded and cleaned with a python script. Graph is made using the plotly open source javascript library.
A record 16 million Americans just filed for unemployment due to the coronavirus pandemic at the end of March and early April 2020. This is an amazingly large number of people and I wanted to visualize how many people this actually is. For context, the US Department of Labor statistics states that in February 2020 (before the pandemic hit the United State) there were 164.2 million workers in the Civilian Labor Force.
The Bureau of Labor Statistics (BLS) site defines “Civilian Labor Force” as such:
This basically means that approximately 10% of the entire workforce of people (both employed and unemployed in Feb 2020) are now out of a job. While 10% is a large, unprecedented number in our lifetimes, comparing these number to the size of the workforce in several states helps to provide more context. The visualization shows a random collection of states whose total labor force is equal to the latest unemployment numbers. If you click the button you can see a different set of states that have the same total labor force.
Predictions are that the number of unemployed will grow as the shutdowns and social distancing measures to contain the virus continue through April and into May. I will update this graph to reflect new numbers as they come out.
And we can only hope that people will be able to manage these tough economic times until we contain the virus and the economy rebounds.
Stay safe out there: stay away from people and wash your hands!
Sources and Tools:
Data on unemployment was obtained from the US Department of Labor website and labor force numbers by state are downloaded from the Bureau of Labor statistics. And the visualization was created using javascript and the open source leaflet javascript mapping library.This map of the United States visualizes over 28,000 cities in the 50 states. The interactive visualization lets you type in a name (or part of a name) and see all of the cities that contain those string of letters. The points on the map show the geographic center of each city.
For example, if you type in “N”, you will highlight all cities that start with an N in the US. As you type in another letter (e.g. “e”, it will narrow down the cities that begin with those two letters (“Ne”). It will progressively narrow down the number of cities as you type in more letters. You can see an scrollable list of the cities (ordered by city population) that contain the string of letter that you have typed.
You can click on the check box to show or hide the outlines of the states.
Sources and Tools:
City name and location data was downloaded from simplemaps.com. And the visualization was created using javascript and the open source leaflet javascript mapping library.
The interface has been updated and you can now also zoom in and look at a specific state’s election results.
Click here to view a visualization that looks more explicitly at the correlation between population density and votes by county.
This interactive map shows the election results by county and you can display the size of counties based on their land area or population size.
Previously, I created a map (cartogram) that showed the state by state electoral results from the Presidential Election by scaling the size of the states based on their electoral votes. The idea for that map was that by portraying a state as Red or Blue, your eye naturally attempts to determine which color has a greater share of the total. On a normal election map, Red states dominate, especially because a number of larger, less populated states happen to vote Republican. That cartogram changed the size of the states so that large states with low population, and thus low electoral votes tended to shrink in size, while smaller states with moderate to larger populations tended to grow in size. Thus, when your eyes attempt to discern which color prevails, the comparison is more accurate and attempts to replicate the relative ratio of electoral votes for each side.
This map looks at the 2024, 2020 and 2016 presidential election results, county by county. An interesting thing to note is that this view is even more heavily dominated by the color red, for the same reasons. Less densely populated counties tend to vote republican, while higher density, typically smaller counties tend to vote for democrats. As a result, the blue counties tend to be the smaller ones so blue is visually less represented than it should be based on vote totals. More than 75% of the land area is red, when looking at the map based on land areas, while shifting to the population view only about 46% of the map is red. Neither of these percentages is exactly correct because each county is colored fully red or blue and don’t take into account that some counties are won by a large percentage and some are essentially tied. However, the population number is certainly closer to reality as Trump won about 48.8% of the votes that went to either Trump or Clinton.
This tool should be relatively straightforward to use. Just click around and play with it.
The map has a few different options for display:
This was my second attempt at using d3 to generate visualizations. I typically use leaflet to do web-based mapping but I wanted the power of d3 which has functions for the circles to prevent overlapping. This map was inspired by Karim Douieb’s cool visualization of 2016 election results. I modified it in a number of different ways to try to make it more interactive and useful.
This visualization does not actually simulate the collisions between the circles on your browser. It is a bit computationally taxing and causes my computer fan to turn on after awhile. So instead I ran the simulation on my computer and recorded the coordinates for where each circle ended up for each category. Then your browser is simply using d3 transitions to shift positions and sizes of the circles between each of the maps, which is simpler, though with 3142 counties, it can still tax the computer occasionally.
Data and Tools
County level election data for 2016 is from MIT Election Lab. The 2020 county level data is downloaded from the New York Times county election data API and processed using a python script. 2024 county data is from the NYTimes as well by individual state (e.g. Maine) Population data used is for 2018. The visualization was created using d3 javascript visualization library.
function resized3map(){ //this function is called from the javascript from within the iframe after the contents of iframe are loaded (and after an additional 150ms delay) setTimeout(function (){ mm = document.getElementById('mapframe'); mm.height = mm.contentWindow.document.body.scrollHeight+20+"px"; console.log("resize"); mobilewarn();
}, 150); }
function reloadiframes(){ document.getElementById('mapframe').contentWindow.location.reload(); }
window.addEventListener("orientationchange", function() { reloadiframes(); resizemap(); }, false);
Based on earlier popularity of the country-by-country animation, this map lets you watch as the world is built-up one state at a time. This can be done along a large range of statistical dimensions:
These statistics can be sorted from small to large or vice versa to get a view of the US and its constituent states plus DC in a unique and interesting way. It’s a bit hypnotic to watch as the states appear and add to the country one by one.
You can use this map to display all the states that have higher life expectancy than the Texas:
select “Life expectancy”, sort from “high to low” and use the scroll bar to move to the Texax and you’ll get a picture like this:
or this map to display all the states that have higher population density than California:
select “Population density, sort from “high to low” and use the scroll bar to move to the United States and you’ll get a picture like this:
I hope you enjoy exploring the United States through a number of different demographic, economic and physical characteristics through this data viz tool. And if you have ideas for other statistics to add, I will try to do so.
Data and tools: Data was downloaded from a variety of sources:
The map was created with the help of the open source leaflet javascript mapping library
function resizemap(){ //this function is called from the javascript from within the iframe after the contents of iframe are loaded (and after an additional 150ms delay) setTimeout(function (){ mm = document.getElementById('USmap'); mm.height = mm.contentWindow.document.body.scrollHeight+20+"px";
mobilewarn();
}, 150); }
function reloadiframes(){ document.getElementById('USmap').contentWindow.location.reload(); }
window.addEventListener("orientationchange", function() { reloadiframes(); resizemap(); mobilewarn(); }, false);
function mobilewarn(){ w=window.innerWidth; if (w<600){ document.getElementById('mobilewarning').innerHTML="On mobile devices, visualizations are best viewed in landscape mode."; } else { document.getElementById('mobilewarning').innerHTML=""; } } mobilewarn();
These statistics can be sorted from small to large or vice versa to get a view of the globe and its constituent countries in a unique and interesting way. It’s a bit hypnotic to watch as the countries appear and add to the world one by one.
You can use this map to display all the countries that have higher life expectancy than the United States:
select “Life expectancy”, sort from “high to low” and use the scroll bar to move to the United States and you’ll get a picture like this:
or this map to display all the countries that have higher population density than the United States:
select “Population density, sort from “high to low” and use the scroll bar to move to the United States and you’ll get a picture like this:
I hope you enjoy exploring the countries of the world through this data viz tool. And if you have ideas for other statistics to add, I will try to do so.
Data and tools: Data was downloaded primarily from Wikipedia: Life expectancy from World Health Organization (2015) | GDP from United Nations (2017) | Population from United Nations (2017) | Land Area from CIA factbook (2016)
The map was created with the help of the open source leaflet javascript mapping library
function resizemap(){ //this function is called from the javascript from within the iframe after the contents of iframe are loaded (and after an additional 150ms delay) setTimeout(function (){ mm = document.getElementById('molleweide'); mm.height = mm.contentWindow.document.body.scrollHeight+20+"px";
mobilewarn();
}, 150); }
function reloadiframes(){ document.getElementById('molleweide').contentWindow.location.reload(); }
window.addEventListener("orientationchange", function() { reloadiframes(); resizemap(); mobilewarn(); }, false);
function mobilewarn(){ w=window.innerWidth; if (w<600){ document.getElementById('mobilewarning').innerHTML="On mobile devices, visualizations are best viewed in landscape mode."; } else { document.getElementById('mobilewarning').innerHTML=""; } } mobilewarn();
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