Red Dots for Dialysis Decline

A third of dialysis clinics in America fail to meet federal standards. This map from CBS News shows you where.

The United States spends more on dialysis treatments than nearly any other country in the world. Yet despite this enormous financial outlay, it has the highest dialysis-related death rate among developed nations. A key reason for this disparity is that healthcare in the U.S. operates as a for-profit business. In other words the entire industry is designed not only to provide healthcare to patients but to maximise returns for shareholders. 

CBS News reports that one-third of dialysis clinics have failed to meet federal standards this year. A significant factor appears to be pressure from clinic owners to implement so-called "bazooka dialysis" - a high-turnover model that prioritizes quantity over quality. Under this model, clinics rush to treat as many patients as possible each day and, as a consequence, some patients do not receive the dialysis treatment that they need. 

To help the public understand the scope of the issue, CBS News has created an interactive map of nearly 7,500 dialysis clinics across the U.S. Clinics that failed to meet federal standards this year are marked in red. Users can search the map by clinic name or address. Clicking on a clinic's marker reveals its federal performance review score (a score of 55 or higher meets the standard).

The Indoor CO2 Map

Over the years I have posted links to a number of interactive maps which visualize the levels of carbon dioxide in the atmosphere. Human activities such as burning fossil fuels, deforestation, and industrial processes help to increase the concentration of CO₂ in the atmosphere. Higher concentrations of CO₂ enhance the greenhouse effect, leading to more heat being trapped in the atmosphere. This means that carbon dioxide plays a significant role in global heating.

Measuring CO₂ levels inside buildings can also be very useful. Like many people in the last couple of years I have begun to ignore all the preventative measures useful for avoiding infections transmitted via air (such as Covid-19). I no longer wear a mask in any situation and no longer avoid enclosed public indoor environments. However not everyone can be so blasé about the risks of infectious diseases.

Many individuals, such as the elderly, the immunocompromised, and those with preexisting health conditions, are at higher risk of severe illness or death from Covid-19 and so still need to wear face masks and avoid enclosed spaces.

The Indoor-CO₂ Atlas has been designed to provide a guide to the likely CO₂ levels in public buildings, eg shops, hospitals and cinemas. The map uses crowd-sourced measurement by the public to show recorded levels of CO₂ in individual buildings.

The concentration of CO₂ in an indoor environment can serve as a proxy for the amount of exhaled air present. Humans exhale CO₂, and higher concentrations can indicate poor ventilation and a higher presence of exhaled air. Since exhaled air can contain aerosols that may carry infectious viruses (like SARS-CoV-2), higher CO₂ levels can correlate with a higher risk of inhaling these infectious aerosols.

Anyone who has a mobile phone and a supported mobile CO₂-Monitor can contribute a reading to the Indoor-CO₂ Atlas. On the map colored markers are used to show the CO₂ levels recorded in a building. If you click on a marker you can also view a graph of the recorded CO₂ levels over time.

John Snow's Proximity Mapping

I've probably seen over 100 modern interactive visualizations of John Snow's famous map of cholera victims during the 1854 cholera outbreak in Soho. John Snow's map shows the locations of cholera deaths clustered around a water pump in Broad Street. The map helped to disprove the prevailing miasma theory on the spread of diseases and establish that cholera was actually spread by contaminated water.

Despite the central importance of proximity to John Snow's interpretation of the Soho cholera outbreak I think that Carto's new 1854 Soho cholera outbreak map is the first of the 100+ reinterpretations of the data to actually visualize which water pump the victims lived closest to. 

On the Carto map straight lines are drawn from John Snow's recorded victim's homes to the nearest pump. The purple lines indicate all those victims which lived closest to the Broad Street Pump. As you can see a number of victims actually lived closer to other pumps. John Snow wrote in a letter to the 'Medical Times and Gazette', "In five of these cases the families of the deceased persons informed me that they always sent to the pump in Broad Street, as they preferred the water to that of the pumps which were nearer. In three other cases, the deceased were children who went to school near the pump in Broad Street."

You can view an interactive version of John Snow's original map on the Wellcome website. The map, was originally published in 1855 in Dr. John Snow's 'Report on the cholera outbreak in the Parish of St. James, Westminster, during the autumn of 1854'. 

America's Drunkest Counties

America's Booziest and Driest Counties is a choropleth map which shows the percentage of the population in each US county who are excessive drinkers. The map is based on data from the Centers for Disease Control and Prevention (CDC), which defines excessive drinking as either binge drinking (men 5 drinks in a single session, women 4 drinks in a single session) or heavy drinking (men drinking more than 15 drinks in a week, women drinking 8 drinks in a week).

The map shows that many of the driest counties are also some of the most religious. For example Utah, whose population is over 50% Mormon, is one the direst states on the map. Consequently Utah has some of the most restrictive laws related to the buying and selling of alcohol. Much of the bible belt also seem to have some of the driest counties in the USA.

At the other end of the scale most counties in Wisconsin have a very high percentage of excessive drinkers. The reason for this may be partly cultural and partly due to the state alcohol laws (in Wisconsin people under the legal drinking age may be served, possess, or consume alcohol if they are with a parent, legal guardian, or spouse who is of legal drinking age).

Kentucky and West Virginia both have a large number of very dry counties. I read an interesting theory last week (I can't remember where so unfortunately I can't credit this) that this may be related to the heavy use of opiates in these states (West Virginia has the highest rate of deaths from drug overdose of all states).

The CDC has also mapped out the percentage of excessive drinking by state. The CDC ranks each state based on the percentage of the population who reported binge or heavy drinking. Utah, Kentucky and Alabama respectively top the rankings with the least percentage of heavy drinkers. North Dakota (50), Montana (49) and Iowa (48) have the highest number of excessive drinkers (Wisconsin comes in at 47th).

The Drug Map of Europe

Cocaine use has been increasing rapidly in Europe over the last seven years. Europeans are also taking more MDMA and ketamine. Every year the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) undertakes an annual wastewater drug survey in order to evaluate trends in the consumption of 'recreational' drugs. By analyzing residues of different recreational drugs in sewage the EMCDDA are able to detect trends in the drug-taking habits of residents in cities across Europe. 

Using the interactive map in the 2023 Wastewater analysis and drugs - a European multi-city study you can explore for yourself the levels of cocaine, cannabis, amphetamine, methamphetamine, MDMA and ketamine detected in around 90 European towns and cities. According to the EMCDDA report "cocaine use remains highest in western and southern European cities". The highest levels of amphetamine use are in cities in the north and east of Europe. The highest levels of cannabis use were detected in Czechia, Spain, the Netherlands and Slovenia.

According to the EMCDDA cannabis is Europe's most commonly used illicit drug. The EMCDDA has been analyzing dug levels in wastewater since 2011 so it is also able to detect trends in drug use over time. For example in 2023 20 cities out of 51 saw an increase in cannabis in wastewater samples compared to 2022, and 15 cities saw a decrease. Cocaine use in Europe has seen a steady increase since 2016. 

Car Commutes are Getting Longer

During the height of the Covid epidemic a number of visualizations were created to show the drop in road traffic and public transit use. As people were forced to quarantine and work from home our roads and transit networks saw an observable fall in traffic.

For example Buzzfeed teamed up with Mapbox to create a series of interactive mapped visualizations showing the reduction in road traffic in a number of American and global cities. In These Traffic Maps Show How The Coronavirus Pandemic Has Emptied Streets Across The Globe Buzzfeed presented a series of before & after maps which revealed traffic levels before & after the outbreak of Covid-19. 

TomTom has now released data which reveals how road traffic has increased again since the 'mid-pandemic era'. Axios has mapped out the Change in average travel time by car, 2021 to 2023 using TomTom's data. The entirely unsurprising result is that now that lock-downs are over there is more traffic on the road and that commuting times are increasing compared to commuting times from 2021.

The Axios map shows that Washington D.C. has seen the largest increase in car commuting times (97 secs for a 6 mile journey). Other densely populated cities such as New York and Boston have also seen increases in average commuting times over one minute.

Axios concludes that these small increases in commuting time prove that 'the heyday of the work-from-home era is no doubt behind us'. I'm not sure I entirely agree with this conclusion. Road traffic has undoubtedly risen since a time when people were being forced into lock-downs. However Axios's analysis is missing the obvious control data of pre-pandemic commuting times. 

It would surely be more revealing to compare the rates of working from home now to pre-pandemic levels rather than comparing commuting times now to those during the enforced lock-down levels during Covid. Therefore I think the Axios map would be far more interesting if they also looked at 2023 commuting times compared to 2019 (pre-pandemic) commuting times.

The TomTom Traffic Index features an analysis of average travel times (per 10km), average speeds in rush hour and travel time changes since 2022 for 387 cities across 55 countries on 6 continents. Globally London has the longest average travel time (37 mins 20 sec to travel 10km) while Oklahoma City has the shortest average travel time (7 min 57 s to travel 10km).

How Long Will You Live?

Thanks to this New Scientist article I decided to ask the AI Death Calculator when I was going to die. According to the New Scientist it can "predict people’s chances of dying more accurately than any existing model, even those used in the insurance industry".

Chatting with the AI Death Calculator proved very difficult. For something called the 'AI Death Calculator' the AI seemed very reluctant to actually calculate my death and it took a lot of cajoling to coax out my life expectancy. Here is a transcript of some of my discussions with the AI Death Calculator

Me:

You say I can expect 'a long and healthy life' but what do you think my life expectancy is?

AI Death Calculator:

As an AI Death Calculator, I can't provide you with an exact life expectancy. Your overall health and longevity greatly depend on various factors, including genetics, lifestyle choices, and your environment. However, by making conscious choices about your habits and taking care of yourself, you can increase your chances of living a long and healthy life.

Me:

Why are you called a 'Death Calculator' if you won't calculate my death?

AI Death Calculator:

My purpose is to provide some light-hearted entertainment by estimating a person's hypothetical expiration date based on general health factors and lifestyle choices. 

Me:

But you have not given me a hypothetical expiration date

AI Death Calculator:

I apologize for the confusion. Based on the information you've shared, my calculations indicate that you might live a long and healthy life. However, it's important to remember that this is just an estimate based on certain lifestyle factors, and should not be taken as an actual prediction of your lifespan or health. The main goal here is to encourage people to be more mindful about their lifestyle choices, and prioritize their health and well-being.

Me:

Saying I might 'live a long and healthy life' is not a hypothetical expiration date

AI Death Calculator:

You’re right. I apologize for giving you a vague answer before. According to our calculation, your hypothetical expiration date is approaching 85 years old.

So according to the AI Death Calculator my life expectancy is approaching 85 years. This tallies fairly well with the Office for National Statistics Life Expectancy Calculator which based on my age and gender says I can expect to live to 84.

Both the AI Death Calculator and the ONS Life Expectancy Calculator seem to ignore the importance of location in life expectancy. The map at the top of this post from the University of Washington shows Life Expectancy at Birth at the county level in the United States. As you can see there can be a huge variation in life expectancy depending on where you live. 

If you live in Oglala Lakota County, South Dakota then frankly you should move. The county ranks last in life expectancy for both men and women. On average citizens have a life expectancy of 65.42 years (so residents can expect to die about 21 months before retirement age). In the county men live an average of 62.8 years and women live an average of 71 years. This is compared to the national averages of 76.7 for men and 81.5 for women. 

At the other end of the scale residents of Summit County, Colorado have a life expectancy of 92.25. This means that on average the citizens of Summit County will live 26.83 years more than the citizens of Ogala Lakota County. 

Also See

Mapipedia's Average Life Expectancy by Country - life expectancy around the world from 1800 to 2018

US Life Expectancy Map - reveals the average life expectancy of nearly every US neighborhood

UK Life Expectancy - ONS maps of UK life expectancy by borough

How Long Will You Live?

According to Population.io I can expect to live for another 26.9 years. This calculation is based on my age, sex and country of birth. I am lucky I don't live in the United States. If I did I'd have 16 months less to live. Mind you if I lived in Japan I'd be able to look forward to living an extra 7 months.

Enter your date of birth, country of birth and sex into Population.io and it will tell you your estimated life expectancy based on United Nations data. Not only does Population.io tell you how many years and months you can expect to live it will also show you an interactive map showing you all the countries in the world where someone of your age and sex has a higher and lower life expectancy. For example if I lived in any of the countries colored green on the map above I would have a longer life expectancy. If I lived in any of the red colored countries I could expect a lower life expectancy.

Population.io can also tell you how many people in the world are currently older and younger than you (84% of the world's population is younger than me). It can also show you your chances of dying in each of your remaining years. For example next year I have around a 2% chance of dying. I'm a little sad that my chance of dying reaches 100% when I reach 107. I was so looking forward to my 108th birthday.

Via: quantum of sollazzo

A Map of the World's Deadliest Epidemics

During the spread of the Bubonic plague in the 14th Century ships arriving in Venice from infected ports would be required to wait 40 days before landing. This practice led to the origin of the word 'quarantine', from the Italian quaranta giorni, meaning 40 days.

This is just one of the many interesting facts in the Bulletin of the Atomic Scientists interactive map An Illustrated History of the World's Deadliest Epidemics. Using Mapbox's popular scrollytelling template this map takes you on an historical tour of some of the world's most lethal viral outbreaks. Starting with the Antonine plague (165–180 C.E.), which was the earliest known major smallpox epidemic, and ending with the COVID-19 epidemic of 2019, this map takes you on both a chronological and geographical tour of deadly epidemics. 

It is estimated that COVID-19 has killed close to 21 million people worldwide. This means that COVID-19 has been one of the world's most lethal viruses in terms of the total number of people killed. However thanks to vaccinations the percentage of people killed by the virus pales into insignificance compared to other major epidemics. For example the Japanese Smallpox Epidemic (735–737 C.E.) killed a third of that country's population; the 1918 Flu pandemic is believed to have killed more people than the First World War; and, in Mexico, the Cocoliztli epidemics are believed to have killed around 80% of the entire Aztec population.

Bat Virus Jump Zones

Over a fifth of the human population lives in areas where there is a large risk that a bat bourne disease will spread to humans. Bats carry tens of thousands of viruses. For most of human history we have been in little danger from these viruses because of the minimal contact between bats and humans. Now, because of human incursions into bat habitats the dangers of a virus jumping from bats to humans is on the increase.

In the Bat Lands: Part 1 Reuters has created a map which shows the level of risk of a virus spreading from bats to humans across the whole world. As you progress through Reuter's article the map pans and zooms to identify areas in China, India Brazil and West Africa where Reuters believe there is a chance that a new global pandemic could be caused by a virus jumping from bats to humans. 

Reuter's five part series begins by looking at the history of bat-borne diseases, and how they have caused outbreaks such as Ebola and SARS. The series also examines the factors that are driving the destruction of bat habitats, such as deforestation, mining, and agriculture. Part 5 of the series looks at the challenges of preventing the next pandemic, and what can be done to reduce the risk. It concludes by calling for a new approach to conservation, one that takes into account the risk of pandemics.

Via: The Top 10 in Data Journalism - the Global Investigative Journalism Network's weekly round-up of the best data stories.

The Drug Map of Europe

According to German newspaper Tagesspiegel drug use in Europe is on the rise again after a brief lull during the Covid pandemic. The paper has used data from the European Monitoring Center for Drugs and Drug Addiction (EMCDDA) to map evidence of the levels of drug use in 104 cities across Europe. You can view the results in the paper's article Drug Analysis in 104 Cities. 

Tagesspiegel's map shows the levels of different drug residues found in the sewage wastewater in each of the sampled cities. The EMCDDA analysis tested for six different types of recreational drug residues: cocaine, crystal meth, MDMA, cannabis, amphetamines and ketamine. The map allows you to observe the levels of residues found for each drug in each of the 104 European cities.

The map reveals that crystal meth is a growing problem in east Germany and the Czech Republic (if you click on a map marker you can view graphs showing the measured levels of each drug in that city over time). Across Europe cocaine use seems to rising, while use of MDMA is falling.

As well as the interactive map Tagesspiegel has created an interactive chart which allows you to view which cities recorded the largest and smallest levels of each drug. Amsterdam had the highest levels of MDMA residues found in its wastewater. Barcelona appears to be the ketamine capital of Europe. Cannabis was highest in Geneva. Antwerp had the highest recorded levels of cocaine. Gävle in Sweden had the highest levels of amphetamines, and Ostrava in the Czech Republic is the city with the largest crystal meth use.

Mapping the Health Effects of Climate Change

 

The Wellcome Trust has mapped out 120 years of climate data in order to explore the health effects of climate change. In Tracking the health effects of climate change you can see where around the world drought, flooding, extreme heat and climate-sensitive disease have affected the health of the local populations.

Select one of the four climate metrics and you can see how every country in the world has been affected by these climate events over the last 120 years on an interactive map. For example if you choose 'Drought' the interactive map will show the number of people affected, the total number of events and the damage caused by flooding in countries around the world.

You can also explore the health effects of climate change over time for individual countries. If you select a country from the drop-down menu you can view a map which shows climate anomalies by area and over time. For example if you select to view the flooding data for India you can see a map  showing where flooding anomalies have most occurred and a timeline of flooding anomalies by year (shown in the screenshot above). The timeline of flood events in India over time clearly reveals the growing problems of flooding in the subcontinent over the last few decades.

The Poor & Poor Health in Brussels

The investigative journalism magazine Médor has undertaken an extremely detailed examination of the health of Brussels. In Bruxelles Malade they explore the extreme inequality of health and health care provision in the capital of Belgium. 

Through a detailed analysis and mapping of health, economic and housing data Médor shows how the poorest neighborhoods in Brussels suffer from low incomes, poor health and high pollution. Despite Brussels being one of the richest cities in Belgium nearly a third of its inhabitants live below the poverty line. 

By mapping out the BIM rate (levels of health insurance benefits) across the city Médor shows how the residents in the northwest of Brussels are in most need of health insurance assistance. Conversely, those people living in the southeast of Brussels are more privileged and less in need of assistance.

By exploring other demographic and economic data Médor explore how income, housing and employment all have a direct impact on the health of the residents of Brussels. Where you live in Brussels can also affect what kind of health care that you receive. Residents in the poorest neighborhoods of Brussels receive poorer dental care, less cervical cancer screenings and are more susceptible to diabetes than residents in the richer neighborhoods. In general those living in the poorest areas have less access to preventative medicine than those in the richest areas.

Médor also maps out the levels of access to green spaces across Brussels and the levels of air pollution. It shows how the poorest neighborhoods also have the worst access to green spaces and have the worst polluted air. Just two more factors that contribute to the inequality of health in the capital city.

Vaccine Rollouts by Country

The Guardian has mapped out the rollout of the Covid vaccine around the world to explore which countries have vaccinated faster and why some countries have been able to vaccinate their populations much faster than others. Which countries rolled out vaccines faster – and why includes two maps which visualize the rollout of vaccines around the globe.

The first map in The Guardian article uses data from Our World in Data to show the rate of the vaccine rollout in countries around the world over the last year. It shows the number of vaccine doses administered per 100 people over time. The second map in the article quickly tweens into a graph which plots country GDP against the number of people vaccinated in a country. This graph shows that  there is a clear correlation between a country's wealth and the rate and scope with which a country was able to rollout vaccinations. 

Wealthy countries, such as the UK and the USA, have in general managed to rollout vaccines more quickly and to more people than poorer countries. The Guardian suggests however that wealth is not the only factor in the speed and expanse of vaccine rollouts. Supply issues have also been a factor in vaccine rollouts. Some wealthier countries may have been able to afford to rollout nationwide vaccine programmes early on but were hampered by limited supplies. 

Over time supply issues have become less extreme and as a result some countries in the Asia-Pacific region have overtaken some European and North American countries in the percentage of the population vaccinated. However, as you can see from the map above, many countries in Africa still have very low levels of their populations vaccinated. This could be dangerous for the whole world. If some countries are left vulnerable to large Covid-19 outbreaks then new variants are more likely to develop. It is therefore clear that wealthier countries around the world have to do more in supporting poorer African countries both economically and through the supply of vaccines.

Mapping Mortality Rates from Covid

El Confidencial has published an interactive map which visualizes the mortality rate from Coronavirus across Spain. The map in The Black Hole of Covid Mortality reveals that there have been quite large differences in the mortality rates across different Spanish municipalities. 

The article accompanying the map does a good job at dismissing some of the possible reasons for the large differences in mortality rates from Covid-19 across the different regions of Spain, without ever really tying down the real reasons why some municipalities have fared far worse from Covid-19 than others.

My first thought was that the differences might be related to age. However El Confidencial discovered that "the municipalities with the most deaths per inhabitant do not coincide with those with older populations". I also wondered if the differences in mortality rates may be related to population density. However El Confidencial was only able to obtain mortality rates for municipalities of more than 500 people. Therefore the overall picture revealed by the interactive map is skewed a little because large areas of Spain (those with the lowest population densities) don't show any mortality rate data on the map.

In both the USA and the UK average income levels has had an impact on infection rates. This may be because those on the smallest incomes are less able to furlough, are more likely to rely on public transit and more likely to work in occupations which require face-to-face interactions with the public.

Left: Mortality rates from Covid-19. Right: Average Incomes

Spanish newspaper El Pais has mapped out the average income per person across the whole of Spain. The Map of Spanish Incomes, Street by Street shows that there is quite a stark divide between the north and south of the country. A comparison of El Confidencial's mortality rate map with El Pais' average income map doesn't reveal an exact correlation between income levels and mortality rates from Covid-19. However a comparison of the two maps does suggest that mortality rates in some of the poorest municipalities in Spain are particular high - especially in the south and in the north-east of the country.

High Vaccination, Low Hospitalization

Kenneth Field has posted a nice critique of a Washington Post bivariate choropleth map showing America’s hospitalization and vaccination divide.The Post's bivariate map uses colors to visualize two different variables on one map - the Covid-19 vaccination rate and the Covid-19 hospitalization rate in every U.S. health region.

Kenneth's Cartoblography post praises the map for making it very clear that "Regions with more vaccinations have fewer hospitalizations." This is where I have to make an embarrassing carto-confession - I often find bivariate choropleth maps difficult to read. When I first looked at the Post's map I thought that the dark colors in Texas, Florida and Oregon identified these areas as having high rates of vaccinations and also high rates of hospitalizations. I also read the map as saying that the light colored Kansas has a low rate of vaccinations and a low rate of hospitalization.

The Post's article also includes a scatterplot of the same two variables which I find a lot easier to read. Looking at this scatterplot reveals that Kansas probably is an outlier (although because the plot isn't interactive I am having to guess that one of those two dots in the bottom-left segment indicates Kansas). However this scatterplot also reveals that there are no regions in the highest vaccination / highest hospitalization sector (however it does appear that parts of Oregon and Florida are in fact close to that sector)

I think that I find the Post's bivariate choropleth map hard to read because it has 16 different colors and it isn't just a matter of light hues = good / dark hues = bad (or vice versa). As Kenneth points out in his critique the Post does help the reader by using annotations both on the map legend and on the map itself to help the reader understand the data.However I do find that the use of so many colors on bivariate maps means that I often have to work towards comprehension. That in itself isn't necessarily a bad thing but it might be a factor worth considering if you want to visualize two different variables on one map.

Obviously the Post's accompanying article also makes clear the correlation between vaccinations and hospitalizations. The data is unequivocal - vaccinations work. Areas of the country which have the highest vaccination rates tend to have the lowest hospitalizations for Covid-19. Conversely those regions of America with the lowest vaccination rates tend to have some of the highest hospitalization rates for Covid-19. 

The message couldn't be clearer. If you haven't done so already then you need to get vaccinated! 

The Life Expectancy Map

People in Mississippi on average die six years younger than people in California. Mississippi has the worst average life expectancy of any state. At birth a Mississippian can expect to live on average 74.9 years. The average Californian can expect 81.3 years on Earth.

The CDC's Life Expectancy at Birth interactive map shows you how long (on average) people in your neighborhood will live. The map reveals that there can be huge differences in life expectancy even between neighborhoods in the same city. 

I can think of a couple of reasons why some neighborhoods might have significantly lower life expectancy than others. For example you could compare life expectancy in your city with CNN's dot map of Race and Ethnicity Across the US. The two maps on this post show roughly the same area of Upper East Side and Harlem in New York City. The blue color on the top map shows areas with the highest life expectancy. Blue on the bottom map equals white people. Red on the top map equals lower life expectancy. Red on the bottom map equates to the Black and Hispanic population.

Perhaps not surprisingly you can also find similar correlations between levels of income and life expectancy in many U.S. cities. You can compare for yourself the CDC's Life Expectancy map with Esri's Predominant Income Range by Households map, which shows how much money people are earning in each census tract in the United States.

Life expectancy in the UK and the USA is falling, bucking a century long trend of people living longer lives. You can read more about falling life expectancy on this Why Are We  Dying Younger post.

Segregated Vaccination Rates

At the beginning of the Covid-19 vaccination roll-out in Australia the government set a target of inoculating the whole country by October. So far (at the beginning of September) just over 35% of the population are fully vaccinated. Among First Nation Australians the rate is even lower, with just over 21% fully vaccinated.

Last week the Australian government released data showing the percentage of the whole population and the population of Indigenous Australians who have been vaccinated in each Statistical Area 4 region. ABC News has used this data to map out the gap in the vaccination rates across the country. The interactive map in Why is the Indigenous COVID-19 vaccination rate 20 per cent lower than the national average? shows that the rate of vaccination of Indigenous Australians is lower in every single region, compared to the vaccination  rate of the whole population.In most regions they are significantly lower.

The Guardian has also been exploring the difference in vaccination rates between Indigenous and non-Indigenous Australians. In Indigenous communities being left behind in NSW vaccine rollout the newspaper has created a map showing this vaccination rate difference across New South Wales. The Guardian's article also lists the gap between Indigenous and non-Indigenous vaccination rates in every NSW health district. In four of these health districts the vaccination rate for non-Indigenous people is over twice the rate of Indigenous people. In none of the districts are Indigenous Australians being vaccinated at the same rate as non-Indigenous Australians.

In March the Australian government claimed that Aboriginal and Torres Strait Islanders were a priority group for Covid-19 vaccinations.

Covid Trends in America

Esri's Which Way Are Things Going interactive map visualizes the week on week trend in Covid-19 cases at the county level. The map can show you at a glance whether the number of cases in your area has risen or fallen in the last week.The map can therefore help you to quickly assess whether things are getting better of worse in your county.

The color and direction of the arrow symbols on the map indicate whether Covid-19 cases have fallen or risen. Purple colored arrows pointing up indicate that cases have risen. Orange arrows pointing down show that cases have gone down in the last week. The size of the arrows show the rate of the increase or decrease in cases. If an arrow points straight up or straight down then it shows that cases have been rising or falling for nearly eight weeks in a row.

Using arrow symbols can be a very effective way to show the direction of travel for trends on a map. For example the New York Times used colored arrows after last year's presidential election to visualize the shift in the numbers voting for the Republican or Democratic candidate in an electoral district since the previous election in 2016. The arrows in the Shift from 2016 map view in Presidential Election Results 2020 show whether electoral districts have moved left or right politically since 2016.

How Covid-19 Conquered the World

Olivia Jack at the Department of Biology, Humboldt University of Berlin has created an interactive animated map which shows the spread of the coronavirus across the world. The COVID-19 Geographical Animation uses data from Johns Hopkins University to visualize the rise of the epidemic and the continuing waves of cases around the globe.

Animated maps showing the spread of an epidemic over time can help us to learn more about how a virus is transmitted and how it spreads. NBC Covid-19 map also maps the spread of the virus over time, showing how Covid-19 quickly spread from Wuhan to the rest of China, through Asia and out to the rest of the world.

Both these animated maps use Johns Hopkins University's Covid-19 Time Series data, which record daily confirmed cases of Covid-19 around the world. If you want to create your own animated map showing the spread of the coronavirus it might be interesting to add context to the spread of the virus. For example it might be interesting to add information to identify and indicate the first cases reported in different locations and to identify significant clusters (for example the Seattle nursing home which saw the first significant fatalities in the USA).

Hat-tip: weeklyOSM