One point per person in the US for the 2010 and 2020 censuses, fully zoomable and interactive using WebGL and [Deepscatter](https://github.com/CreatingData/deepscatter). Since this uses WebGL individual point rendering and quadtiled data, it can be far more responsive than raster-based maps you may have seen in 2010. Plus, if you zoom all the way in in some views it has little person glyphs!
The places that drive the most tend to have the same high share of chain restaurants regardless of whether they voted for Trump or Biden. As car commuting decreases, chain restaurants decrease at roughly the same rate, no matter which candidate most residents supported.
If the link between cars and chains transcends partisanship, why does it look like Trump counties have more chain restaurants? It’s at least in part because he won more of the places with the most car commuters!
About 83 percent of workers commute by car nationally, but only 80 percent of folks in Biden counties do so, compared with 90 percent of workers in Trump counties. The share of car commuters ranges from 55 percent in the deep-blue New York City metro area to 96 percent around bright red Decatur, Ala.
Median property taxes paid vary widely across (and within) the 50 states. The lowest bills in the country are in six counties or county equivalents with median property taxes of less than $200 a year:
Northwest Arctic Borough and the Kusivlak Census Area (Alaska)*
Avoyelles, East Carroll, and Madison (Louisiana)
Choctaw (Alabama)
(*Significant parts of Alaska have no property taxes, though most of these areas have such small populations that they are excluded from federal surveys.)
The next-lowest median property tax of $201 is found in Allen Parish, near the middle of Louisiana, followed by $218 in McDowell County, West Virginia, in the southernmost part of the state.
The eight counties with the highest median property tax payments all have bills exceeding $10,000:
Bergen, Essex, and Union (New Jersey)
Nassau, New York, Rockland, and Westchester (New York)
Falls Church (Virginia)
All but Falls Church are near New York City, as is the next highest, Passaic County, New Jersey ($9,999).
Text is maybe the most underrated element in any data visualization. There’s a lot of text in any chart or map — titles, descriptions, notes, sources, bylines, logos, annotations, labels, color keys, tooltips, axis labels — but often, it’s an afterthought in the design process. This article explains how to use text to make your visualizations easier to read and nicer to look at.
To quote Andy Kirk, “we can look at data, but we cannot really see it. To see data, we need to represent it in a different, visual form.” So, in an attempt to make data more accessible, you may create more visual representations – dots, lines, shapes, and colours. These building blocks combine to create all sorts of charts and pictures helping readers understand numbers.
Although the purpose of visualising data is clear (and universal), the reasons can be different. The reason you visualise data, will help you determine the appropriate visual.
In my case, the graphs I made looked just fine—it’s just that I didn’t understand how copy/pasting graphs between Excel and Word worked (at the time). This was in the mid-2000s, when memory wasn’t quite so plentiful, so many corporate email accounts had memory quotas. If you hit that quota, you would be locked out of your email account. You had to call IT and actually talk to a person!
I was a lowly entry-level person at a financial services company and had done some Monte Carlo modeling involving 1,000,000 scenarios. We were developing a new mutual fund project, based on changing allocations over time as people moved towards retirement, and the company wanted me to model outcomes for different allocation trajectories. After a “full” model run of one million scenarios, I made diagnostic graphs showing the distribution of key metrics (such as the annual accumulation of the fund, how many times the fund decreased while the owner was in retirement, and whether – and when – the money in the fund ran out) so that we could analyze different potential fund strategies. The graphs themselves were fairly simple.
I’m often looking at distributions, and wanting to communicate something about how those distributions change over time, or how distributions compare. Often, I have to simply pick out key percentiles in those distributions, or key aspects, such as mean and standard deviation.
But why not graph all the points in one’s sample directly, if one has them?
The numbers of expected deaths are estimated using statistical models and based on previous 5 years’ (2015 to 2019) mortality rates. Weekly monitoring of excess mortality from all causes throughout the COVID-19 pandemic provides an objective and comparable measure of the scale of the pandemic [reference 1]. Measuring excess mortality from all causes, instead of focusing solely on mortality from COVID-19, overcomes the issues of variation in testing and differential coding of cause of death between individuals and over time [reference 1].
In the weekly reports, estimates of excess deaths are presented by week of registration at national and subnational level, for subgroups of the population (age groups, sex, deprivation groups, ethnic groups) and by cause of death and place of death.
Heart attacks and other cardiovascular issues are a major source of recent excess deaths.
For anti-racist dataviz, our most effective tool is context. The way that data is framed can make a very real impact on how it’s interpreted. For example, this case study from the New York Times shows two different framings of the same economic data and how, depending on where the author starts the X-Axis, it can tell 2 very different — but both accurate — stories about the subject.
As Pieta previously highlighted, dataviz in spaces that address race / ethnicity are sensitive to “deficit framing.” That is, when it’s presented in a way that over-emphasizes differences between groups (while hiding the diversity of outcomes within groups), it promotes deficit thinking (see below) and can reinforce stereotypes about the (often minoritized) groups in focus.
In a follow up study, Eli and Cindy Xiong (of UMass’ HCI-VIS Lab) confirmed Pieta’s arguments, showing that even “neutral” data visualizations of outcome disparities can lead to deficit thinking (and therefore stereotyping) and that the way visualizations are designed can significantly impact these harmful tendencies.
The same dataset, visualized two different ways. The left fixates on between-group differences, which can encourage stereotyping. The right shows both between and within group differences, which may discourage viewers’ tendencies to stereotype the groups being visualized.
Excerpt:
Ignoring or deemphasizing uncertainty in dataviz can create false impressions of group homogeneity (low outcome variance). If stereotypes stem from false impressions of group homogeneity, then the way visualizations represent uncertainty (or choose to ignore it) could exacerbate these false impressions of homogeneity and mislead viewers toward stereotyping.
If this is the case, then social-outcome-disparity visualizations that hide within-group variability (e.g. a bar chart without error bars) would elicit more harmful stereotyping than visualizations that emphasize within-group variance (e.g. a jitter plot).
According to forecasting by Reason Foundation’s Pension Integrity Project, when the fiscal year 2022 pension financial reports roll in, the unfunded liabilities of the 118 state public pension plans are expected to again exceed $1 trillion in 2022. After a record-breaking year of investment returns in 2021, which helped reduce a lot of longstanding pension debt, the experience of public pension assets has swung drastically in the other direction over the last 12 months. Early indicators point to investment returns averaging around -6% for the 2022 fiscal year, which ended on June 30, 2022, for many public pension systems.
Based on a -6% return for fiscal 2022, the aggregate unfunded liability of state-run public pension plans will be $1.3 trillion, up from $783 billion in 2021, the Pension Integrity Project finds. With a -6% return in 2022, the aggregate funded ratio for these state pension plans would fall from 85% funded in 2021 to 75% funded in 2022.
Author(s): Truong Bui, Jordan Campbell, Zachary Christensen
Over the last 50 years, fertility rates have dropped drastically around the world. In 1952, the average global family had five children—now, they have less than three.
This graphic by Pablo Alvarez uses tracked fertility rates from Our World in Data to show how rates have evolved (and largely fallen) over the past decades.
What’s The Difference Between Fertility Rates and Birth Rates?
Though both measures relate to population growth, a country’s birth rate and fertility rate are noticeably different:
Birth Rate: The total number of births in a year per 1,000 individuals.
Fertility Rate: The total number of births in a year per 1,000 women of reproductive age in a population.
