The Number of Illegal Immigrants Apprehended at the Border Near Four-Decade Low
By David Mendoza - Thursday, September 1, 2016
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Donald Trump outlined his immigration policies last night in Phoenix, Arizona. One striking aspect of Trump's speech was how disconnected it was from the actual data on illegal immigration. His speech sounded as if he was unaware that the number of people coming into the United States unlawfully has fallen significantly over the last three decades. As the GIF above shows, the number of people apprehended by the border patrol neared a record low not seen since the 1970s.
The flow of illegal immigration peaked in 2000 with more than 1.6 million apprehensions at the southern border. In 2015, the last fiscal year with data available, less than 332,000 people were caught by the border patrol trying to enter from Mexico. That's an 80% drop. (While the number of apprehensions for fiscal year 2015 were low, the fewest number of apprehensions actually occurred in 2011 when people were caught less than 328,000 times.)
Trump and his supporters simply don't believe empirical evidence, and even when they concede that the data disagrees with them, they attempt to refute it by accusing the people counting of being incompetent or crooked. Trump, for instance, challenged the accuracy of the estimate that 11 million illegal immigrants live in America. "Honestly," Trump wailed, "we've been hearing that number for years. It's always 11 million. Our government has no idea. It could be 3 million. It could be 30 million." PolitiFact and FactCheck.org — citing actual data — proved Trump wrong.
Similarly, Trump surrogate and former Governor of Arizona Jan Brewer admitted, when challenged by Chuck Todd of NBC News, that illegal immigration has declined since her term ended. However, she then went on to imply that the numbers might have been corrupted. "The data that you get is probably skewed," she told Todd. Then adding, "Who's doing the counting? Who's keeping the data?" The answer to both of those questions is U.S. Customs and Border Protection.
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During A Presidential Election Year, 6 Supreme Court Justices Have Been Confirmed Since 1900
By David Mendoza - Sunday, February 14, 2016
Supreme Court Justice Antonin Scalia died yesterday. He was 79.
His untimely death means there is now a vacancy on the precariously divided Supreme Court. Normally, in accordance with Article II, Section 2 of the Constitution, the President would promptly nominate someone to replace the deceased justice. However, Senate Majority Leader Mitch McConnell has a different interpretation of what the Constitution requires of the Senate in this situation.
Last night, Senator McConnell posted this on his Facebook page, "The American people should have a voice in the selection of their next Supreme Court Justice. Therefore, this vacancy should not be filled until we have a new President." (Apparently, a majority of American voters did not speak with enough force in 2012 for their voices to be heard.)
As the chart below shows, although it is unusual for a justice to be confirmed in the last full year of a president's term, it's not unprecedented. Since 1900, there have been 6 justices confirmed by the Senate during a presidential election year. If you use a broader definition of what constitutes a presidential election year, then there have been 9 nominees confirmed one year before Election Day. Either way, this is not a unique situation.
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In case it's not obvious, justices nominated by Democratic presidents are shown in blue and nominees named by Republican presidents are shown in red. The "N" signifies when the nominee was officially nominated and the "C" or "W" indicate whether the nominee was confirmed or withdrew themselves from consideration before the Senate could vote. The data for the chart comes from the Senate's official website.
In June 1968, President Lyndon Johnson nominated two people to the Supreme Court. Even though the Senate was controlled by members of the president's party, Southern Democrats and conservative Republicans like Strom Thurmond stopped both those nominees from being confirmed. Johnson withdrew both nominations just a month before the election of 1968.
Since then, there has been an informal and vague rule known as the Thurmond Rule that states no judicial nominee would be considered that close to a presidential election. However, senators dispute exactly when the Thurmond Rule goes into effect or how strictly it should be followed. Generally, six months before a presidential election is considered the standard. This would mean that the current vacancy on the court falls outside this time frame.
Moreover, President Obama would have more than enough time to nominate and for the Senate to vote to confirm or reject his nominee. As the New York Times points out, President Obama has just under one year left until a new president is inaugurated in 341 days. The longest confirmation process, Louis Brandeis' nomination in 1916 — who, by the way, was nominated during a presidential election year — took 125 days. On average, the process has taken 25 days.
Thanks to Judd Legum at Think Progress for citing my work.
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Throughout last year, The Guardian recorded the number of people killed by the police. They found that in 2015 1,136 people died due to law enforcement action. The Guardian's total is substantially higher than the official numbers kept by the FBI, which averaged only 428 deaths between 2010 and 2014.
This large discrepancy may be due to the fact that the FBI limits its count to "killing of a felon by a law enforcement officer in the line of duty," whereas The Guardian includes "any deaths arising directly from encounters with law enforcement." Additionally, the FBI undercounts the number of people killed by the police because, as The Wall Street Journal reports, several police departments do not submit any data of people they've killed to the FBI.
The chart below shows that on the vast majority of days in 2015 the police killed multiple people. On only 24 days did the police not kill anyone.
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One important thing to note: even though 2015 was replete with examples of the police killing people in apparently unjustifiable circumstances, many — if not most — of the deaths recorded by The Guardian were likely justifiable. For instance, included in their total are people like Syed Farook and Tashfeen Malik, the perpetrators of the massacre in San Bernardino.
Read more about The Counted project by The Guardian here.
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Donald Trump Is Wrong: Most White People Are Killed By Other White People
By David Mendoza - Monday, November 23, 2015
Yesterday, Donald Trump retweeted an egregiously inaccurate graphic from one of his ignorant followers. It claimed that a majority of whites were killed by blacks in 2015 — a lie not supported by a shred of evidence. Anyone even remotely familiar with homicide statistics in America knows that most homicides are intraracial, meaning committed between members of the same race.
Using data from the Bureau of Justice Statistics — a real and credible source, unlike the source used by Trump's dim fan — the area charts below reveal that over a three decade period the vast majority of both white and black homicide victims were killed by someone of their own race.
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The data in these charts span from 1976 to 2005, but the most recent data from the FBI show a similar result. In 2014, 82.4% of white homicide victims were killed by a white person and 90% of black homicide victims were killed by a black person.
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The Disparate Racial Impact of the American Death Penalty
By David Mendoza - Sunday, June 7, 2015
In the current issue of the journal Politics, Groups, and Identities, researchers Frank Baumgartner, Amanda Grigg, and Alisa Mastro document how black homicide offenders are substantially more likely to be executed if they murder a white person. This is in spite of the fact that most victims of black homicide offenders are black. As the chart below reveals, between 1976 and 2005, 85.5% of black homicide offenders killed a black person and only 14.4% of black homicide offenders killed a white person. Yet, between 1976 and 2013, a majority of black homicide offenders were executed for killing a white person.
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The same execution disparity does not exist for white homicide offenders. Nearly commensurate percentages of white murderers are executed for killing white and black people as the percentage of white and black people murdered by white homicide offenders. Specifically, over the same time periods previously mentioned, 93.7% of white homicide offenders killed a white person and 90.7% of executed white homicide offenders killed a white person. That’s a difference of only 3 percentage points between the percentage of white-on-white homicides and executions — vastly smaller than the 46 point difference that exists for black-on-black homicides and executions.
The reality that most murders in America occur between members of the same race is not a particularly well known fact. According to a recent poll by YouGov, only 50% of American adults correctly answered that most white victims of murder were killed by a white person. The other half either incorrectly stated that most white murder victims were killed by a black person or were not sure. The following area charts show that in the thirty years since 1976 white homicide offenders killed a white person 93.7% of the time. Similarly, black homicide offenders killed a black person 85.5% of the time.
So all things being equal, we should expect that the racial composition of people that the government executes would resemble this reality. The data does not support this assumption. Instead, as Baumgartner, Grigg, and Mastro write, the death penalty "is clearly a racial project."
Looking at the data from the perspective of homicide victims, the same racial discrepancy prevails. Over half all homicide victims are black, but less than one-sixth of victims of executed inmates were black.
Click here to enlarge this image.
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Zero Tolerance: Why All Bar Charts Should Have A Zero Baseline
Click here to see the full chart by the Washington Post.
By David Mendoza - Monday, May 4, 2015
This bar chart recently appeared in the Washington Post. It attempts to compare the ages of several presidential candidates with previous presidents. It's a fairly innocuous graphic, except for one major problem: The baseline of the chart starts at 40 — not zero. This is an egregious error, which greatly distorts the data presented in the chart.
Since bar charts visually encode data through length, starting the chart at 40 exaggerates how large the age difference between different candidates and presidents actually are. By doing this, the designer also violates one of Edward Tufte's principles of graphical integrity. In "The Visual Display of Quantitative Information", Tufte wrote, "The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities represented." That's not even remotely true about this chart.
In the figure above, I condensed the original version from the Washington Post to a few bars and measured the length of the longest and shortest bar in millimeters. I did this to calculate the Lie Factor, which is a measure Tufte came up with to determine how inaccurate misleading graphics are. The Lie Factor is calculated by dividing the size of the effect shown in the graphic by the size of the effect in the data.
In this instance, the bars displaying the ages of Ronald Reagan and Marco Rubio are 402 mm and 54 mm, respectively. This means the increase shown in the graphic works out to 644.4%. However, this seriously overstates the effect in the actual data. Reagan was 77 when he was inaugurated into office and Rubio would be 45 on his hypothetical Inauguration Day. That's an increase of only 71.1% from Rubio's age to Reagan's. The Lie Factor of the bar chart, then, is 9.1. As Tufte wrote, a graphic with a Lie Factor larger than 1.05 represents a "substantial distortion."
To put it another way, the effect shown in the chart is commensurate with data that would have Rubio's age as 45 and Reagan's age as 335.
I present a more effective way to visualize this data below.
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The original designer's instincts were correct. The area between 0 and 40 on each bar is superfluous. If we made a bar chart that included this area, it would inhibit the ability of the viewer to easily compare where each bar ends. However, the solution the designer used (i.e., truncating the bars) doesn't work — as I've shown above.
Instead, he should have used a dot chart, since it doesn't require a zero baseline. Unlike bar charts, dot charts don't rely on the viewer to compare the length of each item. Rather viewers compare the position of each dot along a common scale. In "The Elements of Graphing Data," William Cleveland points to this feature of the dot chart as the reason why it's superior to the bar chart. "Ordinary bar charts," he wrote, "have not been used so far in this book." Instead, he explains that he used dot charts because "they are a more flexible display" and "they do not require a meaningful baseline on the scale line."
I will note that my example isn't exactly a "dot" chart because I decided to use the age of each candidate in place of dots. This further improves the ability of the viewer to make meaningful comparisons. Additionally, I only included the ages of the youngest, oldest, and current presidents on the chart in order to save space.
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It’s Still Legal to Discriminate Against LGBT Americans — Even If They Can Get Married
By David Mendoza - Tuesday, April 28, 2015
The Supreme Court heard arguments today in the case that could legalize same-sex marriage across the nation. Currently, same-sex couples can marry in 36 states. That's more than twice as many states that permitted such unions only two years ago.
Yet even as the United States advances toward full marriage equality, several forms of discrimination against LGBT Americans remain legal. Recent controversies in Indiana and Arkansas focused America's attention on Religious Freedom Restoration Acts. Opponents of these laws like Lambda Legal correctly argued that it would provide a new defense for people who want to treat LGBT residents unfairly. However, even without such laws, people and businesses could already discriminate against someone because of his or her sexual orientation or gender identity since these states and many others lack inclusive civil rights laws.
Using data from the Human Rights Campaign and recent news reports, the chart below reveals how inadequate laws protecting LGBT Americans are.
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For the last decade, same-sex marriage has dominated the conversation about LGBT rights. Sometimes so much so that it can seem that LGBT Americans have made more progress than they actually have. Law professors David S. Cohen and Leonore Carpenter made this point in a recent op-ed in USA Today. As they wrote,
Because we as a country have come so far in acceptance of LGBT individuals and seem to be on the precipice of a Supreme Court ruling requiring marriage equality, we like to think there also must be protections against discrimination in the law. However, nothing could be further from the truth. This is truly a gaping hole in American law.
One glance of the previous chart confirms what Cohen and Carpenter suggest.
In particular, anti-discrimination laws in the South are extremely porous. With the exception of Delaware and Maryland — which I included as part of the South because I used regions devised by the Census Bureau — the rest of the South lacks even the most basic bans against firing a person or refusing to serve them because they're gay. In contrast, every state in the Northeast allows same-sex marriage and provides the most comprehensive, though not complete, protection against anti-LGBT discrimination.
A note on the design
Other publications have displayed the same data I used in this post. The example below by the Indianapolis Star is a typical variation that shows the data in a map. But is a map really the best choice? Kaiser Fung of Junk Charts asks this same question about a series of maps made by the New York Times. I think the answer in both cases is no. I tend to agree with a commenter on Fung's post who recommends using the chart form I ended up choosing.
Map by Michael Campbell and Kristine Guerra of the IndyStar.
The primary advantage of my design is that it allows viewers to compare different categories faster than they can with a map. Viewers can determine which types of discrimination are disallowed in a state by looking down a column and compare differences between states by scanning rows horizontally. Such comparisons are substantially harder to do with the map produced by the IndyStar. Additionally, the map distorts the prominence of states in the Midwest with small populations and large land areas compared to states in the Northeast with larger populations but smaller land areas. My chart also includes more specific categories than one map can contain. The conspicuous disparity between the comprehensiveness of California and Nevada's anti-discrimination laws is less apparent in the map than it is in the chart.
One advantage a map might have over a chart is that you can more readily see regional patterns. However, I think that by dividing the chart into four regions you can get a similar effect.
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Despite the overwhelming evidence proving pie charts ineffectively display data, designers continue to use this deficient graphic. Two of the most prominent data visualization experts, Stephen Few and Edward Tufte, both agree that the usefulness of the pie chart is limited. "Of all the graphs that play major roles in the lexicon of quantitative communication," Few maintains, "the pie chart is by far the least effective." Edward Tufte is even more blunt. In The Visual Display of Quantitative Information, he wrote, "Given their low data-density and failure to order numbers along a visual dimension, pie charts should never be used."
Chart via reddit
And yet we continue to find pie charts everywhere. Recently, on /r/dataisbeautiful, this pie chart made it to the front page. The chart has several deficiencies, including the desaturated and nearly monochromatic color scheme, but its biggest flaw is its use of the 3D option. As flawed as pie charts already are, the use of an unnecessary third dimension makes its problems substantially worse.
Below I reveal exactly how much using 3D distorts the data displayed in the chart. On the left, I modified the original pie chart by increasing the color contrast to make the slices easier to differentiate. On the right, I created a 2D pie chart that more accurately displays the same data. I labeled the angle of each slice in blue. As the annotations show, both charts are not the same. For instance, the angle of slice 4 on the 3D pie chart should be almost twice as big, while slice 7 has the opposite problem. The angle of slice 7 should be around 50% smaller than it actually is.
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The distortion of the angles shown in the 3D pie chart also distorts the numbers underlying the visualization. Again, slice 4 and 7 are both incredibly inaccurate. Slice 4 should be 13.4%, but the actual value shown is 6.9%. To put that another way, slice 4 represents the 162 aircraft fatalities caused by the crash of Air Asia QZ8501, but on the 3D pie chart it really shows an angle equivalent to only 83 fatalities. Slice 7, on the other hand, should be 4%, but actually displays a value of 7.7%. That means instead of the 49 fatalities it's supposed to represent it actually shows 93 fatalities — 47% larger than it should be.
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I decided to redesign the original chart by transforming it into a stacked bar chart. Since the original designer intended to show that 44% of aircraft fatalities were attributable to Malaysia Airlines, I grouped those two segments next to one another. This redesign does a more effective job displaying this data because people are better able to compare lengths than they can angles.
Click here to embiggen this image.
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YouGov released a poll last week that asked Americans about the end of the world. It found that most people didn't think an apocalyptic disaster was a likely event. Sixty-nine percent of Americans said the Apocalypse is "somewhat" or "very unlikely" to happen. When asked if the Apocalypse did happen, the largest group of Americans said nuclear war would be the most likely cause of the Earth's destruction. Yet the second most popular answer was nothing. One-fifth of Americans didn't think there even would be a hypothetical Apocalypse.
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Not surprisingly, Democrats think climate change is the most likely event to precipitate the Apocalypse. Democrats were four times more likely than Republicans to think a catastrophic weather event would end the world. The most likely scenario Republicans envision destroying the world is nuclear war. Thirty-six percent of Republicans thought humanity's last slight would be a mushroom cloud.
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Breaking the results down by race, Hispanics are more likely to say climate change would cause the Apocalypse. This corroborates other evidence that Hispanics perceive climate change to be a greater risk, when compared to other racial groups. A recent Pew poll found that Hispanics were 14 percentage points more likely than blacks and 26 percentage points than whites to say "Earth is getting warmer because of human activity."
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Last month, Phoenix Marketing International (PMI) released its yearly estimate of millionaire households in the United States. Maryland claims the top spot for the fourth consecutive year. More than 7% of households in the Old Line State had investable assets worth more than $1 million. The Wall Street Journal notes that PMI defines investable assets as "liquid holdings, such as stocks, bonds, savings accounts and cash," but excludes real estate and retirement investments. Connecticut and New Jersey came in second and third at 7.2% and 7.1%, respectively. Nationally, 5.2% of households have more than $1 million in investable assets.
The GIF above displays PMI's millionaire rankings since 2006. As we can see, the Great Recession temporarily decreased the share of millionaire households, but that trend quickly reversed itself. Since the end of the recession in 2009, the recovery has largely benefited the wealthy. As economist Justin Wolfers points out on The Upshot, "After adjusting for inflation, the average income for the richest 1 percent (excluding capital gains) has risen from $871,100 in 2009 to $968,000 over 2012 and 2013." For everyone else, average income fell over the same time period.
I should mention that to be part of the 1%, you do not need to make over $1 million. A new study by the Economic Policy Institute reveals that in every state you could earn less than that and still be part of your state's top earners.
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At the end of last year, the Centers for Disease Control and Prevention (CDC) released its tally of the number and leading causes of deaths for 2013. The fatality count nearly topped 2.6 million. Diseases of the heart and cancer killed the most people, accounting for 46% of all deaths. Compared to 2012, age-adjusted death rate for these two diseases decreased only slightly.
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However, cancer and heart diseases were not the leading causes of death among all age groups. Using data from the National Vital Statistics System, I made the GIF above which shows that remarkable differences emerge between what kills young and old Americans when divided into 5-year increments. Younger Americans die disproportionately from preventable causes like accidents. The share of deaths caused by accidents peaks among those in their early twenties, making up 42% of the 19,006 deaths of people between 20 and 24 years of age. The two overall leading causes of death — heart disease and cancer — made up only 8% of those deaths. Cancer and heart diseases only begin to eclipse everything else after Americans reach their mid-forties. Cancer predominates between the ages of 45 and 80 and as Americans reach the century mark, they start to die primarily from diseases of the heart.
The chart on the lower half of the GIF shows the total number of deaths in each age group. It illustrates how many more old people die than young people. This explains why cancer and heart diseases can be the overall leading causes of death yet not be the most common cause of death for young people.
Why this is important
Despite the fact that cancer and heart diseases kill substantially more people than other diseases or causes, a Gallup poll from last year found that 17% of Americans thought the Ebola virus — which killed 2 people in the U.S. in 2014 — was the third "most urgent health problem facing this country at the present time." Cancer and heart disease, on the other hand, polled at 10% and 2%, respectively. This is a classic example of the availability heuristic, which states that people reach inaccurate conclusions when they rely only on the most conspicuous data. In this case, the threat that Ebola posed to Americans received an excessive amount of coverage from American journalists and politicians — despite the fact that it's a ridiculously hard disease to catch and the crisis is still primarily concentrated in West Africa.
In order to accurately evaluate the risk certain diseases present to us, we need to pay more attention to the numbers and less to the hype.
Last week, reddit hosted an AMA with Chris Ingraham of the Washington Post, David Yanofsky of Quartz, and Ritchie King of FiveThirtyEight. These three data journalists fielded a variety of questions, but this one caught my eye: "Do you say data is or data are?" Yanofsky deferred to his employer's style guide: Quartz uses "data are." Despite expressing a preference for "data are," King is forced to use "data is" by his tyrannical boss, Nate Silver. Ingraham takes a more agnostic approach, preferring to play it by ear.
As you can see in the chart below, however, Americans are definitive in their preference. By an overwhelming majority, 77% of respondents chose "data is" over "data are" in this sample sentence: "Some experts say it's important to drink milk, but the data is/are inconclusive." Though wrong, a fifth of people surveyed selected "data are." (I explain why they're wrong below.)
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Among all respondents, they were nearly equally divided between those who said they spent time considering if the word "data" was a singular or plural noun. Unsurprisingly, though, "data are" users were over 30 percentage points more likely to say that they have thought about this topic before. And like any good grammar pedant, "data are" users were 24 percentage points more likely to care "a lot" or "some" about this debate than "data is" users.
These results come from a poll Walter Hickey, King's colleague at FiveThirtyEight, commissioned from SurveyMonkey Audience last summer. The sample size for the survey was 1,129 people. You can see the complete results here and read Hickey's original post about it here.
Some other differences emerge between people who thought data was singular and those who thought it was plural. Younger respondents were the most likely to use "data is." Those over 60 were the least likely to. Nearly a third of the most educated used "data are." Perhaps this is explained by the fact that people with graduate degrees work in professions that are more likely to encounter the word datum, data's rarely seen singular form. For instance, the American Psychological Association's style guide requires followers to treat data as plural and therefore use "data are." Regionally, respondents residing in the West were the most likely to use "data are," while those in the Northeast were the least likely to.
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Now here's the reason why people who chose "data are" in the sample sentence are wrong. In the context of the sentence, "data" functions as a mass noun. Mass nouns — if you remember what your grade school English teacher taught you — cannot be counted since it comes "in variable but conceptually undifferentiated quantities," as Professor of Linguistics at the University of Pennsylvania Mark Liberman notes. Thus, like other mass nouns, data is singular. If you substitute a synonymous mass noun like "evidence" in for "data," it only makes sense if you use "is" (i.e., "but the evidence is inconclusive" vs. "but the evidence are inconclusive").
Now this isn't to say that "data is" is always and exclusively correct. There are many instances outside of scientific or technical writing were "data are" could be considered grammatical. Then again, linguist Geoff Nunberg contends that just because "data are" could be considered correct, it doesn't also mean you don't sound ridiculous saying that. As he wrote on Language Log,
My own view is that there are contexts where it’s okay to treat data as a plural, but none in which you can’t treat it as a singular—and that contrary to what many “reasonable” usage writers counsel, this isn't simply a matter of “style and personal preference.” As the Economist example shows, there are times when treating data as a plural makes you sound not simply like a pedant but a fool.
But I'm sure many of you would vehemently disagree.
Americans Think Nursing Is The Most Honest Profession
By David Mendoza - Monday, January 19, 2015
Since 1999, among the 43 jobs that Gallup has asked about, no profession has received a higher evaluation from Americans on its standards of honesty and ethics than nursing. The most recent results from Gallup show that nurses remained on top in 2014. Eighty percent of Americans said nurses had "high" or "very high" standards of honesty and ethics. That's slightly down from last year when 82% of Americans said the same thing. Despite that negligible dip, nurses are still far ahead of other professions such as bankers, lawyers, and business executives.
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In general, Americans tend to rate favorably healthcare professionals. Large majorities said medical doctors and pharmacists also had high ethical standards. Politicians, on the other hand, were perceived very poorly. Less than a quarter of respondents rated local and state officeholders as highly honest and ethical. And members of Congress remain the most reviled profession in America, ranking last among all jobs evaluated.
The chart above displays results from 2013. Unfortunately, Gallup's results from last year only included 11 professions. So I decided to use data from 2013 instead, which asked about 22 professions and compared that to percentages from 1999.
About the design
In "The Visual Display of Quantitative Information," Edward Tufte created the slopegraph, originally dubbed the table-graphic. A slopegraph is essentially a line chart, but with no gridlines or axes. Tufte, a proponent of minimalism, believes that data visualizations should focus entirely on the data eliminating all unnecessary elements from the design. Tufte's innovation is the inspiration for my chart above. Unlike Tufte's original design, though, I added color to help distinguish between lines, marked the end points with dots, placed labels only on the left side, and included faint gridlines. I also split the chart into two to avoid having too many intersecting lines.
In 2006, Radley Balko published "Overkill: The Rise of Paramilitary Police Raids in America," a prescient white paper given the current debate over police militarization. In it, Balko discussed the increasing use of police raids in America since the 1980s, describing scores of cases of police wrongly targeting people who were either innocent or nonviolent offenders. Balko's work confirmed that these transgressions are not aberrations, but the consequence of the overuse of police raids and inadequate oversight. As Balko concluded, "The truth is, mistaken raids continue to happen with disturbing regularity. They can’t all be isolated incidents."
Since the publication of "Overkill," the Cato Institute has continued to collect examples of botched police raids and map the results. Cato's map, shown below, certainly displays significant information, but its purpose is not immediately apparent. Cluttered with so many colorful pins, the map fails to show any compelling trend. Matthew Ericson, Deputy Graphics Director at The New York Times, faults results like this on the axiom "since the data CAN be mapped, the best way to present the data MUST be a map." Instead, Ericson urges designers to avoid mapping data like this because it doesn't show an interesting geographic pattern.
Map by the Cato Institute.
As shown at the top of the page, the data can be revisualized more effectively as a Sankey diagram. The redesign reveals several facts that the original map does not. First, it shows that there have been at least 379 botched raids since 1985 — or more than one a month for the last 29 years. Second, raids on innocent suspects represent the most common mistake. Third, California and New York tied for the most botched raids. However, this is probably because these states are, respectively, the largest and third largest states in the country.
I should acknowledge that the data is limited in many ways. Most importantly, as Balko noted, it is "by no means comprehensive." For instance, the Cato Institute recorded no botched raids in 2012 and 2013, which is an unlikely occurrence. In a recent study by the ACLU, it examined more than 800 SWAT deployments between 2011 and 2012. They found that at least "36 percent of SWAT deployments for drug searches, and possibly in as many as 65 percent of such deployments, no contraband of any sort was found." I also found a few errors in the data. Three examples of a raid on an innocent suspect — one in Florida, one in Georgia, and one in Iowa — were counted twice. One incidence in Alabama incorrectly identified the year a botched police raid was conducted in.
Note: "When Police Raids Go Wrong" is derived from "Botched Paramilitary Police Raids" by the Cato Institute, used under CC BY-NC-SA 3.0. "When Police Raids Go Wrong" is licensed under CC BY-NC-SA 4.0 by David Mendoza.
Hundreds of Police Killings Go Unreported to the FBI
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By David Mendoza - Monday, December 15, 2014
Earlier this month, Rob Barry and Coulter Jones of the Wall Street Journal reported that large discrepancies exist between the number of police killings recorded by police agencies and the number of killings reported to the FBI. Between 2007 and 2012, Barry and Jones discovered that 105 of the largest police agencies in America recorded a total of 1,822 police killings, but reported less than 68% of those deaths to the FBI. These results lead the authors to the grim conclusion that "it is nearly impossible to determine how many people are killed by the police each year." In the wake of several deadly police shootings this year, numerous publications have arrived at the same appalling conclusion.
Police agencies have a variety of explanations for why these inconsistencies occur. Several agencies like the Fairfax County Police Department just decide not to report any police killings to the FBI, which the federal government doesn't actually require them to do. Fairfax County defended its decision by telling the Journal that it didn't believe justifiable police killings were an “actual offense” that should be reported. In other cases, inadequate or outdated technology prevents all agencies in Florida and New York and most agencies in Illinois from reporting any data.
However, among agencies that do report police killings, some of the discrepancies shown in the chart above can be explained by the fact that police killings are reported by the agency where the death occurred. For example, as the Journal noted, "the California Highway Patrol said there were 16 instances in which one of its officers killed someone in a city or other local jurisdiction responsible for reporting the death to the FBI." This reveals why it only reported 3 — or 16% — of the 19 police killings it recorded in 2012. In other instances, this also explains why other agencies reported more killings than their officers committed. Pennsylvania State Police were only responsible for 16 of the 30 killings it reported to the FBI between 2007 and 2012. The other 14 were committed by outside agencies, according to the Journal.
Regardless of the source of the irregularities in the FBI's data, it demonstrates why we need a law that mandates police agencies to report all police killings it commits each year. Otherwise, we'll be stuck with this flawed and inaccurate measure, leaving us perpetually in the dark about this important trend.
Update: I'd like to thank Hayley Munguia at FiveThirtyEight for featuring my post on this week's Ctrl + ← recap.
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Americans Don't Know the Violent Crime Rate Keeps Falling
Click here to enlarge this image and click here to see the trend in property crimes.
By David Mendoza - Monday, November 24, 2014
Last week, Gallup released a poll that showed a majority of Americans believe crime increased last year. Sixty-three percent of Americans incorrectly said there was "more crime in the U.S than there was a year ago" — only 21% correctly said there was less crime. According to the FBI, the number of violent crimes reported fell 4.4% last year compared to 2012. The reported violent crime rate per 100,000 people fell by even more, 5.1%. Similarly, the rate of reported property crimes declined by 4.8% from 2013 to 2012.
The graph above charts the percent change in violent crime for each year and compares it to the percentage of Americans who said there was "more crime" in the previous year than the one before. For instance, in 1996, the reported violent crime per 100,000 people declined by 7% — one of the largest declines in the past two decades. However, according to 71% of Americans, there was more crime. In fact, 14 out of 20 times that Gallup asked this crime question, Americans got it wrong. The exceptions are show in black: 1989, 1990, 2000, 2001, 2005, and 2006. The same trend emerges for property crimes. Americans incorrectly said crime went up 17 out of 20 times, when property crime had actually declined.
Reported violent and property crimes have declined yearly since 2000. The overall drop in crime has been even more substantial. Since 2000, the reported violent crime rate declined 27.4% and the property crime rate fell 24.5%. Despite these amazing statistics, Americans keep contradicting reality. Below is a chart by Gallup showing the persistent belief among Americans that crime keeps increasing.
It's a well-known fact that the United States is awful at electing women. Even though 63% of Americans told Gallup that we would be "governed better" if we had more female officeholders, women remain a minority of elected officials at all levels of government. According to the Center for American Women and Politics, they constitute a minority of governors (5%), mayors (18.4%), national representatives (18.2%) and senators (20%). This is woefully inadequate, especially considering that women comprise 51% of the American population.
Women do slightly better in state legislatures, though: out of 7,383 state lawmakers, 1,789 are women — or just over 24%. As the GIF above shows, the United States has come a long way over the last 35 years in terms of female representation at the state level. In 1979, only one-tenth of state legislators were women. By 2014, female legislators have yet to achieve parity with their male counterparts anywhere, but had increased in total numbers by 132%. The closest any state has come to an equal split between female and male legislators is in Vermont. The Green Mountain State's legislature was composed of 41.1% women in 2013 — more than twice the percentage of women currently in Congress.
Although women get elected to state legislatures at higher rates, this progress has stagnated. Between 1979 and 1989, the number of female state legislators increased by 65%. Between 1999 and 2009, it increased by only 8%. The line chart below shows how the percentage of women in state legislatures peaked in 2010 at 24.5% and then declined the following year for the first time in 4 decades. During the last five years, the percentage of women serving in statehouses across America has flatlined around the low- to mid-twenties.
Click here to embiggen this image.
The poor political representation of women in our country has no passive solution. We can't expect that one day women will make up 51% of elected officials if we just wait long enough. In fact, a projection by the Institute for Women's Policy Research (IWPR) found that if the current drift towards equality continues in Congress, women will not be proportionally represented until 2121 — over a century from now. Of course, the IWPR study dubiously assumes that women will make steady progress from now until then. Long-term linear projections like this are rarely correct because as the previous chart showed, such steady progress is unrealistic.
Chart from the Institute for Women's Policy Research.
So how can we get more women elected? As reductive as the answer sounds, it's by getting more women to run. The obstacles women face are simply less formidable than many potential female candidates perceive them to be. According to Jennifer L. Lawless, associate professor of government at American University, women do as well as men in terms of money raised and elections won. Additionally, though negative gender stereotypes may have historically prevented women from getting elected, research by Kathleen Dolan, political scientist at the University of Wisconsin-Milwaukee, indicates that today sexism plays a smaller role in elections. "There is no evidence that voter beliefs about the abilities and traits of women in the abstract lead voters to evaluate individual women candidates differently than their male opponents," Dolan revealed earlier this year. The gender of a candidate, Dolan found, sways a person's decision to vote for a candidate less than other more salient factors. As Nora Caplan-Bricker of the National Journal eloquently put it: "Faced with flesh-and-blood candidates, voters' biases largely faded into the background. Instead, voters were swayed by things like incumbency, campaign spending—and, above all, partisanship."
The lack of political ambition, however, has hindered women from winning more elections. Men and women differ substantially in terms of their desire to run for elective office. As Lawless and her coauthor, Loyola Marymount University political scientist Richard L. Fox, explained in a study from 2012, "the fundamental reason for women’s under-representation is that they do not run for office. There is a substantial gender gap in political ambition; men tend to have it, and women don’t." Yet, this can be overcome by more effective recruiting by political parties. In 2009, the Center for American Women and Politics studied how to increase the number of women in state legislatures. The authors discovered that many qualified and talented women just needed to be asked to run. Fifty-three percent of female state representatives and 46% of state senators reported they had not "seriously thought about running until someone else suggested it" — compared to 43% of male representatives and 42% of senators who said it was "entirely" their idea to run. This disparity goes a long way to answering why women are so poorly represented politically.
It's crucial that more women are elected to state legislatures because nearly half of female members of Congress and American governors began their political careers there. Equally as important, it would make the grotesque male chauvinism described by Sen. Kirsten Gillibrand less likely to happen.
