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Out of Context: Memorial Day Mentions

**From time to time I’ll post something quick that lacks the context, but is something I found interesting, and might be worth examining later on.**


Memorial Day is coming up in a few days, and granted as a veteran with a large social media network of other veterans it’s a big topic. But I wondered…. do people talk about it more or less now then they used to?

Well, here’s a brief look at that idea. Below is a chart made through Google books’ n-gram search. This shows how popular a word or phrase was in books and magazines published each year. We can use this to see how often “Memorial Day” was used.

The holiday’s origin is a bit interesting and judging by the wikipedia entry doesn’t have a clear beginning, but the story is intriguing. take a look. Also realize there’s a few “citation needed” notes in the history, so take it for what it’s worth.

On May 26, 1966, President Johnson signed a presidential proclamation naming Waterloo, New York, as the birthplace of Memorial Day. Earlier, the 89th Congress adopted House Concurrent Resolution 587, which officially recognized that the patriotic tradition of observing Memorial Day began one hundred years prior in Waterloo, New York.[17] According to legend, in the summer of 1865 a local druggist Henry Welles, while talking to friends, suggested that it might be good to remember those soldiers who did not make it home from the Civil War.[citation needed] Not much came of it until he mentioned it to General John B. Murray, a Civil War hero, who gathered support from other surviving veterans.[citation needed] On May 5, 1866, they marched to the three local cemeteries and decorated the graves of fallen soldiers.[citation needed] It is believed that Murray, who knew General Logan, told Logan about the observance and that led to Logan issuing Logan’s Order in 1868 calling for a national observance.[citation needed]

That’s it. Have fun on this Memorial Day weekend. Remember the men and women who died fighting under this nation’s flags. Put a flag on a veteran’s tombstone. Give a toast the ones who didn’t come home, and enjoy the country they died to protect.

Or, maybe mess around with Google’s N-Gram search yourself.


Allen Iverson’s scoring as a percent of his team’s points, compared to the best in the NBA


In honor of Allen Iverson’s official retirement ceremony today…

I’m sharing this heatmap I put together of some of the top scorers in the NBA over the last decade. The darker the box, the more total team points he scored. Obviously this isn’t the only way to quantify how good or bad a player is. And yes, good players on bad teams might be asked to score a lot more than good players on good teams. And yes, yes, some teams might create offenses designed to facilitate a single player scoring and other coaches might use a system that spreads the ball around.

But this graphic isn’t meant to declare Iverson the best, but hopefully it puts into context how much the 76ers’ offense came from his hands.

Graphic created with R, RColorBrewer library and Adobe Photoshop

Data source:



Minimum Wage Adjusted for Inflation: 21 past increases in today’s dollars

In response to some of the chatter about the federal minimum wage I decided to look at it in a more objective way. The wage has been raised 21 times since it was first introduced at $0.35 in 1938. My contribution is the below chart, which looks at each time the wage was raised and considering the inflation-adjusted value of that wage in 2013 dollars. The red bars represent the new federal minimum wage as set by congress adjusted for inflation into 2013 dollars.


This chart displays the federal minimum wage (black) and the inflation adjusted value each of the 22 times federal laws have changed the minimum wage (red). The light blue shading in the background connecting is the yearly inflation adjusted wage in 2013 dollars.

In the last 25 years when congress has voted to raise the minimum wage, they kept pushing it into a level that equates to a $7-8 rate, often coming back to raise it again, and again to keep it in that place. Prior to the (1950s-1980s) congress kept setting the wage into an equivalent of $9 hours.

Below is a spreadsheet with the federal minimum wage as set by law, and the 2013 inflation-adjusted dollar amount.

CBO Report

The Congressional Budget Office published a report about the miniumum wage, and while plenty of news outlets and blogs have decided what the “news” of it is, I urge anyone who wants to make an assessment of the idea of a new minium wage to at least skim the document. It’s 17 pages with lots of visual illustrations of the different effects of either a $10.10 or $9 wage. We’re lucky that we live in an age where these non-partisan government analysis documents are published online for any of us to pull down and look over. You don’t need some talking head, newspaper column or blog telling you what it says. I tried to pull a few points from the paper that show the variety of effects from the change and don’t lead to a positive or negative conclusion.The full report is here,

  • Once fully implemented in the second half of  2016, the $10.10 option would reduce total employment by about 500,000 workers, or 0.3 percent, CBO projects.
  • 16.5 million people are paid less then the proposed $10.10 minium wage and would receive a raise.
  • 900,000 people would leave the poverty threshold due to the increased wage. In 2016, the CBO projection for the poverty threashold for a family of four is $24,100.
  • Overall real income for all workers would increase $2 billion after accounting for all increases and decreases due to minimum wage change.
  • CBO concludes that the net effect on the federal budget of raising the minimum wage would probably be a small decrease in budget deficits for several years but a small increase in budget deficits thereafter. It is unclear whether the effect for the coming decade as a whole would be a small increase or a small decrease in budget deficits.
  • Deficit reductions would come from less federal spending for workers receiving an increased wage. Spending on cash and near-cash transfers (ie, food stamps) would decline for those workers because they are tied to income levels.

Inflation calculations

The calculation was made by using CPI to create a deflator value (CPI nominal year / CPI 2013 year) and then dividing the wage by that value to get an estimate of what the wage would be in 2013 dollars.  The actual values are provided in an embedded spreadsheet below the chart. There are of course well-known issues with using inflation, but I’ll let the Bureau of Labor and Statistics explain why I’m using it here (source):

The “best” measure of inflation for a given application depends on the intended use of the data. The CPI is generally the best measure for adjusting payments to consumers when the intent is to allow consumers to purchase, at today’s prices, a market basket of goods and services equivalent to one that they could purchase in an earlier period. It is also the best measure to use to translate retail sales and hourly or weekly earnings into real or inflation-free dollars.


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Today is Open Data Day. You should care.

“‘Open Data, proactively disclosing City data, is the foundation of Open Government, is consistent with citizens’ right to public information’ and has benefits to government service delivery.” City of Austin, TX, RESOLUTION NO. 20111208-074, Dec. 8, 2011.

Today is Open Data Day, and it’s a big deal. Open data is the pouring of information from dusty government clerical shelves out onto the information superhighway. It’s part transparency and part relevancy. Each day, your government at a local, state and national level collect information. I’m not talking about a nefarious big brother. I mean the sometimes boring, sometimes tedious logging of things like potholes, traffic accidents or dangerous dogs. Computers allowed this information to move from ledgers into databases. Government analysts could dig through the files to find the most dangerous intersections or offer insight into how economic policy was affecting an entire industry. Open data is the next step forward. It’s the idea of hosting these databases online so everyone in the world can look it over. The internet collective gets a chance to dissect the information and we as a society get a more informed understanding of both the government and our world.

Farmer’s Market

Want to go to a farmer’s market, but not sure where to find one? Worried that a web site won’t have the most updated information? Frustrated at the thought of looking at a list of locations and guessing which is the easiest commute? Open data can fix that.

New York state’s open data page publishes a list with the most current list of every farmer’s market in the state, click on the “visualize” button and…

(Created with Data.NY.Gov’s toolset, Data set:

Foot Traffic

New York City’s open data tells me the Lower East Side gets more foot traffic than Central Harlem, but not as much as Chinatown.

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(Created by NYC.GOV, Data set:

Bad Dogs

Austin, Tx., mapped out every house with a reported “dangerous dog” on a map.

(Created with’s toolset, Data set:

Car Crashes

Denver shows you every traffic accident for the past five years.

(Map displaying last 1,000 accident reports created with Google Fusion Tables. Data set:

More Visualizations

Those are just a few simple examples of the information these governments put at our fingertips. Governments around the world share hundreds of raw data sets and visualized or contextualized data as part of open data initiatives. This brings the vast collections of information governments would make available by request, or that might be accessible in microfiche files in the clerk’s office into digital files that anyone, anywhere, anytime can access. A google search for more visualizations shows a sampling of what is being done with this influx of information. Click the image to see more.

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(Screen shot of a google image search, “open data visualization”)

Comparing Cities

The five cities with the most open data sets (population rank):

  1. New York City (1)
  2. Kansas City (37)
  3. Seattle (22)
  4. Baltimore (26)
  5. Chicago (3)

The federal government and many cities have embraced the idea of open data.  Some have passed resolutions proclaiming their commitment to open data like the City of Austin’s resolution quoted at the top, but not all cities have shown the same enthusiasm. Below is a map with 50 of the largest cities color coded to indicate how many open data sets they offer. If you click on the city you’ll see a link to their collection. (Note: a larger version of the map as a single page can be found here)

(Created with Google Fusion Tables)

Many of the largest cities aren’t leaders in open data. This chart tracking the number of open data sets against the population rank of a city shows there’s an open data effort growing in the second tier group of cities in the mid 20s by population rank.


It’s a big deal

“Openness will strengthen our democracy and promote efficiency and effusiveness in government,” President Barrack Obama, Jan. 21, 2009


When I think about open data there isn’t a single benefit I can rattle off as the reason why I want my government to embrace it. The tools it provides can be useful, but the real significance is what it says about a government embracing the idea of sharing information with its citizens. The city sees citizens, both locally and globally, as partners to make things better. It allows us to be better informed of events (crime, accidents, farmers markets, foot/car traffic), and lets our conversation about those events begin with a smarter first question. The question isn’t IF a street was plowed (that’s updated live), but WHY it took so long. These automated computer systems feeding online databases let us see and display what’s going on. It challenges us to find more useful ways to visualize the information and to put the knowledge to use in our personal or professional lives. For decades this kind of information would sit in a clerk’s office, and an inquiring reporter or shrewd businessperson might walk in and dig into the files. Now, with open data, it’s in our hands and we should celebrate the governments that have made it a priority.

Happy Open Data Day.


For more information on Open Data Day, visit

Open Data Day is a gathering of citizens in cities around the world to write applications, liberate data, create visualizations and publish analyses using open public data to show support for and encourage the adoption open data policies by the world’s local, regional and national governments.


Note: All graphics, visualizations and charts created by me unless otherwise attributed.
NEW YORK --  Sgt. Dakota Meyer, the first living Marine to be awarded the Medal of Honor for actions since Vietnam, reflects during a elevator ride, Sept. 22. (Official Marine Corps photo by Sgt. Randall A. Clinton / RELEASED)

The weight of Dakota Meyer’s medal

He didn’t want it.

This albatross slung around his neck promises to never let him forget the worst day of his life. He carries its weight with him. The combat-hardened Marine seems more affected by this load than any pack he’s carried in Iraq or Afghanistan. The glint of it teases uncomfortable “congratulations” and “good job” platitudes from folks with ignorant minds and misplaced hearts.

“Oooh, I always wanted one of these,” says one. “No you don’t,” he says, almost flinching as the pro athlete smiles and stares at his Purple Heart. The dark humor of the infantry usually labels it the “enemy shooting badge.” It’s presented to any service member wounded in action; not a prestigious award. “Yeah…I just never had the heart for it,” the ignorant athlete continued. He smiled back in a weak attempt to keep his composure. Continue reading


Vignette: The mind of a Combat Correspondent

This country knew him long before his boots sunk into its dusty hell.

After a decade they all look the same. His finger tensing against the trigger reveals his inexperience. Anticipation, not anxiety, pumps a healthy dose of adrenaline through his young body. A lifetime of wanting mixed with too much preparation has left him eager to prove his worth.

Flanked by men bearing arms, he stands out. His weapon is deadly and painless. While the impact of his fellow Marines’ rounds strike immediately, his trigger has no stopping power on the battlefield. While his peers are focused on the vulgar present and all-too-familiar ways this place will keep you from returning home, he is set on the future.

The firefight won’t last long, but regret lasts a lifetime.

Voyeurism spiked with patriotism; the Marine with the unusual weapon heads into the action and begins to fire. Click. With a pull of a trigger the world changes. The intensity of men trying to kill men can’t be described with words, so why try. A picture will speak for those who won’t. His steady hand, his camera, expands the dynamic range of the battlefield. Occupiers and liberators, killers and heroes.

A little less black and white, more gray, more context, more detail.



**NOTE: Way back when I had a sergeant who created an exercise to help us become better writers. He had us each write a 250 word story to explore our writing style. I edited that old piece a bit more and have published it here. I wrote this before I deployed, and therefore was/is not autobiographical.**

Tracking crime against Fordham University students

Every time a Fordham student reports they were the victim of a crime, our school’s campus security office sends out an email blast with a blurb about what happened. The campus is in the Bronx on Fordham Road, and there’s plenty of people who have their own opinion on how safe the area is before even setting foot on campus or in the city. The email blast can play on those feelings if they come in succession. After getting a few in what seemed like a short amount of time I felt like I might make the wrong conclusion about how often fellow students report being victims of crime off campus. I decided that it might be more helpful to look at them on a map, and charted out by time. The plots are color coded by gender, too.

As of Sept. 1, 2013, there have been 15 reports campus security has announced to students for this year. I’ll do my best to keep this map updated with future alerts. If you click on the pins on the map you can read a description of the crime.

Here’s a link to view the map full screen:



It’s important to note that there have been ONLY 15 reports of students as victims of crime since January 2013 (as of Sept. 1). This small sample size should not be used to make further extrapolations, but can be useful in understanding where and when students become victims of crime. I specifically labeled these as students who reported crimes instead of assuming this was a listing of all crime toward students at Fordham. Obviously not all crime will be reported, or reported to both the NYPD and the campus security office to be announced to the rest of the student body.

Keep in mind the words from our campus security office:

Members of the University community are reminded to be aware of their surroundings when traveling off campus, and, as much as possible, to avoid displaying cell phones or other valuable items in public.

For more information visit Fordham Campus Security web site:



  • I’ll continue to update the data for this post. The information here is current as of Sept. 15, 2013 and the last crime report was Sept. 15 at midnight. There have been 22 reports
  • The Fordham Ram newspaper ran an article about this blog post in their Sept. 11 issue, you may find it here
  • I will continue to update the data, the information is current as of Sept 15, 2013.
  • I used Google spreadsheets and fusion tables to create the graphics for this post. I usually use Excel, but I wanted to try out the geomapping functions of the fusion tables and be able to continue updating the information without having to create new artwork. Every time I add a new row of data into my Google Drive hosted spreadsheet it will automatically be reflected in the pie and scatter point graph here. While the graphics aren’t as nice as what came out of Excel, the functionality in this situation should be worth the change.
  • Featured image of Keating Hall, Fordham University, is listed as a creative commons image, by Chriscobar hosted at Wikipedia

The Iraq and Afghanistan Wars were (statistically) different

casualties breakdownfix
The age and rank distributions of the active duty military force was the same during the Iraq and Afghanistan wars, but since I don’t have information from the Vietnam war I choose to compare only our most recent conflicts to each other

Since 9/11 our country concurrently waged two of its longest wars. The invasion of Afghanistan began Oct. 7, 2001. Less than two years later American forces crossed the Kuwait-Iraq border and began the invasion of Iraq. During that time American troops deployed, came home and deployed again for multiple combat tours. Sometimes they returned to the same country or city as their previous deployment. 6,638 (and counting) service members didn’t come home. This post is going to dive deeper into the charts I shared a week ago putting the casualties of the Iraq, Afghanistan and Vietnam Wars in focus by rank and by age.

At the time I mentioned that the lack of some data points and context made it unwise to jump to any conclusions. After seeking guidance from my professors and friends I am able to present this statistical analysis.

I’ve compared the data from Iraq and Afghanistan to see if the distribution (either by rank or by age) is statistically different. I dropped the Vietnam data from the analysis. The Iraq and Afghanistan Wars happened over the same time period so the age/rank distribution for the entire active duty force was the same, I don’t know (but doubt) if that is similar to the force structure during the Vietnam War.

There were more casualties in Iraq than Afghanistan (4,477 vs 2,161 in my dataset), so I used the distributions of the Iraq War deaths to estimate and then test the Afghanistan War deaths using a statistical analysis called Chi-Square. The test requires a null and alternative hypothesis and the analysis demands a level of significance to reject the null hypothesis, and keep the alternative. The rejection of a hypothesis means that it is unlikely that the results would have happened by chance.

Chi-Square: Ranks

Null Hypothesis: The rank distribution of casualties from the Afghan War is the same as the Iraq War.

Alternative Hypothesis: The distribution is not the same

The casualty distribution from the Iraq War is used to calculate expected casualties of the Afghanistan War and the compared with the actual deaths. The red line marks the test statistic for each value. The greater the value the more incorrectly the expected value matched the actual value.

There are 14 degrees of freedom and I’ve used the standard 5% level of significance. The result is a test statistic of 146 and a critical value of 23.68. Since the test statistic is larger than the critical value I reject the Null H. The distribution is not the same.

The greatest variance in rank was in the senior enlisted ranks (E-7 and E-8). These are usually advisers to unit commanders and unlikely to be on regular patrols. They are ranks many troops retire at with names iconic of their service: Gunnery Sergeant (E-7), Chief Petty Officer (E-7) and Master Sergeant (E-8). I’ll come back to the implications of this later.

Chi-Square: Ages

I repeated the test for age distributions where I split the ages of the casualties into four-year blocks.

Null Hypothesis: The age distribution of casualties from the Iraq War is the same as the Afghanistan War.

Alternative Hypothesis: The distribution is not the same

The casualty distribution from the Iraq War is used to calculate expected casualties of the Afghanistan War and the compared with the actual deaths. The red line marks the test statistic for each value. The greater the value the more incorrectly the expected value matched the actual value.

There are 14 degrees of freedom and I’ve used the standard 5% level of significance. The result is a test statistic of 61 and a critical value of 13.36. Therefore I reject the Null H. The distribution is not the same.

The results add weight to our assumptions that the wars produced a different distribution of casualties, but why?

Off hand I can think of two possible causes for the significant difference and why the casualties in Afghanistan have been more senior: Special Forces and helicopters.

More Special Forces in Afghanistan

The below chart is a list of the units with the largest number of casualties, I’ve boldfaced special operations units because they tend to have higher ranking troops out in the field than traditional units. Notice the SEALs have one of the highest casualty rates of all units in Afghanistan.

Units with most casualties (Afghanistan)

  1. 3rd Battalion, 5th Marine Regiment, 1st Marine Division, I Marine Expeditionary Force
  2. SEAL
  3. 2nd Battalion, 8th Marine Regiment, 2nd Marine Division, II Marine Expeditionary Force
  4. 2nd Battalion, 508th Parachute Infantry Regiment, 4th Brigade Combat Team, 82nd Airborne Division
  5. 2nd Battalion, 327th Infantry Regiment, 1st Brigade Combat Team, 101st Airborne Division (Air Assault)
  6. 1st Battalion, 508th Parachute Infantry Regiment, 4th Brigade Combat Team, 82nd Airborne Division
  7. 2nd Battalion, 6th Marine Regiment, 2nd Marine Division, II Marine Expeditionary Force
  8. 1st Battalion, 327th Infantry Regiment, 1st Brigade Combat Team, 101st Airborne Division (Air Assault)
  9. 3rd Battalion, 75th Ranger Regiment
  10. 2nd Battalion, 502nd Infantry Regiment, 2nd Brigade Combat Team, 101st Airborne Division (Air Assault)

Units with most casualties (Iraq)

  1. 1st BN, 3rd Marines, 3rd Marine Division, III Marine Expeditionary Force
  2. 3rd BN, 1st Marines, 1st Marine Division, I Marine Expeditionary Force
  3. 2nd BN, 4th Marines, 1st Marine Division, I Marine Expeditionary Force
  4. 3rd BN, 25th Marine REG, 4th Marine Division
  5. 3rd BN, 7th Marines, 1st Marine Division, I Marine Expeditionary Force
  6. 1st BN, 5th Marines, 1st Marine Division, I Marine Expeditionary Force
  7. 5th Squadron, 73rd Cavalry Reg, 3rd Brigade Combat Team, 82nd Airborne Division
  8. 3rd BN, 5th Marines, 1st Marine Division, I Marine Expeditionary Force
  9. 3rd Squadron, 3rd Armored Cavalry REG
  10. 2nd BN, 2nd Marines, 2nd Marine Division, II Marine Expeditionary Force

Helicopter crashes

Next is a list of the casualties from an Aug 6, 2011 helicopter crash. Thirty American troops died in the crash; 70% of the casualties were senior enlisted ranks (E-6 to E-9). While not every DoD release includes a cause of death, 197 service members have been identified as killed in helicopter crashes. 60 of the 197 (29%) were in the E-6 to E-9 range.

  • Lt. Cmdr. (SEAL) Jonas B. Kelsall, 32, of Shreveport, La.,
  • Special Warfare Operator Master Chief Petty Officer (SEAL) Louis J. Langlais, 44, of Santa Barbara, Calif.,
  • Special Warfare Operator Senior Chief Petty Officer (SEAL) Thomas A. Ratzlaff, 34, of Green Forest, Ark.,
  • Explosive Ordnance Disposal Technician Senior Chief Petty Officer (Expeditionary Warfare Specialist/Freefall Parachutist) Kraig M. Vickers 36, of Kokomo, Hawaii,
  • Special Warfare Operator Chief Petty Officer (SEAL) Brian R. Bill, 31, of Stamford, Conn.,
  • Special Warfare Operator Chief Petty Officer (SEAL) John W. Faas, 31, of Minneapolis, Minn.,
  • Special Warfare Operator Chief Petty Officer (SEAL) Kevin A. Houston, 35, of West Hyannisport, Mass.,
  • Special Warfare Operator Chief Petty Officer (SEAL) Matthew D. Mason, 37, of Kansas City, Mo.,
  • Special Warfare Operator Chief Petty Officer (SEAL) Stephen M. Mills, 35, of Fort Worth, Texas,
  • Explosive Ordnance Disposal Technician Chief Petty Officer (Expeditionary Warfare Specialist/Freefall Parachutist/Diver) Nicholas H. Null, 30, of Washington, W.Va.,
  • Special Warfare Operator Chief Petty Officer (SEAL) Robert J. Reeves, 32, of Shreveport, La.,
  • Special Warfare Operator Chief Petty Officer (SEAL) Heath M. Robinson, 34, of Detroit, Mich.,
  • Special Warfare Operator Petty Officer 1st Class (SEAL) Darrik C. Benson, 28, of Angwin, Calif.
  • Special Warfare Operator Petty Officer 1st Class (SEAL/Parachutist) Christopher G. Campbell, 36, of Jacksonville, N.C.,
  • Information Systems Technician Petty Officer 1st Class (Expeditionary Warfare Specialist/Freefall Parachutist) Jared W. Day, 28, of Taylorsville, Utah,
  • Master-at-Arms Petty Officer 1st Class (Expeditionary Warfare Specialist) John Douangdara, 26, of South Sioux City, Neb.,
  • Cryptologist Technician (Collection) Petty Officer 1st Class (Expeditionary Warfare Specialist) Michael J. Strange, 25, of Philadelphia, Pa.,
  • Special Warfare Operator Petty Officer 1st Class (SEAL/Enlisted Surface Warfare Specialist) Jon T. Tumilson, 35, of Rockford, Iowa,
  • Special Warfare Operator Petty Officer 1st Class (SEAL) Aaron C. Vaughn, 30, of Stuart, Fla., and
  • Special Warfare Operator Petty Officer 1st Class (SEAL) Jason R. Workman, 32, of Blanding, Utah.
  • Special Warfare Operator Petty Officer 1st Class (SEAL) Jesse D. Pittman, 27, of Ukiah, Calif., and
  • Special Warfare Operator Petty Officer 2nd Class (SEAL) Nicholas P. Spehar, 24, ofSaint Paul, Minn.
  • Chief Warrant Officer David R. Carter, 47, of Centennial, Colo. He was assigned to the 2nd Battalion, 135th Aviation Regiment (General Support Aviation Battalion), Aurora, Colo.;
  • Chief Warrant Officer Bryan J. Nichols, 31, of Hays, Kan. He was assigned to the 7th Battalion, 158th Aviation Regiment (General Support Aviation Battalion), New Century, Kan.;
  • Staff Sgt. Patrick D. Hamburger, 30, of Lincoln, Neb. He was assigned to the 2nd Battalion, 135th Aviation Regiment (General Support Aviation Battalion), Grand Island, Neb.;
  • Sgt. Alexander J. Bennett, 24, of Tacoma, Wash. He was assigned to the 7th Battalion, 158th Aviation Regiment (General Support Aviation Battalion), New Century, Kan.; and
  • Spc. Spencer C. Duncan, 21, of Olathe, Kan. He was assigned to the 7th Battalion, 158th Aviation Regiment (General Support Aviation Battalion), New Century, Kan.
  • Tech. Sgt. John W. Brown, 33, of Tallahassee, Fla.;
  • Staff Sgt. Andrew W. Harvell, 26, of Long Beach, Calif.; and
  • Tech. Sgt. Daniel L. Zerbe, 28, of York, Pa.

What does this mean?

They’re different. We can say that the distribution of casualties both by age and rank are significantly different. The greater proportional use (and casualties from) special operations units along with more deaths (proportionally) from helicopter crashes could explain the “why.” Beyond those more obvious causes there are the evolving  battlefield tactics in both theaters over nearly a decade of war in each to consider. Sometimes units were moving house to house in all out urban gun fights or sprinting across poppy fields to close with and destroy Taliban fighters. Other times they were training local security forces, escorting convoys of supplies or just taking a helicopter ride between bases.

The limited number of casualties in Afghanistan over such a long period of time makes it hard to parse down and analyze some elements. If the casualties were broken down by year and by either age or rank single incidents could cause an abnormal skew in the data.

Follow my friends

On the topic of deployed troops, a handful of my good friends are currently deployed in Kandahar Province, Afghanistan serving with Regional Command Southwest. They’re part of the public affairs team telling the story of the Marines on the ground and detailing what the last remaining forces in Afghanistan are doing to prepare their Afghan partners. Follow them on Facebook:

 Data Sources:

casualties breakdownfix

Comparing casualties from Vietnam, Iraq, Afghanistan (a primer)

I’ve been working on this data set for a while and I’ve decided to post a pair of basic charts to share with you what I’m looking at before I’m ready to make any conclusions about it or finished running some other tests on it. It’s a way to keep me blogging and get a chance to think out loud (in an internet age kind of way) about the way I want to approach this information.

Feel free to drop a comment at the bottom with your thoughts. In a future post I’ll get into some of the context, conclusions and narratives to go along with it. Here they are:

First is a chart showing the distribution of casualties from the Vietnam, Iraq and Afghanistan wars by ranks. The shaded area behind the bar graphs represent the current distribution of rank within the active duty ranks. The ranks E-1 through E-9 are enlisted personnel, starting with private to sergeant (E-5) and ending at sergeant major (E-9). The officer ranks are lieutenant (O-1) to captain (O-3) and moving into one, two, three and four star generals (O-7 to O-10). The CWO ranks designate chief warrant officers. They’re a specialized kind of officer commission available for enlisted troops and as the grey shaded area shows, they represent a small fraction of the force.

casualties breakdownfix


This chart shows a comparison of ages of the causalities broken down into four-year age blocks.

casualties by age

This last month marked the 10 year anniversary of the beginning of the Iraq war and the 40th year since the last combat troops left Vietnam. For years I heard talking heads try and compare the two wars as a slander against either political opponent, but I wondered if I could find something a big more insightful in examining them in a more unique way. I pulled down a data set of all American casualties from Vietnam and Iraq and went looking. The biggest challenge I have in looking into data is the limitations of the data sets I’m using. After I cleaned up the data I started asking some questions. Trouble was, the answers to those questions are in variables I don’t have yet. I’d been thinking over whether or not to publish a post with this data for at least a week. I didn’t want the incompleteness of the data or a chart derived from it make an implication that came from missing information. What I decided to do is publish this post more as a question than as a statement.

What are your thoughts when seeing this information?

What questions do you have?


Data Sources:


PS: In honor of our Vietnam veterans here is a multimedia piece I produced while still on active duty about the New York City Vietnam Memorial here in New York City.


Edit : I added a bit more context regarding what the rank abbreviations meant.

Edit (April, 9, 2013): The original rank distribution of casualties was wrong. I mistakenly didn’t account for the Army and Marine Corps having the same rank, private first class, but at a different pay grade, E-2 (Marines) and E-3 (Army). The chart has been corrected in this post.***

Letter to the Editor

Don’t ask us (veterans) if we’re injured

My school news paper published a set of Q&A style interviews with student veterans. In each of them they asked some form of a “were you  injured” question. Infantrymen and mechanics all got the same question. Links to the original pieces are here, here , here, and here with an excerpt below.

Any injuries or wounds?

“A purple heart was one of those things I didn’t want (laughs).”

I contacted the paper staff and was allowed to submit a letter to the editor response. It was published in the March 7th edition of the Fordham Observer.

“What’s wrong with you?” Don’t just think those words to yourself. Say them out loud. Tweet them. Share them with your friends and let the world know. Then ask each other why anybody might think it’s acceptable to ask them to a combat veteran.

Because here’s what that veteran is hearing: “we know something’s not quite right with you, that something broke you over there. Now tell us. Give us your name, let us take a picture or at least be descriptive enough that we can imagine the details.”

I realize the writer of the six-part series on veteran students was, like many Americans, unaware that question such as “Any injuries or wounds?” can be extremely off-putting, painful even, to veterans. That’s why I sought him out to discuss his series in person, to explain that while he had noble intentions—introduce Fordham’s growing population of veterans to the rest of the student body—the question implied some newsworthy element. But as far as I can tell, there was none. What was accomplished by asking?

I spent eight years as a Marine combat correspondent, and I can promise your readers there are countless veterans among us with interesting and inspiring stories. There’s the infantryman studying to become a journalist or the intelligence analyst who could probably be a guest lecturer in his Middle Eastern studies class. Instead, we have awkward questions about injuries— a carnival sideshow approach.

Fordham has one of the deepest military connections of any American university. The patron saint of the Jesuits, St. Ignatius, began his meditations as a wounded soldier lying in bed after a cannonball struck his legs in the early 1500s. Fordham’s reserve officer training program has been training future military leaders since 1929. Among the distinguished alumni are three Medal of Honor recipients and more than a dozen general officers, including Marine Lt. Gen. John A. Toolan, Jr., who served as commander of all coalition forces in southwest Afghanistan in 2011.

There are more than 150 student veterans attending class at the Lincoln Center campus. I urge everyone to reach out and begin a conversation with one of them, and maybe do some digging on your own about the military and veterans. As I write this, there are more than 68,000 troops still deployed to our 12-year-old war in Afghanistan. We don’t need to agree on the war, politics or the decision to serve, but we should agree that deeper understanding of each other doesn’t start with, “Any injuries or wounds?”

I’d appreciate your feedback as well. Have you ever been asked a similar question? How did you respond?