Religious buildings (churches, mosques, synagogues, temples, and other places of worship) often have an intentional orientation, largely to assist with fixing the direction people face when praying. The altar in Christian churches is often pointed toward the liturgical east. Islamic mosques are traditionally oriented toward the Qibla (direction of Mecca).

For these calculations, I selected five countries that are dominated by five different religions (Thailand – Buddhism; Italy – Catholicism; Israel – Judaism; Pakistan – Islam; India – Hinduism). The shapefile containing the Israel buildings was merged with Palestine, which is predominantly Islamic. Though these could be separated, the exact border between the two countries is a bit tenuous, so I opted to leave it as a single region.

The method for the calculation is shown on the graphic. For each building footprint, a bounding rectangle is defined. This rectangle is oriented to minimize its width. The orientation of the building is then measured as the azimuth of the rectangle’s height (longer sides). Orientation is counted in both directions, so a building facing due east is also considered to face west. The plots show the frequency of a given orientation in 5° bins.

As you can see, most religious buildings in these countries are aligned east-west. Pakistan is slightly north of east from Mecca, which may explain why many of the religious buildings there are orientated WSW-ENE.

Data source: http://download.geofabrik.de/

If you’re a government employee, your salary usually depends on your position and years worked.  But it also depends heavily on the state in which you live.  Each pair of maps illustrates average annual salaries (extrapolated from one month of full-time payroll in March, 2012) for a specific government function; state government salaries (left-hand maps) are compared to local government salaries (right-hand maps).  The scale applies to all maps, so any map can be compared to any other.

Some of the differences are striking because the roles are fundamentally different.  For example, in education, most local government employees are grade school teachers, whereas state employees are university professors. Others may be due to how governments allocate their resources or emphasize the importance of a given function. For example, Nevada seems to fund local parks more heavily than other states.  Perhaps it’s time for Leslie Knope to leave Pawnee…

Data source: https://www.census.gov/govs/apes/

This map shows the percentage of each county’s civilian population (aged 18+) who have veteran status. For Census data, as shown here, the term “veteran” is based on the Department of Veteran Affairs’ definition:

A veteran is someone 18 years and older (there are a few 17-year-old veterans) who is not currently on active duty, but who once served on active duty in the United States Army, Navy, Air Force, Marine Corps, or Coast Guard, or who served in the Merchant Marine during World War II. There are many groups whose active service makes them veterans including: those who incurred a service-connected disability during active duty for training in the Reserves or National Guard, even though that service would not otherwise have counted for veteran status; members of a national guard or reserve component who have been ordered to active duty by order of the President or who have a full-time military job. The latter are called AGRs (Active Guard and Reserve). No one who has received a dishonorable discharge is a veteran.

http://www.census.gov/prod/2011pubs/12statab/defense.pdf

Based on this definition, military personnel on active duty are not counted as veterans, but several paths of active service qualify people for veteran status. As such, military bases will have a notable effect on this map, though not as significant as they could (if those on active duty were counted). For each of the top counties list, I included the names of the largest military installations in the respective county.

Data source: 

http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml (table S2101)

Disclaimer – I’ve never read any of the Game of Thrones series (though I do enjoy the show).  But my wife has, and has the Jezebel knowledge to make a more informed comment on this graphic.  So, in her words…

George R. R. Martin claims that he is a feminist, but there has still been ample debate over whether his popular series, Game of Thrones, reflects those values. While the women of Westeros may strive for power just as the men do, a word-frequency analysis of the books thus far reveals a clear bias towards male terminology. One interesting pattern? In books 4/5, “girl” starts to overtake “boy.” We just may be seeing a future queen.

Data source: https://archive.org/details/1.AGameOfThrones

For all the gamers who love the classic arcade games, here’s a map to guide your Pac-Man efforts. Darker areas are considered more dangerous, as you have a farther distance to travel before reaching an intersection. The lower half of the board is generally considered more difficult to clear, particularly the bottom row, where it is easy to be trapped by ghosts on either side.

There are a couple caveats/glitches, which affect this map. One is that there are four intersections (blue outline) where the ghosts cannot turn upward in scatter and chase modes.  Only when frightened can they turn upward at these intersections. There is also the infamous safe spot (hiding spot), outlined in green, where you can sit indefinitely without being touched assuming the ghosts didn’t see you move there.

Happy gaming!

Data source: http://home.comcast.net/~jpittman2/pacman/pacmandossier.html#CH2_Home_Sweet_Home

Back when I was a graduate student, I spent a lot of time as a chess coach and tutor. Because I was teaching at a K-12 school, parents of some of the younger children asked if their kids were ready to play chess. It’s an interesting question, and the answer is different for each person. In general, my experience was that most students could easily learn how the pieces move when they were five, but it took another year before they understood how the pieces work in combination (i.e., strategy).

I was curious about the age at which most board games can be played, so I gathered data from BoardGameGeek (link below). These graphs include data on the 100 board games with the highest number of voters, which I’m interpreting as a proxy for popularity.

In the top left graph, I compare the manufacturer’s suggested minimum age to the players’ suggested minimum age, which is determined by a poll on the website. Out of the 100 games, the ages are the same for 50. In 34 cases, the manufacturer has an older minimum, and in the remaining 16, the players selected an older minimum.

The top right graph shows the slight trend that longer games are generally designed for older players. I’ve noted that it means older players have a longer attention span, but of course, a lot of it has to do with the complexity of the game and strategy, not just how long the player can stay seated at the board.

The bottom left graph suggests that players tend to give newer games higher ratings. I’m not sure I agree that we are creating better board games than we have in the past; as I said, I’m a big chess fan (as well as go…and I’m pretty partial to Carcassonne, which came out in 2000). But there is often a tendency for people to correlate newer with better.

The bottom right graph confirms that popularity is not indicative of quality. At best, this graph shows a weak positive correlation, but many of the highest rated games do not appear in the 100 most popular list.

Data source: http://boardgamegeek.com/browse/boardgame?sort=numvoters&sortdir=desc

These graphs were created by dividing the girls growth chart by the boys growth chart. Lower percentiles (blue lines) represent smaller (shorter/lighter) people, while the highest percentiles correspond to larger people. The yellow line represents the typical person (50th percentile = median). Note that the y-axis scales are different for the two graphs; weight ratios vary more significantly than height ratios.

The graphs shows that girl and boy babies are born at close to the same size, but boys get larger more quickly in the first six months of growth. The ratios then increase, approaching one as girls catch up in size.  Around the ages of 7-9, the typical girl is about the same size as the typical boy. Then girls hit puberty first, and from 10-13 they are generally larger than boys. Buy once boys hit puberty, they catch up quickly, and become taller and heavier after the age of 13.

Data source: http://www.cdc.gov/growthcharts/

To create this map, I derived the average color of each state flag and assigned it to its respective state. Only the obverse side of each flag was used. The notion of average color depends on the color space used; for the purpose of this map, I used a Lab color space (CIELab D50). The background is set to the average color of the USA national flag.

It’s interesting to see that bluer averages tend to be associated with northern states.  Redder hues (including purples and pinks) are more southern, and particularly focused in the southeast. The states that chose not to conform to red, white, and blue also stand out (e.g., Washington, New Mexico, Maryland, New Jersey).

Data source: http://flaglane.com/category/american/

Theoretically, visiting a dentist should help you keep your natural teeth. Then again, the lack of a strong correlation between the two variables shown here suggests otherwise. Perhaps those who have worse oral health actually visit the dentist more often to address the issues they’ve encountered. After all, in order to have all your natural teeth extracted, you have to visit the dentist, right?  Unless you do it at home, which is a really bad idea.

Despite the lack of correlation, I think the maps are still interesting to view independently. You can identify states and metropolitan areas where people don’t visit the dentist. Or you can determine how likely you are to be donning complete dentures later in life.

Data source: http://www.cdc.gov/brfss/ (2010 GIS data)

I made this graphic over a year ago, but it included defunct wheels.  It was reposted on reddit today, and given that Las Vegas now has the biggest wheel in the world, I decided to make a quick update. This will only be current for so long, because both NY and Dubai have plans for larger wheels.

Data source: http://en.wikipedia.org/wiki/Ferris_wheel

These maps show the density of submarine communications cables around the world. The top map displays unweighted line density, while the bottom has been weighted by bandwidth (e.g., Gbps).

The data come from Greg Mahlknecht’s website (see link below). He states that the accuracy of the layout varies, as he was able to obtain exact routes for some cables, but other routes are schematic depictions based on landings. In addition, he generally excludes cables below 1 Gbps. It should also be noted that some of these cables are defunct, or are not yet activated. So these maps should be considered generalizations of global submarine communications; they are not exact, but provide an overall sense of the layout.

Data source: http://www.cablemap.info/

Happy World Cup watching, everyone!  This graph represents all historic scoring data.  Looks like Brazil wins by outscoring their opponents.  Who would have thought?

To be consistent with the data source, I kept each listing separate, even though some would merge them (e.g., West Germany and Germany).

Data source: http://www.worldcup-history.com/index.php?siden=statistikk

I’ve been thoroughly enjoying Mario Kart 8.  Sure, the battle “arenas” are terrible and the lack of a mini-map is a little disappointing, but the HD is amazing and the tracks are well designed.

I’ve been watching ghosts to improve my track times and thought it would be fun to do a graphic on world records. Because there are going to be new records daily for MK8, this graphic looks at world records for Mario Kart Wii. Note that I’m using non-shortcut records – players have accomplished much faster times using some remarkable glitches.

Most of the time, the record holders use one mushroom per lap, and the consistency among lap speeds for a given track is incredible. The first lap is typically the slowest (24 of 32 tracks) and the last lap, the fastest (17 of 32 tracks). Looking at the graph, you can easily distinguish the tracks where the driver used all mushrooms on one lap (Mario Circuit and Dry Dry Ruins). On GCN Peach Beach, the driver uses one mushroom on the first lap, and two on the last lap.

Data source: http://www.mkwrs.com/mkwii/ (YouTube videos)

There are a lot of cows in the US. The headcount at the beginning of 2014 was about 87.7 million, which is a notable decline from ten years earlier, when it was close to 95 million. Still, it’s an impressive number – approximately one cow for every 3.6 people, or 0.278 cows per capita. These data include both cattle and calves.

For this graphic, I’ve mapped the cattle per capita values by state. No surprises here: the states in the middle of the country, which are generally less populated and home to many farms, have the highest values. South Dakota leads the pack, with 4.32 cows per person, followed by Nebraska at 3.29. I also graphed the population values on a scatter plot for those interested in the raw data. From this, you can see that Texas has the most cattle at 10.9 million.

Data source: http://usda.mannlib.cornell.edu/usda/current/Catt/Catt-01-31-2014.txt

When it comes to making data visually engaging, sometimes nature does the work for you. That’s the case with these scatter plots, which simply show how attributes of solar eclipses vary as a function of latitude. The data come from the supplementary material for “Five Millennium Catalog of Solar Eclipses: -1999 to +3000” (NASA/TP-2009-214174), which includes 11,898 past and future solar eclipses. Similar to my Earth’s spinning speed graphic, I’ve plotted latitude on the y-axis for geographic purposes, even though it should be thought of as the independent variable in most of the graphs.

I’ve defined some of the terms on the graphic to help with the interpretation. If you aren’t familiar with the concepts, check out the second data source, which is a good resource for understanding solar eclipses.

Data sources:

Eclipse Predictions by Fred Espenak (NASA’s GSFC)

http://eclipse.gsfc.nasa.gov/SEpubs/5MKSE.html

http://eclipse.gsfc.nasa.gov/solar.html