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Explore the diagrams here.

Did you know that different translations insert section headings at different places in the Bible text? Some translations might want shorter sections to break up the text into more-easily digestible units, while others may prefer fewer sections to better preserve the flow of thought.

This project takes twenty English Bibles (BSB, ERV, ESV, ISV, NCV, CEB, CEV, CSB, GNT, GW, LEB, NABRE, NASB, NCB, NET, NIV, NKJV, NLT, NRSVue, and REB), identifies where each section starts and ends, and presents the aggregated data.

Specifically, it uses Sankey diagrams to plot section breaks for each book of the Bible. For example, here’s the diagram for Ruth (also in png format):

Here’s how to read this diagram: The height of each solid bar indicates the number of translations with a heading at that verse. Lighter bands emanate from each bar to where the section ends. For example, from 1:1, you can see a small band that ends at 1:7, larger bands that end at 1:14 and 2:1, and a much-larger band that ends at 1:6. The size of the bands shows the number of translations. So we can see that most translations treat 1:1-5 as a single section, and they start a new section at 1:6. Then, starting in 1:6, there’s much more variety in how long the sections are (you can see that the bands fan out to five different vertical bars).

What can we learn from this visualization? The high bars at 1:1, 2:1, 3:1, and 4:1 indicate that translations insert headings at the chapter breaks in Ruth. (Ruth is unusual in this respect; most books don’t break so cleanly and unanimously.) In chapter one, you can see somewhat-large divisions at verses 6 (Naomi hears about God’s work) and 19 (Ruth and Naomi arrive in Bethlehem). But other translations pick different divisions in chapter 1: verse 7 (Naomi starts heading out to Bethlehem), verse 8 (Naomi asks her daughters-in-law to go back, verse 14 (Ruth clings to Naomi), verse 16 (“Where you go I will go”), and verse 18 (Naomi stops asking Ruth to go back). And still other translations don’t break up chapter one at all. So different translators see different moments as deserving headings, which shapes how you read the text.

Similarly, in chapter four, many translations see 4:13 as a turning point (when Boaz officially marries Ruth). The bar at 4:18 is showing that some translations have a heading for David’s genealogy, but most don’t.

Lamentations is another favorite. Some translations make the acrostic structure visible to the English reader through headings, but most don’t:

Is this kind of analysis helpful? I’m not really sure. And the data complexity for most books—Ruth is manageable, but longer books are less so—is perhaps pushing Sankey diagrams past where they’re useful. But explore and decide for yourself. As usual, the data is freely available to download under a CC-BY license. I used SankeyMATIC to generate the Sankey diagrams; you can click through to SankeyMATIC to interact with the diagrams by highlighting certain bands and moving things around.

See all the Sankey diagrams here.

Update: to follow on with my previous post, here are two AI-generated podcasters discussing these diagrams. The part where they discuss Exodus is especially interesting to me, since I don’t discuss it in the text. The only way they’d draw their conclusions is by looking at and understanding the Sankey diagram for Exodus, knowing that Exodus 32 is about the golden calf, and interpreting it as they do. It’s impressive. Listen here.

Try Bible Verse Photo Composites. Move your mouse over the image to see a photo composite for a particular verse, and click to see composites for the words in that verse.

An example:

Here’s the idea: Use the Flickr API to find photos matching each of the words in the Bible. Then download the photos for each word and layer them on top of each other to produce a composite image for word. Once you do that, layer the important words from each verse on top of each other to produce a composite image for each verse.

Then put all the verses together in sequence to create the orangeish image you see above. About 300,000 images comprising 13,000 words make up the image. Each verse occupies about six pixels.

Technical Background

For layering the photos, I wanted the brightest (most-saturated) colors possible; I used a simple formula. It finds the difference between the brightest and darkest channels in a particular pixel. The brighter colors will tend have bigger differences.

function weight_pixel($pixel)
{
$rgb = array(( $pixel >> 16 ) & 0xFF, ( $pixel >> 8 ) & 0xFF, $pixel & 0xFF);
sort($rgb, SORT_NUMERIC);
return $rgb[2] - $rgb[0];
}

This formula differs from the usual conversion formula from RGB to HSL. I didn’t like the results of the RGB-HSL formula as much; the images were slightly darker.

Then I placed the darkest pixels on the bottom layer of the composite image, layering brighter (but about 50% transparent) pixels on top. The results for each word resemble abstract art:

(11) (begrudge) (liquid) (sharp) (waterfalls)

I did a similar procedure to produce verse composites, layering the important words from each verse on top of each other. The results are generally less spectacular (in my opinion) than the word composites, as many of the verses look like each other. I’d like to explore other compositing algorithms to try to differentiate the verses more. (Feel free to leave a comment if you have any suggestions for an algorithm!)

(Gen 1:2) (Josh 3:10) (Matt 5:4)

The hardest part was the waiting. It took a long time to download the 300,000 images, and nearly as long to process them all. Plus, it takes a while to upload half a gigabyte of data after processing the images.

The verses composites omit images for about 200 common words (as listed in the Crossway Comprehensive Concordance of the ESV). I didn’t want common words to overwhelm the important ones.

From a coding standpoint, this project let me try out the jQuery Javascript library, which I’ve been wanting to do for some time.

Future Directions

Well, the result is awfully orange. As I said above, I would’ve liked to see some more differentiation. (I tried a few different formulas but didn’t come up with anything that works much better.) You can see some bands that are more orange than others, but I’m not sure how significant they are.

It would be interesting to try to calculate cross-references based on the similarity of the verse composites—are verses that look alike actually similar?

Any work of literature would lend itself to this kind of project. Project Gutenberg has lots of public-domain e-texts available. It would be interesting to compare the composite footprint of, say, Moby Dick to the Bible’s.

Inspiration

The inspiration for this project comes from the 80 Million Tiny Images project by Antonio Torralba, Rob Fergus, and William T. Freeman at MIT. They created a map of nouns in the English language by downloading images from search engines, combining the images, and then arranging them into a graphic based on the words’ semantic distance from each other. Fascinating stuff. Via ReadWriteWeb.

Try it out.

Screenshots

All the occurrences of the word said in the Bible. Note the narrative bands in Genesis, the Old Testament history books, and the gospels.

Occurrences of the words father and son. Clusters in Chronicles and a couple of places in the New Testament show where to find the genealogies.

Occurrences of the words Moses, David, and Jesus. It’s easy to tell where the main stories about each person are.

Background

Part of the Similar Diversity work includes a visualization of the word you in various holy books. This visualization provided me the impetus to produce something more interactive for the Bible.

How It Works

First, I counted the number of words in the ESV Bible (767,847, including headings but excluding footnotes, if you’re interested).

Next I assigned each word in the Bible a unique, incremented number. Then it was just a matter of going through the positions and grouping them by word. The result is a database table with two columns: word (varchar(18)) and positions (mediumtext). The positions column consists of a space-separated string of numbers.

When you enter a search query, the program finds the positions of words matching your query and then plots those positions on a chart. (The chart is 1/4 the size it would be if each word position got one pixel; it would have to be 1,083×709 pixels at a one-pixel-per-word ratio.)

The Code

The code is in PHP, using the GD library. I decided this project would be a good time to try out the SQLite database that comes with PHP instead of going with MySQL. I have no complaints, though Perl creates incompatible tables with current PHP versions (5.2.4) unless you use PDO in PHP to access the tables. It worked fine after a bit of Googling revealed the workaround.

Limitations

Since the chart is 1/4 full-size, each pixel represents four words, and each dot occupies nine pixels. Given the coarse resolution, it’s best to use the locator to identify trends and then switch to the Bible text for further analysis.

It doesn’t do exact matches, only beginning-of-word matches. Only want to search for Eve, omitting results for evening? You’re out of luck.

It would be interesting to be able to click a dot and see the context of each occurrence.

Introducing Labs

Launching this tool gives me an excuse to launch the new Labs section of this site, which houses small, one-off experiments like this one and the Chapter Browser.