My Airport History: Toledo (TOL)

I’ve written before about the “doughnut zone,” which is the area that’s generally too far from home to fly out of, but too close to fly to. At about three hours away by car, Toledo falls easily within that zone.

Nonetheless, I did manage to take a single trip out of Toledo back in 2011. I had a work trip immediately before the 4th of July weekend that year, when I was planning to meet up with friends in South Bend, Indiana. Since I was planning to return from my work trip and immediately drive from the airport to South Bend without stopping home first, I realized that I could realistically fly out of any airport between Dayton and South Bend equally easily. Toledo had substantially cheaper flights for my trip than Dayton, so I asked my employer and got approval to take the cheaper flights out of Toledo instead.

I didn’t find the Toledo airport itself to be particularly memorable; it was rather small, which made sense given its proximity to the much larger Detroit (DTW).

GPX and KML downloads added to Flight Historian

My Flight Historian has always used Great Circle Mapper for its maps. However, I’ve occasionally had the need to use other map formats, like GPX (used by many of my mapping projects) or KML (used by Google Earth).

A while ago I wrote some internal methods in Flight Historian’s code to let me generate GPX or KML files. However, they weren’t available for public use.

I’ve changed that with a small update today. Every map on Flight Historian now has download links immediately below it for GPX and KML versions of the map.

Creating Song Lyrics Graphs

A couple of months ago, I wrote myself a tool which could take a text file of song lyrics and generate an image showing how frequently each word appeared in the song (like a word cloud, where more frequent words were larger), and which words followed which words (unlike a word cloud, since it had arrows between the words).

After trying it on quite a few different songs, I came up with the idea of feeding it a very repetitive song, such as the road trip song 99 Bottles of Beer.

A directed graph of the lyrics of "99 Bottles of Beer," with words as nodes and edges between subsequent lyrics.

Yesterday, I decided to post this image to the Reddit r/dataisbeautiful community, and it received a lot of interest. I’ve had some people ask how I created an image like this, which this post will try to answer.

Directed Graphs

While I’ll try to keep from getting too technical, one thing we need to understand is that this song lyric image is a directed graph.

Simplified, a directed graph is a bunch of nodes (the circles, each with a unique word of the song) and edges (the arrows showing how the words are related).

For example, an edge (arrow) from “99” → “bottles” means that “99” comes just before “bottles” in the song lyrics.

I can create a directed graph with a (free!) tool like yEd Graph Editor, which lets me draw nodes (circles) and drag edges (arrows) between them.

The first two lines of the song – “99 bottles of beer on the wall / 99 bottles of beer” – in graph form

So with this alone, I could create an entire song lyrics graph, but it would take a very long time – there are thousands of words in all ninety-nine verses of the song, so I’d have to draw thousands of arrows.

Automatically Generating a yEd Graph

To save time, I want to be able to take a text file of song lyrics and automatically convert it into a yEd document.

yEd files are in a format called GraphML. Here’s a sample of a very simple graph, and the GraphML that describes it:

A simple graph, with the following nodes: 99, bottles, of, beer. Arrows join these nodes in that order.

Lines 1–6 tell us that this is the start of a GraphML document, and lines 17–18 end the document. What we care most about is the nodes (lines 8–11) and edges (lines 13–15).

You can see that each <node> has an id. Each <edge> has a source (where the arrow comes from) and target (where the arrow points to), and they use those same node ids. So, for example, <edge source="99" target="bottles"/> means “draw an arrow from the node with an id of 99 to the node with an id of bottles.”

Notice that each node can have multiple edges, so we only need to define each word as a node once – even though “bottles” is used hundreds of times throughout the song, we only need a single node with an id of bottles, and then we can refer to it with as many edges as we need.

Effectively, what I need to do is create a script which will loop through the lyrics text and create a <node> for each unique word. Then I need to go back through the lyrics and, looking at each pair of adjacent words, create an <edge> between them.

The resulting code is my song-lyrics-graph Python script. It’s built using the basic concept above, though it has some additional features too – plain vanilla GraphML doesn’t allow things like specifying the size of nodes, but yEd adds extensions to the GraphML document that let me do that.

As long as Python is installed on your computer and you’ve downloaded my script, you can drag and drop a .txt file of song lyrics onto the file, and it will generate a .graphml file with a directed graph of your song.

yEd Layouts

My script does generate all the nodes and edges, but it doesn’t position them in a pretty layout – the file it generates will just have all the nodes on top of each other.

Screenshot of yEd

Fortunately, yEd has a layout engine that will try to figure out a good arrangement of the nodes. Open the Layout menu, and you’ll see a large selection of layouts to choose from.

Screenshot of yEd's Layout menu

For most songs, I’ve found out that the Tree / Balloon layout seems to work best, though you can certainly experiment with the others.

When you select Layout / Tree / Balloon, a set

Screenshot of the yEd Balloon Layout settings menu. Root Node Policy: Weighted Center Root. Routing Style for Non-Tree Edges: Straight-Line. Preferred Child Wedge: 200. Preferred Root Wedge: 360. Minimal Edge Length: 10. Compactness Factor: 0.5. Place Children Interleaved and Straighten Chains is checked, all other checkboxes are unchecked.

Again, you can play around with the settings to try to make the graph look good, but these are the settings I usually use.

Click OK, and yEd will arrange your nodes as it sees fit.

From there, you can export your graph as a .png image by using the File / Export menu!

Converting GPS Data Between GPX and KML

Part of the GPS Mapping Tutorials series.

GPX (GPS Exchange format) and KML (Keyhole Markup Language) are both file types used to store GPS data. While many applications can use either file formats, Google products (Google Earth, Google My Maps) tend to prefer KML, so it’s often helpful to be able to convert between them.

(Note that both .kml and .kmz file extensions represent KML files; the latter is just a zipped version to reduce file size.)

This tutorial will teach you how to convert between GPX and KML (in both directions) using GPS Visualizer.

Continue reading “Converting GPS Data Between GPX and KML”

Extracting GPX Files From a Garmin Automotive GPS

Part of the GPS Mapping Tutorials series.

This tutorial will teach you how to record route data on a Garmin automotive GPS and extract it into a GPX file (which can then be used by mapping software).

I wrote this tutorial using a Garmin DriveSmart 50 LMT. However, I’ve had success using the same steps with other variations of the Garmin nüvi and DriveSmart series.

Continue reading “Extracting GPX Files From a Garmin Automotive GPS”