TIGER road clumps

I pulled together some info I wanted to share: what is the longest connected group of untouched TIGER imported roadways in the U.S.? The answer is this 1258.6km group of ways in Brooks County, Texas. And then below will be the rest of the info I found. I haven’t done anything with it, I just wanted to share.

Note: the Overpass queries I link directly to are all relatively quick since they’re just lists of way IDs. But for some of them I link to pastebin since they were too long to pass in a url.

Overpass Turbo - Brooks County, TX

Idea

The full results are here as a spreadsheet but I also want to explain.

I was looking at the results of the query for unedited ways and nodes in an area near me and noticed there were way more untouched roads than I thought there would be.

All the roads I’ve looked at in my area were pretty detailed and accurate to my eye. But then I noticed the unmodified ways were mostly separate from each other, only connected by roads that HAD been edited in the last 15 years.

So a large number of unedited roads in an area can mean they were good to begin with? As long as they’re not still touching at least?

Then, where is the other extreme?

Method

So what is the farthest distance you can travel only on mapped roads unmodified since about 2008? The answer is it depends on the state you’re in.

Basically I ran the “TIGER unmodified ways and nodes” query for each state and wrote a program to go through the results and group ways by connections. There is probably an easier and smarter way to do it but I couldn’t find it and so I got to have fun creating my own. I can share the code if anyone cares but it isn’t very clean.

I also ran the same query as before again with one change. I replaced the last line with:

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node(way_cnt.result:2-)->.result2;
.result2 out geom;

This returns just the nodes connecting two or more ways. I used this info to ignore all of the nodes only belonging to a single way, which greatly sped up my horribly inefficient program trying to match 10s to 100s of thousands roads to each other by common nodes.

Once grouped by connections, I calculated each group’s total length using the latitude and longitude of their nodes.

Another note: this data does not include any individual ways, only connected groups of at least two. Also I made no attempt to find connections across state lines.

Finally, the data

Here’s everything I found in spreadsheet form. The file is about 5MB. Columns are: state, number of connected ways in the group, total length of ways, and then a comma-separated list of way IDs you can view by pasting them into the following Overpass query:

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[out:json][timeout:25];
way(id:WAY_IDS_HERE);
out geom;

There were no results for RI, PA, MA, HI, or DC. Also if you want to replicate this, don’t query all of MO, OK, TX, or VA in one go. You’d probably need to split up AL, KS, CA, and NM too. There are too many results. I assumed built in limits would stop me from hurting anything by trying to run too big a query. If that’s wrong, I’m very sorry for taxing the Overpass server when I ran all of this.

Some of the results visualized:

The 10 largest (longest) groups in each state:

Query text on Pastebin // Higher res

Every group longer than 100 km:

Query text on Pastebin // Higher res

Between 10 and 100 km:

This query was too long even for Pastebin… Higher res

Everything 10km and up in blue and 100km and up in orange using mapshaper:

Higher resolution

One 535km group in Rawlins County, Kansas that is clearly visible on satellite and at a quick glance looks ok but I haven’t checked how the tags on it are:

Overpass Turbo - Rawlins County, KS

A 61km group in Greenbrier County, West Virginia in a forested area that could still be spruced-up? (pun intended, but I don’t actually know if this is a an example of needing work or not)

Overpass Turbo - Greenbrier County, WV

I hope some of this was helpful. Thank you for reading!