I’ve been out of the Cincinnati transit scene for a while now so I was surprised to see today on the Interwebs that the Better Bus Coalition has put out a new “Better Bus Map” based to some degree on the frequent transit map that got me started on this blog. There certainly is a family resemblance, but this is a whole new creation, and its designer, Mark Samaan, has clearly put a lot of work into it.
Follow the link for a closer look. It updates my now somewhat out-of-date frequent transit map, and adds more information, especially on destinations like grocery stores, shopping centers, etc. that were squeezed out of my map by the need to fit it on an 8.5″x11″ page.
My deepest hope for this map is that it stays updated longer than mine did. While I have no idea what Mark’s or the Better Bus Coalition’s financial situation is, I can say that I never really updated my map because it takes SO MUCH TIME and no one was finally willing to pay for the work in a consistent way. I had rent to pay and life goes on. I hope SORTA, the Chamber, the Better Bus Coalition, the University, or some other institution can see the value in what might be termed “attractively topological transit mapping” and will pony up the dough needed to keep this not only on life support, but keep it updated, improved, and circulated in coming years.
And what if everyone had an even probability of visiting some other fellow cyclist who lived between 3 and 8 kilometers away from them? This would be a beautiful and strange world. Here is what the traffic might look like in that world, assuming there were no effects of congestion. Thicker lines have more bikes:
Line thickness is scaled to the log of a measure of betweenness, based on optimal paths for bicycles, as defined according to current OSM data and OSRM‘s default bicycle routing profile. ‘People’ were located randomly inside their 2010 home census block and routes were calculated between random pairs of people where the straight-line distance between them was between 3 and 8 kilometers. The distance limits are to simulate reasonable cycling trips and work against MAUP effects.
This is the first step in a project to develop mode-specific street hierarchies, which can be used in transport maps where auto-based classification schemes are undesireable or unavailable. In the coming days, I’ll work on a better weighting scheme (than population density) and look at other modes and cities. I’ll be working the results into a poster for NACIS 2017, showing the different hierarchical classifications that result for cycling, walking, and driving modes, hopefully across three cities with widely different development patterns (Cincinnati, Toronto, …?)
Headsigns are trimmed and included. There is still some more cleaning to do though. Headsigns are quite inconsistent and I’m trying to just pull out prominent destinations.
Heading arrows have been dimmed and placed behind the text headings. These only show up after all of the shapes have loaded. Clearly, there are some legibility issues right now, but I think the positioning is the way I like it and legibility can maybe be resolved with color changes.
I’ve removed the randomly placed ‘vehicle locations’ that I’d been playing with. For now ;-)
Thoughts and comments much appreciated! If you follow the link above, you’ll be taken to a page where you can select stops. Pick one or a few and hit the button that says ‘use selected stops’. Then wait for something to go wrong and then tell me about it.
I said when I was working on the bike map that I would get around to publishing the ‘source code’, and now that I’m preparing to leave the city, I feel I ought to finally do that. So! The basic idea of this post will be a step by step instruction for how to make yourself (or your city) an updated Cincinnati Bike Map. Strictly speaking, this will work for other cities too, but please note that the map was designed for Cincinnati in 2014 and other cities in other times, with different (data) structures may simply not work well at all.
Let’s get started!
Step 1: Get the software you’ll need: QGIS, PostGIS(a PostgreSQL extension), osm2pgsql, osm2po, GRASS, PHP(for one of the scripts), and Inkscape and GIMP for the final layout. All are free and open source and run on Linux (but probably other things too).
Step 2:Update the data! A big and important step. The vector data is all from OpenStreetMap and the process of editing OSM is well documented elsewhere, so I needn’t go into it at all here.
Step 3: Go to OpenStreetMap and navigate to the area you want to download. Be generous and include at least ten extra miles on all sides of the map you’ll be making. Click the ‘export’ tab and use the ‘Overpass API’. It will prompt you to download a large .osm XML file to your computer.
Step 4: Import that data into a PostGIS database twice: once with osm2pgsql and once with osm2po. The first will bring in the OSM data as-is, with as many tags as you care to import. To do it the way I did it, you should use this osm2pgsql.style file. The second one, osm2po will slice the linear path data (the streets and stuff) into a table of routable nodes and edges. For that one, you may want to try this configuration file. If that doesn’t work, the real point of it is to include paths that bikes can use (paths, pedestrian streets, stairs, etc), which are not included by default, while leaving out the rest.
Step 5: Process the data from osm2pgsql using this SQL script. It does quite a few things, including setting (short) street labels, calculating speed in mph, setting default speeds and lane-counts for no-data streets, identifying landmark buildings, and pulling a number of features into a consistent format for better/easier rendering.
Step 6: Run this SQL script to merge the two tables you’ve imported into one table that is both routable and has all of the important attributes/tags from OSM.
Step 7: Run tarjan.php on the new/duplicate segments table. This script uses Tarjan’s Algorithm to identify edges that connected at both ends to the main street network, and those that are not, leaving the results in a boolean field on that table.
Step 8: Once the dangling edges are identified, run this SQL script to drop the minor paths that go nowhere. Major dead-ending streets will be kept. Things like driveways will be dropped.
Step 9: Get the elevation data. I used data from the USGS (use their national map tool to download). I found that of the two decent resolutions available, one was too course (I could see pixels) and the other was too fine (I could see buildings). I chose to smooth out the finer data, using a neighborhood average in GRASS. I suppose you could also go at that the other way though too, increasing the resolution and then smoothing. The point is to get an amount of detail that just looks right and doesn’t have any visible pixelation: use your gut!
Step 10: Now you have all the data ready to go in your PostGIS database, and you just need to drop it into QGIS and style it. I wish things were easy enough that I could share a simple stylesheet with you; the way QGIS does it, the style information is all bound up with information about the table. That means that if your table/database/column/everything names are different from the ones I used, you’re going to have trouble making this run smoothly. In the interest of giving something here though instead of nothing, I’ll link to the QGIS map files used to render the main and inset maps (hills, transit, and trails). These may not be directly useful, but you could look at them as XML files, and see precisely how things were styled including line widths, hex colors, etc. It may also be useful to sample colors directly from the digital version of the map using something like GIMP. Once ready, export these maps as 300+DPI rasters using the following templates: main map, 1/3 scale inset maps.
Step 11: Now we have the base maps, we’re finally ready for the layout! I did the layout in Inkscape SVG, linking to the exported raster maps which I placed in an adjacent directory. You’ll have to re-link those, but the frames should still be in the right position.
Step 12: Profit.
Well, that’s about the gist of it. … I don’t actually think that hardly covers it, but there’s not enough time in the world to document everything for an uncertain future that may or may well not contain good bike maps. And anyway, I don’t expect anyone to slavishly duplicate my approach. We’ll call it a limited edition ;-)
If someone does actually want a real update though, I’m always available to answer questions, or if you’re the type to cut right to the chase, for hire. Email me!
Well, it will also be up for a week or so after that, but tomorrow is the night with all the people and the free food. If y’all haven’t been to DAAPworks before, it’s DAAP’s senior show, and it’s actually got a lot of interesting work in the areas of transport, civic design, cartography, and architecture1.
My quick run through the building today, on my way to see the cranky ogres who run the plotting machines in the photo cave, dredged up at least five truly gorgeous maps, most of those local, and two of them definitely using OSM! It’s so rarely we get to see such pretty things. Here, to whet your desires, is a map from a DAAPworks of a couple years past, not my own, that I think is just incredible.
Like this one, a lot of the maps that are most beautiful don’t actually make much sense if you think about them too hard, or even a little. But such things, maps, landscapes, transit plans, buildings, are worth seeing, I think, because they remind us of the potential for grace and beauty in all things, good, bad, or nonsensical — and perhaps also of the excitement of the mind untamed by repetitive mental labor and cautious hedging.