It’s a great move for bicycle advocacy and helps increasing awareness about cycling as serious urban transportation mode. According to that blog post on the Google Lat Long Blog, the Google Maps engineers developed some solid bicycle routing algorithms. The data is apparently coming from Rails-to-Trails, public sources and Google’s Street View and Imagery. In the areas I viewed, all data was (c) Google, which is, after we saw the parcel layer (c) by Google, not surprising at all.

Nice side effect: bicycling is available in Google Maps API V3 (which recently made it into OpenLayers).

Even though the bicycle layer looks pretty impressive at a first glance – lot’s of green lines in the Boston area – it’s clearly in beta stage. For my daily commute it suggested three basically realistic alternatives, only with 1 to 3 blocks detours from the route I usually take. My usual route includes going up a quiet one-way street which Google doesn’t suggest, instead it would send me to the car packed 4-lane highway.

Playing around with the marker, the routes get a little funkier:

1. It’s not the shortest/quickest route. Bike trails are clearly weighted very high in the algorithm.
2. You’re allowed to ride through the Boston Common, but not through the Public Garden as the algorithm suggests; missing restriction.
3. The shown bike facilities in Cambridge are pretty messy and Boston looks way better than it actually is – Mass Ave preferred for bicycling? That’s one of the craziest streets you could possibly ride on.
4. At Kendall the algorithm clearly gets confused with some turn restrictions and would send you up and down the street.

Despite those few glitches, Google has done an amazing job in introducing bicycle transportation to Google Maps – very exciting!

At least it was a cop on a bike, so we were all somewhat equal. The first cyclist tried to fight it, claiming the light was green. “Here is a $20 fine that you can fight”, said the cop, who apparently was not in arguing mood at all that morning. I and the cute blond girl switched to “Yes Sir, sorry Sir” mode and got away with a warning. Or maybe it was just the cute blond girl effect.

My favorite: “Car Alarm Over 10 Minutes” for $300 – nervous car security systems can be expensive in The People’s Republic of Cambridge!

My personal highlight was the first session I attended, about Open 311. It’s clearly not my core area, but I’m interested in the current development and felt that I learned a lot in that session. One of the most interesting points during the discussion was the evaluation of “Resistat”. Resistat is an initiative to include residents in 311 statistics in Somerville. It works very simple: a mailing-list facilitates communication and statistics and results are sent out to residents as powerpoint files. Not rocket science, but it’s enough communication technology that even though only about 25% of involved residents have been to in-person meetings, 85% of them say that they feel better engaged with their local government (by sending powerpoints to a mailing list, it’s as simple as that!). Anyway, great insights when talking about the “town hall meeting divide”, can’t wait to read the entire study about the program.

Our session about Open Data Strategies was “merged” together with I think 5 other sessions that had the word “data” in the title. Unfortunately it didn’t really work out as we intended it and the discussion went somehow all over the place. Better luck next time I guess.

The last session I picked was all about data visualization. Two young IBM researchers showed and demoed amazing data visualization tools – Many Eyes and sense.us among them. Their latest project, called “IBM Visual Bill explorer”, should make it easy for citizens to visually explore and analyze legal texts. Tremendously valuable when trying to understand or to find potential pitfalls in 1000+ pages documents written by lawyers, as bills usually are.

On a side note: I had absolutely no idea that IBM is running such a great research department. Where are marketing departments when you really need them?

Happy hour – free beer was involved – went straight to talking GIS and Open Source. As it turned out, there are very similar problems across gov agencies (surprising, huh!). One notion during the discussion was, that, instead of throwing money individually at our problems, why not join forces and contribute to and customize Open Source projects where we all benefit from? Interesting thought, will be continued…

import flickrapi
api_key = '1234567890'
flickr = flickrapi.FlickrAPI(api_key, cache=True)

…replace some dots with underscores in the Flickr API methods…

photos = flickr.photos_search(tags='boston', lat='42.355056', lon='-71.065503', radius='5')

…and loop through the parsed results…

for photo in photos[0]:
	print photo.attrib['title']
	photoLoc = flickr.photos_geo_getLocation(photo_id=photo.attrib['id'])
	print photoLoc[0][0].attrib['latitude']
	print photoLoc[0][0].attrib['longitude']
	photoSizes = flickr.photos_getSizes(photo_id=photo.attrib['id'])
	print photoSizes[0][1].attrib['source']

…done.

The above code example lists title, latitude, longitude and thumbnail-source of photos found in a 5km radius search around the Boston Common.

Bicycle routing is in my opinion far more complex than car routing. Car routing is mostly based on well known and documented rules, also known as road traffic regulations. Mix in estimated traffic figures, average speeds and fuel consumptions and you get pretty decent car directions.

For cyclists, a similar rule set exists, but it’s maybe a little more, let’s call it, elastic. Cyclists use short-cuts, turn where cars can’t, go against traffic, ride through parks and on poorly documented trails. High traffic doesn’t mean slowdown for cyclists. They ride by on the bike lane on the right side of a traffic jam at almost the same speed as without traffic. But high traffic creates a security risk some cyclists aren’t comfortable with taking and rather choose a different route.

A perfect route from A to B for speedy messengers doesn’t necessarily mean it’s also an ideal route for kids. For your daily commute you probably pick another route than for weekend rides, even though it connects the same points.

Bicycle routing criteria is manifold, sometimes psychological, hard to measure and to quantify. Researching how cyclists are going, for what purpose and under what conditions, is a very smart way to get started on that topic.

How?

There is this great library PROJ.4, that does everything you’d ever want in terms of cartographic projections. A few smart people have ported PROJ.4 to JavaScript, called Proj4js then.

Proj4js works great in combination with OpenLayers, a popular JavaScript web mapping framework, and allows on-the-fly projections between any spatial reference systems browser applications.

<script src="proj4js-compressed.js"></script>
<script src="http://openlayers.org/api/OpenLayers.js"></script>

Define the spatial reference you’re planning to use. Check Spatial Reference for the exact projection parameters and include them in your code.

Proj4js.defs["EPSG:26986"] = "+title=Massachusetts Mainland NAD83 +proj=lcc +lat_1=42.68333333333333 +lat_2=41.71666666666667 +lat_0=41 +lon_0=-71.5 +x_0=200000 +y_0=750000 +ellps=GRS80 +datum=NAD83 +units=m +no_defs";

Adding all desired projections to the OpenLayers script…

projOSM = new OpenLayers.Projection("EPSG:900913");
projWGS84 = new OpenLayers.Projection("EPSG:4326");
projMassGIS = new OpenLayers.Projection("EPSG:26986");

map = new OpenLayers.Map ("map", {
	maxExtent: new OpenLayers.Bounds( -20037508.34, -20037508.34, 20037508.34, 20037508.34),
	maxResolution: 156543.0399,
	units: 'm',
	projection: projOSM,
	displayProjection: projWGS84,
	allOverlays: false
});

osm = new OpenLayers.Layer.OSM(
	"OpenStreetMap",
       "http://tile.openstreetmap.org/${z}/${x}/${y}.png"
);

openspace = new OpenLayers.Layer.WFS("Open space", "http://giswebservices.massgis.state.ma.us/geoserver/wfs", {
	typename: "massgis:GISDATA.OPENSPACE_POLY"
}, {
	projection: projMassGIS
	attribution: "<a href='http://www.mass.gov/mgis/'>MassGIS</a>"
});

…results in an interactive map with MassGIS Open Space WFS vector features overlayed on an OpenStreetMap base layer, using WGS84 lat/lon as display coordinates.

On a sidenote: OpenLayers comes with a Python proxy to retrieve information from remote servers via an XMLHttpRequest. Here is a good how-to get Python play well with IIS 6 on Windows Server 2003, which was quite useful.

Don’t forget to add the domains you’re trying to access to the Python proxy. For MassGIS you would add following string for instance:

allowedHosts = ['giswebservices.massgis.state.ma.us']

To bring some map content from ArcMap to Raphaël I used the VBA Macro I wrote 4 years ago in ArcMap. It still does the job and gives me clean vector graphics the way I want them. I couldn’t find a decent SVG export option for QGIS, although there are some efforts to improve that kind of functionality.

AsSVG, a Python geoprocessing script for ArcGIS is pretty good too. It provides some nice export options, such as pick style and data attribute fields, and I actually ended up using it a lot.

However, it’s 2009 and there are other ways available for sharing code then just providing a plain text file. So I ended up wrapping a bitbucket repository around it. Just in case if somebody is interested in working on or improving the script…

• OpenStreetMap is successfully based on open crowdsourcing, a horizontal multi-directional work-flow model, to build and maintain the world’s largest free geospatial database.
• Open crowdsourcing helps to collect local knowledge across your residents, improve local geospatial data, engage residents and provide a 24/7 feedback loop for them.
• Wide variety of data and information distribution: OpenStreetMap allows output from raw data access for developers to print map renderings for tourists.
• Built-in data interoperability: no matter how many or in what part of the world people are contributing to the project, it all fits together to one piece.

Bottom line: towns should take a serious look at OpenStreetMap and the underlying model. It’s proven to work in many places and provides some valid points town administrations can benefit from.

There should be videos of all presentations online at some point. My colleagues Holly and Chris talked about our 3D video game/planning participation project in Chinatown and about the 10 most wanted data sets (and one state GIS department at stake) we would like to see to for better planning decisions in the Metro Boston region.

Update: Videos of Ignite Spatial: Boston are now available on YouTube. That’s me, struggling through the format

GM’s strategy, in a nutshell, was to pull public transport into a vicious circle. GM’s funding apparently helped to buy up local transportation companies, mainly with the goal to reduce their service. Cutting down quality led to loosing riders. Bad service and decreasing ridership caused troubles justifying public money and investments in public transportation. The downward spiral was completed and public transportation stagnated or was even shut down entirely in some places.

The really sad aspect of that story is, that America’s public transport was just about the same level as in European countries in the 1950’s. It could have made the same development as it did in the rest of the industrialized world during the past 50 years. It could have reached a status, were people are still free to choose their preferred transportation vehicle. Most Americans nowadays are left with only one choice for their daily commute: the car.

It’s hard and expensive for America to catch up and repair what GM’s lobbying has caused. Even though the highway lobbying might be less in the year 2009, car companies found new, mostly ridiculous, ways to use public tax money for their own interest and to eventually sell more cars: IntelliDrive is definitely among them.

Their motivation is quite simple: as public agency they collect, produce and hold lots of data and information. Eventually they want to see this information and data out there used by and helping people through services and applications. Instead of putting to much energy in internal developments, they decided to approach developer communities and ask what they’d need to build applications around EOT data. A very smart move if you ask me. They save costs on their side and attract a big creativity and innovation potential from a broad developer community at the same time.

The first result of that initiative several open data feeds, posted on the EOT developers page. If you’re interested in (Massachusetts) transit data you should do two three things:

1. check out the EOT developers page
2. join their Google Group for getting support or leaving feedback
3. and sign-up for the Open Government Hack Day held on Sep 27th, hosted by BetaHouse in Cambridge.