this is aaronland

Zebra in a crosswalk

Stating the obvious one post at a time

Meanwhile, in the So obvious it hurts Department : Timelines for weblog archives. This is still very much a work in progress but gradually I will add them across the board.

Update: I've started experimenting with grouping posts by date but labeling them by tag. It gives a better sense of the shape of things and makes it easier to handle years with (too) many posts. It is also a good tool for spotting posts that are poorly tagged or not tagged at all.

There are a lot of design-y / layout issues that still need to be accounted for, particularly labels that are too long to be displayed properly. There may be ways to tweak things in the JavaScript code but I haven't poked at it too much yet. I was a little terrified to find so much of the CSS inlined but maybe there's a reason for that. We'll see.

Related : a timeline of my Flickr photos from January, 2006.


I think the first thing I ever seriously considered writing in Series 60 Python was an application to talk to Upcoming.

Last winter, I decided to go a Dorkbot meeting for the first time. As is my fashion I didn't bother to write down the directions, instead quickly looking at the address before I left the office and making a ballpark mental map of the location. By the time I'd gotten there I had forgotten most of the details and found myself walking straight in to the heart of the Tenderloin.

The Tenderloin doesn't really bother me, at least not in the way it is talked about it in hushed tones, but I knew it wasn't where I was supposed to be going. I also didn't feel like it was the best place to whip out a laptop and start poking around for wireless hot-spots. I wouldn't have thought twice about getting out my phone, though. In fact, I employed cutting edge mashup technology to call a friend who I knew would be sitting in front of the Interweb and got him to tell me the address.

But really : It's a no-brainer. Why not just sync events in Upcoming with the calendar on my phone? Store the name of the event, an address and phone number and optionally an alarm so that I'm not caught by surprise. That's it. Which isn't much. Until the moment when you need that information — and not, say, your social network's tags for that event — at which point it's eveything.

 upcalendar (0.1) says hello!
 Reading configs
 Testing network connection
 Testing authentication token
 Welcome, straup - upcalendar is ready to go!
 To begin, select 'Merge your watchlist' from the Options menu
 Collecting events on your watchlist
 Found 13 events

 # At this point, the application actually starts to store
 # the events in your calendar. When run in debug (emulation)
 # mode on the desktop it dumps event data to the screen.

 Stern Grove, 19th Ave & Sloat Blvd. (San Francisco) / 415.252.6252
 1156104000 - 1156104000

 Snakes on a mother-f***ing plane
 TBD, check the website (San Francisco)
 1155931200 - 1155931200

 Imaging Environment: Maps, Models, and Metaphors
 Stanford University, Stanford Campus (Palo Alto)
 1163095200 - 1163206800

 Presidential Lecture "Thinking About Blasphemy and Secular Criticism" by Talal Asad
 Stanford School of Education - Cubberley Auditorium, 485 Lasuen Mall (Stanford)
 1160449200 - 1160449200

 # And so on...


Some notes:

Information wants to be fre... in your pocket : upcalendar.

We are all watching each other

I saw Eric Paulos speak at Dorkbot, last week. Like a lot of people, these days, he is thinking about what networked computers all over the place will mean for urban spaces and the people living in them. He demo-ed something called Sashay which is a little application that logs all of the cell towers you pass during the day and then generates images mapping not their location but their relationship to each other.

He had already won me over when he talked about doing something more interesting than another application to find the closest restaurant and I thought : I should build one of those too. So I did.

# I have a lot of time to kill on the shuttle between
# the city and the Valley.

1154620655;bt;00:0e:ed:19:2a:64;00:13:70:6a:f7:b2; \
1154620721;bt;00:13:70:6c:27:ce;00:0e:ed:19:2a:64; \
1154620787;bt;00:0e:ed:19:2a:64;00:13:70:6a:f7:b2; \
1154620855;bt;00:0e:ed:19:2a:64;00:13:70:6a:f7:b2; \
   00:13:70:6c:27:ce;00:13:70:6d:24:55;00:07:e0:07:9a:a7; \
1154620921;bt;00:13:70:6c:27:ce;00:0e:ed:19:2a:64; \
   00:13:70:6a:f7:b2;00:13:70:6d:24:55;00:07:e0:07:9a:a7; \
1154620988;bt;00:13:70:6c:27:ce;00:0e:ed:19:2a:64; \
   00:13:70:6a:f7:b2;00:13:70:6d:24:55;00:07:e0:07:9a:a7; \
1154621058;bt;00:13:70:6a:f7:b2;00:13:70:6d:24:55; \
   00:07:e0:07:9a:a7;00:14:51:d4:a5:af;00:07:e0:22:b7:69; \

Exciting, huh? Most data collection isn't. What you're looking at is a Unix timestamp followed by a data source and either the available cell tower information (gsm) or the list of Bluetooth addresses within range (bt). Here's another way to look at it :

Unsocial Networking #1154758685
Untitled Signals #1154758685

On the left are all the Bluetooth devices that I was nearby during the day, and their nearby-ness to each other. On the right, cell towers. These are not nearly as sexy as the graphs that Paulos showed but you get the idea. The GSM diagram is especially interesting to me since it received no more special magic than anything Graphviz does and is a more than better representation mapping my day : I started in the Mission and went down the 101 to Santa Clara, and then further South for lunch looping back to the office and then finally up and over to the mothership, in Sunnyvale.

I wonder whether that was just a fluke or if it will always be possible to recognize the shape of the relationships. Speaking of relationships, the other other fun bit would be to write a small app to scan the room for available Bluetooth addresses and then associate known devices with individuals in your address book. The when you were crunching your Bluetooth logs you could cross-reference the (device) addresses and display meaningful names instead of Zorgon or Pam's N72. Or, if you did that and also merged their Flickr account data (with your address book) you could : Walk in to a room, scan it for all the Bluetooth addresses with corresponding Flickr IDs, fetch each users tags and...uh...

Profit! is a Series 60 Python application to monitor and record nearby cell tower and bluetooth address information. The data can be stored locally to disk or to a network location over HTTP. It is only a tool for collecting data but since there aren't a whole lot of those out there I thought I would share mine.

Aside from the regular Series 60 Python, you'll also need the socket libraries from the nice PDIS people. The application can run happily in the background for many hours and probably all day if left uninterrupted. The nature of mobile phone operating systems may mean that another application running at the same time, or gobbling up all the available memory, will cause Python to be terminated without warning. Life sucks and so on.

Update : I've released a new version of the application that modifies the log string to include the interval, in seconds, between polling. Because mobile phone connections can be flakey, and because Series 60 Python will sometimes just silently die, this helps when generating reports and graph-y bits and determining whether two events were contiguous.

Be seeing you.