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— urbantick

November 2008 Monthly archive

I have been playing around with the Plymouth365 data set and managed to produce a collaged GPS file. The track data that was collected over the period of one year is displayed simultaneously.
It is an aquarium again where I recalculated the height according to the time. As time passes the track rises up. This has been done with simple spreadsheet calculation and then re-pasting into the gpx file. The new altitude is now the indication of process.
This image uses the simple transformation of the time into seconds as the height. In this example the altitude is between 32000m and 85000m. It is very difficult to read on the level of everyday Plymouth activities, but it draws nice progress lines from long distance and day trips.

Image by urbanTick – Plymouth 365 aquarium

Image by urbanTick – Plymouth 365 aquarium

The second image here has the height reduced by 50%. Much more detail is visible on smaller scale where long distance trips lose their quality. An interesting feature is the “wall” that emerges between the place where I lived and my work place. Along the path I used to take emerges a vertical mess of lines at all times/heights. I must have used this route pretty much at any time in the day during this one year period.
Although I have tried to “clean” the data today, there are still a large number of error lines showing up. Also seem there to be new error lines occurring because of the method I used to collage the gpx file. The problem is that I pasted it as one track and not as a set of tracks. This would involve some more computing, but it’s probably worth a try. With such a method some more specific queries would be possible.

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Amazing how time passes…
The London Aquarium in new light under the sky.

Image by urbanTick – London Aquarium 2008-11 data

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I have been working with the data material Plymouth365.
Different approaches have now been tested and it looks promising that this could lead to something.
I have been focusing on the analysis of the data in the context of a 24h day. Basically what I did is squeezed in all the days into one sample day and plot it. In other words, all the days are superimposed onto one day.

Image by urbanTick – activity graph

(These are screenshot movies and shows Google Earth playing the tracks over 24hours. It is a first shot at it, so needs some cleaning and tweaking.)

Image by urbanTick – aquarium, Time space diagram referred to as the aquarium. After Kwan (2004)

This data is actually pretty new, these are the new London tracks from October and November in 2008)

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A first image of the emerging tracks in London. This record starts in September 2008.
The two hotspots already emerge. UCL and Tuffnel Park connected via the 390 bus line. I do really bad in crossing the Thames. Maybe I should start deliberately crossing the river. If only for the purpose of this image.

Image by urbanTick – London, generated 20081118

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SlogBase is an open source tool to track the movement of avatars in Second Life. The code offers a database that can be attached to objects in Second Life and sends sensor data to an external database.
It was develop with having in mind the marketing aspect. It is advertised by saying how important it is to know what visitors do and what they are interested in. By monitoring the movement on ones owned land the offers can be adjusted or exchanged based on the analysis of the logged data.
A lot of information can be logged with this tool. Beside the location, speed and direction other additional data can also be logged. This can be age or mode of transport.
Image taken from: SlogBase

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Someone in Apeldoorn, the Netherlands has set up a small network of five Bluetooth sensors. The sensors are placed in different locations in the city, as said on the projects homepage “with friends and family”, to keep it a low budget project. Each location has a simple USB Bluetooth adaptor connected to the internet. All the information is then stored in one location in a database.
The sensor will pick up mobile devices with Bluetooth turned on. They are identified by a unique MAC address. Through the network of sensors within Apeldoorn it is then possible to roughly track individual devices.
The amount of data collected from just five locations is quite a lot. Within the first four weeks the network registered 15’000 unique devices.
As it occurs, some devices are picked up by two or more sensors and are therefore reveal information about movement within Apeldoorn of individual devices. Data from one sensor over a period of time reveals a picture of the usage pattern of the area it covers. An example from the project homepage at bluetoothtracking.org.
Below you see the statistics of the Apeldoorn Driehuizen Bluetooth scanner. The location of this scanner is near a couple of office buildings. You can clearly see the early morning and late afternoon traffic it even shows that they usually go for a walk after lunch. One afternoon peaks at 12, Friday afternoon, and this is because many people take Friday afternoon off.

Image taken from Bluetooth tracking – Weekday activity

Dutch people like to enjoy long weekends and often take Friday afternoon off.”
This simple chart visualizes how working hours create a pattern in everyday movement. The chart only represents on e week, but every week is most likely the same as the pattern is repetitive.
The University of Bath has, it was revealed by a Guardian article on Monday July 21 2008, undertakes a very similar study. For this study the university has already three years ago, installed 10 Bluetooth scanners to capture signals from mobile devices. The data is used to study how people move around cities. The project is called Cityware and, similar to the previously mentioned example based in the Netherlands, stores the data centrally to allow analysis.
On the Cityware project home page the team publishes a map of data collected back in 2007. During a day on 9 different locations, 6 time sessions have been scanned.

Image taken from: Cityware

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