Emrg nodebox Bluetooth#
As you can see from the image below sometimes the BlueTooth name has been modified to reveal more about the person. The BlueBall data is more revealing on people's identities in some ways. Future versions might be on the much cheaper and smaller Gumstix hardware. He used Nodebox to create the visualizations. He wasn't able to detect signal strength via the native BlueTooth stack on the Minis so there is no proximity or geolocationbut he was able to collect some interesting info like the number of devices.
Emrg nodebox mac#
He took on the challenge and turned three Mac Minis into sensors to be used at the conference. We got to talking about how on could use BlueTooth to do the same thing at a more contained conference. A couple of weeks ago Leonard created Fireball (also profiled on TechCrunch) to let attendees check in locations at the Web 2.0 Expo (like Dodgeball). Leonard Lin, co-founder of Upcoming, has put together a fun reality mining project for the conference. ( Disclosure: OATV has invested in the company) The team behind Path Intelligence were on-hand to discuss the application and they were usually busy explaining See the TechCrunch post for more information on the company. All of the data that was collected is anonymous. One of the slides also shows the path of a single cellphone. The embedded slides show where our attendees are from and when they were in the session rooms vs. We used it to learn more about the attendees of Where 2.0. Path Intelligence is able to detect GSM signals from cellphones. We received a little bit of useful data, but there definitely would need to be refinements before it could be considered one of our tools for judging how well a talk was received (the number of new http requests might be more accurate than the movement of attendees). Secondly, as an event organizer knowing even a little more about attendees group actions is very valuable. Though our attendees' devices are undoubtedly tracked without their knowledge all the time we wanted to make the applications. Reality Mining applications definitely fit that description. First, Where 2.0 is a conference on location, It's the goal of the conference to show the latest technology at the intersection of location and technology. The goal in having them at Where 2.0 was two-fold. Several times through out the conference I told the attendees about the projects and showed them the same visualizations shared in this post.
They each took advantage of the signals that our inadvertent sensors (mobile phones and laptops primarily) broadcast. We had two such collection projects this year at Where 2.0. A formal definition of Reality Mining is " the collection of machine-sensed environmental data pertaining to human social behavior". Collecting this data is known as Reality Mining. Your cellphone and laptop leave an invisible trail.
Emrg nodebox for mac os#
NodeBox version 1.9.5 for Mac OS X.The Results of Reality Mining at Where 2.0 for i in range(100):ĭe Bleser F., De Smedt T., Nijs L. It draws a number of ellipses to the canvas, in random shades of transparent red, with random size and position. This example demonstrates a simple NodeBox script. NodeBox 1 has been stable for a long time and is now in “maintenace mode”: we only release new versions to fix critical issues. Ports exist for other platforms that use the same API, such as Shoebot. NodeBox 1 is built on Mac OS X’s Quartz rendering engine, using PyObjC. For an exponent of generative art made with NodeBox, see Spamghetto (Olivero, 2009). Several (free) plug-in libraries have been developed for NodeBox, for example for Core Image filters, SVG import, organic systems such as graphs, boids, L-systems, and an OSC wrapper.
Emrg nodebox mac os x#
NodeBox 1 is a Mac OS X application that lets you create 2D visuals (static, animated or interactive) using Python programming code and export them as a PDF or a QuickTime movie.
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