:::: MENU ::::

Quantified Self & Mapping

The quantified self (QS) movement is intrinsically about knowing more about your most important asset in order to make better decisions. There are many applications out there that allow you to record daily activities – be it mood, food consumed or movement. For the great majority of these they probably, in truth, gain little more actual utility than simply writing these down with pen and paper would provide.

When can these quantified self applications be effective? Some applications such as ‘OptimizeMe‘ use integrated approach of these measurements and give intelligent suggestions such as ‘If you get more sleep on Sunday you will have a better mood on Monday’ ; with these technologies emerging, it feels as if the next step, integration of self-data with real data will be combined – such as the application suggesting : ‘Today the sunniest morning of the week – maybe you should walk to work’. Can having these kind of suggestions available make people, en masse, happier, healthier or more active than they already are?

As part of our MRes coursework we are working on creating visual representations of ‘the active city’. One element my group deduced was physical activity and we are currently exploring different QS datasets and how these might be involved.

At the moment I am testing out the ‘Moves‘ application. This application seamlessly records your movement throughout the day which can then be extracted and visualised using the Moves API. So far it has been impressive in its ability to deduce when I am walking by foot, using transport and which venues I spend hours on the computer.

unnamed

There is also a GitHub example of visualising this data through Processing available!

screenshot_1 More information about this viz.