On investigation, we identified a historical correlation between averaged smartphone battery temperatures and the ambient temperature readings made by dedicated weather stations. We had been collecting readings of battery temperature from our connection-toolkit app called OpenSignal. One good example of this is the experiment which ultimately led to our creating WeatherSignal. It is this that buttresses the Big Data philosophy of ‘more data is better data’ you do not necessarily know what use the data you are collecting will have until you can investigate and compare it with other datasets. The true benefit of Big Data is that it drives correlative insights, which are achieved through the comparison of independent datasets. It is also important to remember that Big Data when used on its own can only provide probabilistic insights based on correlation. Flu Trends recently majorly overestimated an epidemic in the US – possibly because increased media coverage led to an increase in false positive searches for flu symptoms. Despite this, however, the system is not perfect.
In comparison, relying on Doctors to report flu cases as they were observed resulted in a comparative lag of up two weeks in the identification of outbreaks. Despite the inevitable noise, the sheer volume of Google search data meant that flu outbreaks could now be successfully identified and tracked in near real-time. One good example of the success of the ‘Big Data’ approach can be seen in Google’s Flu Trends which uses Google searches to track the spread of flu outbreaks worldwide. The philosophy of Big Data is that insights can be drawn from a large volume of ‘dirty’ (or ‘noisy’) data, rather than simply relying on a small number of precise observations – a subject covered in detail by Viktor Mayer-Schönberger and Kenneth Cukier in their recent book ‘Big Data’. First, we can combine sensor readings (if light reading is sub x then phone is not outdoors, for instance) and second, given appropriate volume we can arrive at valid averages – an answer that gets to the heart of what Big Data really means. We are often asked how we can trust the data, as mobile phones are often indoors or in pockets. The prospect of a granular network of millions of inter-connected weather stations is an exciting one for meteorology. While the S4 is the most advanced phone in terms of sensors, valuable readings can be gathered from many other phones as well. The most recent Galaxy phone, the S4, contains a barometer, hygrometer (humidity), ambient thermometer and lightmeter – all of which is important data for meteorology.
BIG BIG WEATHER ANDROID
WeatherSignal works by repurposing the sensors that already exist in Android devices in order to build a live map of atmospheric readings. Five months ago, we at OpenSignal (a project to map global cell phone signal coverage) launched an app called WeatherSignal to collect atmospheric data from smartphones.
Smartphone-collected Big Data has the potential to transform the way we can understand and predict weather systems.
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