The need to just do something is a real reason people aggregate data from real-time sensors, create maps and share their findings online. Crowd sourcing is a manifestation of that desire to contribute in a way that helps us understand and better define the overwhelming amount of data available.
In Japan, crowdsourcing is emerging as a way for people to know the radiation levels around the country that are spiking at times due to the pollution escaping from the Fukushima nuclear power plant.
Pachube is being used to monitor Geiger counters across Japan.
Sponsor
That data from Pachube and other sensor networks is being aggregated into various forms.
Marie Steinbach is a data visualization specialist. She has provided a listing of open data related to radiation in Japan.
She uses data acquired by the “System for Prediction of Environment Emergency Dose Information” (SPEEDI), to create a real-time video of radiation levels.
This is a classic example of how the Internet of Things is helping us understand real-time radiation levels in multiple locations across Japan and other parts of the world. Sensors, APIs, the cloud, various devices and real-time, geospatial data give us a visual representation of radiation levels.
The toughest part comes in understanding what the data means. Haiyan of Failed Robot created a map based upon Pachube data. He explained the issue this way:
The toughest part of this visualisation is really understanding what the numbers mean and what impact they have on human health. The first step to this process is standardising the units of measurement, as the crowd-sourced measurements and visualisations may use a number of representations. Units here are in µSv/h (or microSieverts) and we’ve been hearing CNN and NHK World refer to the unit Milisieverts (1 miliSievert = 1000 microSieverts). I also urge other mappers out there to use the µSv/h unit, so we speak a common language.
The Internet of Things is a powerful way to collect radiation data and visualize it. The challenge over our lifetimes will be in interpreting this type of data so we can really understand what it means and what we should do about it when disasters strike.
Lead image: Associated Press
0 Responses
Stay in touch with the conversation, subscribe to the RSS feed for comments on this post.