Climate change is a very real problem facing our planet. The term “climate change” can cover a great many things, some natural and some man made, including global warming and loss of wildlife habitat. Each of these brings its own challenges but, increasingly, big data and analytics are being put to use to come up with new solutions and research methods.
Climate scientists have been gathering a great deal of data for a long time, but analytics technology’s catching up is comparatively recent. Now that cloud, distributed storage, and massive amounts of processing power are affordable for almost everyone, those data sets are being put to use. On top of that, the growing number of Internet of Things devices we are carrying around are adding to the amount of data we are collecting. And the rise of social media means more and more people are reporting environmental data and uploading photos and videos of their environment, which also can be analyzed for clues.
Perhaps one of the most ambitious projects that employ big data to study the environment is Microsoft’s Madingley, which is being developed with the intention of creating a simulation of all life on Earth. The project already provides a working simulation of the global carbon cycle, and it is hoped that, eventually, everything from deforestation to animal migration, pollution, and overfishing will be modeled in a real-time “virtual biosphere.” Just a few years ago, the idea of a simulation of the entire planet’s ecosphere would have seemed like ridiculous, pie-in-the-sky thinking. But today it’s something into which one of the world’s biggest companies is pouring serious money. Microsoft is doing this because it believes that analytical technology has finally caught up with the ability to collect and store data.
Another data giant that is developing tools to facilitate analysis of climate and ecological data is EMC. Working with scientists at Acadia National Park in Maine, the company has developed platforms to pull in crowd-sourced data from citizen science portals such as eBird and iNaturalist. This allows park administrators to monitor the impact of climate change on wildlife populations as well as to plan and implement conservation strategies.
Last year, the United Nations, under its Global Pulse data analytics initiative, launched the Big Data Climate Challenge, a competition aimed to promote innovate data-driven climate change projects. Among the first to receive recognition under the program is Global Forest Watch, which combines satellite imagery, crowd-sourced witness accounts, and public datasets to track deforestation around the world, which is believed to be a leading man-made cause of climate change. The project has been promoted as a way for ethical businesses to ensure that their supply chain is not complicit in deforestation.
Other initiatives are targeted at a more personal level, for example by analyzing transit routes that could be used for individual journeys, using Google Maps, and making recommendations based on carbon emissions for each route.
The idea of “smart cities” is central to the concept of the Internet of Things – the idea that everyday objects and tools are becoming increasingly connected, interactive, and intelligent, and capable of communicating with each other independently of humans. Many of the ideas put forward by smart-city pioneers are grounded in climate awareness, such as reducing carbon dioxide emissions and energy waste across urban areas. Smart metering allows utility companies to increase or restrict the flow of electricity, gas, or water to reduce waste and ensure adequate supply at peak periods. Public transport can be efficiently planned to avoid wasted journeys and provide a reliable service that will encourage citizens to leave their cars at home.
These examples raise an important point: It’s apparent that data – big or small – can tell us if, how, and why climate change is happening. But, of course, this is only really valuable to us if it also can tell us what we can do about it. Some projects, such as Weathersafe, which helps coffee growers adapt to changing weather patterns and soil conditions, are designed to help humans deal with climate change. Others are designed to tackle the problem at the root, by highlighting the factors that cause it in the first place and showing us how we can change our behavior to minimize damage.
These projects are built around the principle of predictive modeling. Once a working simulation of a climate change system – deforestation, overfishing, ice cap melt, or carbon emissions – has been built based on real, observed data, then by adjusting variables we can see how it might be possible to halt or even, in some cases, reverse the damage that is being done. After all, the whole point of big data analysis, in climate science or otherwise, is to generate actionable insights that can drive growth or change (or, in the case of the climate, prevent too much change).
Just a decade or so back, climate change was seen by many as an insurmountable problem. Thanks to the growth of big data analysis, it is becoming apparent that the actions of individuals can make a difference when they are able to make decisions based on sophisticated analysis of accurate data.
Bernard Marr is a bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and big data guru. He helps companies to better manage, measure, report, and analyze performance. His leading-edge work with major companies, organizations, and governments across the globe makes him an acclaimed and award-winning keynote speaker, researcher, consultant, and teacher.
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