When Big Data Doesn’t Tell The Whole Story – Megaregions And Commuting

  It’s tempting sometimes to think that we can grab all the lovely data lying around, feed it into a computer algorithm and then get results that magically tell us something new and amazing. That though is the tired data scientist’s fantasy – and thinking about problems that way doesn’t really help solve them! We’re always going to need what’s now being called “domain expertise” in data science circles – that deep understanding of your subject and the expertise that lets you understand when data is valuable, what insights really are insights and when to use the data scientist’s vast array of tools.   A study that was published in   PLOS One   today is a perfect example of how a data scientist typically works through problems in the clean tech space – together with all the associated complications.   The question that was asked in this study was this – “Can I use data about how people commute to understand which regions are economically dominant – that is megaregions?”   So, starting of

Making Clean Tech And Data Science Work: From Micro To Mega…

  Several interesting research studies have been written highlighting how data science is being embedded in clean tech. What I find fascinating is that these stories showcase research at very different scales – at the micro level and at the scale of the Earth System. The first study that came out this week was by teams from the University of California, Irvine and NASA’s Jet Propulsion Laboratory evaluating the melting of Antarctica’s glaciers. The teams used satellite data to monitor the location and movement over the years of the glaciers “grounding line” – the point at which the glacier begins to float while still attached to the land. The reason that’s important is because it helps determine how much ice is melting into the oceans – with all the associated implications for understanding sea level rise in a changing climate. The recent data (2014-2016) came from the Sentinel-1 mission launched by the European Space Agency and the previous years data (1992,1996 and 2011) that was use

Water, Water Everywhere – But Where’s The Funding?

  How many times did you think about water this month? If you’re like most people in developed countries, you probably only thought about it when paying your water bill – or if there were news articles about floods or droughts or oceans. If you’re in parts of the world where water is not plentiful, the chances are that you thought about it if you had to plan your day around when water was going to come out of the tap. If, like many of the poor, you had to walk miles or stand in queues to collect drinking water, you probably spent a large part of your day thinking about it. Water is essential to life and yet, we don’t hear a great deal about innovation or venture capital funding or startups that are changing the world in this sector in popular media or news. Which brings up the question – where is the funding for innovation in this sector? Venture capital funding in water is a relatively small investment compared to the investment in high-tech or even in some of the other clean tech sec

Nature’s Supply And Demand Problem

  “Supply and demand” is a phrase that’s more commonly associated with economics and business than with the environment. And yet, when we think about it – Nature provides several services that we take for granted… until they aren’t there anymore. Clean air for example – natural systems have filtered and purified air around cities and homes for many years, until the output from our cities becomes too much for the natural system and then we start noticing the smog and pollution. Or flood control – mangroves in the coastal areas of the tropics provide buffers against storm surges and flooding from hurricanes, until they are cut down for development and then we are faced with multi-million dollar damages from a storm.   Several ecologists and economists have worked together to try and figure out how best to quantify or price the services that natural systems provide, often called ecosystem services. But what happens as the environment changes, the climate warms and several ecosystems are t

Snippets in Clean Technology and Data Science: Urban Sustainability

  Most of us working in the sustainability and clean tech space have heard of “ Smart Cities ” – one of the buzzwords in the clean tech and data science space since 2014. It’s usually used in the context of building better sensors or using artificial intelligence so that certain aspects of living in cities become automated, efficient and   sustainable.   These could be a number of things – better waste management, more efficient lighting, energy efficient buildings across the city, increased green spaces, less water use and so on and so on… As more of the world’s population starts living in cities, it’s critical that we make our cities as livable and sustainable as possible. And that means using all the latest tools at our disposal, especially the new methods by which data are collected and stored in the cloud today. One of the most fascinating aspects of working in the data science space has been the explosion in data that are freely available or available at a relatively low cost as

Snippets in Clean Technology and Data Science: Biomimicry and sustainable materials

  One of the newest entrants in the clean tech arena is the   field of biomimicry and sustainable materials . Research in nanotechnology and biological systems is driving a lot of innovation in how we design materials so that they can be easily recycled/reused or degraded naturally to return to the environment. And not just material design, but also a whole suite of novel solutions to problems that are based on biological systems. First off – designing monitoring systems that can track and monitor wildlife and natural systems .  An interesting interplay between clean technology and data science lies in biomimicry –where natural systems are used as templates for better design. Often, what happens is that there’s an interesting technological advance that is used to collect large amounts of data – and then, researchers figure out by using data science that there is a natural system that could make it even better! A really interesting   invention out of MIT   looks at how robots can be bui

Helping The Environment Recover With Data Science

  After the Deepwater Horizon oil spill, a question that came up frequently was – how long would it be before the environment recovered? This was a concern both for the ocean surrounding the spill area as well as the marshes and beaches inland where the oil washed up. Not all spills are as bad as the Deepwater Horizon one – but a concern that comes up repeatedly when anything needs to be extracted from the Earth is –   what is the impact on the local environment and how long will it take for it to recover or at least return to a state as close to the original one as possible? This is true whether it’s minerals being extracted or gold or oil and natural gas. The recovery of land after oil and gas wells have been drilled is a question that has been studied for quite a while now. In general, most of the work has looked at individual sites and evaluated how they are doing after the extraction is complete and the system has been shut down, but there are very few studies that have been able