Big Data and Weather Forecasting

In his article “How Big Data Can Boost Weather Forecasting” for Wired, author Steve Hamm discusses how Big Data analysis techniques are being used to more accurately predict weather patterns. As his first example, Hamm discusses how the Korean Metrological Administration (KMA) is working to upgrade its predictive systems in order to better prepare the Korean peninsula for storms that “[carry] dense clouds of yellow dust from China’s Gobi Desert that are sometimes loaded with heavy metals and carcinogens”. As part of their upgrades, the KMA is dramatically increasing the agency’s storage capabilities in hopes of more accurately forecast weather patterns through an increased ability to quickly analyze large amounts of information.

Such efforts to increase predictive capabilities are also being made in other parts of the world. Following the destruction caused by Hurricane Sandy, “leaders of the city of Hoboken…are considering building a wall around the city to keep the tidal Hudson River at bay”, but such efforts will be in vain if scientists are unable to predict how the changing climate will affect the river’s future depth and behavior. Due to the scale of the issue, IBM is assisting in researching more accurate predictive methods through a project called Deep Thunder, a “long-term weather analysis project”.

Currently, Deep Thunder has been used to accurately predict “the snowfall totals in New York City during the mammoth snowstorm” in February, and was also able to predict “when the snowfall would start and stop”. IBM is currently working to implement Deep Thunder in Rio de Janeiro for the 2016 Olympics, and provide attendees access to the predictive information through “iPad and cloud applications”. The accuracy and speed of Deep Thunder have great implications for the future of climate prediction; if the planet’s weather can be consistently be predicted, the damages and injuries caused by catastrophic weather could be greatly mitigated during future events.

Crowdsourcing Killer Outbreaks

Nanowerk’s article “Crowdsourcing killer outbreaks” presents an idea that is both a forward-thinking and efficient technological step for science, and a world-shaking challenge to the traditional concept of priority in the sciences. Here we see two dangerous pathogens, one that threatens human lives and the other that is scarring ecosystems. In both cases speed is of the essence for treatment as the danger increases exponentially as time goes on. The solution is met not by individual labs competing with each other to reach the cure before the other, but by collaboration on a global scale.

The “crowdsourcing” method, which is simply technologically mediated mass collaboration, allowed for different genomic labs to instantly share their findings with each other to reach a solution much faster than could have been possibly done with the competition method. By combining resources and exchanging information, the project became one of an international lab working together.

However, this does present a problem on the legal and financial levels. Universities and research labs depend on patents and priority (the concept that whoever discovers something first gets the credit) in order to fund themselves. With the research being shared through the creative commons, the determination of who gets priority is left very much up in the air.

I feel that there is much to be gained by the crowdsourcing of science. Even though there are some legal and economic details to be ironed out, there is too much to be gained to let the traditional methods hold us from making some serious progress in situations of dire need.

Opportunities Ripe for Data

With today’s technology, we are able to record a large amount of data on our daily lives.  We leave a history of the websites that we visit, our phones can track our location and companies can see what you are buying with your credit card.  Big Data is about taking in this huge amount of data and turning it into useful information. The Wall Street Journal article “Leveraging Data to Drive Innovation” included comments from  Chris Anderson, CEO of 3D Robotics: “We are drowning in data.  But we don’t have enough ability to analyze it.”

If we could come up with efficient ways to crawl through data, we would be better informed and be able to make better decisions that benefit our health.  We could keep track of our vitals constantly, giving us a more complete picture of what we need to do in order to be healthy.  Alternatively, that information can be commercialized. Insurance companies, the food industry, and other commercial interests would use the information to refine their advertising.

By not taking advantage of these pieces of data, we are missing out on potential innovations that can move science and the economy forward.  If homeowners could easily identify what in their home was consuming the most energy, they could take steps towards reducing energy costs.  If automobile manufacturers could reduce emissions from cars by just 1%, that would create a significant decrease in pollution for some cities. It is just a matter of taking the physical world and putting it into measurable data.

Map-Based Visualizations

The trend in computing today is questioning every aspect of life and compiling data on it.  We can take massive volumes of data and identify deficiencies in the way we run our lives. Directions Magazine’s article, “New map-based visualization provides insight into Seattle commuting data” is an excellent example of this.  IDV Solutions is a data visualization company that took information from the U.S. Census Bureau and other public sources to create an infographic that displays detailed information on the geography of Seattle’s commuting trends.  Infographics help translate the data that we take from the physical world and put it in a data structure that takes the shape of the physical world as well, allowing the reader to quickly spot trends and make decisions based on data that we would not be able to see otherwise.

The implementation of these infographics can bring attention to the efficiency of the public transportation system in Seattle. It can also help determine where improvements can be made.  Even more exciting is the prospect of applying the same techniques to every major population center in the world.  A one percent improvement in efficiency can produce massive reductions in fuel expenditures and gas emissions when applied on a global scale.  The technology exists for the repetition of this process to be a real possibility; it just requires that someone act on it.  The application of this technology is an investment that can save time and resources for commuters. It can also open up other avenues of savings.  The aspects of life that can be improved are endless. We just need to figure out how to put the world in a database.