R bloggers has a nice post on the theory behind database organization, and some tools that can used to manage and manipulate data through R. Maybe this seems very specialized, but many of our jobs involve dealing with data these days, so this knowledge and tools is potentially relevant to us, and yet I don’t think many of us even in technical fields outside math and computer science learn this stuff in school.
Structure Sensor is a gadget that can supposedly measure an entire room and make a 3D computer model of it in seconds.
Capture dense 3D models with the push of a button
When used as a 3D scanner, Structure Sensor allows you to capture dense geometry in real-time. This enables you to simulate real world physics and create high-fidelity 3D models with high-resolution textures in seconds. The possibilities are incredible.
Measure entire rooms all at once
The magic of 3D depth sensing begins with the ability to capture fast, accurate, dimensions of objects and environments.
And Structure Sensor doesn’t just capture one dimension; it captures everything in view, all at once. Large-scale reconstruction tasks are easy with Structure Sensor & Structure SDK.
It’s $379. I don’t deal with interior design personally, but I know that surveying on engineering projects can be incredibly expensive and time-consuming. If there are technologies that could make it quick, cheap and easy, that would be a game changer.
This is pretty cool – an interactive website that lets you explore a real-world carbon trading research problem while learning new tricks in R.
Many economists would agree that the most efficient way to fight global warming would be a world-wide tax or an emmission trading system for greenhouse gases. Yet, if only a part of the world implements such a scheme, a reasonable concern is that firms may decide to relocate to other parts of the world, causing job losses and less effective emmission reduction…
In their article ‘Industry Compensation under Relocation Risk: A Firm-Level Analysis of the EU Emissions Trading Scheme’ (American Economic Review, 2014), Ralf Martin, Mirabelle Muûls, Laure B. de Preux and Ulrich J. Wagner study the most efficient way to allocate a fixed amount of free permits among facilities in order to minimize the risk of job losses or carbon leakage. Given their available data, they establish simple alternative allocation rules that can be expected to substantially outperform the current allocation rules used by the EU.
As part of his Master’s Thesis at Ulm University, Benjamin Lux has generated a very nice RTutor problem set that allows you to replicate the insights of the paper in an interactive fashion. You learn about the data and institutional background, run explorative regressions and dig into the very well explained optimization procedures to find efficient allocation rules. At the same time you learn some R tricks, like effective usage of some dplyr functions.
It’s an interesting question at a time when some U.S. states and Canadian provinces have started introducing carbon trading and taxation schemes that differ from their neighbors (sometimes because their neighbors have nothing at all). Perhaps there is a win-win where a policy can gradually phase out less productive, dirtier industries while replacing them with cleaner and higher-value-added industries, then sharing enough of the wealth so everyone benefits.
I thought Nate Silver only looked at professional sports. I was wrong – here is a cool interactive web page he has put together for college football. The numbers don’t always give you the answers you want to hear though – even if my beloved Gators somehow win all the rest of their games, which would include beating Alabama in the conference championship game, he gives them only a 13% chance of winning the national championship. Another nice thing about Nate Silver – he always explains his methodology.
We’ll be updating the numbers twice weekly: first, on Sunday morning (or very late Saturday evening) after the week’s games are complete; and second, on Tuesday evening after the new committee rankings come out. In addition to a probabilistic estimate of each team’s chances of winning its conference, making the playoff, and winning the national championship, we’ll also list three inputs to the model: their current committee ranking, FPI, and Elo. Let me explain the role that each of these play…
FPI is ESPN’s Football Power Index. We consider it the best predictor of future college games so that’s the role it plays in the model: if we say Team A has a 72 percent chance of beating Team B, that prediction is derived from FPI. Technically speaking, we’re using a simplified version of FPI that accounts for only each team’s current rating and home field advantage; the FPI-based predictons you see on ESPN.com may differ slightly because they also account for travel distance and days of rest…
Our college football Elo ratings are a little different, however. Instead of being designed to maximize predictive accuracy — we have FPI for that — they’re designed to mimic how humans rank the teams instead.4 Their parameters are set so as to place a lot of emphasis on strength of schedule and especially on recent “big wins,” because that’s what human voters have historically done too. They aren’t very forgiving of losses, conversely, even if they came by a narrow margin under tough circumstances. And they assume that, instead of everyone starting with a truly blank slate, human beings look a little bit at how a team fared in previous seasons. Alabama is more likely to get the benefit of the doubt than Vanderbilt, for example, other factors held equal.
Thanks to Nate Silver for posting all his polling data in a convenient text file that anyone can read! It’s a nice thing to do. Even though not many of us can do as interesting things with it as Nate Silver, it is a fun data set to play and practice with. Here is an R-bloggers post with some ideas on how to play with it.
Canva has a helpful article with links to a large number of sources of free visuals – photos, videos, even Infographics. There is more than just Google Images and Youtube out there. There is even more here than it seems like at first because as you drill down some of the links are to additional lists…of lists…of…you get the idea.
This week I discovered several websites that show you cool snapshots of current weather. My colleagues are laughing at me because apparently I am the last to know. I think this is one example of how a complex visualization can sometimes be much better than a simpler one. Compared to the typical “synoptic” maps of warm and cold fronts, which are confusing to most people, this is something I think even an elementary school student could begin to grasp.
https://www.windytv.com/?pressure,38.836,-77.338,4
https://www.ventusky.com/?p=30.2;-78.2;4&l=pressure
I just learned of two companies that will negotiate with your cable company on your behalf, in exchange for a share of the savings. Shrinkabill.com and BillFixers.com.
There should be a lot of business opportunities out there like this because we have so many subscriptions and bills now and they are so complex and screwed up. Beyond utilities, you have screwed up medical bills obviously. Shopping around for homeowners and car insurance periodically can really pay off. Then there are simple repairs and maintenance that can lower energy and water bills. Property tax assessments can sometimes be challenged successfully. Mortgage and other lending terms can be negotiated, and if companies are not willing to negotiate they can be refinanced or consolidated. And yet most of us are too busy to spend time doing all this. It wouldn’t make sense to take time off work to do it, and we don’t want to give up our limited family and leisure time. But if there are businesses out there who will do it for you and it puts a little money in both your pockets that wasn’t there before, it’s a win-win.
Can you make Venn diagrams in R? Yes, of course you can!
Here is an article about new “financial technology” by the author of a book called Money Changes Everything: How Finance Made Civilization Possible.
For example, even as we debate the relevance and usefulness of traditional financial institutions such as banks, another revolution is underway in the world of money. A mere decade after we thought we had mastered the intricacies of asset securitization, shadow banking and credit default swaps, an entirely new financial phenomenon has emerged. It is called FinTech – short for financial technology. FinTech involves the plumbing and wiring of the financial system. It is changing how we borrow, how we save, how we raise money for companies even how we assess our future; its possibilities, risks and relationships.
Some of these innovations you may already know: PayPal, Bitcoin, Financial Engines, Kickstarter, Prosper.com and Venmo. They are apps, payment systems, crowdfunding vehicles and peer to peer lending sites. Their use has spread rapidly along with other technological improvements in how we get things done. However these companies are the tip of a very large iceberg.
Many of the innovations in finance are buried in the complex, business to business infrastructure of the economy. These include new ways of detecting fraud, recording transactions, routing orders, valuing assets and even discovering hidden patterns in big data; massaging the fast, continuous flow of news, trades, tweets, satellite images, and Facebook posts. Financial companies – from the big players like Goldman Sachs and Blackrock down to your local bank and financial advisor believe FinTech will fundamentally alter their businesses — and they are rushing to get out ahead of competitors. This is because FinTech innovation tends to disrupt the existing structure. It disintermediates customers and providers of financial services, replacing them with peer-to-peer lending, instant money transfers, loans without loan officers, and investment without investment banks. These innovations are transformative, empowering and create a new infrastructure for exploring even greater opportunities but they threaten the status quo in ways that the securitization wave of the 2000’s never approached. Securitization mostly involved the same big players that ruled the markets in prior decades. FinTech brings a different cast of characters who are defining new communities of investors, new sources of knowledge and unfortunately new kinds of scams and risks. The top FinTech companies today include a lot of new names. How many of us have been following the likes of Credit Karma, Market Axcess, Square, Stripe and SoFi?
I’m all for cutting out the middlemen trying to rip us off. And I’m still looking for the perfect app for splitting a restaurant bill among a large party.