The collaborative innovation landscape in data science
This article is originally published at https://ekonometrics.blogspot.com/
Computing platforms should be like Lego. That is, they should provide the fundamental building blocks and enable the users' imagination to innovate. The latest issue of Stata Journal exemplifies how Stata and, by the same account, R provide the platform for the users to innovate beyond the innate capacity of the core group responsible for software development.
Earlier in July, I received in an email the table of contents for the Stata Journal’s latest issue. I was expecting to see one or maybe two articles of interest. What I found was quite surprising. I was intrigued by almost every article, which made me wonder if I had lost my academic focus so that almost anything is now of interest?
As I browsed through the journal, I noticed that the authors contributing to the journal were truly international. From academic colleagues in Germany and the United States to colleagues working for central banks in Europe, the diversity was hard to ignore. And that’s where I spotted the apparent similarity between R and Stata. Even though Stata is a proprietary computing platform, the innovation landscape is not restricted to the core team at Stata. This is similar to the R environment where literally thousands of packages (algorithms) for R are contributed by independent researchers.
For R, such collaborative ecosystem comes naturally for R being free software. Stata, on the other hand, follows a more traditional market approach of charging for the use of the software. Yet, Stata and R are able to attract leading data scientists (my preferred term for statisticians, econometricians, and others) to volunteer their innovation expertise that they readily share with the larger community.
Returning to the latest issue, I was first attracted to the article on assessing inequality using percentile shares. As the author, Ben Jann from the University of Bern, noted income inequality has come to the forefront of academic and social discourse since the publication of Thomas Piketty’s Capital in the Twenty-First Century. I have been intrigued by the topic for years, primarily influenced by the incredible works of Joseph Stiglitz, Angus Deaton, and others. Piketty’s Capital, despite the criticism (watch Deidre Mccloskey’s careful, yet blunt, review of the Capital), has made percentile shares familiar to analyze distributional inequalities.
Ben Jann has contributed pshare to Stata that readily estimates inequalities with the convenience of a single-line syntax. Using the data from the 1988 US National Study of Young Women, the command easily computes the income distribution showing that the top 10-percent women received 27% of the wages.
For R users, I would recommend the ineq package by Achim Zeileis and Christian Kleiber for generating inequality and poverty indices.
My primary area of interest lies at the intersection of real estate and transportation in urban settings. I am always struggling with how location impacts rents, growth, and other socio-economic outcomes. Determining the location or, for that matter, distances between entities is usually a struggle. Thanks to the GIS software, such as QGIS, MapInfo, and Maptitude, the task of spatial computation has become a lot easier. Still, one has to get proficient on several computing platforms to achieve the necessary tasks of getting distances or travel times to and from locations. Stata offers two interesting solutions for these tasks. The latest one is reported in the latest issue. Stephan Huber and Christoph Rust from the University of Regensburg have a contributed a new command that computes network distances (not just the straight-line Euclidean distances) and network travel times for the shortest paths that rely on Open Source Routing Machine and OpenStreetMap.
Earlier in 2011, Adam Ozimek and Daniel Miles contributed commands to geocode and compute travel times between origins and destinations for different modes of travel, including public transit.
R is equally equipped for similar tasks. Timothée Giraud, Robin Cura, and Matthieu Viry programmed an R package osrm to determine travel time and distances. Other R packages include gdistance and gmapdistance, to name a few.
In summary, I remain delightedly optimistic about the future of both open source and proprietary computing platforms. Altruism is the name of the game where thousands of innovators are making their generous contributions available for the larger benefit of the society making it easier for applied data scientists to satisfy their curiosities by applying readily available algorithms to solve riddles.
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This article is originally published at https://ekonometrics.blogspot.com/
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