Congrats to Revolution Analytics for being named a top analytics company in the Hurwitz Victory Index

Hurwitz and Associates recently published their Victory Index in a whitepaper that describes the current state of analytics in business management and applications.  The index rates the top companies that offer analytics products and services to their clients.
Revolution Analytics is a business that packages and supports enterprise version of the R software as well as their high performance libraries that extend R and make it more useful for workgroups and enterprise applications that need to integrate with business systems.  Revolution was called out as one of the top analytics companies in this year's list.
Revolution is the only one on the list that directly supports the open source community by contributing to R, providing an open source version of their enterprise software, and many R related training courses, webinars, and events each year.  
So congrats to the Revolution team, and thanks for all you do to support R and the community.

Spatial predictive analytics helps find Medicare Fraud

It's staggering the level of Medicare fraud in the US.  IEEE Spectrum just published an article that describes a recent investigation by the FBI to uncover a fraud ring that were collecting improper payments by filing fraudulent claims to Medicare.  Of particular note to us is that the article calls out GIS and predictive analytics as a key tool in the fight against fraud.  The article doesn't go far enough to say that it takes modeling and analysis techniques in addition to the GIS software, but instead gives a plug to ESRI saying that their software is being used to plot these events.  Mere plotting is not enough, obviously, and the potential for more effective predictive techniques in the geospatial data environment is great in this area.  It's incumbent on all of us practitioners or OR and related disciplines to make sure that we are doing everything we can to make sure people know that there are more advanced methods than simple plotting and kriging.  I'm on the mission, hope you are too.

Review of the Hastie and Tibshirani course: Statistical Learning and Data Mining

I just got back from a two day short course by Trevor Hastie and Robert Tibshirani based on their Stanford graduate stats course and book: Elements of Statistical Learning. The first edition of this book has been a near constant companion in my career, and the new second edition has some notable additions, including new sections on Random Forests, and Elastic Nets, which combines some of the elements of ridge and lasso regression. H&T generously give away their book in PDF form at the book and course page (http://www-stat.stanford.edu/~tibs/ElemStatLearn).
The course was held at the Georgetown University Hotel and Conference Center. Registration for the course was

Optimization algorithms using OpenStreetMap data

Recently I've been making some edits to the map data served up by the good folks at OpenStreetMap.org

View Larger Map

OSM is an open, free map of the world built by thousands (over 220,000 as of this post) of volunteers worldwide. When you visit the site, you're greeted by a user friendly mapping experience not unlike the basic features of any of the modern online map browsers. But what makes OSM really special for us spatial data analysis researchers and practitioners is

• GIS and Spatial Analysis - Univ. Of Maryland
• Introduction to Bayesian Inference: Hierarchical Models and Spatial Analysis - Brown Univ.
• Special topics in Applied Spatial Statistics - Texas A&M
• Reasoning in Spatial Information Systems - U. of Maine
  

Google shows trends in Ops Research activity

How much can you tell by the volume of Google searching on a particular topic? Apparently enough to predict a flu epidemic faster than the CDC. So I thought it would be interesting to search for a few terms in Google Trends to see if there were any interesting results. The volume of searches for "operations research" has been steadily declining during the period from 2004 to the present.













Interesting...

GIS at massive scales... who will get us there? Understanding high performance computing for GIS.

As a GIS practitioner for over ten years I've been subject to all of the same limitations and headaches as my peers. GIS has some powerful capabilities for analysis and research, but boy can it be slow. Many GIS engines have operations that work in active memory, others are limited to working on single files or databases, and we've all experienced the blasted slowness of it all. So how can we get over this hurdle? We've taken major strides in the digital mapping field in the last several years - tiled map interfaces are commonplace online now, and the user experience is far better than the first generation mapping services; analytically we're doing more every day. ESRI's toolbox capabilty in the 9.x series allows them and others to develop special purpose toolsets that perform almost any spatial operation known. But how can we do more. Every GIS practitioner I know wishes that we could perform all these calculations on more and more data at faster and faster speeds. As GIS capability grows, so does the demand for higher resolution data. As a result, we've been at a standstill in terms of processing speed for 5 years or so.