11:20am - 12:05pm Deep-Dive Geospatial Case Study from Data Science DC
Monday Sep 15 - Room: Constitution B
The data science revolution is caused by a fundamental shift in the cost of building analytical systems, and the complexity of learning to use them. Now, a single person can quickly perform data acquisition, integration, prediction, and presentation tasks, over a weekend that previously would have taken teams of people months. To demonstrate this, I will walk through how I quickly got a good-enough answer to a question for the Data Science DC Meetup, a volunteer organization that holds monthly professional education and networking events. Where, on a map, should I schedule events, to make most people at least sometimes happy with the location? This location analysis question is similar to problems faced repeatedly by companies and other organizations. Where should I put my regional offices, or chain of convenience stores, or satellite dishes? Techniques that will be discussed, complete with examples of R code, include: working with latitude/longitude data, constructing geometric cost functions, mapping, global function optimization, and dynamic report generation.