R is perhaps the most powerful computer environment for data analysis that is currently available. R is both a computer language, that allows you to write instructions, and a program that responds to these instructions. R has core functionality to read and write files, manipulate and summarize data, run statistical tests and models, make fancy plots, and many more things like that. This core functionality is extended by hundreds of packages (plug-ins). Some of these packages provide more advanced generic functionality, others provide cutting-edge methods that are only used in highly specialized analysis such as geospatial computation.
Tim Appelhans joins me on the show today to talk about his journey from learning R too developing new packages and extending the geospatial visualization capabilities of R
This episode is sponsored by HiveMapper
A platform that takes video and creates 3D mapping layers based on that data. The video can be from avariety of different sensors, does not need to be vertically looking down on the geography and each 3D output is georeferenced!
To put it simply, point clouds are a collection of XYZ points that represent some real world object of nearly any scale.They can be generated in a few ways. As geospatial scientists, we mostly work with LAS/LAZ data collected by aerial LiDAR (light detection and ranging) scanners at varying scales, from landscapes, down to project sites. We may also derive point clouds from highly detailed orthoimagery of an area, such as from the products of a drone flight.
As a data scientist, you don’t just go in and solve problems. You make recommendations to multi-faceted issues so that you get a fantastic model in the end. You’ll also be advocating a better use and understanding of the data while you do that.