randgeo generates random points and shapes in GeoJSON and WKT formats foruse in examples, teaching, or statistical applications. Points and shapes are generated in the long/lat coordinate system and withappropriate spherical geometry; random points are distributed evenly acrossthe globe, and random shapes are sized according to a maximum great-circledistance from the center of the shape.

I’ve recently released the new package ccafs, which provides accessto data from Climate Change, Agriculture and Food Security(CCAFS; http://ccafs-climate.org/) General Circulation Models (GCM) data.GCM’s are a particular type of climate model, used for weather forecasting,and climate change forecasting - read more athttps://en.wikipedia.org/wiki/General_circulation_model.

I've published a short note on my work on geophylogenies and GeoJSON in PLoS Currents Tree of Life : At the time of writing the DOI hasn't registered, so the direct link is here. There is a GitHub repository for the manuscript and code. I chose PLoS Currents Tree of Life because it is (supposedly) quick and cheap.

For the last few weeks I've been working on a little project to display phylogenies on web-based maps such as OpenStreetMap and Google Maps. Below I'll sketch out the rationale, but if you're in a hurry you can see a live demo here: http://iphylo.org/~rpage/geojson-phylogeny-demo/, and some examples below.

Previously on this blog we have discussed making geojson maps and uploading to Github for interactive visualization with USGS BISON data, and with GBIF data, and on my own personal blog. This is done using a file format called geojson , a file format based on JSON (JavaScript Object Notation) in which you can specify geographic data along with any other metadata.

Previously on this blog and on my own personal blog, I have discussed how easy it is to create interactive maps on Github using a combination of R, git and Github. This is done using a file format called geojson , a file format based on JSON (JavaScript Object Notation) in which you can specify geographic data along with any other metadata.

We have a number of packages for getting species occurrence data: rgbif and rbison. The power of R is that you can pull down this occurrence data, manipulate the data, do some analyses, and visualize the data - all in one open source framework. However, when dealing with occurrence data on maps, it is often useful to be able to interact with the visualization.

Bob Mesibov (who has been a guest author on this blog) recently published a paper on data quality in in ZooKeys :In this paper Bob documents some significant discrepancies between data in his Millipedes of Australia (MoA) database and the equivalent data in the Atlas of Living Australia and GBIF (disclosure, I was a reviewer of the paper, and also sit on GBIF's science committee). This paper spawned a thread on TAXACOM, and also came up