Maperitive Geographic maps in R | Smart Data Collective The maps library for R is a powerful tool for creating maps of countries and regions of the world. For example, you can create a map of the USA and its states in just three lines of code: library(maps) map("state", interior = FALSE) map("state", boundary = FALSE, col="gray", add = TRUE) The coordinate system of the graph is latitude and longitude, so it's easy to overlay other spatial data on this map. Unfortunately, the data for the maps library isn't sufficient for some applications. GADM is a spatial database of the location of the world's administrative boundaries, and as Claudia Engel discovered the map information is available as native R objects that can be plotted directly with the spplot function (from the sp package). library(sp)con <- url(" Sweet! AnthroSpace: Download Global Administrative Areas as RData files Link to original post
Polymaps Programming R | Beginner to advanced resources for the R programming language stamen/modestmaps-js - GitHub GPS Visualizer map input form: Plot quantitative data This is a special version of the GPS Visualizer map form that's designed for plotting quantifiable data on a map. You can colorize and/or resize the points according to a generic frequency field named "N", or you can use a more typical field, such as altitude, population, or category. If you have track data, or if you don't need to automatically colorize/resize your data points by a particular parameter, you'll probably be better served by the normal Google Maps form, the Google Earth KML form, or the JPEG/PNG/SVG form. If you want your map to load markers dynamically (e.g., from a Google Docs spreadsheet), you definitely need to use the standard Google form. The coordinates of your data can be given as latitude/longitude, as geographic places (city-state pairs, states, or countries), U.S. Please contact Adam Schneider, using the address on the bottom of this page, if you have any questions about how this form works!
[map=yes] the code-y bits map=yes is a collaboration between MapQuest Open and Stamen Design, using data from the OpenStreetMap project. The project is an exploration of new frontiers in online cartography and the mapping of open data. More and more open data is coming online every day. Companies like MapQuest are explore ways of building businesses in this new ecosystem, where curation of data and the accessibility and always-on nature of the internet enable new kinds of interaction, visualization and mapping. All the code used to generate these maps is available for download and liberal re-use. This is a short tutorial designed to show you how to make your own [map=yes] style map tiles. Open Street Map The first thing we need to do is start by explaining some of the basic building blocks of OpenStreetMap (OSM): nodes, ways and tags and how you can query them using MapQuest's XAPI service. nodes nodes are points on the Earth. As you can see, they are an exciting bunch. ways tags How awesome is that? Tiles TileStache Okay!
The R Project for Statistical Computing bjornharrtell/jsts - GitHub Wallpapering Fog: Losing touch... or why Excel and VBA won't cut it any more Thinking through this post is making me feel old. There's going to be a lot of 'in my day' type reminiscing and I'm only 34. It's all this new fangled technology that's doing it. The world's changing fast. I got my first proper job twelve years ago this month, as a junior analyst with a small econometrics consultancy and although the statistical techniques I use are roughly the same as back then, I've started to realise that our software tools are going through a revolution. Fairly quickly after starting that first job, I discovered that data processing in Excel was a hell of a lot faster and easier if you learned Visual Basic for Applications (VBA), so I did. Up until fairly recently, if an aspiring analyst asked what they should do to get ahead at work, I'd say get good in Excel. The trouble is, VBA's getting left behind. There's also a problem for the next generation in that they need to get luckier with where they start work to get exposed to the right kit. Collect data Process data