maps

This post presents a data exploration tool developed by EDJNet. If you have a spreadsheet with longitude and latitude columns and would like to have a look at those data on a map or associate them with administrative units across Europe, you may well find this tool useful. You can try this online, or check out this screencast for an overview of its main features. The interface is available also through the R package latlon2map.

In this video, I point at some of the issues that characterise data grids and that may generate misunderstandings. In the video, I mention how gridded data can be generated via satellite imagery and machine learning techniques. Of course, such data can be generated also via other means. References This video is based on the data used for this article: Climate warming in Europe, municipality by municipality Municipality-level data for Italy can be accessed through this interactive interface: In marcia con il clima

A structured approach and a new R package to deal with a recurrent question

(this post is now published also on balcanicaucaso.org) A few weeks ago, out of curiosity, Davide Denti asked me if I knew how to parse OpenStreetMap data in order to find all streets and squares dedicated to Tito in the former Yugoslavia. I was unfamiliar with how OpenStreetMap stores data, but after a few quick attempts I managed to extract some data and produce a map that looked meaningful. Early feedback suggested that the data were incomplete, and I did not fully polish the data, but – aware that I would not have the time to work on this in the following weeks, and perhaps I would never pick it up again – I decided to make public the draft document I created.