Cartographic tools for data journalists

Last Saturday I attended the Jornadas de Herramientas Cartográficas organized by Geoinquietos and the Periodismo de Datos group in Medialab-Prado. Several professionals, cartographers and journalists mostly, gave an insight of their work and the tools they most commonly use. The event was focused on election maps (#electionmaps) and you can find a GitHub repository with all the slides and scripts which were displayed during the workshop.

Thanks to Xosé Manuel Vilán and Jesús García we learnt how to make hex maps with Quantum GIS, Graeme Herbert showed us how to make prediction models applying the not-well-called D’Hont Law and a couple of epidemiologists from the Instituto de Salud Carlos III, Rebeca Ramis and Diana Gómez, explained to us how to analyze spatial data with Geoda software. Carlos Gil and Beatriz Martinez also introduced us into mapping with R. A very interesting talk was given by Jorge Sanz from CartoDB.  He showed us how to solve the eternal geographical problem of the Spanish geography: putting the Canary Islands on the map. And he did it with just a few lines of SQL:

You can observe the result thanks to the following map. Before and after running the code.

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How to geocode and visualize your data with CartoDB

In the last post we geocode our data with R. But the geocoding process did not work enterely right. Some points were badly geolocated. In this post, I will explain how to subset these outliers, correct their coordenates and finally, visualize the entire dataset both using R and CartoDB.

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How to geocode your data with R

In my first post I would like to introduce you one of the easiest ways to geocode your data. Geocoding is one of the most essential functionalities within the spatial analysis. It allow us to transform a description of a location, generally an address, postcode or similar, into a pair of coordinates -longitude, latitude or X, Y-. Then we can map our data.


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