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Technologies and resources employed

Due to the nature of this work, a large amount of libraries and electronics resources have been employed. Here we mention some of the most intensively used

Distributed algorithms simulation

python python-networkX python-threading

Data preprocessing and results analysis

R R-igraph R-plyr R-multicore R-sp R-maps

Figures generation an edition

R-ggplot2 R-ggmaps inkscape GRACE


php PostgreSQL PostGIS bootstrap jquery d3.js jsnetworkx Google Maps API


We would like to thank Jameson Toole (@jltoole), Luis Ubeda (@lubeme23), Juan Fernado Sánchez (@balkian) and Andrés Archila (@AFArchila) for their useful commnets about the manuscript, and Alberto Asuero (@alasarr) for sharing his experience about Geographic Information Systems (GIS).