geographic data

Understanding the World

Recent Posts

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Normally when we visualize monthly precipitation anomalies, we simply use a bar graph indicating negative and positive values with red and blue. However, it does not explain the general context of these anomalies. For example, what was the highest or lowest anomaly in each month? In principle, we could use a boxplot to visualize the distribution of the anomalies, but in this particular case they would not fit aesthetically, so we should look for an alternative. Here I present a very useful graphic form.

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When we try to estimate the correlation coefficient between multiple variables, the task is more complicated in order to obtain a simple and tidy result. A simple solution is to use the tidy() function from the {broom} package. As an example, in this post we are going to estimate the correlation coefficients between the annual precipitation of several Spanish cities and climate teleconnections indices.

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Publications

More Publications

(2019). Introducción a los SIG con R. Publicaciones de la Universidad de Zaragoza.

PDF 978-84-1340-002-0

(2019). Wildfire burnt area patterns and trends in Western Mediterranean Europe via the application of a concentration index. In Land Degradation and Development.

10.1002/ldr.3450

(2019). Spatiotemporal variability of daily precipitation concentration and its relationship to teleconnection patterns over the Mediterranean during 1975-2015. In International Journal of Climatology, Early View.

10.1002/joc.6278

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Projects

[2019-2022] AVODIS - Understanding and building on the social context of rural Portugal to prevent wildfire disasters. (University of Porto)

[2018-2020] CLICES- CLImate of the last CEntury in Spanish mainland. (University of Barcelona, University of Zaragoza, CSIC)

[2017-2020] ELECTRONET - Atmospheric Electricity Network: coupling with the Earth System, climate and biological systems. info (Cost Action 15211)

[2016-2019] FIREXTR - Prevent and prepare society for extreme fire events: the challenge of seeing the forest and not just the trees. info (University of Porto)

[2018-2020] Prevalence of consumption of alcohol, cannabis and other drugs in students between 14 and 18 years of age in Lugo. (University of Santiago de Compostela)

[2017-2019] MEDEA3 - Study of socioeconomic and environmental inequalities in the geographical distribution of mortality in large cities (1996-2015). info (Multicenter Research Project)

[2017-2019] Intensive alcohol consumption: characterization of a new trajectory towards alcoholism. (University of Santiago de Compostela)

[2014-2016] Biometeorological approach to study the spatial variability of influenza in the Iberian Peninsula. (University of Cantabria)


Courses

  • 2019

Introduction in Geographic Information Systems and Cartography with R, II Edition [15h] (Summer School, University of Santiago de Compostela)

Introduction in Geographic Information Systems with R [30h] (University of Porto)

  • 2018

Introduction in Geographic Information Systems and Cartography with R, I Edition [15h] (Summer School, University of Santiago de Compostela)

  • 2014

Practical applications of R with climatological data [25h] (University of Barcelona)

Climatic applications of Geographic Information Systems [12h] (University of Santiago de Compostela)

  • 2013

Introduction to Statistics with R [20h] (University of Santiago de Compostela)

Introduction in data management, manipulation, visualization and statistics with R [40h] (University of Barcelona)