153 search results for "geographical"

Working with geographical Data. Part 1: Simple National Infomaps

December 21, 2012
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Working with geographical Data. Part 1: Simple National Infomaps

There is a popular expression in my country called “Gastar polvora en chimangos”, whose translation in English would be “spending gunpowder in chimangos”. Chimango is a kind of bird whose meat is useless for humans. So “spending gunpowder in chimangos” … Continue reading →

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A brief script on Geographical data analysis in R

A brief script on Geographical data analysis in R

I saw this post and I decided to replicated that good example but with data closer to me, particulary data of my country. So, I've got the shape data of the capital of my country (You can download the data from here). The data comes from the 2002 CENSO...

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How to map geographically-detailed survey responses?

January 17, 2012
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David Sparks writes: I am experimenting with the mapping/visualization of survey response data, with a particular focus on using transparency to convey uncertainty. See some examples here. Do you think the examples are successful at communicating both local values of the variable of interest, as well as the lack of information in certain places? Also, The post How...

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Creating 3D geographical plots in R using RGL

July 11, 2011
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Creating 3D geographical plots in R using RGL

I've been playing around with the rgl package in the last week, as part of an ongoing quest to come up with nice-looking (but more importantly, useful) data vizualisations. It's a nice little package, and once you've run through the excell...

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Bootstrap Evaluation of Clusters

September 4, 2015
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Bootstrap Evaluation of Clusters

Illustration from Project Gutenberg The goal of cluster analysis is to group the observations in the data into clusters such that every datum in a cluster is more similar to other datums in the same cluster than it is to datums in other clusters. This is an analysis method of choice when annotated training data … Continue reading...

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GEOSTAT 2015: a write-up

August 30, 2015
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GEOSTAT 2015: a write-up

The week before last I attended the GEOSTAT summer school in Lancaster. GEOSTAT is an annual week-long meeting devoted to ‘geostatistics’ (or ‘spatial statistics’ - we’ll come on to the difference subsequently). Having seen the impressive range of materials from previous ‘GEOSTATs’, I was greatly looking forward to the event as a hub of learning, research and community-building, organised...

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Spatio-Temporal Kriging in R

August 27, 2015
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Spatio-Temporal Kriging in R

PrefaceI am writing this post more for reminding to myself some theoretical background and the steps needed to perform spatio-temporal kriging in gstat. This month I had some free time to spend on small projects not specifically related to my primary occupation. I decided to spend some time trying to learn this technique since it may become useful in...

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Importing Data Into R – Part Two

August 18, 2015
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Importing Data Into R – Part Two

In this follow-up tutorial of This R Data Import Tutorial Is Everything You Need-Part One, DataCamp continues with its comprehensive, yet easy tutorial to quickly import data into R, going from simple, flat text files to the more advanced SPSS and SAS files. As a lot of our readers noticed correctly from the first post, The post

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R tutorial on the Apply family of functions

July 28, 2015
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R tutorial on the Apply family of functions

Introduction In our previous tutorial Loops in R: Usage and Alternatives , we discussed one of the most important constructs in programming: the loop.  Eventually we deprecated the usage of loops in R in favor of vectorized functions. In this post we highlight some of the most used vectorized functions: the apply functions. In the present post we show the use The post

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Time series outlier detection (a simple R function)

July 8, 2015
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Time series outlier detection (a simple R function)

(By Andrea Venturini) Imagine you have a lot of time series – they may be short ones – related to a lot of different measures and very little time to find outliers. You need something not too sophisticated to solve quickly the mess. This is – very shortly speaking – the typical situation in which you can adopt washer.AV()...

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