123 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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Adding Google Drive Times and Distance Coefficients to Regression Models with ggmap and sp

September 24, 2014
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Adding Google Drive Times and Distance Coefficients to Regression Models with ggmap and sp

Space, a wise man once said, is the final frontier. Not the Buzz Alrdin/Light Year, Neil deGrasse Tyson kind (but seriously, have you seen Cosmos?). Geographic space. Distances have been finding their way into metrics since the cavemen (probably). GIS seem to make nearly every science way more fun…and accurate! Most of my research deals with

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Seeing the (day)light with R

September 23, 2014
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Seeing the (day)light with R

The arrival of the autumnal equinox foreshadows the reality of longer nights and shorter days here in the northeast US. We can both see that reality and distract ourselves from it at the same time by firing up RStudio (or your favorite editor) and taking a look at the sunrise & sunset times based on

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ASDAR book Review

September 8, 2014
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ASDAR book Review

I was recently invited to write a book review for Applied Spatial Analysis and Policy (ASAP). The book, I conclude, “is the authoritative resource on R’s spatial capabilities” and should be of interest to many R users. Below is a preprint of the full review, now published on ASAP’s website. As a geographer and heavy R user,...

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Transport Map Book

September 8, 2014
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Transport Map Book

The Transport Map Books are available for each local authority district in England and present a series of maps related to commuting behaviour. The data are derived from multiple sources including: the 2011 Census, Department for Transport estimates and the results of a research project looking at carbon dioxide emissions linked to the...

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Reasonable Inheritance of Cluster Identities in Repetitive Clustering

August 15, 2014
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Reasonable Inheritance of Cluster Identities in Repetitive Clustering

… or Inferring Identity from Observations Let’s assume the following application: A conservation organisation starts a project to geographically catalogue the remaining representatives of an endangered plant species. For that purpose hikers are encouraged to communicate the location of the plant … Continue reading →

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Vtreat: designing a package for variable treatment

August 7, 2014
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Vtreat: designing a package for variable treatment

When you apply machine learning algorithms on a regular basis, on a wide variety of data sets, you find that certain data issues come up again and again: Missing values (NA or blanks) Problematic numerical values (Inf, NaN, sentinel values like 999999999 or -1) Valid categorical levels that don’t appear in the training data (especially Related posts:

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