recogeo: A new R package to reconcile changing geographic boundaries (and corresponding variables)

Demographics information is usually reported in relation to precise boundaries: administrative, electoral, statistical, etc. Comparing demographics information reported at different point in time is often problematic because boundaries keep changing. The recogeo package faciliates reconciling boundaries and their data by a spatial analysis of the boundaries of two different periods. In this post, I explain how to install the package, reconcile two spatial objects and check the results.

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Friday, 1 February 2019

Are you parallelizing your raster operations? You should!

If you plan to do anything with the raster package you should definitely consider parallelize all your processes, especially if you are working with very large image files. I couldn’t find any blog post describing how to parallelize with the raster package (it is well documented in the package documentation, though). So here my notes.
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Thursday, 17 January 2019

How to (quickly) enrich a map with natural and anthropic details


In this post I show how to enrich a ggplot map with data obtained from the Open Street Map (OSM) API. After adding elevation details to the map, I add water bodies and elements identifying human activity. To highlight the areas more densely inhabitated, I propose to use a density-based clustering algorithm of OSM features.

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Thursday, 9 August 2018

Explicit semantic analysis with R

Explicit semantic analysis (ESA) was proposed by Gabrilovich and Markovitch (2007) to compute a document position in a high-dimensional concept space. At the core, the technique compares the terms of the input document with the terms of documents describing the concepts estimating the relatedness of the document to each concept. In spatial terms if I know the relative distance of the input document from meaningful concepts (e.g. ‘car’, ‘Leonardo da Vinci’, ‘poverty’, ‘electricity’), I can infer the meaning of the document relatively to explicitly defined concepts because of the document’s position in the concept space.

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Tuesday, 26 April 2016

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RSS r-bloggers.com

  • RObservations #31: Using the magick and tesseract packages to examine asterisks within the Noam Elimelech
    Introduction Since my last blog on Tesseract-OCR I have been playing around casually with it to see what it is possible of doing. Tesseract supports optical character recognition for over 100 languages. That together with straight forward usage for implementing it in R inspired me to try using it for Hebrew ... Continue reading: RObservations […]
  • How to add labels at the end of each line in ggplot2?
    The post How to add labels at the end of each line in ggplot2? appeared first on Data Science Tutorials How to add labels at the end of each line in ggplot2?, Using the ggplot2 R library, this article shows how to display the last value of each line as ... Continue reading: How to […]
  • Top 3 Tools to Monitor User Adoption in R Shiny
    Can you monitor user adoption for R Shiny apps? What is user adoption anyway? We’ll answer these questions and show you how to do it yourself in this article. Put simply, user adoption is the process by which new users become familiar with your product and/or service and ... Continue reading: Top 3 Tools to […]
  • Artificial Intelligence Examples-Quick View
    The post Artificial Intelligence Examples-Quick View appeared first on Data Science Tutorials - Are you curious about Artificial Intelligence Examples? If you answered yes, then this article is for you.  We’ll go over some Artificial Intelligence instances here. So, spend a few minutes reading this article to learn everything ... Continue reading: Artificial Intelligence Examples-Quick […]
  • Bayesian sampling without tears
    Following a question on Stack Overflow trying to replicate a figure from the paper written by Alan Gelfand and Adrian Smith (1990) for The American Statistician, Bayesian sampling without tears, which precedes their historical MCMC papers, I looked at the R code produced by the OP and could not spot an ... Continue reading: Bayesian […]

RSS Simply Statistics

RSS Statistical Modeling, Causal Inference, and Social Science

  • “Stylized Facts in the Social Sciences”
    Sociologist Daniel Hirschman writes: Stylized facts are empirical regularities in search of theoretical, causal explanations. Stylized facts are both positive claims (about what is in the world) and normative claims (about what merits scholarly attention). Much of canonical social science … Continue reading →
  • New Yorker : Spy :: Kieran Healy : Statistical Modeling, Causal Inference, and Social Science
    Back in the day, the New Yorker magazine had an Olympian attitude and did not run letters. Spy magazine rectified this with a column, Letters to the Editor of the New Yorker. The New Yorker now runs letters, but Kieran … Continue reading →
  • Webinar: Design of Statistical Modeling Software
    This post is by Eric. On Wednesday, Juho Timonen from Aalto University is stopping by to tell us about his work. You can register here. Abstract Juho will present what he thinks is an ideal modular design for statistical modeling … Continue reading →