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

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  • Introducing scale model in greybox
    At the end of June 2021, I released the greybox package version 1.0.0. This was a major release, introducing new functionality, but I did not have time to write a separate post about it because of the teaching and lack of free time. Finally, Christmas has arrived, and I could spend several ... Continue reading: […]
  • Plotting Bee Colony Observations and Distributions using {ggbeeswarm} and {geomtextpath}
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  • Non-linear model of serial dilutions with Stan
    In chapter 17 “Parametric nonlinear models” of Bayesian Data Analysis1 by Gelman et al., the authors present an example of fitting a curve to a serial dilution standard curve and using it to estimate unknown concentrations. Below, I build t... Continue reading: Non-linear model of serial dilutions with Stan
  • Predicting future recessions
    Even if this sounds incredible, yes, we can predict future recessions using a couple of time series, some simple econometric models, and … R !  The basic idea is that the slope of the yield curve is somewhat linked to the probability of future recessions. In other words, the difference between the ... Continue reading: […]
  • Detecting multicollinearity — it’s not that easy sometimes
    By Huey Fern Tay with Greg Page When are two variables too related to one another to be used together in a linear regression model? Should the maximum acceptable correlation be 0.7? Or should the rule of thumb be 0.8? There is actually no single, ‘one-size-fits-all’ answer to this question. As an ... Continue reading: Detecting multicollinearity […]

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