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

2018 Italian general election: Details on my simulation

This article describes the simulation behind the app that you find here

This simulation of the results for the 2018 general election is based on the results from the last two national elections (the Italian parliament election in 2013 and the European Parliament election 2014) and national polls conducted until 16 February 2018. The simulation is based on one assumption, which is reasonable but not necessarily realistic: the relative territorial strength of parties is stable. From this assumption derives that if the national support for a party (as measured by national voting intention polls) varies, it varies consistently and proportionally everywhere. A rising tide lifts all boats and vice versa. The assumption has some empirical justification. If we compare the difference from the national support (in percentage) for each district in 2013 and 2014 we see a significant correlation, especially in the major parties.

Votes to party in the 2018 Chamber districts

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Tuesday, 27 February 2018

NDVI, risk assessment and developing countries

The Normalized Difference Vegetation Index (NDVI) estimates the greenness of plants covering the surface of the Earth by measuring the light reflected by the vegetation into space. The main idea behind the NDVI is that visible and near-infrared light is absorbed in different proportions by healthy and unhealthy plants: a green plant will reflect 50% of the near infrared-light it receives and only 8% of the visible light while an unhealthy plant will reflect respectively 40% and 30%. NDVI can then be used to quantitatively compare vegetation conditions across time and space (and indeed is quite widely used, a Google Scholar search on NDVI produced 60,500 hits).

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Thursday, 14 February 2013

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • Backcast a Time Series for Covid-19 Truths
    A couple of months ago, Turkey’s Health Minister announced that the positive cases showing no signs of illness were not included in the statistics. This statement made an earthquake effect in Turkey, and unfortunately, the articles about covid-19 I have wrote before came to nothing. The reason for this ... The post Backcast a Time […]
  • The Impact of the COVID-19 Pandemic on My Walking Behavior in 2020
    In this post, we will take a look back at 2020, and analyze my step count data to understand some of the impacts that the COVID-19 crisis had on my walking behavior during that crazy year. The Data Step Counts & Measurement Devices The step count data come from 2 sources in 2020 - ... […]
  • Share R shiny apps with brightRserver: 70-second sneak-peek
    Building, maintaining, and improving interactive R web apps has never been easier. YakData’s brightRserver seamlessly combines the best-in-class R editor and R web app server with Secure FTP publishing and synchronization. The post Share R shiny apps with brightRserver: 70-second sneak-peek first appeared on R-bloggers.
  • Making a Solar Insolation Map for Alberta (For novices!)
    Been a while since I've blogged here; wrapping up an MSc and moving continents from Europe to North America is all the excuse I need. This blog post is not going to be revolutionary, and obviously it builds on a lot of what others have done before (see... The post Making a Solar Insolation Map […]
  • Counting Missing Values (NA) in R
    To check for missing values in R you might be tempted to use the equality operator == with your vector on one side and NA on the other. Don’t! If you insist, you’ll get a useless results. x The post Counting Missing Values (NA) in R first appeared on R-bloggers.

RSS Simply Statistics

  • The Four Jobs of the Data Scientist
    In 2019 I wrote a post about The Tentpoles of Data Science that tried to distill the key skills of the data scientist. In the post I wrote: When I ask myself the question “What is data science?” I tend to think of the following five components. Data science is (1) the application of design […]
  • Palantir Shows Its Cards
    File this under long-term followup, but just about four years ago I wrote about Palantir, the previously secretive but now soon to be public data science company, and how its valuation was a commentary on the value of data science more generally. Well, just recently Palantir filed to go public and therefore submitted a registration […]
  • Asymptotics of Reproducibility
    Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Hierarchical stacking
    (This post is by Yuling) Gregor Pirš, Aki, Andrew, and I wrote: Stacking is a widely used model averaging technique that yields asymptotically optimal predictions among linear averages. We show that stacking is most effective when the model predictive performance is heterogeneous in inputs, so that we can further improve the stacked mixture by a […]
  • The norm of entertainment
    Someone pointed me to a comment that a psychology researcher wrote that he almost never reads our blog and that it “too quickly bores me.” That’s ok. I’m sure that lots of people have stumbled upon our blog, one way or another, and have been bored by it. We don’t have a niche audience, exactly; […]
  • Tessa Hadley on John Updike
    Lots to think about here. To start with, this is the first New Yorker fiction podcast I’ve heard where they actually criticize the author instead of just celebrating him and saying how perfect the story is. This time, they went right at it, with the interviewer, Deborah Treisman, passing along some criticisms of Updike and […]