This week I will attend the GEOMED2017 conference in Porto, in Portugal. Researchers from different horizons will be gathering here to attend to some talks about spatial statistics, spatial epidemiology and public health. This will be the perfect occasion for me to learn more on those subjects during the three days of the conferences. I will also attend a workshop given by Lee Duncan from Glasgow University on modelling spatial data in R with the package CARBayes).

# Is Big Data Good or Evil?

Yesterday, Arthur Charpentier (@freakonometrics) and I joined the Eco Club at the Franco-American Institute (@IFArennes). We were kindly invited by Alice Bernard to talk about Big Data with the members of the club. It was really fun!

The slides that were projected during the presentation are available inside the post.

# Where’s Waldo? Here he is!

Yesterday, I came across a nice article untitled “Here’s Waldo: Computing the optimal search strategy for finding Waldo“, written by Randal S. Olson. I used the data he shared to apply a correction to the kernel density estimation of Waldo’s location.

In this article, Randal explains that he has devoted some time to try to compute the optimal search strategy for finding Waldo. To that end, he has used some machine learning techniques.

From an image provided by Slate (Here’s Waldo, 2013, by Ben Blatt), Randal S. Olson retrieved the coordinates of 68 different locations of Waldo, and kindly shared the data afterwards.

[…]

# Maps with R: slides

The other day, someone asked me if it was easy to do maps with R. My first thought was: “of course, it is”. I offered to prepare a few slides. I guess it was not **that simple** after all because one actually needs quite a lot of basic knowledge to be able to create its own map.

# French dates in R – From Mac OS to Windows

The other day during my R lecture, something I did not expect happen… I should have known, or at least guess that it would not work… But I wanted to believe it would. When I gave examples on how to read both English and French formatted dates in R, what was working perfectly on Mac OS X did not on Windows Seven. The example was the following:

```
d_char_fr <- "Mer 04 Fév 2015"
d_char <- "Wed 04 Feb 2015"
as.Date(d_char_fr, format = c("%a %d %b %Y"))
as.Date(d_char, format = c("%a %d %b %Y"))
```

# Coordinates of “hot-spots”

Last week I uploaded a note about the paper @freakonometrics and I have submitted again after we added quite a few revisions. We have already made available most of the `R`

code to reproduce our applications (on this GitHub repository). However, we did not share our method to obtain the coordinates of the “hot-spots” areas

# Kernel Density Estimation with Ripley’s Circumferential Correction

This is it! The revised version of a paper written with Arthur Charpentier has been submitted yesterday! It is untitled “Kernel Density Estimation with Ripley’s Circumferential Correction”. The paper is available on https://hal.archives-ouvertes.fr/hal-00725090.

# Maps with R

Last month, @freakonometrics posted a bunch of articles on his blog about the North pole (in French):

- We are winter
- On retourne au pôle nord ?
- Dernières cartes du pôle nord
- Retour sur les enjeux du Pôle Nord

There are some maps on these articles that were produced using ggplot2 excellent package in R. We want to share our code here.

# Closest distance between countries

We sometimes need the distance between two countries in econometric models, for instance in gravity models. Bur for some countries, I think this measure could cause problem. Let’s take an example. If we want to model the volume of trade between country (i) and country (j), the economic theory says it will depend on the distance between (i) and (j). Let (i) be China, (j_1) Japan, and (j_2) India. The distance between (i) and (j_1) will be lower than between (i) and (j_2). Can we consider, though, that Japan is farther from China than India is?

So, rather than compute the distance between two head cities, it might be more accurate to compute the closest distance between the borders. If a border is shared by country (i) and country (j), then the distance should be zero.

# Drawing a basketball court with R

Basketball court with ggplot2.

Following freakonometrics‘ tweet on basketball and big data, linking to this really cool javascript animation, as seen on @kirkgoldsberry‘s article on Grantland, I have spent some time drawing a basketball court in R. I have tried to be the most accurate possible, using this layout.