Benjamin Nowak
@BjnNowak
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French lecturer in agronomy with teaching and research activities focused on soil sciences and remote sensing for crop monitoring.
Clermont-Ferrand
Joined February 2023
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#Day6
(Asia) of
#30DayMapChallenge
A tribute to
@TerribleMaps
book's cover
My only regret is having this idea after
#Day4
(a bad map)...
#RStats
code:
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This is what a large farm would look like, growing all the world's crops.
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How do 👉YOU👈 map the distribution of human population?
#gischat
More like Judith Olson or Waldo Tobler? Or another way?
#Day8
of
#30DayChartChallenge
(distribution/human)
🔗to
#Rstats
code for the 6 maps :
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#Day13
(choropleth) of
#30DayMapChallenge
A trivariate choropleth highlighting soil texture differences in France (clay is the smallest soil particle size, silt the intermediate particle size, sand the largest particle size)
#RStats
code:
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38
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👋As many of you joined following the boost from
@R_Graph_Gallery
, let me introduce myself with a few posts!
My research activities focus mainly on the characterization of the performances of farming systems using large scale datasets, especially remote sensing data
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29
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🦓For
#Day23
(time series X tiles) of
#30DayChartChallenge
, a repost of the yield stripes (radial version), showing the evolution of wheat and maize yields since 162
🔗Link to
#RStats
code:
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#Day10
of
#30DayChartChallenge
: Hybrid distribution
Number of farmers is decreasing in France, farm managers are also older
🔗to
#Rstats
code :
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#Day9
(hexagons) of
#30DayMapChallenge
Re-sharing an old map with... hexagons on Hexagon (the nickname of France) to show the comparison between animal and human density
#RStats
code:
Data and map available in {frex}:
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The Yield Stripes, radial version. Also added maize.
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🍷This week,
@SergeZaka
published a very interesting graph on the evolution of grape harvest dates in France
📊As I also love these long phenological time series, I couldn't resist adapting this plot
🤒Increasingly early harvests illustrate the impact of climate change
Vous avez aimé ces séries phénologiques historiques. Eh bien, voici les vignobles français ! Même constat : la hausse des températures accélère le développement du vignoble et les vendanges (i.e. récolte des grappes) sont plus précoces ! IMPLACABLE !
La source est sur l'image.
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For
#MapPromptMonday
(sports), 1st try to mimick a Lego map with R!
I used a combination of grid and centroid to try and replicate the bricks
#RStats
code:
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For
#Day25
of
#30DayChartChallenge
(Global change) a
#dataviz
to compare the impacts of various solutions to reach drawdown, the moment when greenhous gases concentrations in atmosphere stop ↗️ and start ↘️
Data from
@ProjectDrawdown
#RStats
code
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🗺️
#Day29
(Population) of
#30DayMapChallenge
🧑🤝🧑Resharing this old map illustrating different ways of mapping populations!
🔗
#RStats
code for the maps:
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🗺️
#30DayMapChallenge
|
#Day1
(point)
🐞Because of its use in agriculture for biological control, Harmonia axyridis has spread throughout the world (especially in Europe and Northern America)
🔗
#Rstats
code for the map :
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🆕I have updated my map gallery with some maps made for this year
#30DayMapChallenge
🔗Link to the gallery:
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The relationship illustrated for
#Day16
(family) of
#30DayChartChallenge
is the evolution of the regularity of meals as my son grew: from the chaos of the first months to 4 regular meals a day
#DataViz
made with
#RStats
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🗺️
#Day30
of
#30DayMapChallenge
🔚To end this, three tutorials to reproduce with
#rstats
some of the maps I made during the challenge. All data included, just have to follow the code
🔗Part 1: How to make choropleths and cartograms
⏳For
#Day19
(5 min map) of
#30DayMapChallenge
, a Dorling cartogram of countries GDP per capita
🔗
#RStats
code:
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🆕New tutorial for
@R_Graph_Gallery
: 'customized' Dorling cartogram with
#RStats
Dorling cartogram replaces polygons with circles proportional to one given variable. This tutorial show how it can be adapted to also plot the value of sub-variables.
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🗺️
#Day18
(atmosphere) of
#30DayMapChallenge
🤒How much CO2 emissions per country? Well, it depends on the indicator! I read a nice article about that on
@OurWorldInData
, tried to summarize it with maps here.
🔗
#RStats
code:
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🔵🔴🟡Wanted to try the trivariate choropleth script of
#Day13
of
#30DayMapChallenge
on another country, so here is a map of soil texture for contiguous USA
🔗
#RStats
code:
#Day13
(choropleth) of
#30DayMapChallenge
A trivariate choropleth highlighting soil texture differences in France (clay is the smallest soil particle size, silt the intermediate particle size, sand the largest particle size)
#RStats
code:
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277
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14
124
As suggested by
@Nari_Dorey
, an animation of NDVI seasonal evolution in France to complete my little
#gischat
project on vegetation mapping
It clearly shows a more favorable summer 2021 for vegetation
#RStats
code
(all data online so fully reproducible)
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🎉Very happy to present the cover illustration for the 2nd edition of "Geocomputation with R" by
@robinlovelace
,
@jakub_nowosad
and Jannes Muenchow!
🔗Blog post explaining the design process:
🔗Full
#RStats
code for the cover:
Having withdrawal symptoms from the completion of the
#30DayMapChallenge
yesterday, want to make more maps AND contribute to an open source educational project? The second Geocomputation with front cover competition may be for you!
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Hexagons in hexagon for
#Day12
of
#30DayChartChallenge
(Distribution X BBC style) !
Wheat distribution in France : soft winter wheat is by far the most cultivated crop in the country (mainly in the North, where yields are the highest)
#RStats
code
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🇪🇺For
@EU_Eurostat
data
#Day18
of
#30DayChartChallenge
, a
#DataViz
showing the cropping patterns of the member states in a shape inspired by the European Parliament
🔗
#RStats
code
🔗data
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Following the trend of the
#30DayChartChallenge
wrap-up (also thx to
@bigdatadipper
kind words!), a basic website with all my
#dataviz
for the challenge:
🔗
👏Thx to
@CedScherer
,
@dr_xeo
,
@ShijiaWendy
and
@shinysci
for organizing!
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🗺️
#Day14
(Europe) of
#30DayMapChallenge
💧Soil drought in Europe: my contribution to the
@esaclimate
Little Picture
#dataviz
challenge, using Copernicus data
🔗
#RStats
code:
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🕳️For
#Day9
of
#30DayChartChallenge
(distribution X major/minor), a
#dataviz
showing the creation of the yield gap between countries with the lowest and highest wheat yields
🔗Link to
#RStats
code:
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#Day9
of
#30DayChartChallenge
(high/low) : an old
#TidyTuesday
to illustrate the age gap between Hollywood movies couples
🔗
#Rstats
code
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Picked a flora related theme for
#day3
of
#30DayChartChallenge
🌽vs☘️: Crops or grasslands, what is the main land use for the agricultural land of each country?
#gischat
🔗
#Rstats
code
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#Day27
of
#30DayChartChallenge
(good/bad)
Old
#TidyTuesday
to compare the good/bad feelings associated with specific words between three African languages in Twitter
🔗
#rstats
code here
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🗑️For
#TidyTuesday
, the analysis of items collected by Trash Wheels in Baltimore harbour show that plastic bottles are by far the main trash found in the water. One more reason to replace them with reusable bottles!
🔗
#RStats
code:
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For
#Day11
(retro) of
#30DayMapChallenge
, a map reproducing Bertin's valued points, updated with recent population density for France
Original & new map below
#RStats
code:
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As the theme is inspiring, I couldn't help but try a new type of map with
#Rstats
for
#MapPromptMonday
: kind of a "nested" Dorling cartogram
It clearly shows the agricultural specialization of the different continents
Inspiration for this map is presented in the next tweet ⬇️
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🌐For theme
#Day24
UN Woman of
#30DayChartChallenge
, a bivariate choropleth showing both the % of farm workers and the % of women among them, using data from FAOStats (UN database related to farming)
🔗
#Rstats
code
#gischat
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An adaptation of last
#TidyTuesday
with a
#DataViz
to compare the feelings associated with specific words between three languages
#rstats
code here
Ethiopian Language Sentiment analysis for
#TidyTuesday
: religious terms are mostly tweeted in a positive way, whereas the opinion of others is poorly considered...
#RStats
code :
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For
#Day8
(Africa) of
#30DayMapChallenge
, a map about locust invasions in the continent
#RStats
code:
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To illustrate
#Day17
of
#30DayChartChallenge
, some
#DataViz
from a paper where we used network analysis to (i) evaluate the return time of crops and (ii) compare this return time with the use of pesticides
🔗to paper
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#30DayChartChallenge
|
#Day17
| Network
🌍The global food trade, a
#dataviz
showing the exchange networks associated with some commodities. Agricultural products are often associated with a particular country/region
🔗
#RStats
code:
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🚆Combining
#Day2
(line) and
#Day3
(polygon) of
#30daymapchallenge
to analyze the railway network density per department in France.
Data:
@SNCFVoyageurs
Also discovered that there is one train track for the army in the South of France
#Rstats
code :
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📷
#Day21
(raster) of
#30DayMapChallenge
🌱NDVI evolution in France (🟢= more vegetation)
🔗
#RStats
code:
⬇️More countries in next posts, to compare their "degree of greeness" ⬇️
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Hard to imagine that Bertin was doing that in the 1960’s … that is 40 years before {ggplot2} !
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For
#Day19
of
#30DayChartChallenge
(time series X anthropocene), I picked a time serie about bird population evolution in France from 1989 to 2017
It shows the various impact of human activities on bird species
🔗
#RStats
code :
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#Day7
of
#30DayChartChallenge
(distributionXhazards): geographic distribution of locust swarms that cause massive crop damage in Africa, especially in recent years
#Rstats
code
Data
@FAOstatistics
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⏳For
#Day19
(5 min map) of
#30DayMapChallenge
, a Dorling cartogram of countries GDP per capita
🔗
#RStats
code:
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List of topics for this year
#30DayMapChallenge
is out and if you're looking for an idea for
#Day13
(choropleth),
@R_Graph_Gallery
just posted a tutorial to reproduce the Lego map I created last week:
🍾Really happy to be part of this amazing site!
Subtle change, but after a discussion with
@LouisNadalin
I added a shading effect on the right to give a 3D effect to the
#Lego
blocks
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🌱For
#Day1
of
#30DayChartChallenge
(part-to-whole), a comparison of the amount of wheat produced by different regions and countries of the world
🔗Link to
#RStats
code :
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A glimpse of some textless figures sent to my editor for the cover of my first book: a collection of
#dataviz
about farming!
Book will be in French, but it will be published in February 2024, so there's still plenty of time to learn the language if you're interested 😉
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As i rely heavily on
@esaclimate
data for my research activities, I couldn't miss this dataviz contest!
Here is my submission, using Combined Drought Indicator data for year 2022 (a very dry year)
#RStats
code for the map:
Are you into
#climate
#scicomms
&
#dataviz
?
Our new competition challenges you to use satellite data to create a compelling visual to illustrate how our climate is changing or being impacted, to spark awareness and drive
#climateaction
.
Winning submission wto be presented
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🗺️
#Day28
(chart or map) of
#30DayMapChallenge
⚠️This is not a map⚠️
My fav topic, so I tried to comply as closely as possible with the prompt with a treemap inside a map
🔗
#RStats
code if you're curious (map only, annotations added with Figma):
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Crop distribution in France: a comparison of total crop area vs organic fields only
For most crops, organic production areas are not the same as conventional ones
Color palette by
@fcrameri
#useBatlow
#RStats
code example at
#gischat
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🧇Today's prompt of
#30DayChartChallenge
is "waffle".
🥧Classically used to represent proportions, waffle charts can also be adapted to reproduce distribution curves.
🔗Here is a tutorial to create that kind of plots with
#RStats
:
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#Day30
of
#30DayChartChallenge
:
@worldbankdata
dataset can help reduce spatial uncertainty in agricultural economic statistics
🇫🇷Case study in France shows the regional specialisation (livestock in Brittany, pine wood in South-West...)
🔗
#Rstats
code
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🗺️
#Day10
(North America) of
#30DayMapChallenge
📚"Fifty Shapes of North America". A (non-erotic) comparison of world map projections
The shape of the continent changes depending on the projection used to plot in 2D
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Printed version as a gift for a friend who teaches zootechnics !
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🐮For
#Day4
of
#30DayChartChallenge
(waffle), a comparison of the share of cereals used as animal feeds per continent
🔗
#RStats
code :
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🛸UFO sightings for
#TidyTuesday
👽Seems that the aliens who travel by lighted flying saucer visit the United States more often than those who travel by round flying saucer...
🔗
#RStats
code:
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Old
#TidyTuesday
dataset and a spiral chart for
#Day11
of
#30DayChartChallenge
(circular) : evolution of drought in California
Two multiannual drought episodes (beyond summer) are visible on the spiral
#RStats
code for the spiral:
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🧵As the
#30DayMapChallenge
is approaching, here is a short thread showing the steps to create this simple map with
#rstats
!
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🗺️
#Day23
(3D) of
#30DayMapChallenge
⏱️Hope time can count as a third dimension, coz here it is the annual vegetation evolution in Europe (based on Modis NDVI)
🔗
#RStats
code:
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🌻For
#Day8
(distributionXcircular) of
#30DayChartChallenge
, a repost of the distribution of sunflower yields between the Top10 producers
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10
71
🗺️
#Day26
(minimal) of
#30DayMapChallenge
🚴Schematic comparison of cycle ways density in some European capitals
🔗
#RStats
code:
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For this
#TidyTuesday
about Valentine's day spending in the US:
As we age, spending for Valentine's Day declines and evolves: greeting cards are becoming the main expense item, while the amount dedicated to celebrations is declining.
#RStats
code:
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Took the opportunity of this
#TidyTuesday
to try a "tidy" version of
@neocartocnrs
method to create a dot cartogram with
#RStats
(thx for sharing!)
Also
#mappromptmonday
(world)
#RStats
code:
Dans
#neocarto
, on vous propose deux billets où on vous cause des
#dotcartograms
.
#geoviz
#dataviz
👉
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For
#Day1
of
#30DayChartChallenge
here is a
#dataviz
to illustrate the concept of the eco-holobiont
#RStats
code to create the circles with the abundance for each compartment:
All data used are directly available in the {phyloseq} package
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Our main activities are relatively similar between countries, with some differences depending on economic development (based on this week
#TidyTuesday
)
#RStats
code:
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🎉Très heureux de vous présenter le premier exemplaire de mon livre, "Mémo visuel d'agronomie", publié chez
@dunod_editeur
!
👀Voici un petit aperçu de l'ouvrage...
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#Day4
of
#30DayChartChallenge
(historical) : this
#dataviz
show the evolution and the percentage of world population fed by nitrogen fertilizers since the discovery of the Haber-Bosch process (perhaps the most important invention ever!)
🔗
#Rstats
code
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Following France, the evolution of vegetation for 3 more countries, with highly contrasting situations: Italy, Ireland and Niger
🔗to
#RStats
code:
Italy:
Ireland:
Niger:
#gischat
As suggested by
@Nari_Dorey
, an animation of NDVI seasonal evolution in France to complete my little
#gischat
project on vegetation mapping
It clearly shows a more favorable summer 2021 for vegetation
#RStats
code
(all data online so fully reproducible)
4
26
120
2
11
63
🗺️
#Day15
(Open Street Map) of
#30DayMapChallenge
🚲Evaluation of the degree of bicycle-friendliness of some european capitals (🟡= more cycleways than roadways)
Data from OSM,
#RStats
code for maps at this link:
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10
63
#Day21
of
#30DayChartChallenge
: Time seriesXDown/Up
A very long time series collected by Y. Aono to highlight the influence of human activities on cherry flowering. Fascinating process for data gathering!
#Rstats
code
2
10
63
Small update to
#RStats
{bertin} package: it may now works with raster too, with the make_points_rst() function
Vignette at this link:
⬇️Small example on the pic below ⬇️
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7
63
Old
#TidyTuesday
for
#Day15
of
#30DayChartChallenge
to illustrate the positive/negative differences of local time and sun time around the world
🔗
#RStats
code
0
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#Day29
of
#30DayChartChallenge
| uncertainties X black'n'white
"Sometimes life is merely a matter of coffee and whatever intimacy a cup of coffee affords."
Richard Brautigan
Link to
#Rstats
code:
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🗺️
#Day16
(Oceania) of
#30DayMapChallenge
🇦🇺🌏Recycling an old
#TidyTuesday
about numbat population in Australia
🔗
#RStats
code:
1
9
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👻Haunted places per US state for
#TidyTuesday
🇺🇸California is the state with the most haunted places in the contiguous United States (1,070 locations)
🔗
#RStats
code here:
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