TTT
Flux et champs vectoriel
pour la visualisation de matrice OD

Installation de ttt

remotes::install_github("tributetotobler/ttt")
library(ttt)
library(sf)
library(dplyr)

Exercise 1 : Un exemple historique, les dollars de Tobler

Lire des données

Importez les données dollars contenues dans le package ttt et regardez leurs structure.

Indices

Utilisez data(dollars) et str(dollars)

Solution

data(dollars)
str(dollars)

List of 3
 $ OD       : int [1:12, 1:12] 2040 602 143 68 150 122 97 31 14 20 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:12] "Boston" "New York" "Philadelphia" "Cleveland" ...
  .. ..$ : chr [1:12] "Boston" "New York" "Philadelphia" "Cleveland" ...
 $ coords   :'data.frame':  12 obs. of  5 variables:
  ..$ V1: logi [1:12] NA NA NA NA NA NA ...
  ..$ V2: num [1:12] 65.4 59.7 59.8 53.2 57.8 ...
  ..$ V3: logi [1:12] NA NA NA NA NA NA ...
  ..$ V4: num [1:12] 37.2 33.3 29.9 27.2 22.6 ...
  ..$ V5: logi [1:12] NA NA NA NA NA NA ...
 $ polygones:Classes 'sf' and 'data.frame': 12 obs. of  2 variables:
  ..$ geom:sfc_POLYGON of length 12; first list element: List of 1
  .. ..$ : num [1:10, 1:2] 64 67 66 66 69 67 65 64 62 64 ...
  .. ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
  ..$ name: chr [1:12] "Boston" "New York" "Philadelphia" "Cleveland" ...
  ..- attr(*, "sf_column")= chr "geom"
  ..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA
  .. ..- attr(*, "names")= chr "name"

Structure des données

Vous devriez avoir dans votre environnement une liste dollars avec trois champs :

• un champ OD contenant une matrice OD de taille 12 x 12 contenant des flux
• un champ polygones contenant les contours des 12 régions monétaires des USA

Calculez les balances

Calculez les balances en chaque nœuds de la matrice OD et stockez le résultats dans une colonne delta de la data.frame polygones.

Indices

Utilisez ?rowSums et ?colSums

Solution

delta = rowSums(dollars$OD)-colSums(dollars$OD)
dollars$polygones$delta=delta

Carte +/-

Faire une carte en +/- des balances.

Indices

Utilisez la fonction ?plus_minus_map.

Solution

plot(st_geometry(dollars$polygones))
plus_minus_map(dollars$polygones,"delta")

Calcul du potentiel

Calculez le potentiel de poisson de ce jeu de données.

Indices

Utilisez la fonction ?poisson.potential.

Solution

pot.dollars = compute_poisson_potential(dollars$polygones,nb_it = 5000)

Building grid
Cleanning borders
Grid is build, it contains 1614 cells
Grid weighting performed
Solving poisson equations
mse: 22.3897897645124
mse: 20.197521032228
mse: 18.6065988326793
mse: 17.4846707417539
mse: 16.4880604660814
mse: 15.7189885653791
mse: 14.9856094686621
mse: 14.3952472203219
mse: 13.81594707792
mse: 13.3351743172512
mse: 12.8590743127507
mse: 12.4533527610829
mse: 12.0518450536298
mse: 11.7012285411363
mse: 11.356253268265
mse: 11.0480427831814
mse: 10.7473199874747
mse: 10.4728568118532
mse: 10.2076082289713
mse: 9.96068579042728
mse: 9.72440835534995
mse: 9.50040716834261
mse: 9.28815240706927
mse: 9.08353263102317
mse: 8.89145495724106
mse: 8.70344058033982
mse: 8.52849987200206
mse: 8.35487247449119
mse: 8.19463057607209
mse: 8.03358923615973
mse: 7.88606640585639
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mse: 7.20171980086058
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mse: 0.000562113136512569
mse: 0.000564737073095511
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mse: 0.000560925231801974
mse: 0.00055705592062194
mse: 0.000559663278431652
mse: 0.000555802406606513
mse: 0.00055840563065647
mse: 0.000554553179124492
mse: 0.00055715227379105
mse: 0.000553308223538097
mse: 0.00055590319319989
mse: 0.000552067525259849
mse: 0.000554658374296042
mse: 0.000550831069751827
mse: 0.000553417802544295
mse: 0.000549598842526667
mse: 0.000552181463457969
mse: 0.000548370829146136
mse: 0.000550949342600442
mse: 0.000547147015220735
mse: 0.000549721425584179
mse: 0.000545927386409978
mse: 0.000548497698068949
mse: 0.000544711928422487
mse: 0.000547278145765996
mse: 0.0005435006270168
mse: 0.000546062754434173
mse: 0.000542293467998452
mse: 0.000544851509881691
mse: 0.000541090437223086
mse: 0.000543644397963979
mse: 0.000539891520592667
mse: 0.000542441404584762
mse: 0.000538696704058651
mse: 0.000541242515696753
mse: 0.00053750597361973
mse: 0.000540047717300116
mse: 0.000536319315323131
mse: 0.000538856995442882
mse: 0.000535136715262709
mse: 0.000537670336219705
mse: 0.000533958159581108
mse: 0.000536487725774585
mse: 0.000532783634466561
mse: 0.000535309150297532
mse: 0.000531613126156244
mse: 0.000534134596025271
mse: 0.000530446620932778
mse: 0.000532964049242504
mse: 0.00052928410512635
mse: 0.000531797496280412
mse: 0.000528125565113732
mse: 0.00053063492351607
mse: 0.000526970987318148
mse: 0.000529476317373995
mse: 0.000525820358209039
mse: 0.000528321664324265
mse: 0.000524673664302535
mse: 0.000527170950883861
mse: 0.00052353089215971
mse: 0.000526024163615297
mse: 0.000522392028389084
mse: 0.000524881289126854
mse: 0.000521257059643438
mse: 0.000523742314072699
mse: 0.000520125972621204
mse: 0.000522607225151918
mse: 0.000518998754067485
mse: 0.000521476009110426
mse: 0.000517875390771835
mse: 0.000520348652737902
mse: 0.000516755869568315
mse: 0.000519225142869497
mse: 0.000515640177336381
mse: 0.000518105466385584
mse: 0.000514528301000819
mse: 0.000516989610210753
mse: 0.000513420227530146
mse: 0.000515877561314521
mse: 0.000512315943938094
mse: 0.000514769306710743
mse: 0.000511215437282183
mse: 0.000513664833457834
mse: 0.000510118694664145
mse: 0.00051256412865791
mse: 0.000509025703230087
mse: 0.000511467179457598
mse: 0.00050793645016984
mse: 0.000510373973046903
mse: 0.00050685092271692
mse: 0.0005092844966597
mse: 0.000505769108147961
mse: 0.000508198737573659
mse: 0.00050469099378387
mse: 0.00050711668310883
mse: 0.000503616566988202
mse: 0.000506038320630328
mse: 0.000502545815168589
mse: 0.000504963637544876
mse: 0.000501478725774104
mse: 0.000503892621302289
mse: 0.000500415286297324
mse: 0.000502825259396098
mse: 0.000499355484274565
mse: 0.000501761539361506
mse: 0.000498299307282607
mse: 0.000500701448775882
mse: 0.000497246742942124
mse: 0.000499644975260492
mse: 0.000496197778915536
mse: 0.00049859210647787
mse: 0.000495152402908259
mse: 0.00049754283013283
mse: 0.00049411060266593
mse: 0.000496497133971931
mse: 0.00049307236597786
mse: 0.000495455005784119
mse: 0.000492037680673787
mse: 0.00049441643339923
mse: 0.000491006534625714
mse: 0.000493381404688495
mse: 0.000489978915746339
mse: 0.000492349907565423
mse: 0.00048895481198991
mse: 0.000491321929983935
mse: 0.000487934211351889
mse: 0.000490297459939674
mse: 0.000486917101868687
mse: 0.000489276485468421
mse: 0.000485903471617447
mse: 0.000488258994647805
mse: 0.000484893308715877
mse: 0.000487244975595136
mse: 0.000483886601323554
mse: 0.000486234416469468
mse: 0.000482883337638363
mse: 0.000485227305468458
mse: 0.000481883505899562
mse: 0.000484223630831758
mse: 0.000480887094386775
mse: 0.000483223380837915
mse: 0.000479894091418916
mse: 0.000482226543806045
mse: 0.000478904485354935
mse: 0.000481233108095104
mse: 0.000477918264594423
mse: 0.000480243062103509
mse: 0.00047693541757525
mse: 0.000479256394269335
mse: 0.000475955932775641
mse: 0.000478273093069994
mse: 0.000474979798712525
mse: 0.000477293147022861
mse: 0.000474007003943026
mse: 0.000476316544683785
mse: 0.00047303753706178
mse: 0.000475343274647237
mse: 0.000472071386703798
mse: 0.000474373325547622
mse: 0.000471108541542069
mse: 0.00047340668605791
mse: 0.000470148990288492
mse: 0.00047244334488888
mse: 0.000469192721693571
mse: 0.000471483290790855
mse: 0.000468239724546459
mse: 0.000470526512551946
mse: 0.00046728998767301
mse: 0.000469572998998356
mse: 0.000466343499939433
mse: 0.000468622738994374
mse: 0.000465400250248195
mse: 0.000467675721442979
mse: 0.000464460227540928
mse: 0.00046673193528481
mse: 0.000463523420796516
mse: 0.00046579136949744
mse: 0.000462589819030517
mse: 0.000464854013096865
mse: 0.000461659411297774
mse: 0.000463919855136522
mse: 0.000460732186689198
mse: 0.000462988884706584
mse: 0.000459808134333229
mse: 0.000462061090934877
mse: 0.00045888724339565
mse: 0.000461136462985968
mse: 0.000457969503078318
mse: 0.000460214990061502
mse: 0.000457054902621529
mse: 0.000459296661400684
mse: 0.00045614343130092
mse: 0.000458381466278788
mse: 0.000455235078429592
mse: 0.000457469394006695
mse: 0.000454329833356483
mse: 0.000456560433933834
mse: 0.000453427685467375
mse: 0.000455654575443904
mse: 0.000452528624182836
mse: 0.000454751807958356
mse: 0.000451632638962956
mse: 0.000453852120934258
mse: 0.000450739719300437
mse: 0.000452955503864309
mse: 0.000449849854724514
mse: 0.000452061946276913
mse: 0.000448963034801554
mse: 0.000451171437736771
mse: 0.000448079249131774
mse: 0.000450283967843907
mse: 0.000447198487352286
mse: 0.000449399526233488
mse: 0.000446320739134818
mse: 0.000448518102577092
mse: 0.000445445994186571
mse: 0.000447639686579929
mse: 0.000444574242250061
mse: 0.000446764267983685
mse: 0.000443705473102058
mse: 0.000445891836564788
mse: 0.000442839676555742
mse: 0.000445022382133756
mse: 0.000441976842457378
mse: 0.000444155894537134
mse: 0.000441116960688589
mse: 0.000443292363654642
mse: 0.000440260021165457
mse: 0.000442431779401078
mse: 0.000439406013839064
mse: 0.00044157413172677
mse: 0.000438554928694488
mse: 0.000440719410614683
mse: 0.000437706755750226
mse: 0.0004398676060825
mse: 0.000436861485059288
mse: 0.000439018708182549
mse: 0.000436019106709235
mse: 0.00043817270700003
mse: 0.000435179610820092
mse: 0.000437329592654544
mse: 0.000434342987547171
mse: 0.000436489355299466
mse: 0.000433509227078679
mse: 0.000435651985121961
mse: 0.000432678319636332
mse: 0.000434817472341997
mse: 0.000431850255474896
mse: 0.000433985807213673
mse: 0.000431025024883084
mse: 0.000433156980023308
mse: 0.000430202618181792
mse: 0.000432330981091307
mse: 0.000429383025726441
mse: 0.000431507800770375
mse: 0.000428566237903467
mse: 0.000430687429447143
mse: 0.00042775224513374
mse: 0.000429869857539855
mse: 0.000426941037870316
mse: 0.00042905507550002
mse: 0.000426132606597985
mse: 0.000428243073811325
mse: 0.00042532694183531
mse: 0.000427433842990641
mse: 0.000424524034132649
mse: 0.00042662737358702
mse: 0.000423723874072547
mse: 0.000425823656181049
mse: 0.000422926452269582
mse: 0.000425022681385831
mse: 0.00042213175937064
mse: 0.000424224439846612
mse: 0.000421339786054448
mse: 0.00042342892224045
mse: 0.000420550523031458
mse: 0.000422636119276452
mse: 0.000419763961043882
mse: 0.000421846021695197
mse: 0.000418980090865558
mse: 0.00042105862026856
mse: 0.000418198903301921
mse: 0.000420273905799919
mse: 0.000417420389189598
mse: 0.000419491869125004
Post-processing

Visualisation du potentiel

Regardez la structure de la data.frame retournée et visualisez le champs potentiel stocké dans la colonne attractivity.

Indices

Utilisez la fonction ?str et ?plot ou la librairie ggplot si vous le souhaitez.

Solution

str(pot.dollars)

Classes 'sf' and 'data.frame':  1614 obs. of  10 variables:
 $ grid_in     :sfc_POLYGON of length 1614; first list element: List of 1
  ..$ : num [1:5, 1:2] 33.4 34.5 34.5 33.4 33.4 ...
  ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
 $ i           : num  1 1 2 2 2 2 3 3 3 3 ...
 $ j           : num  31 32 29 30 31 32 29 30 31 32 ...
 $ attractivity: num  309 309 303 304 307 ...
 $ residuals   : num  0.0198 -0.019 -0.0192 0.0197 -0.019 ...
 $ b           : num  2.66 2.66 2.66 2.66 2.66 ...
 $ dx          : num  0 0 0 2.39 2.16 ...
 $ dy          : num  0 0 0 0 -1.94 ...
 $ x           : num  33.9 35 31.9 32.9 33.9 ...
 $ y           : num  4.51 4.51 5.52 5.52 5.52 ...
 - attr(*, "sf_column")= chr "grid_in"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA
  ..- attr(*, "names")= chr [1:9] "i" "j" "attractivity" "residuals" ...

plot(pot.dollars%>% select(attractivity))

Visualisation du champ vectoriel

Visualisez de manière statique le champ vectoriel calculée à l’aide de la fonction poisson_flow_map. N’oubliez pas de régler le paramètre normfact pour obtenir quelque chose de lisible.

Solution

plot(st_geometry(dollars$polygones))
poisson_flows_map(pot.dollars,normfact=0.01)

Exercice 2 : Données de migrations résidentielles

Données

Importez les données FD_MIGCOM_2018AGG.rds à l’aide de la fonction readRDS. Cette data.frame correspond aux données de détails sur les migrations résidentielles française de l’INSEE (https://www.insee.fr/fr/statistiques/5395755?sommaire=5395764#dictionnaire). Cette base de données à juste été agrégée pour ne garder qu’un facteur de variation la catégorie socio-professionnelle CSM de manière à la rendre un peu plus légère.

Solution

migr_agg=readRDS("FD_MIGCOM_2018AGG.rds") 

Gestion des arrondissement municipaux

Dans cette base, la commune de destination est renseignée avec les codes des arrondissements municipaux pour les villes ayant plusieurs arrondissement. Nous allons commencer par créer une table ou les mouvements sont des mouvements de communes à communes. Pour ce faire, créez à partir de la table précédente une table com_arr contenant pour chaque communes ayant des arrondissements le code d’arrondissement (colonne ARM) et le code commune (colonne COMMUNE). Vous devriez obtenir la table suivante :

# A tibble: 45 × 2
   COMMUNE ARM  
   <chr>   <chr>
 1 13055   13201
 2 13055   13202
 3 13055   13203
 4 13055   13204
 5 13055   13205
 6 13055   13206
 7 13055   13207
 8 13055   13208
 9 13055   13209
10 13055   13210
# … with 35 more rows

Solution

com_arr = migr_agg %>% select(COMMUNE,ARM) %>% filter(ARM!="ZZZZZ") %>% distinct()

Gestion des arrondissement municipaux

Utilisez cette table pour faire une jointure et récupérez les colonnes :

• COMMUNE : commune de résidence
• COMMUNE_ANM1 : commune de résidence (année n-1)
• CSM : catégorie socio professionnelle
• IPONDI : poids de la ligne
• DEP : département de résidence
• DEP_ANM1 : département de résidence (année n-1)

Solution

migr_com = migr_agg  %>%
  left_join(com_arr,by=c("DCRAN"="ARM")) %>%
  mutate(COMMUNE_ANM1 = if_else(is.na(COMMUNE.y),DCRAN,COMMUNE.y),COMMUNE=COMMUNE.x) %>%
  select(COMMUNE,COMMUNE_ANM1,CSM,IPONDI) %>%
  mutate(DEP_ANM1= substr(COMMUNE_ANM1,1,2),DEP = substr(COMMUNE,1,2))

Filtrage

Filtrez la table pour ne conserver que :

• les lignes avec CSM == 7
• les lignes ou COMLUNE et COMMUNE_ANM1 diffèrent
• les lignes correspondant à des déménagement de France métropolitaine (hors corse) vers la France métropolitaine (hors corses).

Solution

migr_clean = migr_com %>%
  filter(CSM==7) %>%
  filter(COMMUNE!=COMMUNE_ANM1) %>%
  filter(!(DEP %in% c("2A","2B",96:100)),!(DEP_ANM1 %in% c("2A","2B",96:100))) %>%
  arrange(desc(IPONDI))

Calcul des balances

Calculez pour chaque communes en utilisant la table précédente :

• le total des flux entrants
• le total des flux sortants
• la balance

Solution

migr_in = migr_clean %>% group_by(COMMUNE) %>% summarize(NIN = sum(IPONDI))
migr_out = migr_clean %>% group_by(COMMUNE_ANM1) %>% summarize(NOUT = sum(IPONDI))

delta = migr_in %>% full_join(migr_out,by=c("COMMUNE"="COMMUNE_ANM1")) %>%
  tidyr::replace_na(list(NIN=0,NOUT=0)) %>%
  mutate(delta = NIN-NOUT)

Données géographique

Utilisez la fonction read_sf pour charger le fond de carte communal.

Solution

#remotes::install_github("antuki/CARTElette/CARTElette@RPackage")
#library(CARTElette)
#COM <- charger_carte(COG=2020,nivsupra="COM")
#library(sf)
#library(dplyr)
#write_sf(COM,"COM.gpkg")
COM= read_sf("COM.gpkg")

Données géographique

Filtrez le fond de carte pour ne conserver que les communes de France métropolitaine (hors corse). Faites la jointure avec votre table contenant les flux entrants, sortants totaux et les balances. Enfin projetez en Lambert 93 (code EPSG 2154).

Solution

delta_geo = COM %>%
  filter(!(INSEE_DEP %in% c("2A","2B",971:976))) %>%
  left_join(delta,by=c("INSEE_COM"="COMMUNE")) %>%
  tidyr:: replace_na(list(delta=0)) %>%
  st_transform(2154)

Carte des balances

Réalisez une carte symbole proportionnelle des balances, la taille des symboles encodera la valeur absolue de la balance et la couleur le signe. Réalisez ensuite une carte en symboles +/-.

Solution

delta_dep = delta_geo %>% group_by(INSEE_DEP) %>% summarize(delta=sum(delta)) %>% ungroup() %>% select(delta)
library(ggplot2)
ggplot(delta_dep)+ 
  geom_sf()+ 
  geom_sf(data=delta_dep %>% st_centroid(),aes(size=abs(delta),color=delta))+
  scale_color_gradient2()

Calcul du champ potentiel

Calculez le champ potentiel de poisson sur ce jeu de données.

Solution

pot = compute_poisson_potential(delta_geo,cellsize = 10000,nb_it = 5000)

Building grid
Cleanning borders
Grid is build, it contains 5366 cells
Grid weighting performed
Solving poisson equations
mse: 405.39876469907
mse: 247.364366745157
mse: 182.472691999892
mse: 143.948923615445
mse: 120.516655985184
mse: 102.832768551319
mse: 90.6395876893888
mse: 80.3777364300776
mse: 72.8483841461762
mse: 66.1076536113184
mse: 60.9703805562947
mse: 56.1908046314325
mse: 52.4498326493866
mse: 48.8805351665585
mse: 46.0289430044971
mse: 43.2613958789151
mse: 41.0129681354212
mse: 38.8047728984595
mse: 36.985152545801
mse: 35.1829296905831
mse: 33.679553481871
mse: 32.1813298168493
mse: 30.9181265280262
mse: 29.6533965019274
mse: 28.5770219672638
mse: 27.4954604204795
mse: 26.5673517165228
mse: 25.6320761109438
mse: 24.8236359938068
mse: 24.0070051162004
mse: 23.2965623545823
mse: 22.5774595147078
mse: 21.9482828553549
mse: 21.3103041034935
mse: 20.7492656357087
mse: 20.1794792174317
mse: 19.6761313480264
mse: 19.1642086339881
mse: 18.7101321336659
mse: 18.2477264524846
mse: 17.8360606779774
mse: 17.4163553803642
mse: 17.0414536904889
mse: 16.6588280787972
mse: 16.3160010194589
mse: 15.9657798490253
mse: 15.6511009855399
mse: 15.3293641827033
mse: 15.0395213817484
mse: 14.7429577865861
mse: 14.4751379625304
mse: 14.2009316879155
mse: 13.9527305235237
mse: 13.6984717735628
mse: 13.46782231034
mse: 13.2314367471306
mse: 13.0165523948323
mse: 12.7962447167503
mse: 12.5955733961268
mse: 12.3897819093673
mse: 12.2019688734099
mse: 12.0093286431835
mse: 11.8331861231011
mse: 11.652498876511
mse: 11.4869811383889
mse: 11.3171905218232
mse: 11.1613732452635
mse: 11.0015443592429
mse: 10.854607492526
mse: 10.7039098670328
mse: 10.5651232969556
mse: 10.4228166519221
mse: 10.2915281663364
mse: 10.1569504405462
mse: 10.0325755690599
mse: 9.9051328072504
mse: 9.78714620874043
mse: 9.66630397919524
mse: 9.55423210965591
mse: 9.4395081889086
mse: 9.33292303411078
mse: 9.22388114587263
mse: 9.12239484357799
mse: 9.01863927887946
mse: 8.92189948737764
mse: 8.82307046459101
mse: 8.73075635993744
mse: 8.63652600865641
mse: 8.54834481358069
mse: 8.45841368665893
mse: 8.3740976507558
mse: 8.28819168521631
mse: 8.20749544954477
mse: 8.12536331038511
mse: 8.04806160062656
mse: 7.96947235239559
mse: 7.89535795375431
mse: 7.82009901366091
mse: 7.74898098812198
mse: 7.67685632174174
mse: 7.60855843443991
mse: 7.53938696111849
mse: 7.47374628766091
mse: 7.4073604677135
mse: 7.34422615853439
mse: 7.28047073850262
mse: 7.21970291986363
mse: 7.15843381556755
mse: 7.0999026097805
mse: 7.04098590981708
mse: 6.98457055975501
mse: 6.92788163454508
mse: 6.87346971960689
mse: 6.81889242316089
mse: 6.76637915562896
mse: 6.71380510895244
mse: 6.66309270118769
mse: 6.61242064773367
mse: 6.56341774193179
mse: 6.51455296677358
mse: 6.46717412009667
mse: 6.42002792557521
mse: 6.37419314440549
mse: 6.32868237593147
mse: 6.28431669379235
mse: 6.24036331027803
mse: 6.19739640465387
mse: 6.1549270887339
mse: 6.11329293261037
mse: 6.07223873640123
mse: 6.03187528085786
mse: 5.99217130351622
mse: 5.95302018814366
mse: 5.91460528192697
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mse: 0.0364007124887769
mse: 0.03639231231079
mse: 0.0363440472387599
mse: 0.0363356699155196
mse: 0.0362874749529054
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mse: 0.036230995473722
mse: 0.0362226637078493
mse: 0.0361746086439963
mse: 0.036166299580965
Post-processing

Calcul du champ potentiel

Représentez le champ potentiel et le champ vectoriel.

Solution

plot(pot %>% select(attractivity))

Analyse par catgéorie socio-professionelle

Reprenez la démarche précédente pour construire et représentez les champs associés à d’autres catégories professionnelles. Vous pouvez également étudier les différences de résultats obtenues lorsque les balances sont calculées en prenant en compte les flux hors de la France métropolitaine.

Droplets

Exportez les données associées à un calcul de champ à l’aide de la fonction jsonlite::write_json . Utilisez le notebook Observable disponible à l’adresse https://observablehq.com/@comeetie/ttt-poisson-potential pour l’explorer à l’aide d’une visualisation en droplets. Profitez en pour observez l’influence des paramètres graphiques influençant cette visualisation :

• Nombre de particules
• Vitesse des particules
• Durée de vie des particules
• Coefficient de traînée

jsonlite::write_json(pot,"./potential_migrfr.json")

reproducibility

sessionInfo()

R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C               LC_TIME=fr_FR.UTF-8       
 [4] LC_COLLATE=fr_FR.UTF-8     LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
 [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.3.5 ttt_0.1.0     dplyr_1.0.7   sf_1.0-3      knitr_1.36   

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7         lattice_0.20-44    tidyr_1.1.3        class_7.3-19      
 [5] assertthat_0.2.1   digest_0.6.28      utf8_1.2.2         R6_2.5.1          
 [9] plyr_1.8.6         evaluate_0.14      e1071_1.7-9        httr_1.4.2        
[13] highr_0.9          unilur_0.4.0.9000  pillar_1.6.3       rlang_0.4.11      
[17] lazyeval_0.2.2     rstudioapi_0.13    data.table_1.14.0  jquerylib_0.1.4   
[21] Matrix_1.3-4       rmarkdown_2.8      labeling_0.4.2     stringr_1.4.0     
[25] foreign_0.8-81     htmlwidgets_1.5.4  igraph_1.2.6       munsell_0.5.0     
[29] proxy_0.4-26       compiler_4.1.1     xfun_0.26          pkgconfig_2.0.3   
[33] rgeos_0.5-8        htmltools_0.5.2    tidyselect_1.1.1   tibble_3.1.4      
[37] fansi_0.5.0        viridisLite_0.4.0  withr_2.4.2        crayon_1.4.1      
[41] grid_4.1.1         jsonlite_1.7.2     gtable_0.3.0       lifecycle_1.0.1   
[45] DBI_1.1.1          magrittr_2.0.1     units_0.7-2        scales_1.1.1      
[49] KernSmooth_2.23-20 cli_3.0.1          stringi_1.7.4      farver_2.1.0      
[53] reshape2_1.4.4     cartograflow_1.0.3 sp_1.4-5           bslib_0.3.0       
[57] ellipsis_0.3.2     generics_0.1.0     vctrs_0.3.8        tools_4.1.1       
[61] glue_1.4.2         purrr_0.3.4        fastmap_1.1.0      yaml_2.2.1        
[65] colorspace_2.0-2   maptools_1.1-2     classInt_0.4-3     plotly_4.9.4      
[69] sass_0.4.0