Résumer visuellement un désordre de segments de ligne dirigés

J'ai un ensemble de données de millions de segments de ligne dirigée. Les segments de ligne sont séquentiels - c'est une variable climatique (chaleur sensible), avec des valeurs observées et simulées à intervalles d'une demi-heure. J'essaie de rechercher des modèles dans la façon dont la simulation fonctionne. Je regarde un diagramme de dispersion des valeurs obs vs simulation et les relie aux segments de ligne (les flèches indiquent la direction du temps). Si je les trace, je reçois juste un désordre flou qui est impossible à interpréter, comme ceci:

Il s'agit d'un sous-ensemble de 10000 lignes, tracé fin et à faible opacité:

ggplot(d, aes(x=Qh_obs, xend=lead(Qh_obs), y=Qh_sim, yend=lead(Qh_sim))) +
  geom_segment(size=0.1, alpha=0.2, arrow=arrow(length=unit(2, units='mm')))

dput() des 700 premières lignes de données (limitées par la longueur du message):

structure(
    list(
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Je suis intéressé à essayer de résumer visuellement le flux de ces lignes. Existe-t-il des moyens courants de résumer ce type d'informations? Je pourrais simplement faire un tracé de champ vectoriel, mais je pense que je perdrais des informations pertinentes - en particulier, certaines des zones les plus denses ont probablement plusieurs groupes de segments. J'envisage de regrouper les segments (en utilisant les quatre variables comme caractéristiques), puis de tracer les moyennes, ou peut-être de faire un champ vectoriel de roses des vents, mais je voulais voir s'il y avait de meilleures façons de procéder.

Vous pouvez également vous diviser en morceaux de temps et créer des tracés à facettes. Cela aiderait si le flux allait et venait sur lui-même plusieurs fois.

Il peut être possible de lisser les segments pour avoir une idée du flux aussi, mais je pense que le facettage serait une première étape.

Une possibilité serait de faire la moyenne sur plusieurs jours, pour obtenir un ou quelques cycles quotidiens "moyens" au lieu de 1000+ jours individuels.

Une autre idée pourrait être une représentation 3D comme dans Tominski, Schumann, Andrienko et Andrienko (2012) .

Référence:

Tominski, C., Schumann, H., Andrienko, G., Andrienko, N. (2012). Visualisation basée sur l'empilement des données d'attributs de trajectoire. Visualization and Computer Graphics, IEEE Transactions on , vol.18, no.12, pp.2565,2574, décembre 2012. doi: 10.1109 / TVCG.2012.265. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6327262&isnumber=6327196