Résumer visuellement un désordre de segments de ligne dirigés

9

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:

lignes

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.

rien101
la source
1
Si vous les tracez dans des couleurs qui représentent la direction, verrez-vous des motifs? Ce qui me fait penser à cela, c'est que lorsqu'il y a de fortes tempêtes, j'ai vu des images radar à la télévision où la couleur est basée sur la direction du vent, et des tornades peuvent être repérées en recherchant des endroits où il y a un changement de couleur brutal.
Russ Lenth
C'est une bonne idée. J'ai totalement oublié d'utiliser d'autres variables visuelles. Je suppose que je pourrais peut-être aussi indiquer le nombre d'éléments dans un cluster avec un poids de ligne.
naught101
L'autre chose à laquelle je peux penser est un type de graphique dynamique ou animé, avec un curseur qui sélectionne une fenêtre étroite dans le temps mais peut être déplacé en douceur pour que vous puissiez voir comment les choses changent.
Russ Lenth
Oui, cela vaut également la peine d'essayer, mais je regarde principalement un cycle quotidien ici, et les données sont à des intervalles de 30 minutes et durent de 3 à 7 ans, donc cela pourrait fonctionner, mais cela pourrait être difficile à traiter.
naught101
4
Vous écrivez que vous avez des segments , mais dans le graphique, vous semblez en fait avoir une trajectoire linéaire par morceaux (c'est-à-dire que les segments sont tous joints bout à bout)? Est-ce vrai? Si oui, vous voudrez peut-être modifier cela dans votre question, car cela peut influencer les réponses possibles (dont je n'ai malheureusement aucune idée).
Stephan Kolassa

Réponses:

4

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.

Dianne Cook
la source
1

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

shadowtalker
la source
Ce sont des tracés sympas, mais ils traitent en fait de données 3D qui ont du sens pour la conception humaine des relations spatiales (lat, long, temps; bien qu'en réalité ce ne soit que 2D: temps et distance le long de la route). Je ne sais pas comment je traduirais cela dans cette situation. Les tracés quotidiens moyens sont intéressants, mais ils perdent beaucoup d'informations (par exemple, ce qui se passe les jours de matinée chaude et d'après-midi pluvieux, etc.
naught101