Visuelles Zusammenfassen eines Durcheinanders gerichteter Liniensegmente

Ich habe einen Datensatz von Millionen gerichteter Liniensegmente. Die Liniensegmente sind sequentiell - es handelt sich um eine Klimavariable (fühlbare Wärme) mit beobachteten und simulierten Werten in halbstündlichen Intervallen. Ich versuche nach Mustern für die Leistung der Simulation zu suchen. Ich betrachte ein Streudiagramm von obs gegen Simulationswerte und verknüpfe sie mit Liniensegmenten (Pfeile geben die Richtung der Zeit an). Wenn ich sie zeichne, bekomme ich nur ein verschwommenes Durcheinander, das unmöglich zu interpretieren ist:

Dies ist eine Teilmenge von 10000 Linien, die fein und mit geringer Deckkraft aufgetragen sind:

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() der ersten 700 Datenzeilen (begrenzt durch die Postlänge):

structure(
    list(
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Ich bin daran interessiert, den Fluss dieser Linien visuell zusammenzufassen. Gibt es übliche Möglichkeiten, diese Art von Informationen zusammenzufassen? Ich könnte einfach ein Vektorfelddiagramm erstellen, aber ich denke, ich würde einige relevante Informationen verlieren - insbesondere haben einige der dichteren Bereiche wahrscheinlich mehrere Gruppen von Segmenten. Ich denke darüber nach, die Segmente zu gruppieren (unter Verwendung der vier Variablen als Merkmale) und dann die Durchschnittswerte zu zeichnen oder vielleicht ein Vektorfeld aus Windrosen zu erstellen, aber ich wollte sehen, ob es bessere Möglichkeiten gibt, dies zu tun.

Sie können auch in Zeitabschnitte aufteilen und facettierte Diagramme erstellen. Dies würde helfen, wenn der Fluss einige Male über sich selbst hin und her geht.

Es mag möglich sein, die Segmente zu glätten, um auch einen Eindruck vom Fluss zu bekommen, aber ich denke, dass das Facettieren ein erster Schritt wäre.

Eine Möglichkeit wäre, über Tage zu mitteln, um einen oder einige "durchschnittliche" tägliche Zyklen anstelle von mehr als 1.000 einzelnen Tagen zu erhalten.

Eine andere Idee könnte eine 3D-Darstellung wie in Tominski, Schumann, Andrienko und Andrienko (2012) sein .

Referenz:

Tominski, C., Schumann, H., Andrienko, G., Andrienko, N. (2012). Stapelbasierte Visualisierung von Trajektorienattributdaten. Visualization and Computer Graphics, IEEE Transactions on , Band 18, Nr. 12, S. 2565, 2574, Dezember 2012. doi: 10.1109 / TVCG.2012.265. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6327262&isnumber=6327196