有向線分の混乱を視覚的に要約する

何百万もの有向線分のデータセットがあります。ラインセグメントは連続しています。これは気候変数（顕熱）であり、30分間隔で観測およびシミュレーションされた値を持ちます。シミュレーションのパフォーマンスのパターンを探しています。obs対シミュレーション値の散布図を見て、それらを線分にリンクしています（矢印は時間の方向を示しています）。それらをプロットすると、次のように解釈することが不可能なぼやけた混乱を得るだけです：

これは、10000行のサブセットであり、細かく、不透明度が低くプロットされています。

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() 最初の700行のデータ（投稿の長さによって制限されます）：

structure(
    list(
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これらの線の流れを視覚的に要約することに興味があります。この種の情報を要約する一般的な方法はありますか？ベクトルフィールドプロットを実行することもできましたが、関連する情報が失われると思います。特に、より密度の高い領域の一部には、複数のセグメントグループがある可能性があります。（4つの変数を特徴として使用して）セグメントをクラスター化し、平均をプロットするか、おそらくは風配図のベクトルフィールドを実行することを検討していますが、これに対処するためのより良い方法があるかどうかを確認したいと思いました。

また、時間チャンクに分割してファセットプロットを作成することもできます。これは、フローが数回往復する場合に役立ちます。

セグメントをスムーズにしてフローの感覚をつかむことも可能かもしれませんが、ファセットは最初のステップだと思います。

1日の平均では、1,000日以上の個別の日数ではなく、1つまたはいくつかの「平均」日サイクルを取得できます。

別のアイデアは、Tominski、Schumann、Andrienko、Andrienko（2012）のような3D表現かもしれません。

参照：

Tominski、C.、Schumann、H.、Andrienko、G.、Andrienko、N.（2012）。軌跡属性データのスタッキングベースの視覚化。Visualization and Computer Graphics、IEEE Transactions on、vol.18、no.12、pp.2565,2574、Dec. 2012. doi：10.1109 / TVCG.2012.265。http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6327262&isnumber=6327196