Secara visual meringkas kekacauan dari segmen garis terarah

Saya memiliki kumpulan data jutaan segmen saluran terarah. Segmen garis berurutan - ini adalah variabel iklim (panas yang masuk akal), dengan nilai yang diamati dan disimulasikan pada interval setengah jam. Saya mencoba mencari pola bagaimana kinerja simulasi. Saya sedang melihat sebidang plot nilai obs vs simulasi, dan menghubungkannya dengan segmen garis, (panah menunjukkan arah waktu). Jika saya merencanakannya, saya hanya mendapatkan kekacauan yang kabur yang tidak mungkin untuk ditafsirkan, seperti ini:

Ini adalah bagian dari 10.000 baris, diplot dengan baik dan opacity rendah:

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() dari 700 baris data pertama (dibatasi oleh panjang pos):

structure(
    list(
        Qh_sim = c( 56.401439666748, 33.9568634033203, 16.2147789001465, 0.797790050506592, -3.19529962539673, -10.3250732421875, -11.6082448959351, -21.5074787139893, -21.5963478088379, -21.4389324188232, -19.8912830352783, -18.5908279418945, -19.2523441314697, -19.663516998291, -19.1126575469971, -18.4237308502197, -16.6181221008301, -14.7601175308228, -14.5604763031006, -14.3527803421021, -14.6219816207886, -14.791407585144, -15.452392578125, -15.8962726593018, -11.9349966049194, -7.97028636932373, 12.4507570266724, 32.1654815673828, 56.9330673217773, 82.0748443603516, 110.501235961914, 138.745697021484, 150.787033081055, 161.838928222656, 172.895263671875, 184.098617553711, 187.657043457031, 192.906372070312, 193.122268676758, 192.274002075195, 179.315124511719, 164.562591552734, 163.265823364258, 161.703796386719, 133.756286621094, 105.635498046875, 97.0240097045898, 87.8632583618164, 58.3389205932617, 33.2009048461914, 23.2682571411133, 13.6356563568115, 1.51271843910217, 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Saya tertarik mencoba merangkum secara visual alur dari garis-garis ini. Apakah ada cara umum untuk merangkum informasi semacam ini? Saya hanya dapat melakukan plot bidang vektor, tetapi saya pikir saya akan kehilangan beberapa informasi yang relevan - khususnya, beberapa area yang lebih padat mungkin memiliki beberapa kelompok segmen. Saya sedang mempertimbangkan mengelompokkan segmen (menggunakan empat variabel sebagai fitur), dan kemudian merencanakan rata-rata, atau mungkin melakukan bidang vektor mawar angin, tetapi saya ingin melihat apakah ada cara yang lebih baik untuk mengatasinya.

Anda juga dapat memecah menjadi potongan waktu dan membuat plot yang difas. Ini akan membantu jika aliran mundur dan maju sendiri beberapa kali.

Dimungkinkan untuk memuluskan segmen untuk mendapatkan beberapa aliran, tetapi saya pikir facetting akan menjadi langkah pertama.

Satu kemungkinan akan menjadi rata-rata setiap hari, untuk mendapatkan satu atau beberapa siklus harian "rata-rata" alih-alih 1.000 hari per hari.

Gagasan lain mungkin representasi 3D seperti di Tominski, Schumann, Andrienko, dan Andrienko (2012) .

Referensi:

Tominski, C., Schumann, H., Andrienko, G., Andrienko, N. (2012). Visualisasi berdasarkan data atribut lintasan. Visualisasi dan Grafik Komputer, Transaksi IEEE aktif , vol.18, no.12, pp.2565.2574, Desember 2012. doi: 10.1109 / TVCG.2012.265. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6327262&isnumber=6327196