Resumindo visualmente uma bagunça de segmentos de linha direcionados

Eu tenho um conjunto de dados de milhões de segmentos de linha direcionados. Os segmentos de linha são seqüenciais - é uma variável climática (calor sensível), com valores observados e simulados em intervalos de meia hora. Estou tentando procurar padrões no desempenho da simulação. Estou olhando para um gráfico de dispersão de valores obs vs simulação e vinculando-os a segmentos de linha (as setas indicam a direção do tempo). Se eu traçá-los, fico com uma bagunça turva que é impossível de interpretar, assim:

Este é um subconjunto de 10.000 linhas, plotadas com precisão e com baixa opacidade:

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() das primeiras 700 linhas de dados (limitadas pelo tamanho da postagem):

structure(
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Estou interessado em tentar resumir visualmente o fluxo dessas linhas. Existem maneiras comuns de resumir esse tipo de informação? Eu poderia apenas fazer um gráfico de campo vetorial, mas acho que perderia algumas informações relevantes - em particular, algumas das áreas mais densas provavelmente têm vários grupos de segmentos. Estou pensando em agrupar os segmentos (usando as quatro variáveis ​​como recursos) e depois traçar as médias, ou talvez fazer um campo vetorial de rosas do vento, mas queria ver se havia alguma maneira melhor de fazê-lo.

Você também pode dividir em pedaços de tempo e fazer gráficos facetados. Isso ajudaria se o fluxo voltar e avançar algumas vezes.

Pode ser possível suavizar os segmentos para ter uma noção do fluxo também, mas acho que a faceta seria o primeiro passo.

Uma possibilidade seria fazer a média entre dias, obter um ou alguns ciclos diários "médios" em vez de mais de 1.000 dias individuais.

Outra idéia pode ser uma representação 3D como em Tominski, Schumann, Andrienko e Andrienko (2012) .

Referência:

Tominski, C., Schumann, H., Andrienko, G., Andrienko, N. (2012). Visualização baseada em empilhamento de dados de atributos de trajetória. Visualização e computação gráfica, transações IEEE on , vol.18, no.12, pp.2565.2574, dez. 2012. doi: 10.1109 / TVCG.2012.265. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6327262&isnumber=6327196