Resumindo visualmente uma bagunça de segmentos de linha direcionados

9

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:

linhas

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(
    list(
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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.

naught101
fonte
11
Se você as plotar em cores que representam a direção, verá padrões? O que me faz pensar nisso é que, quando há fortes tempestades, eu vejo imagens de radar na TV, onde a cor é baseada na direção do vento, e tornados podem ser vistos procurando lugares onde há uma mudança abrupta de cor.
Russ Lenth
Essa é uma ótima ideia. Esqueci completamente de usar outras variáveis ​​visuais. Suponho que também possa indicar o número de elementos em um cluster com espessura de linha.
precisa saber é o seguinte
A outra coisa em que consigo pensar é em algum tipo de plot dinâmico ou animado, com um controle deslizante que seleciona uma janela estreita no tempo, mas pode ser movido suavemente para que você possa ver como as coisas mudam.
Russ Lenth
Sim, também vale a pena tentar, mas eu estou olhando principalmente para um ciclo diário aqui, e os dados têm intervalos de 30 minutos e 3 a 7 anos, para que possam funcionar, mas pode ser difícil de processar.
precisa saber é o seguinte
4
Você escreve que possui segmentos , mas no gráfico você realmente parece ter uma trajetória linear por partes (ou seja, todos os segmentos são unidos de ponta a ponta)? É assim mesmo? Se sim, convém editá-lo em sua pergunta, pois isso pode influenciar possíveis respostas (das quais, infelizmente, eu não tenho idéia).
Stephan Kolassa

Respostas:

4

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.

Dianne Cook
fonte
1

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

shadowtalker
fonte
Essas são algumas tramas legais, mas na verdade estão lidando com dados 3D que fazem sentido para a concepção humana de relações espaciais (lat, longo, tempo; embora na verdade seja apenas 2D: tempo e distância ao longo da estrada). Não sei como traduzir isso para essa situação. Parcelas médias diárias são interessantes, mas solta um monte de informações (por exemplo, o que acontece em dias com manhãs quentes e tardes chuvosas, etc.
naught101