Resumen visual de un desorden de segmentos de línea dirigida


9

Tengo un conjunto de datos de millones de segmentos de línea dirigida. Los segmentos de línea son secuenciales: es una variable climática (calor sensible), con valores observados y simulados a intervalos de media hora. Estoy tratando de buscar patrones en el rendimiento de la simulación. Estoy viendo un diagrama de dispersión de valores de obs vs simulación, y vinculándolos con segmentos de línea (las flechas indican la dirección del tiempo). Si los trazo, me sale un desorden borroso que es imposible de interpretar, así:

líneas

Este es un subconjunto de 10000 líneas, trazadas bien y con baja opacidad:

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() de las primeras 700 filas de datos (limitado por la longitud de la publicación):

structure(
    list(
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Estoy interesado en tratar de resumir visualmente el flujo de estas líneas. ¿Hay alguna forma común de resumir este tipo de información? Simplemente podría hacer un diagrama de campo vectorial, pero creo que perdería información relevante, en particular, algunas de las áreas más densas probablemente tienen múltiples grupos de segmentos. Estoy considerando agrupar los segmentos (usando las cuatro variables como características) y luego trazar los promedios, o tal vez hacer un campo vectorial de rosas de viento, pero quería ver si había alguna forma mejor de hacerlo.


1
Si los traza en colores que representan la dirección, ¿verá patrones? Lo que me hace pensar en esto es que cuando ocurren tormentas severas, he visto imágenes de radar en la televisión donde el color se basa en la dirección del viento, y los tornados se pueden detectar buscando lugares donde haya un cambio abrupto de color.
Russ Lenth

Es una gran idea. Olvidé por completo el uso de otras variables visuales. Supongo que también podría indicar el número de elementos en un clúster con grosor de línea.
naught101

La otra cosa que se me ocurre es algún tipo de trama dinámica o animada, con un control deslizante que selecciona una ventana estrecha en el tiempo pero se puede mover sin problemas para que pueda ver cómo cambian las cosas.
Russ Lenth

Sí, también vale la pena intentarlo, pero aquí estoy mirando principalmente un ciclo diario, y los datos están en intervalos de 30 minutos y duran de 3 a 7 años, por lo que podría funcionar, pero podría ser difícil de procesar.
naught101

44
¿Escribe que tiene segmentos , pero en el gráfico realmente parece tener una trayectoria lineal por partes (es decir, los segmentos están todos unidos de extremo a extremo)? ¿Es eso así? En caso afirmativo, es posible que desee editar esto en su pregunta, ya que puede influir en las posibles respuestas (que desafortunadamente no tengo idea).
Stephan Kolassa

Respuestas:


4

También podría dividirse en fragmentos de tiempo y hacer trazados facetados. Esto ayudaría si el flujo retrocede y avanza sobre sí mismo varias veces.

Es posible suavizar los segmentos para tener una idea del flujo también, pero creo que el facetado sería un primer paso.


1

Una posibilidad sería promediar los días, obtener uno o unos pocos ciclos diarios "promedio" en lugar de más de 1,000 días individuales.

Otra idea podría ser una representación en 3D como en Tominski, Schumann, Andrienko y Andrienko (2012) .

Referencia:

Tominski, C., Schumann, H., Andrienko, G., Andrienko, N. (2012). Visualización basada en apilamiento de datos de atributos de trayectoria. 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


Esas son algunas tramas geniales, pero en realidad están tratando con datos 3D que tienen sentido para la concepción humana de las relaciones espaciales (lat, largo, tiempo; aunque en realidad solo es 2D: tiempo y distancia a lo largo del camino). No estoy seguro de cómo traduciría eso en esta situación. Las parcelas diarias promedio son interesantes, pero pierden mucha información (por ejemplo, lo que sucede en días con mañanas cálidas y tardes lluviosas, etc.)
nada101
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