직접 선 세그먼트의 혼란을 시각적으로 요약

수백만 개의 직접 선 세그먼트 데이터 세트가 있습니다. 선분은 순차적입니다. 기후 변수 (현열)이며 30 분 간격으로 관찰 및 시뮬레이션 된 값을 갖습니다. 시뮬레이션이 수행되는 방식에서 패턴을 찾으려고합니다. obs vs 시뮬레이션 값의 산점도를보고 선분과 연결합니다 (화살표는 시간 방향을 나타냄). 내가 플롯하면 다음과 같이 해석하기 어려운 흐릿한 혼란이 생깁니다.

이것은 10000 라인의 하위 집합으로, 잘 그려지고 불투명도가 낮습니다.

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() 첫 700 행의 데이터 중 (게스트 길이로 제한) :

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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        ),
    .Names = c( "Qh_sim", "Qh_obs"),
    row.names = c( NA, -700L),
    class = c( "data.table", "data.frame"),
    .internal.selfref = <pointer: 0x66d468>
    )

이 라인의 흐름을 시각적으로 요약하려고합니다. 이런 종류의 정보를 요약하는 일반적인 방법이 있습니까? 벡터 필드 플롯을 할 수는 있지만 관련 정보를 잃을 것이라고 생각합니다. 특히 일부 밀도가 높은 영역에는 여러 그룹의 세그먼트가있을 수 있습니다. 세그먼트를 클러스터링 (4 개의 변수를 기능으로 사용) 한 다음 평균을 플로팅하거나 풍향의 벡터 필드를 수행하는 것을 고려하고 있지만 더 좋은 방법이 있는지 알고 싶었습니다.

또한 시간 청크로 분할하여 패싯 플롯을 만들 수도 있습니다. 이는 흐름이 몇 번 반복해서 진행되는 경우에 도움이됩니다.

흐름의 일부 감각을 얻기 위해 세그먼트를 부드럽게하는 것이 가능할 수도 있지만 패싯이 첫 번째 단계라고 생각합니다.

하나의 가능성은 하루에 평균 1,000 개 이상이 아닌 하루에 하나 또는 몇 개의 "평균"주기를 얻는 것입니다.

또 다른 아이디어는 Tominski, Schumann, Andrienko 및 Andrienko (2012)와 같은 3D 표현 일 수 있습니다 .

참고:

Tominski, C., Schumann, H., Andrienko, G., Andrienko, N. (2012). 궤적 속성 데이터의 스택 기반 시각화. 시각화 및 컴퓨터 그래픽, IEEE 트랜잭션 on vol.18, no.12, pp.2565,2574, 2012 년 12 월. doi : 10.1109 / TVCG.2012.265. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6327262&isnumber=6327196