流程图（旅行路径）在R中使用Lat和Long [英] Flow map(Travel Path) Using Lat and Long in R

问题描述

我试图绘制流程图（新加坡）。我有Entry（Lat，Long）和Exit（Lat，long）。

 结构（list（token_id = c（1.12374e + 19，1.12374e + 19,1.81313e + 19，
 1.85075e + 19,1.30752e + 19,1.30752e + 19,1.2828e + 19,1.70088e + 19，
 1.70088e + 19， 1.70088e + 19,1.05536e + 19,1.44818e + 19,1.4736e + 19 
 1.44736e + 19 1.44736e + 19 1.44736e + 19 1.89909e + 19 1.15795e + 19 
 1.15795e + 19 1.15795e + 19 1.70234e + 19 1.70234e + 19 1.44062e + 19 
 1.21512e + 19 1.21512e + 19 1.95909e + 19 1.95909e + 19,1.50179e + 19，
 1.50179e + 19,1.24174e + 19,1.36445e + 19,1.98549e + 19,1.92068e + 19，
 1.18468e + 19,1.186468e + 19， 1.92409e + 19,1.92409e + 19,1.21387e + 19，
 1.9162e + 19,1.9162e + 19,1.40385e + 19,1.40385e + 19,1.32996e + 19，
 1.32996e +19,1.69103e + 19,1.69103e + 19,1.57387e + 19,1.40552e + 19，
 1.40552e + 19,1.00302e + 19），Entry_Station_Lat = c（1.31509,1.33261，
 1.28425,1.31812,1.333858,1.29287,1.339692,1.37773,1.333858，
 1.33322,1.28179,1.30036,1.43697,1.339752,1.27677,1.24752。 39752，
 1.41747,1.35733,1.28405,1.37773,1.33598,1.42948,1.32774，
 1.42948,1.349,1.36017,1.34971,1.38451,1.31509,1.31509，
 1.37002,1.34971,1.31231,1.339169， 1.31812,1.444909,1.29341，
 1.41747,1.33759,1.44062,1.31509,1.38451,1.29461,1.332388，
 1.41747,1.27614,1.339752,1.339449,1.33261,1.31231），Entry_Station_Long = c（103.76525，
 103.84718,103.84329,103.89308,103.70611,103.8526,103.90902，
 103.76339,103.70611,103.74217,103.859,103.85563,103.7865，
 103.74745,103.84596,103.74745,103.83298,103.9884,103.85152，
 103.76339 ，103.75191,103.83505,103.67828,103.83505,103.74956，
 103.88504,103.87326,103.74437,103.76525,103.76525,103.84955，
 103.87326,103.83793,103.89548,103.89308,103.82004,103.78479，
 103.83298,103.69742 ，103.80098,103.76525,103.74437,103.80605，
 103.93002,103.83298,103.79156,103.74745,103.90051,103.84718，
 103.83793），Exit_Station_Lat = structure（c（48L， 34L，118L，60L，
 14L，54L，10L，49L，49L，74L，71L，65L，102L，5L，102L，119L，
 116L，10L，13L，88L，117L，66L， 40L，62L，117L，37L，67L，34L，
 85L，44L，102L，44L，115L，29L，92L，17L，121L，70L，120L，52L，
 85L，34L， 11L，4L，115L，62L，48L，92L，14L）。标签= c（1.27082，
1.27091，1.27236，1.27614，1.27637，1.27646，1.27935 ，
1.28221，1.28247，1.28405，1.28621，1.28819，1.28932，
1.29287，1.29309，1.29338，1.29341 1.29461，1.29694，
1.29959，1.29974，1.30034，1.30252，1.30287，1.30392，
1.30394，1.30619，1.30736 ，1.30842，1.31139，1.3115，
1.31167，1.31188，1.31509，1.31654，1.31756，1.31913，
1.31977 1.32008，1.3205，1.32104，1.32388，1.32573，
，1.32725，1.32774，1.33119，1.33155，1.33261，1.33322，
1.33474，1.33554，1.33759，1.33764，1.33858，1.33921，
1.34037，1.34225，1.34293，1.3432，1 .34426，1.34857，
1.349，1.34905，1.35158，1.35733，1.35898，1.36017，
1.3625，1.36849，1.37002 1.37121，1.37304，1.37666，
1.37775，1.3786，1.37862，1.38001，1.38029，1.3803，
1.38178 1.38269，1.38295，1.38399，1.38423，1.38451，
1.38671，1.38672，1.38777，1.38814，1.3894，1.39147 $ b1.39169，1.39189，1.39208，1.39389，1.39449，1.39452，
，1.39628，1.39692，1.39717，1.39732，1.39752， 1.39821，
1.39928，1.39962，1.4023，1.40455，1.40511，1.40524，
1.40843，1.40961，1.41184，1.41588 ，1.41685，1.41747，
1.42526，1.42948，1.43256，1.43697，1.44062，1.44909
），class =factor Exit_Station_Long =结构（c（59L，19L，
 27L，4L，65L，3L，63L，6L，6L，21L，93L，121L，9L，56L，9L，32L，
 16L，63L， 44L，23L，50L，12L，54L，11L，50L，71L，87L，19L，7L，
 118L，9L，118L，49L，90L，96L，31L，45L，61L，38L，2 L，7L，19L，
 117L，47L，34L，49L，11L，59L，96L，65L）。标签= c（103.67828，
103.69742，103.70611，103.72092 103.73274103.74217
103.74437，103.74529，103.74745，103.74905，103.74956，
103.75191，103.7537，103.75803， 103.76011，103.76215，
103.76237，103.76449，103.76525，103.76648，103.76667，
103.76893，103.7696，103.77082，103.77145 ，103.77266，
103.774，103.77866，103.78185，103.78425，103.78479，
103.7865，103.78744，103.79156，103.79631 103.79654，
103.79836，103.80098，103.803，103.80605，103.80745，
103.80781，103.80978，103.81703，103.82004，103.82592 ，
103.82695，103.83216，103.83298，103.83505，103.83918，
103.83953，103.83974，103.84387，103.84496，103.84596 
103.84673，103.84674，103.84718，103.84823，103.84955，
103.85092，103.85152，103.85226，103.8526，103.85267 
103.85436，103.85446，103.85452，103.86088，103.86149，
103.86275，103.86291，103.86395，103.86405，103.86896 $ b103.87087，103.87135，103.87534，103.87563，103.8763，
103.87971，103.88003，103.88126，103.88243，103.88296 103.88504，103.8858，103.88816，103.8886，103.88934，
103.89054，103.89237，103.89313，103.8938，103.89548，
103.89719 ，103.89723，103.89854，103.9003，103.90051，
103.90208，103.90214，103.9031，103.90484，103.90537，
103.90597 103.90599，103.90663，103.9086，103.90902，
103.9126，103.9127，103.91296，103.91616，103.9165，
103.93002，103.94638 ，103.94929，103.95337，103.9884
），class =factor）），.Names = c（token_id，Entry_Station_Lat，
Entry_Station_Long，Exit_Station_Lat ，Exit_Station_Long
），row.names = c（10807L，10808L，10810L，10815L，10817L，10818L，
 10819L，10820L， 10823L，10824L，10826L，10827L，10829L，10831L，$ b $ 10832L，10833L，10834L，10835L，10836L，10838L，10840L，10841L，10816L，10818L，10847L，10850L，10852L，10854L，10855L， 10861L，
 10869L，10872L，10883L，10886L，10891L，10895L，10896L，10897L，
 10900L，10902L，10903L，10906L，10910L，10911L，10912L，10913L， 10921L，10924L），class =data.frame）
  

地图流量

解决方案

使用传单的替代答案和 geosphere
$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $
$ b #format data
a $ Entry_Station_Long = as.numeric（as.character（a $ Entry_Station_Long））
a $ Entry_Station_Lat = as.numeric（as.character（a Entry_Station_Lat））
a $ Exit_Station_Long = as.n （as.character（a $ Exit_Station_Lat））
a $ Exit_Station_Lat = as.numeric（as.character（a $ Exit_Station_Lat））
a $ id = as.factor（as.numeric（as.factor a $ token_id）））

＃创建一些颜色
factpal< - colorFactor（heat.colors（30），pathList $ id）

#create a插入路径列表
pathList = NULL
for（i in 1：nrow（a））
{
tmp = gcIntermediate（c（a $ Entry_Station_Long [i]，
a $ Entry_Station_Lat [i]），
c（$ Exit_Station_Long [i]，
a $ Exit_Station_Lat [i]），n = 25，
addStartEnd = TRUE）
tmp = data.frame（tmp）
tmp $ id = a [i，] $ id
tmp $ color = factpal（a [i，] $ id）
pathList = c（pathList，list （tmp））
}

#create空的基本单张对象
leaflet（）％>％addTiles（） - > lf

＃将路径列表的每个条目添加到传单对象中
for（path in pathList）
{
lf％>％addPolylines（data = path，
lng =〜lon，
lat =〜lat，
color =〜color） - > lf

}
＃显示输出
lf

请注意，正如我之前提到的，没有办法在这样一个小地方找到路径 - 大圆圈实际上是直线。如果你为了美观而需要圆形边缘，你可能需要使用我的其他答案中所描述的geom_curve方式。

stack.imgur.com/wIrEg.jpgrel =nofollow noreferrer>

I am trying to plot flow map (for singapore) . I have Entry(Lat,Long) and Exit (Lat,long). I am trying to map the flow from entry to exit in singapore map.

structure(list(token_id = c(1.12374e+19, 1.12374e+19, 1.81313e+19, 
1.85075e+19, 1.30752e+19, 1.30752e+19, 1.32828e+19, 1.70088e+19, 
1.70088e+19, 1.70088e+19, 1.05536e+19, 1.44818e+19, 1.44736e+19, 
1.44736e+19, 1.44736e+19, 1.44736e+19, 1.89909e+19, 1.15795e+19, 
1.15795e+19, 1.15795e+19, 1.70234e+19, 1.70234e+19, 1.44062e+19, 
1.21512e+19, 1.21512e+19, 1.95909e+19, 1.95909e+19, 1.50179e+19, 
1.50179e+19, 1.24174e+19, 1.36445e+19, 1.98549e+19, 1.92068e+19, 
1.18468e+19, 1.18468e+19, 1.92409e+19, 1.92409e+19, 1.21387e+19, 
1.9162e+19, 1.9162e+19, 1.40385e+19, 1.40385e+19, 1.32996e+19, 
1.32996e+19, 1.69103e+19, 1.69103e+19, 1.57387e+19, 1.40552e+19, 
1.40552e+19, 1.00302e+19), Entry_Station_Lat = c(1.31509, 1.33261, 
1.28425, 1.31812, 1.33858, 1.29287, 1.39692, 1.37773, 1.33858, 
1.33322, 1.28179, 1.30036, 1.43697, 1.39752, 1.27637, 1.39752, 
1.41747, 1.35733, 1.28405, 1.37773, 1.35898, 1.42948, 1.32774, 
1.42948, 1.349, 1.36017, 1.34971, 1.38451, 1.31509, 1.31509, 
1.37002, 1.34971, 1.31231, 1.39169, 1.31812, 1.44909, 1.29341, 
1.41747, 1.33759, 1.44062, 1.31509, 1.38451, 1.29461, 1.32388, 
1.41747, 1.27614, 1.39752, 1.39449, 1.33261, 1.31231), Entry_Station_Long = c(103.76525, 
103.84718, 103.84329, 103.89308, 103.70611, 103.8526, 103.90902, 
103.76339, 103.70611, 103.74217, 103.859, 103.85563, 103.7865, 
103.74745, 103.84596, 103.74745, 103.83298, 103.9884, 103.85152, 
103.76339, 103.75191, 103.83505, 103.67828, 103.83505, 103.74956, 
103.88504, 103.87326, 103.74437, 103.76525, 103.76525, 103.84955, 
103.87326, 103.83793, 103.89548, 103.89308, 103.82004, 103.78479, 
103.83298, 103.69742, 103.80098, 103.76525, 103.74437, 103.80605, 
103.93002, 103.83298, 103.79156, 103.74745, 103.90051, 103.84718, 
103.83793), Exit_Station_Lat = structure(c(48L, 34L, 118L, 60L, 
14L, 54L, 10L, 49L, 49L, 74L, 71L, 65L, 102L, 5L, 102L, 119L, 
116L, 10L, 13L, 88L, 117L, 66L, 40L, 62L, 117L, 37L, 67L, 34L, 
85L, 44L, 102L, 44L, 115L, 29L, 92L, 17L, 121L, 70L, 120L, 52L, 
85L, 34L, 42L, 11L, 4L, 115L, 62L, 48L, 92L, 14L), .Label = c("1.27082", 
"1.27091", "1.27236", "1.27614", "1.27637", "1.27646", "1.27935", 
"1.28221", "1.28247", "1.28405", "1.28621", "1.28819", "1.28932", 
"1.29287", "1.29309", "1.29338", "1.29341", "1.29461", "1.29694", 
"1.29959", "1.29974", "1.30034", "1.30252", "1.30287", "1.30392", 
"1.30394", "1.30619", "1.30736", "1.30842", "1.31139", "1.3115", 
"1.31167", "1.31188", "1.31509", "1.31654", "1.31756", "1.31913", 
"1.31977", "1.32008", "1.3205", "1.32104", "1.32388", "1.32573", 
"1.32725", "1.32774", "1.33119", "1.33155", "1.33261", "1.33322", 
"1.33474", "1.33554", "1.33759", "1.33764", "1.33858", "1.33921", 
"1.34037", "1.34225", "1.34293", "1.3432", "1.34426", "1.34857", 
"1.349", "1.34905", "1.35158", "1.35733", "1.35898", "1.36017", 
"1.3625", "1.36849", "1.37002", "1.37121", "1.37304", "1.37666", 
"1.37775", "1.3786", "1.37862", "1.38001", "1.38029", "1.3803", 
"1.38178", "1.38269", "1.38295", "1.38399", "1.38423", "1.38451", 
"1.38671", "1.38672", "1.38777", "1.38814", "1.3894", "1.39147", 
"1.39169", "1.39189", "1.39208", "1.39389", "1.39449", "1.39452", 
"1.39628", "1.39692", "1.39717", "1.39732", "1.39752", "1.39821", 
"1.39928", "1.39962", "1.4023", "1.40455", "1.40511", "1.40524", 
"1.40843", "1.40961", "1.41184", "1.41588", "1.41685", "1.41747", 
"1.42526", "1.42948", "1.43256", "1.43697", "1.44062", "1.44909"
), class = "factor"), Exit_Station_Long = structure(c(59L, 19L, 
27L, 4L, 65L, 3L, 63L, 6L, 6L, 21L, 93L, 121L, 9L, 56L, 9L, 32L, 
16L, 63L, 44L, 23L, 50L, 12L, 54L, 11L, 50L, 71L, 87L, 19L, 7L, 
118L, 9L, 118L, 49L, 90L, 96L, 31L, 45L, 61L, 38L, 2L, 7L, 19L, 
117L, 47L, 34L, 49L, 11L, 59L, 96L, 65L), .Label = c("103.67828", 
"103.69742", "103.70611", "103.72092", "103.73274", "103.74217", 
"103.74437", "103.74529", "103.74745", "103.74905", "103.74956", 
"103.75191", "103.7537", "103.75803", "103.76011", "103.76215", 
"103.76237", "103.76449", "103.76525", "103.76648", "103.76667", 
"103.76893", "103.7696", "103.77082", "103.77145", "103.77266", 
"103.774", "103.77866", "103.78185", "103.78425", "103.78479", 
"103.7865", "103.78744", "103.79156", "103.79631", "103.79654", 
"103.79836", "103.80098", "103.803", "103.80605", "103.80745", 
"103.80781", "103.80978", "103.81703", "103.82004", "103.82592", 
"103.82695", "103.83216", "103.83298", "103.83505", "103.83918", 
"103.83953", "103.83974", "103.84387", "103.84496", "103.84596", 
"103.84673", "103.84674", "103.84718", "103.84823", "103.84955", 
"103.85092", "103.85152", "103.85226", "103.8526", "103.85267", 
"103.85436", "103.85446", "103.85452", "103.86088", "103.86149", 
"103.86275", "103.86291", "103.86395", "103.86405", "103.86896", 
"103.87087", "103.87135", "103.87534", "103.87563", "103.8763", 
"103.87971", "103.88003", "103.88126", "103.88243", "103.88296", 
"103.88504", "103.8858", "103.88816", "103.8886", "103.88934", 
"103.89054", "103.89237", "103.89313", "103.8938", "103.89548", 
"103.89719", "103.89723", "103.89854", "103.9003", "103.90051", 
"103.90208", "103.90214", "103.9031", "103.90484", "103.90537", 
"103.90597", "103.90599", "103.90663", "103.9086", "103.90902", 
"103.9126", "103.9127", "103.91296", "103.91616", "103.9165", 
"103.93002", "103.94638", "103.94929", "103.95337", "103.9884"
), class = "factor")), .Names = c("token_id", "Entry_Station_Lat", 
"Entry_Station_Long", "Exit_Station_Lat", "Exit_Station_Long"
), row.names = c(10807L, 10808L, 10810L, 10815L, 10817L, 10818L, 
10819L, 10820L, 10823L, 10824L, 10826L, 10827L, 10829L, 10831L, 
10832L, 10833L, 10834L, 10835L, 10836L, 10838L, 10840L, 10841L, 
10843L, 10847L, 10850L, 10852L, 10854L, 10855L, 10859L, 10861L, 
10869L, 10872L, 10883L, 10886L, 10891L, 10895L, 10896L, 10897L, 
10900L, 10902L, 10903L, 10906L, 10910L, 10911L, 10912L, 10913L, 
10915L, 10920L, 10921L, 10924L), class = "data.frame")

I am trying to get something this : Map Flow

解决方案

Alternative answer using leaflet and geosphere

#get Packages
require(leaflet)
require(geosphere)

#format data
a$Entry_Station_Long = as.numeric(as.character(a$Entry_Station_Long))
a$Entry_Station_Lat = as.numeric(as.character(a$Entry_Station_Lat))
a$Exit_Station_Long = as.numeric(as.character(a$Exit_Station_Long))
a$Exit_Station_Lat = as.numeric(as.character(a$Exit_Station_Lat))
a$id = as.factor(as.numeric(as.factor(a$token_id)))

#create some colors
factpal <- colorFactor(heat.colors(30), pathList$id)

#create a list of interpolated paths
pathList = NULL
for(i in 1:nrow(a))
{
tmp = gcIntermediate(c(a$Entry_Station_Long[i],
                 a$Entry_Station_Lat[i]),
               c(a$Exit_Station_Long[i],
                 a$Exit_Station_Lat[i]),n = 25,
               addStartEnd=TRUE)
tmp = data.frame(tmp)
tmp$id = a[i,]$id
tmp$color = factpal(a[i,]$id)
pathList = c(pathList,list(tmp))
}

#create empty base leaflet object
leaflet() %>% addTiles() -> lf

#add each entry of pathlist to the leaflet object
for (path in pathList)
{
  lf %>% addPolylines(data = path,
                      lng = ~lon, 
                      lat = ~lat,
                      color = ~color) -> lf

}
#show output
lf

Note that as I mentioned before there is no way of geosphering the paths in such a small locality - the great circles are effectively straight lines. If you want the rounded edges for sake of aesthetics you may have to use the geom_curve way described in my other answer.

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