Функция для удаления геометрии геолокации в непосредственной близости в R - PullRequest
Новая
       50

Функция для удаления геометрии геолокации в непосредственной близости в R

0 голосов
/ 13 декабря 2018

Я хочу удалить каждый город, который находится в пределах 20 км от другого города, сохранив первый город.Я уже рассчитал расстояние между каждым городом и ближайшим общественным аэропортом. 

geocitylist["OSA"][,c("airport_code","cityname","tmpkey","Population","latitude","longitude","distance")]

    airport_code      cityname           tmpkey Population latitude longitude   distance
 1:          OSA     Kishiwada     jp kishiwada     205563   34.467   135.367  12.103398
 2:          OSA         Izumi         jp izumi     189087   34.483   135.433  18.389912
 3:          OSA  Tondabayashi  jp tondabayashi     132875   34.500   135.600  33.600850
 4:          OSA     Kashihara     jp kashihara     126224   34.450   135.767  47.995914
 5:          OSA      Habikino      jp habikino     121052   34.534   135.583  33.238086
 6:          OSA       Kaizuka       jp kaizuka      92633   34.450   135.350  10.036157
 7:          OSA     Izumiotsu     jp izumiotsu      80773   34.500   135.400  16.417087
 8:          OSA         Tenri         jp tenri      71054   34.583   135.833  56.642112
 9:          OSA        Tanabe        jp tanabe      69564   33.733   135.367  77.976596
10:          OSA       Kashiba       jp kashiba      69391   34.535   135.709  44.241938

ожидаемый результат (для первых 10): 

airport_code      cityname           tmpkey Population latitude longitude   distance
         OSA     Kishiwada     jp kishiwada     205563   34.467   135.367  12.103398
         OSA  Tondabayashi  jp tondabayashi     132875   34.500   135.600  33.600850
         OSA         Tenri         jp tenri      71054   34.583   135.833  56.642112
         OSA        Tanabe        jp tanabe      69564   33.733   135.367  77.976596

причины ожидаемого результата (для первых 10):

    airport_code      cityname           tmpkey Population latitude longitude   distance
 1:          OSA     Kishiwada     jp kishiwada     205563   34.467   135.367  12.103398
 2: --DELETED-- (6km from first surviving row, because it was already filtered out it won't be checked with all the other rows)
 3:          OSA  Tondabayashi  jp tondabayashi     132875   34.500   135.600  33.600850
 4: --DELETED-- (16km from first surviving row, because it was already filtered out it won't be checked with all the other rows)
 5: --DELETED-- (survived first row check but not second row check;  4km from second surviving row)
 6: --DELETED-- (2km from first surviving row, because it was already filtered out it won't be checked with all the other rows)
 7: --DELETED-- (5km from first surviving row, because it was already filtered out it won't be checked with all the other rows)
 8:          OSA         Tenri         jp tenri      71054   34.583   135.833  56.642112
 9:          OSA        Tanabe        jp tanabe      69564   33.733   135.367  77.976596
 10: --DELETED-- (survived first row check but not second row check; 11km from second surviving row)
(the third row and fourth row were >20km from each other so both were safe)

дальнейшее объяснение: 

for all rows of the same airport_code the function would calculate the distance between each row. As far as I'm aware it only needs forward comparing. 
Here's how I made the expected output: I took the latlongs from row 1 and I plugged the pair into a distance calculator as the first pair of latlongs. For the second pair of the distance calc I plugged in the second row to see if it was closer than 20km. 
The distance was 6km so row 2 failed the check. 
Then I went and compared row 1 and row 3: pass. 
row 1 and row 4: fail. 
r1 & r5: pass. 
r1 & r6: fail. 
r1 & r7: fail. 
r1 & r8: pass. 
r1 & r9: pass. 
r1 & r10: pass. 

checking r1 is finished so there should be no other cities near r1, now we proceed to r2. 

r2 & r3:r10: skip (r2 already failed). 

now we check r3. 

r3 & r4: skip (r4 already failed). 
r3 & r5: fail. 
r3 & r6,r7: skip (r6,r7 already failed). 
r3 & r8: pass. 
r3 & r9: pass. 
r3 & r10: fail. 
r4:r7 & r5:r10: skip (r4:r7 already failed). 
r8 & r9: pass. 
r8 & r10: skip (r10 already failed). 
r9 & r10: skip (r10 already failed). 
DONE

Моя идея состоит в том, чтобы поместить все в список, а затем иметь какую-то функцию, которая будет определять, какие строки удалять.

list <- split(df[, -1], df$airport_code)
require(gmt)
lapply(list, function(x) 

geodist(geocitylist$city1[i],geocitylist$city1[i],geocitylist$city2[i],geocitylist$city2[i],units="km")), something...)

Я не уверен, куда идти отсюда ...

dput: 

structure(list(airport_code = c("OSA", "OSA", "OSA", "OSA", "OSA", 
"OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", 
"OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", 
"OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", 
"OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", "OSA", 
"OSA", "OSA", "OSA", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", "ILO", 
"ILO"), cityname = c("Kishiwada", "Izumi", "Tondabayashi", "Kashihara", 
"Habikino", "Kaizuka", "Izumiotsu", "Tenri", "Tanabe", "Kashiba", 
"Sennan", "Sakurai", "Hannan", "Takaishi", "Osakasayama", "Hashimoto", 
"Iwade", "Kainan", "Sumoto", "Gojo", "Gose", "Tawaramoto", "Gobo", 
"Kawai", "Kumano", "Haibara", "Asuka", "Awaji", "Kamitonda", 
"Kawachinagano", "Kimino", "Koya", "Kozagawa-Cho", "Minabe", 
"Misaki", "Nachikatsuura", "Nosegawa", "Shirahama", "Susami", 
"Taiji", "Tenkawa", "Uda", "Yoshino", "Yura", "Iloilo", "Barotac Nuevo", 
"Trapiche", "Tuyom", "Inayawan", "Jordan", "Alimodian", "Guimbal", 
"Dingle", "Cabatuan", "Igbaras", "Pavia", "Cabano", "Patnongon", 
"Ungka", "Leon", "Bulata", "Tumcon", "Caliling", "Hamtic", "Belison", 
"Buray", "Cagbang", "Masaling", "Duenas", "Linaon", "Bingawan", 
"Maasin", "Igang", "Cartagena", "Tiling", "Maribong", "Napnapan", 
"Zarraga", "Concordia", "New Lucena", "Dao", "Aglalana", "Bugasong", 
"Alibunan", "Jamabalud", "Egana", "Calaya", "Constancia", "Pakiad", 
"Nueva Valencia", "Jibao-an", "Mina", "Bolilao", "San Enrique", 
"Cordova", "Lawigan", "Piape", "Aganan", "Ponong", "Gines", "Leganes", 
"Jaguimitan", "East Valencia", "Morobuan", "Atabayan", "Avila", 
"Catungan", "Ermita", "Igcocolo", "Tiwi", "Balibagan", "Sulangan", 
"Jalaud", "Tiring", "Abangay", "Guisijan", "Abilay", "Monpon", 
"Aureliana", "Tigum", "Quinagaringan", "Abaca", "Mapili", "Da-an", 
"Cabilauan", "Getulio", "Pina", "Oracon", "Badlan", "Lucmayan", 
"Cauayan", "San Jose De Buenavista"), tmpkey = c("jp kishiwada", 
"jp izumi", "jp tondabayashi", "jp kashihara", "jp habikino", 
"jp kaizuka", "jp izumiotsu", "jp tenri", "jp tanabe", "jp kashiba", 
"jp sennan", "jp sakurai", "jp hannan", "jp takaishi", "jp osakasayama", 
"jp hashimoto", "jp iwade", "jp kainan", "jp sumoto", "jp gojo", 
"jp gose", "jp tawaramoto", "jp gobo", "jp kawai", "jp kumano", 
"jp haibara", "jp asuka", "jp awaji", "jp kamitonda", "jp kawachinagano", 
"jp kimino", "jp koya", "jp kozagawa-cho", "jp minabe", "jp misaki", 
"jp nachikatsuura", "jp nosegawa", "jp shirahama", "jp susami", 
"jp taiji", "jp tenkawa", "jp uda", "jp yoshino", "jp yura", 
"ph iloilo", "ph barotac nuevo", "ph trapiche", "ph tuyom", "ph inayawan", 
"ph jordan", "ph alimodian", "ph guimbal", "ph dingle", "ph cabatuan", 
"ph igbaras", "ph pavia", "ph cabano", "ph patnongon", "ph ungka", 
"ph leon", "ph bulata", "ph tumcon", "ph caliling", "ph hamtic", 
"ph belison", "ph buray", "ph cagbang", "ph masaling", "ph duenas", 
"ph linaon", "ph bingawan", "ph maasin", "ph igang", "ph cartagena", 
"ph tiling", "ph maribong", "ph napnapan", "ph zarraga", "ph concordia", 
"ph new lucena", "ph dao", "ph aglalana", "ph bugasong", "ph alibunan", 
"ph jamabalud", "ph egana", "ph calaya", "ph constancia", "ph pakiad", 
"ph nueva valencia", "ph jibao-an", "ph mina", "ph bolilao", 
"ph san enrique", "ph cordova", "ph lawigan", "ph piape", "ph aganan", 
"ph ponong", "ph gines", "ph leganes", "ph jaguimitan", "ph east valencia", 
"ph morobuan", "ph atabayan", "ph avila", "ph catungan", "ph ermita", 
"ph igcocolo", "ph tiwi", "ph balibagan", "ph sulangan", "ph jalaud", 
"ph tiring", "ph abangay", "ph guisijan", "ph abilay", "ph monpon", 
"ph aureliana", "ph tigum", "ph quinagaringan", "ph abaca", "ph mapili", 
"ph da-an", "ph cabilauan", "ph getulio", "ph pina", "ph oracon", 
"ph badlan", "ph lucmayan", "ph cauayan", "ph san jose de buenavista"
), Population = c(205563, 189087, 132875, 126224, 121052, 92633, 
80773, 71054, 69564, 69391, 66460, 62966, 60796, 60512, 57170, 
57115, 55634, 43369, 39546, 34343, 32871, 32660, 27169, 20106, 
19517, 18472, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 387748, 11641, 11539, 11117, 9963, 8255, 7302, 7232, 6171, 
6106, 5974, 5928, 5812, 5810, 5598, 5172, 5151, 5067, 4840, 4816, 
4711, 3863, 3856, 3829, 3705, 3669, 3657, 3514, 3468, 3396, 3332, 
3308, 3258, 3253, 3091, 3059, 2893, 2868, 2828, 2798, 2742, 2723, 
2713, 2713, 2689, 2681, 2677, 2665, 2651, 2620, 2588, 2571, 2543, 
2522, 2513, 2476, 2463, 2461, 2444, 2442, 2362, 2349, 2318, 2304, 
2297, 2269, 2263, 2262, 2239, 2232, 2207, 2196, 2195, 2189, 2172, 
2168, 2153, 2139, 2104, 2085, 2077, 2063, 2054, 2045, 2041, 2024, 
0, 0), latitude = c(34.467, 34.483, 34.5, 34.45, 34.534, 34.45, 
34.5, 34.583, 33.733, 34.535, 34.348, 34.5, 34.333, 34.517, 34.517, 
34.317, 34.25, 34.15, 34.35, 34.35, 34.45, 34.55, 33.883, 34.233, 
33.904, 34.533, 34.48, 34.485, 33.691, 34.45, 34.187, 34.212, 
33.536, 33.752, 34.304, 33.578, 34.118, 33.685, 33.553, 33.602, 
34.269, 34.473, 34.365, 33.977, 10.697, 10.894, 10.684, 9.977, 
9.9, 10.658, 10.821, 10.663, 10.999, 10.879, 10.716, 10.776, 
10.587, 10.913, 10.75, 10.781, 9.86, 10.917, 9.98, 10.702, 10.838, 
10.715, 10.7, 9.982, 11.067, 9.95, 11.233, 10.892, 10.916, 9.82, 
9.974, 11.1, 10.708, 10.82, 10.508, 10.879, 10.515, 11.18, 11.044, 
11.147, 10.879, 10.747, 10.492, 10.596, 10.7, 10.511, 10.75, 
10.931, 10.862, 11.071, 10.73, 10.549, 10.729, 10.783, 11.083, 
10.933, 10.787, 11.142, 10.668, 10.626, 10.683, 10.692, 10.771, 
10.9, 10.69, 10.93, 10.8, 10.811, 10.893, 10.853, 10.967, 11.093, 
10.733, 10.912, 10.885, 10.783, 11.122, 11.134, 11.108, 11.232, 
10.861, 10.747, 10.64, 10.478, 11.141, 10.473, 9.844, 10.775), 
    longitude = c(135.367, 135.433, 135.6, 135.767, 135.583, 
    135.35, 135.4, 135.833, 135.367, 135.709, 135.268, 135.85, 
    135.25, 135.433, 135.563, 135.617, 135.317, 135.2, 134.9, 
    135.7, 135.733, 135.8, 135.15, 135.85, 136.122, 135.95, 135.82, 
    134.853, 135.408, 135.574, 135.491, 135.591, 135.79, 135.325, 
    135.159, 135.931, 135.616, 135.343, 135.479, 135.945, 135.881, 
    135.92, 135.862, 135.07, 122.564, 122.704, 122.432, 122.558, 
    122.434, 122.596, 122.431, 122.323, 122.671, 122.486, 122.266, 
    122.546, 122.7, 121.994, 122.55, 122.389, 122.402, 122.667, 
    122.481, 121.982, 121.96, 122.459, 122.499, 122.537, 122.619, 
    122.448, 122.567, 122.436, 122.639, 122.4, 122.654, 122.533, 
    122.393, 122.608, 122.55, 122.597, 121.946, 122.657, 122.066, 
    122.459, 122.621, 122.01, 122.626, 122.642, 122.467, 122.532, 
    122.5, 122.575, 122.747, 122.656, 122.401, 121.986, 121.972, 
    122.533, 122.626, 122.483, 122.589, 122.69, 122.71, 122.555, 
    122.417, 122.709, 122.015, 122.717, 122.319, 122.734, 122.517, 
    122.664, 122.748, 122.511, 122.65, 122.046, 122.5, 122.638, 
    121.977, 122.567, 122.588, 122.716, 122.739, 122.421, 122.573, 
    122.666, 122.638, 122.584, 122.52, 122.519, 122.383, 121.931
    ), distance = c(12.1033983706715, 18.3899116757047, 33.6008502207034, 
    47.9959144975438, 33.2380857241381, 10.0361570266128, 16.4170866375552, 
    56.6421123629711, 77.9765955762285, 44.2419380564569, 9.08236338629334, 
    56.1040561615424, 10.4929819561705, 19.9777109028803, 30.8756212480724, 
    36.3394509305111, 20.8088759721094, 31.0772190785225, 32.6932917835482, 
    42.6875344478932, 44.8846250107249, 52.7169940204742, 61.0971131463108, 
    59.6523210207629, 99.4873860406245, 65.7262133303869, 53.1011249132666, 
    36.3948919070862, 83.1987994010293, 30.3473005687047, 35.0284306100351, 
    39.8307891352326, 111.08591952332, 75.4085566830835, 15.7641843048534, 
    113.628384359301, 48.4569715473876, 82.9862748769935, 99.5353589260391, 
    112.155589656905, 61.030002562045, 62.1503754896085, 57.0858456256784, 
    52.5304774496695, 16.9678516007278, 23.9650848510695, 17.8622353497786, 
    95.3826087011773, 103.879664442242, 22.4451633033778, 6.93563300043443, 
    26.5105095696934, 26.7555548147453, 5.17234173583494, 28.0185700131311, 
    8.55316337771447, 35.4454504812041, 55.2125344446071, 11.1051435584871, 
    12.7732336937756, 108.581863736287, 21.121988580694, 94.7968710054594, 
    57.6888894096445, 58.2126963678581, 13.6398306134487, 14.7936754865746, 
    94.6852187356986, 29.3921004979045, 98.2460045794196, 45.1659952539469, 
    9.06298808500626, 18.3744243779213, 113.028151284642, 97.0551648543418, 
    29.980690030871, 17.6919106840833, 12.5955991980882, 36.6417019118572, 
    12.4111967318849, 69.435007170183, 42.4879227652078, 52.187645377298, 
    35.0905087142823, 14.8373207506573, 53.6199130632313, 40.567800302352, 
    30.9360522555993, 15.0583064589121, 36.0304676987285, 9.25329531036705, 
    14.0682148292176, 27.8678028846033, 31.84581492357, 15.2538294715758, 
    63.7579385401013, 58.0725944947238, 7.04357919640105, 31.3211844599149, 
    11.1674571270427, 11.6242282860789, 40.4839601053959, 29.9264324776362, 
    23.9679606967483, 18.6378718796601, 28.279040989485, 52.6660699937347, 
    25.5152625993047, 24.7961086588726, 28.3847007995859, 4.4854253785277, 
    18.7845096002248, 28.5769530854182, 2.93900551228868, 22.6670596650714, 
    56.7127941524606, 11.1375128973757, 18.0600792892105, 56.6455596931624, 
    9.77233832993428, 33.7306342063393, 41.3331620404634, 40.6417161096453, 
    45.0319001319029, 9.23116854868285, 21.130213848193, 26.6342321513515, 
    40.6723663618788, 34.3462448184846, 40.1029725617726, 110.559706361562, 
    61.7191590665721)), class = "data.frame", row.names = c(NA, 
-132L))

1 Ответ

0 голосов
/ 17 декабря 2018

edit4: он работает сейчас: D

этот код, вероятно, выглядит довольно плохо, но он хорошо работает для моего случая использования и не слишком медленный 

options("scipen"=100)
library(geosphere)
# split up data into regions
splitdt<-split(geocitylist[, -1],geocitylist$airport_code)

# function to get the total number of regions in the list
NROW(splitdt)

# function to get the total number of rows in a given region
NROW(splitdt[[1]])

## reduce cities
dat=geocitylist[FALSE,][]
currentregion=1
currentorigin=1

while (currentregion <= NROW(splitdt)){
workingregion <- as.data.frame(splitdt[[currentregion]]) ## set region
workingregion$remove = FALSE
setDT(workingregion)
plot(workingregion$longitude,workingregion$latitude)

while (currentorigin <= NROW(workingregion)) {
  # choose which row to use
  # as the first part of the distance formula
  workingorigin <- workingregion[,c("longitude","latitude")] %>% slice(currentorigin) ## set LeadingRow city
  setDT(workingorigin)

  # calculate the distance from the specific row chosen 
  # and only keep ones which are further than 20km
  workingregion<-workingregion %>% rowwise() %>% mutate(remove = 
      ifelse(distHaversine(c(longitude, latitude), workingorigin) != 0 &  # keep workingorigin city
          distHaversine(c(longitude, latitude), workingorigin) < 20000,TRUE,workingregion$remove))

  # remove matched cities
  workingregion <- workingregion[workingregion$remove!=TRUE,]

  currentorigin = currentorigin+1
}
currentregion = currentregion+1
# save results
#dat <- workingregion
dat <- rbind(dat, workingregion, fill=TRUE)
}
plot(dat$longitude,dat$latitude)

edit3: я думаю, что почтисделай это.

но это не работает, и я не знаю почему.Он говорит, что индекс за пределами границ 

for (currentregion in seq_along(1:NROW(splitdt))){

workingregion <- splitdt[[currentregion]] ## set region
workingregion$remove = FALSE

for (currentorigin in seq_along(1:NROW(splitdt[[currentregion]]))){
# choose which row to use 
# as the first part of the distance formula
LeadingRow <- workingregion[,c("longitude","latitude")] %>% slice(currentorigin) ## set LeadingRow city
workingregion <- workingregion[workingregion$remove!=TRUE,]

# calculate the distance from the specific row chosen 
# and only keep ones which are further than 20km
workingregion<-workingregion %>% rowwise() %>% mutate(remove = 
    ifelse(distHaversine(c(longitude, latitude), LeadingRow) != 0 &  # keep LeadingRow city
        distHaversine(c(longitude, latitude), LeadingRow) < 20000,TRUE,workingregion$remove))
}
# save results
dat <- rbind(dat, workingregion)
}

edit2: хорошо, я понял это больше

теперь мне просто нужно добавить циклы и / или лапы, я думаю ... 

# split up data into regions
splitdt<-split(geocitylist[, -1],geocitylist$airport_code)

# function to get the total number of regions in the list
NROW(splitdt)

# function to get the total number of rows in a given region
NROW(splitdt[[1]])

## loop here
workingregion <- splitdt[[1]] ## set loop iteration
workingregion$remove = FALSE

# choose which row to use as the first part of the distance formula
LeadingRow <- workingregion[,c("longitude","latitude")] %>% slice(1) ## set loop iteration

## another loop here
workingregion <- workingregion[workingregion$remove!=TRUE,]

# calculate the distance from the specific row chosen and only keep ones which are further than 20km
workingregion<-workingregion %>% rowwise() %>% mutate(remove = 
    ifelse(distHaversine(c(longitude, latitude), LeadingRow) != 0 &  #keep LeadingRow city
        distHaversine(c(longitude, latitude), LeadingRow) < 20000,TRUE,workingregion$remove))

edit1

Ах.Я только что понял, что это отменит работу предыдущих итераций, потому что, если он только добавляет, он добавит строки обратно, в которых должен был быть удален, поэтому, вместо поднабора, мне нужно найти способ пометить данные как подлежащие удалению или, возможно, дажелучше удалить его из списка напрямую

оригинальный ответ

У меня есть половина ответа. 

options("scipen"=100)
library(geosphere)
setDT(dt)    

# split up data into regions
splitdt<-split(dt[, -1],dt$airport_code)

# function to get the total number of regions in the list
NROW(splitdt)

# function to get the total number of rows in a given region
NROW(splitdt[[1]])

# choose which row to use as the first part of the distance formula
LeadingRow = splitdt[[1]][,c("longitude","latitude")][1]

# calculate the distance from the specific row chosen and copy only ones which are further than 20km
df<-splitdt[[1]] %>% rowwise() %>% mutate(distanceFromLeadingRow = distHaversine(c(longitude, latitude), LeadingRow))
res<-subset(df,df$distanceFromLeadingRow > 20000)

Теперь мне просто нужно выяснить, как выполнять итерацию по каждомустрока и каждый регион всех данных и добавить его в новый фрейм данных.

...