在海岸线附近的ggplot中选择栅格

时间：2018-03-09 12:29:09

标签： r ggmap

所以我有一张地图正在绘制加泰罗尼亚的气压：

enter image description here

这里有一个特写：

enter image description here

我现在想要选择气压高于97 kPa（深蓝色）的所有观测值，并创建一个新的数据框用于进一步分析。这是一个棘手的问题，我想选择符合高度过滤器AAAAND的观测沿着地中海沿岸。大多数97 kPa以上的观测位于地中海沿岸，但有一些异常值位于内陆。

最后，我想某种对角线纬度，必须进行经度过滤，但我不知道如何指定它。是否有某种方式来绘制，或在数据上绘制多边形然后让它选择内部的所有内容？

这是我的数据：

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91.4775120851854, 90.690550948402, 86.9395606892669, 87.1589246647255, 
95.0585647311234, 95.3441686169426, 94.5920252209444, 92.8377339061359, 
89.8456477071546)), .Names = c("final.Latitude", "final.Longitude", 
"final.airpressure"), row.names = c(NA, -379L), class = "data.frame")

这是根据这篇文章绘制的代码：Plotting contours on an irregular grid 

library(akima)
library(ggplot2)
library(reshape2)

x <- newfinal$final.Longitude
y <- newfinal$final.Latitude
z <- newfinal$final.airpressure

require(akima)

fld <- interp(x,y,z)

df <- melt(fld$z, na.rm = TRUE)
names(df) <- c("x", "y", "Rain")
df$Lon <- fld$x[df$x]
df$Lat <- fld$y[df$y]


ggplot(data = df, aes(x = Lon, y = Lat, z = Rain)) +
  geom_tile(aes(fill = Rain)) +
  stat_contour() +
  ggtitle("Air pressure Catalonia") +
  xlab("Longitude") +
  ylab("Latitude") +
  scale_fill_continuous(name = "kPa",
                        low = "white", high = "blue") +
  theme(plot.title = element_text(size = 20,),
        legend.title = element_text(size = 10),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10, vjust = -0.5),
        axis.title.y = element_text(size = 10, vjust = 0.2),
        legend.text = element_text(size = 10))

1 个答案:

答案 0 :(得分：5)

概述

在地中海西部使用shape file，我计算了巴利阿里（伊比利亚海）和西部盆地部分边界的坐标。然后，我计算了df中每个379坐标对与巴利阿里和西部盆地部分边界的每个坐标对的距离（以千米为单位）。

将distance过滤为仅包含最小距离值后，我确定了df中哪些点包含的距离小于或等于20公里（based on the distance from Traiguera to Vinaròs)。

最后，df2是通过返回final.airpressure大于97且坐标距离地中海小于或等于20公里的行而创建的。

可重复的示例

注意：下面没有写下df来减少代码行数。要创建df，只需复制并粘贴dput()并存储即可。

此外，在导入shape file之前，需要在我的浏览器中下载地中海西部的并在我的工作目录中解压缩。

SS of Western Basin Download with Instructions

阅读Plotting contours on an irregular grid后，我使用df2中的数据使用base R重现该图。administrative boundaries of the counties of Catalonia的GeoJSON文件取自他们的开放数据门户。 

# load necessary packages
library( akima )
library( sf )
library( leaflet )
library( geosphere )

# create sf data frame
# of Catalonia
catalonia.polygon <-
read_sf( dsn = "https://analisi.transparenciacatalunya.cat/api/geospatial/txvb-mhz6?method=export&format=GeoJSON" 
         , layer = "OGRGeoJSON"
         , stringsAsFactors = FALSE )

# dissolve into one polygon
catalonia.polygon <-
  st_union( x = catalonia.polygon )

# create sf data frame
# of the western basin
western.basin <-
  read_sf( dsn = getwd()
           , layer = "iho"
           , stringsAsFactors = FALSE )

# view first version of the map
my.map <-
  leaflet() %>%
  setView( lng = 1.514619
           , lat = 41.875227
           , zoom = 8 ) %>%
  addTiles() %>%
  addPolygons( data = catalonia.polygon
               , fill = "#D24618"
               , color = "#D24618" ) %>%
  addCircleMarkers( data = df
                    , lng = ~final.Longitude
                    , lat = ~final.Latitude
                    , fillColor = "#10539A"
                    , fillOpacity = 0.75
                    , stroke = FALSE
                    , radius = 10
                    , group = "No Filter" ) %>%
  addPolygons( data = western.basin
               , label = ~name )

# display map
my.map

SS of Map without Filtering 

# get the boundaries of each
# polygon within the western basin
list.of.polygon.boundaries <- 
  sapply( 
    X = methods::as( object = western.basin, Class = "Spatial" )@polygons
    , FUN = function( i )
      sp::coordinates( obj = i@Polygons[[1]] )
  )

# label each set of boundaries
names( list.of.polygon.boundaries ) <- western.basin$name

# from Google maps
# it looks like we only care about the
# the Balearic and Western Basin parts of the sea
# since they are nearest Catalonia
list.of.polygon.boundaries <-
  list.of.polygon.boundaries[ c("Balearic (Iberian Sea)", "Mediterranean Sea - Western Basin" ) ]

# calculate each points distance (in kilometers)
# from each boundary point within
# each polygon in list.of.polygon.boundaries
# ~1 minute to complete
distance <-
  apply(
    X = df[ c("final.Longitude", "final.Latitude") ]
    , MARGIN = 1
    , FUN = function( i )
      lapply(
        X = list.of.polygon.boundaries
        , FUN = function( j )
          distGeo(
            p1 = i
            , p2 = j
          ) / 1000 # to transform results into kilometers
      )
  )

# find the minimum distance value
# for each list in distance
distance.min <-
  lapply(
    X = distance
    , FUN = function( i )
      lapply(
        X = i
        , FUN = function( j )
          min( j )
      )
  )

# set the maximum distance
# allowed between a point in df
# and the sea to 20 kilometers
max.km <- 20

# identify which points in df
# are less than or equal to max.km
less.than.or.equal.to.max.km <-
  sapply(
    X = distance.min
    , FUN = function( i )
      sapply(
        X = i
        , FUN = function( j )
          j <= max.km
      )
  )

# convert matrix results into
# vector of TRUE/FALSE indices
less.than.or.equal.to.max.km <-
  apply(
    X = less.than.or.equal.to.max.km
    , MARGIN = 2
    , FUN = any
  )

# create subset from df
# where the air pressure is greater than 97 kpa
# AND the coordinate pairs are located
# along the Mediterranean coast
df2 <-
    df[ which( df$final.airpressure > 97 &
             less.than.or.equal.to.max.km ), ]

# View our results
my.map %>%
  hideGroup( group = "No Filter" ) %>%
  addCircleMarkers( data = df2
                    , lng = ~final.Longitude
                    , lat = ~final.Latitude
                    , fillColor = "#10539A"
                    , fillOpacity = 0.75
                    , stroke = FALSE
                    , radius = 10
                    , group = "Filter" )

SS of Filtered Map

在不规则网格上绘制轮廓

# plot intended results
bivariate.interpolation <-
  akima::interp(
    x = df2$final.Longitude
    , y = df2$final.Latitude
    , z = df2$final.airpressure
    )
# using base R
filled.contour(
  x = bivariate.interpolation$x
  , y = bivariate.interpolation$y
  , z = bivariate.interpolation$z
  , color.palette = colorRampPalette( c("white", "blue" ) )
  , xlab = "Longitude"
  , ylab = "Latitude"
  , main = "Catalonia Air Pressure"
  , key.title = title(
    main = "Air Pressure (kPa)"
    , cex.main = 0.5 )
)

# end of script #

SS of Plotting Contour

来自df2

的最终数据 
structure(list(final.Latitude = c(42.104854, 40.544337, 41.26267, 
40.745573, 40.881084, 41.201499, 41.494183, 40.873663, 41.197585, 
41.141899, 40.883885, 40.814408, 40.710754, 41.581905, 41.61424, 
41.137955, 41.553355, 42.17195, 41.447145, 41.398371, 41.954854, 
40.974225, 41.675766, 41.220308, 41.009161, 41.136268, 41.747716, 
42.306365, 41.525909, 41.187937, 41.862027, 41.074787, 41.724519, 
41.590543, 42.422663, 42.374163, 41.639757, 41.277658, 41.175305, 
42.403712, 41.417024, 41.165596, 41.208101, 41.197377, 41.594469, 
42.391185, 40.671331, 41.053748, 41.207852, 42.344326, 41.041842, 
40.655163, 41.607816, 41.731762, 42.028479, 41.102165, 41.360861, 
41.743718, 42.073092, 41.826875, 42.296039, 41.699929, 41.646217, 
40.734082, 40.716001, 41.480477, 41.18225, 41.039296, 42.360065, 
41.265454, 41.468488, 41.191922, 41.248929, 41.354354, 41.316667, 
42.218238, 41.666667, 41.917381, 41.175447, 41.971022, 41.437188, 
42.316667, 41.191613, 41.186914, 42.315525, 41.188829, 41.327836, 
41.051187, 41.278987, 41.002665, 41.224484, 41.155913, 41.496859, 
41.168999, 41.138704, 42.227089, 41.224079, 41.54329, 41.076036, 
41.383333, 41.26126, 41.694361, 41.253569, 42.13269, 42.323734, 
41.808423, 41.237045, 41.384189, 41.116667, 41.482015, 40.90787, 
41.699143, 41.146003, 41.952427, 40.812492, 40.597668, 42.036402, 
41.880549, 41.648215, 41.386759, 41.243112, 41.286117, 41.220468, 
42.061813, 41.247787, 42.283618, 41.316881, 42.138901, 42.246931, 
42.196145, 42.217381, 42.375733, 42.254854, 41.504069, 42.105996
), final.Longitude = c(3.085332, 0.48089, 1.170103, 0.618666, 
0.498917, 1.046086, 2.294718, 0.400167, 1.11167, 1.375338, 0.801686, 
0.714752, 0.578086, 2.549358, 2.539721, 0.908485, 2.401142, 3.074909, 
2.244789, 2.1741, 3.207173, 0.517667, 2.789078, 1.439364, 0.383916, 
0.989194, 2.558458, 2.967557, 2.368952, 1.569665, 3.074338, 1.052436, 
2.464097, 2.581156, 2.926337, 2.920236, 2.357387, 1.968755, 1.324377, 
3.150642, 1.919698, 1.444914, 1.672893, 1.635581, 2.405817, 2.999929, 
0.519665, 0.438496, 1.247073, 3.064954, 0.632897, 0.468583, 2.289721, 
2.503212, 3.10289, 0.752582, 2.110014, 2.628836, 3.010814, 2.893647, 
2.912027, 2.845646, 2.741505, 0.37308, 0.530085, 2.316382, 1.045336, 
0.596649, 3.000214, 1.365168, 2.280459, 1.208506, 1.294718, 1.849072, 
1.816667, 2.907316, 2.75, 3.163098, 1.269737, 3.148144, 2.009243, 
3.116667, 1.255497, 1.209089, 2.921769, 0.85608, 2.094718, 0.871539, 
1.297127, 0.599667, 1.310832, 1.107423, 2.157387, 0.976254, 1.051562, 
3.042647, 1.21775, 2.109423, 1.139829, 2.083333, 1.772075, 2.444646, 
1.550857, 2.978193, 3.186045, 2.745075, 1.807316, 1.796288, 1.25, 
2.267024, 0.512357, 2.71888, 1.400214, 3.126837, 0.521602, 0.446585, 
3.034546, 3.126409, 2.509957, 1.932048, 1.249001, 1.249929, 1.533654, 
3.046003, 1.330192, 2.981263, 2.016775, 2.888187, 2.936294, 3.056888, 
2.970593, 3.068024, 2.889007, 2.392219, 2.993361), final.airpressure = c(101.021480506879, 
100.453323066872, 98.4130259181779, 101.215628071472, 101.154916909799, 
98.1888998199279, 100.236578057928, 97.3442020744334, 97.8595387529882, 
100.694700579387, 101.094242163897, 101.227774675623, 101.227774675623, 
101.203482924827, 99.8643906852719, 97.2274590917429, 100.260637723706, 
101.239922737453, 101.252072257138, 101.215628071472, 98.9221495412202, 
101.094242163897, 101.167056228173, 99.1955517955091, 97.9065226068125, 
98.9696435706551, 99.2908252491736, 101.009358656544, 99.5532993411879, 
100.513613145927, 101.057854786839, 101.033603811924, 97.4961775397119, 
101.142779048056, 98.9221495412202, 100.032303869205, 99.0052790563431, 
101.288529565072, 100.610152546045, 101.203482924827, 97.2507964822998, 
100.74304563755, 101.179197003352, 101.203482924827, 99.5532993411879, 
99.8284459750613, 99.8044900228565, 97.0060330066725, 99.7326566531564, 
100.646378721282, 101.009358656544, 99.302740863126, 99.5771950003942, 
99.1955517955091, 101.142779048056, 98.6139934081953, 101.227774675623, 
99.0528129975234, 100.791413906912, 99.3981172677039, 99.613049243971, 
101.264223234853, 101.276375670773, 97.1808011101903, 100.670536751023, 
100.997238260744, 98.7560998507858, 100.803509602308, 100.610152546045, 
98.5784987599884, 101.082111582687, 100.068321981469, 98.5548427591196, 
98.15355817787, 98.7916584468953, 100.140397117231, 101.13064264277, 
100.549804560724, 99.8763751312065, 100.658457011412, 99.6967593586619, 
100.924546421398, 99.8164672802803, 100.140397117231, 100.803509602308, 
97.554692799058, 101.227774675623, 98.389409626026, 98.3067972240066, 
99.2312686227987, 99.2193615850005, 99.9123370990196, 100.368977705507, 
97.7539075022777, 99.8164672802803, 101.239922737453, 99.9483120154717, 
99.0409273731154, 101.300684917927, 99.8524076773845, 100.791413906912, 
98.8272298464039, 100.104353062587, 100.284703164497, 100.74304563755, 
100.405117042172, 101.203482924827, 98.4130259181779, 100.501552236019, 
99.6847964653268, 101.167056228173, 100.912436202455, 101.142779048056, 
100.791413906912, 101.179197003352, 99.7206894524044, 100.960885798805, 
100.730957197395, 98.6613394871811, 99.1955517955091, 99.3861902092696, 
98.7442498298192, 100.730957197395, 101.045728571852, 98.2832064236821, 
101.094242163897, 101.106374200865, 99.8763751312065, 100.670536751023, 
101.264223234853, 100.779319662913, 99.2908252491736, 100.140397117231, 
101.203482924827, 100.332851376663)), .Names = c("final.Latitude", 
"final.Longitude", "final.airpressure"), row.names = c(12L, 13L, 
15L, 16L, 17L, 18L, 19L, 20L, 26L, 29L, 30L, 31L, 32L, 35L, 36L, 
38L, 39L, 40L, 45L, 49L, 53L, 58L, 64L, 66L, 68L, 69L, 70L, 73L, 
74L, 76L, 78L, 80L, 81L, 84L, 85L, 87L, 88L, 95L, 97L, 102L, 
107L, 109L, 110L, 112L, 115L, 116L, 127L, 131L, 133L, 135L, 138L, 
141L, 144L, 146L, 147L, 148L, 150L, 151L, 154L, 163L, 166L, 168L, 
169L, 175L, 176L, 177L, 178L, 185L, 186L, 190L, 191L, 195L, 199L, 
201L, 206L, 211L, 214L, 215L, 216L, 217L, 218L, 219L, 221L, 227L, 
230L, 235L, 238L, 239L, 243L, 248L, 249L, 250L, 253L, 255L, 257L, 
258L, 260L, 261L, 264L, 270L, 271L, 274L, 275L, 277L, 278L, 280L, 
281L, 289L, 296L, 299L, 301L, 303L, 308L, 310L, 312L, 315L, 317L, 
318L, 322L, 323L, 324L, 325L, 326L, 327L, 329L, 330L, 333L, 334L, 
336L, 339L, 340L, 341L, 342L, 347L, 349L), class = "data.frame")

会话信息

R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.2

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets 
[6] methods   base     

other attached packages:
[1] geosphere_1.5-7    leaflet_1.1.0.9000
[3] sf_0.6-0           akima_0.6-2       

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.15    magrittr_1.5    units_0.5-1    
 [4] xtable_1.8-2    lattice_0.20-35 R6_2.2.2       
 [7] rlang_0.2.0     udunits2_0.13   tools_3.4.3    
[10] grid_3.4.3      e1071_1.6-8     DBI_0.8        
[13] htmltools_0.3.6 crosstalk_1.0.0 class_7.3-14   
[16] yaml_2.1.17     digest_0.6.15   shiny_1.0.5    
[19] htmlwidgets_1.0 mime_0.5        sp_1.2-7       
[22] compiler_3.4.3  pillar_1.2.1    classInt_0.1-24
[25] httpuv_1.3.6.2
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