在r/python/gis中，如何在地图中覆盖多个lat/long坐标，并创建一个变量，指示坐标是否叠加匹配

Question

我有两个带有坐标的数据框。

• 第一个数据帧包含无线电发射器的LAT和长期均匀的无线电波坐标。
• 第二个包含纬度和长期选举区。

我正在尝试i）在BR地图中绘制两个数据范围，以使无线电波覆盖选举区域，ii）合并数据框并创建一个额外的列（coved_by_by_radio_wave），表示LAT和LONG选举区被无线电波覆盖（因此躺在其中）。 R中的代码：

df_1 <- data.frame(Radio_id=c(13,22),
                    frequency=c(76.1, 84.1),
                    transmitter_latitude=c(-8.060833,-23.552222),
                    transmitter_longitude=c(-34.88111,-46.67528),
                    radio_name=c('A','B'),
                    radio_wave=c("-7.312 -34.7498, -7.3233, -34.6224,-7.3571, -34.5029,-7.4123, -34.3949,-7.4872, -34.3017,-7.5795, -34.2261,-7.6864, -34.1704,-7.8047, -34.1363,-7.9308, -34.1248,-8.0608, -34.1363,-8.1909 -34.1704,-8.317, -34.2261,-8.4353, -34.3017,-8.5422, -34.3949,-8.6345, -34.5029,-8.7094, -34.6224,-8.7645, -34.7498,-8.7983, -34.8811,-8.8097, -35.0124,-8.7983, -35.1398,-8.7645 -35.2593,-8.7094, -35.3673,-8.6345, -35.4605,-8.5422, -35.5361,-8.4353, -35.5918,-8.317, -35.626,-8.1909, -35.6375,-8.0608, -35.626,-7.9308, -35.5918,-7.8047, -35.5361,-7.6864, -35.4605,-7.5795, -35.3673,-7.4872, -35.2593,-7.4123, -35.1398,-7.3571, -35.0124,-7.3233, -34.8811",
                              "-22.9276 -46.5569,-22.937 -46.4422,-22.9652 -46.3346,-23.0112 -46.2373,-23.0737 -46.1533,-23.1507 -46.0851,-23.2399 -46.0349,-23.3386 -46.0042,-23.4438 -45.9939,-23.5522 -46.0042,-23.6607 -46.0349,-23.7659 -46.0851,-23.8646 -46.1533,-23.9537 -46.2373,-24.0307 -46.3346,-24.0932 -46.4422,-24.1392 -46.5569,-24.1674 -46.6753,-24.1769 -46.7936,-24.1674 -46.9083,-24.1392 -47.016,-24.0932 -47.1133,-24.0307 -47.1973,-23.9537 -47.2654,-23.8646 -47.3156,-23.7659 -47.3464,-23.6607 -47.3567,-23.5522 -47.3464,-23.4438 -47.3156,-23.3386 -47.2654,-23.2399 -47.1973,-23.1507 -47.1133,-23.0737 -47.016,-23.0112 -46.9083,-22.9652 -46.7936,-22.937 -46.6753"),
                    radio_show=c("News","Local hits"))

df_2<- data.frame(election_section=c(13,19),
                  municipality=c("Abadia de goias", "Agua boa"),
                  state=c("GO","MT"),
                  result=c("elected","not elected"),
                  latitude=c(-18.490394, -23.56),
                  longitude=c(-47.40096, -46.69))

df_merged_desired<-data.frame(Radio_id=c(13,22),
                              frequency=c(76.1, 84.1),
                              transmitter_latitude=c(-8.060833,-23.552222),
                              transmitter_longitude=c(-34.88111,-46.67528),
                              radio_name=c('A','B'),
                              radio_wave=c("-7.312 -34.7498, -7.3233, -34.6224,-7.3571, -34.5029,-7.4123, -34.3949,-7.4872, -34.3017,-7.5795, -34.2261,-7.6864, -34.1704,-7.8047, -34.1363,-7.9308, -34.1248,-8.0608, -34.1363,-8.1909 -34.1704,-8.317, -34.2261,-8.4353, -34.3017,-8.5422, -34.3949,-8.6345, -34.5029,-8.7094, -34.6224,-8.7645, -34.7498,-8.7983, -34.8811,-8.8097, -35.0124,-8.7983, -35.1398,-8.7645 -35.2593,-8.7094, -35.3673,-8.6345, -35.4605,-8.5422, -35.5361,-8.4353, -35.5918,-8.317, -35.626,-8.1909, -35.6375,-8.0608, -35.626,-7.9308, -35.5918,-7.8047, -35.5361,-7.6864, -35.4605,-7.5795, -35.3673,-7.4872, -35.2593,-7.4123, -35.1398,-7.3571, -35.0124,-7.3233, -34.8811",
                                           "-22.9276 -46.5569,-22.937 -46.4422,-22.9652 -46.3346,-23.0112 -46.2373,-23.0737 -46.1533,-23.1507 -46.0851,-23.2399 -46.0349,-23.3386 -46.0042,-23.4438 -45.9939,-23.5522 -46.0042,-23.6607 -46.0349,-23.7659 -46.0851,-23.8646 -46.1533,-23.9537 -46.2373,-24.0307 -46.3346,-24.0932 -46.4422,-24.1392 -46.5569,-24.1674 -46.6753,-24.1769 -46.7936,-24.1674 -46.9083,-24.1392 -47.016,-24.0932 -47.1133,-24.0307 -47.1973,-23.9537 -47.2654,-23.8646 -47.3156,-23.7659 -47.3464,-23.6607 -47.3567,-23.5522 -47.3464,-23.4438 -47.3156,-23.3386 -47.2654,-23.2399 -47.1973,-23.1507 -47.1133,-23.0737 -47.016,-23.0112 -46.9083,-22.9652 -46.7936,-22.937 -46.6753"),
                              radio_show=c("News","Local hits"),
                              election_section=c(13,22),
                              municipality=c("Abadia de goias", "Agua boa"),
                              state=c("GO","MT"),
                              result=c("elected","not elected"),
                              latitude=c(-18.490394, -23.56),
                              longitude=c(-47.40096, -46.69),
                              covered_by_radio_wave=c("No","Yes"))

使用GGPLOT我只能添加一组LAT和我的一个数据范围之一。

library(geobr)
library(ggplot2)
mun <- read_municipality(code_muni="all", year=2010)
ggplot() + 
  geom_sf(data=mun, fill = NA) + scale_fill_gradientn(colours= brewer.pal(2, "RdYlGn"))+
  geom_point(data = df_1, mapping = aes(x = transmitter_longitude, y = transmitter_latitude, colour = factor(radio_show)), size = 1) + 
  coord_sf()+
  theme(panel.grid.major = element_blank(), panel.background = element_blank(), panel.grid.minor = element_blank())

参考 geobr

Answer 1

因此，对于您的第一部分：

i）在BR地图中绘制两个数据框，以使无线电波覆盖选举区

您可以将radio> radio_wave坐标转换到多边形（>） SF）。这有点棘手，因为坐标有点混乱，但是我设法使用自定义功能来完成i）提取坐标ii）使用适当的顺序（LON/LAT）它。请参阅：

library(tidyverse)
library(sf)


df_1 <- data.frame(
  Radio_id = c(13, 22),
  frequency = c(76.1, 84.1),
  transmitter_latitude = c(-8.060833, -23.552222),
  transmitter_longitude = c(-34.88111, -46.67528),
  radio_name = c("A", "B"),
  radio_wave = c(
    "-7.312 -34.7498, -7.3233, -34.6224,-7.3571, -34.5029,-7.4123, -34.3949,-7.4872, -34.3017,-7.5795, -34.2261,-7.6864, -34.1704,-7.8047, -34.1363,-7.9308, -34.1248,-8.0608, -34.1363,-8.1909 -34.1704,-8.317, -34.2261,-8.4353, -34.3017,-8.5422, -34.3949,-8.6345, -34.5029,-8.7094, -34.6224,-8.7645, -34.7498,-8.7983, -34.8811,-8.8097, -35.0124,-8.7983, -35.1398,-8.7645 -35.2593,-8.7094, -35.3673,-8.6345, -35.4605,-8.5422, -35.5361,-8.4353, -35.5918,-8.317, -35.626,-8.1909, -35.6375,-8.0608, -35.626,-7.9308, -35.5918,-7.8047, -35.5361,-7.6864, -35.4605,-7.5795, -35.3673,-7.4872, -35.2593,-7.4123, -35.1398,-7.3571, -35.0124,-7.3233, -34.8811",
    "-22.9276 -46.5569,-22.937 -46.4422,-22.9652 -46.3346,-23.0112 -46.2373,-23.0737 -46.1533,-23.1507 -46.0851,-23.2399 -46.0349,-23.3386 -46.0042,-23.4438 -45.9939,-23.5522 -46.0042,-23.6607 -46.0349,-23.7659 -46.0851,-23.8646 -46.1533,-23.9537 -46.2373,-24.0307 -46.3346,-24.0932 -46.4422,-24.1392 -46.5569,-24.1674 -46.6753,-24.1769 -46.7936,-24.1674 -46.9083,-24.1392 -47.016,-24.0932 -47.1133,-24.0307 -47.1973,-23.9537 -47.2654,-23.8646 -47.3156,-23.7659 -47.3464,-23.6607 -47.3567,-23.5522 -47.3464,-23.4438 -47.3156,-23.3386 -47.2654,-23.2399 -47.1973,-23.1507 -47.1133,-23.0737 -47.016,-23.0112 -46.9083,-22.9652 -46.7936,-22.937 -46.6753"
  ),
  radio_show = c("News", "Local hits")
)

df_2 <- data.frame(
  election_section = c(13, 19),
  municipality = c("Abadia de goias", "Agua boa"),
  state = c("GO", "MT"),
  result = c("elected", "not elected"),
  latitude = c(-18.490394, -23.56),
  longitude = c(-47.40096, -46.69)
)


# Create radio waves polygon
radio_wave_pol <- lapply(seq_len(nrow(df_1)), function(z) {
  x <- df_1[z, ]

  # Split coords
  coords <- x$radio_wave %>%
    str_split(",| ", simplify = TRUE) %>%
    as.double()

  m <- coords[!is.na(coords)] %>%
    matrix(ncol = 2, byrow = TRUE)

  pol <- m[, 2:1] %>%
    st_linestring() %>%
    st_convex_hull() %>%
    st_sfc(crs = 4326)

  pol
})

radio_wave_pol <- do.call(c, radio_wave_pol)

# Create sf from df1

df_1_sf <- st_as_sf(df_1, radio_wave_pol)

# Create sf from df2
df_2_sf <- st_as_sf(df_2, coords = c("longitude", "latitude"), crs = 4326)

# Part 1: Plot
ggplot(df_1_sf) +
  geom_sf(aes(fill = radio_show)) +
  geom_sf(data = df_2_sf)

对于第二个问题 ii）合并数据框并创建一个额外的列（coveed_by_radio_wave），指示是否涵盖了选举区域的LAT和LONG RECTINCS /em>，您可以使用st_coverred_by（）创建covee_by_radio_wave。

我注意到您想要的最终数据框架是通过添加df_1和df_2的Cols产生的，因此我对此进行了复制，但请注意，这不是两个数据集的加入。尽管如此，我还是为您制作了。检查如何完成：

# Part 2: df_merged_desired
# Check if covered
covered <- st_covered_by(df_2_sf,
  st_union(st_geometry(df_1_sf)),
  sparse = FALSE
) %>%
  as.data.frame() %>%
  select(logical = 1) %>%
  mutate(covered_by_radio_wave = if_else(logical == TRUE, "Yes", "No")) %>%
  select(-1)

covered
#>   covered_by_radio_wave
#> 1                    No
#> 2                   Yes

df_desired <- bind_cols(df_1, df_2) %>%
  bind_cols(covered)

glimpse(df_desired)
#> Rows: 2
#> Columns: 14
#> $ Radio_id              <dbl> 13, 22
#> $ frequency             <dbl> 76.1, 84.1
#> $ transmitter_latitude  <dbl> -8.060833, -23.552222
#> $ transmitter_longitude <dbl> -34.88111, -46.67528
#> $ radio_name            <chr> "A", "B"
#> $ radio_wave            <chr> "-7.312 -34.7498, -7.3233, -34.6224,-7.3571, -34~
#> $ radio_show            <chr> "News", "Local hits"
#> $ election_section      <dbl> 13, 19
#> $ municipality          <chr> "Abadia de goias", "Agua boa"
#> $ state                 <chr> "GO", "MT"
#> $ result                <chr> "elected", "not elected"
#> $ latitude              <dbl> -18.49039, -23.56000
#> $ longitude             <dbl> -47.40096, -46.69000
#> $ covered_by_radio_wave <chr> "No", "Yes"

^{在2022-06-14创建的 reprex package （v2）。 0.1）}