geoarea {pipenostics}

Calculate geographical metrics

Description

Calculate geographical metrics (distance, area) for two or three geographical locations.

Usage

geoarea(lat1, lon1, lat2, lon2, lat3, lon3, earth = 6371008.7714)

geodist(lat1, lon1, lat2, lon2, earth = 6371008.7714)

Arguments

lat1

latitude of the first geographical location, [DD]. Type: assert_double.

lon1

longitude of the first geographical location, [DD]. Type: assert_double.

lat2

latitude of the second geographical location, [DD]. Type: assert_double.

lon2

longitude of the second geographical location, [DD]. Type: assert_double.

lat3

latitude of the third geographical location, [DD]. Type: assert_double.

lon3

longitude of the third geographical location, [DD]. Type: assert_double.

earth

Earth radius, [m]. See Details. Type: assert_numeric.

Details

geodist calculates distance between two geographical locations on Earth, whereas geoarea calculates the area of spherical triangle between three geographical locations. Both functions use absolute positions of geographical locations described by geographical coordinate system in decimal degrees units denoted as DD. The haversine formula is applied to calculate the distance, and so the spherical model of Earth is considered in both functions.

Since several variants of Earth radius can be accepted, the user is welcome to provide its own value. WGS-84 mean radius of semi-axes, R_1, is the default value.

The resulting distance is expressed in metres (m), whereas the area is expressed in square kilometers(km^2).

Value

For geodist:

distance between two geographical locations, [m].

For geoarea:

area of spherical triangle between three geographical locations, [km^2].

Type: assert_double.

See Also

Other utils: meteos(), mgtdhid(), wth_d()

Examples

library(pipenostics)

# Consider the longest linear pipeline segment in Krasnoyarsk, [DD]:
pipe <- list(
  lat1 = 55.98320350, lon1 = 92.81257226,
  lat2 = 55.99302417, lon2 = 92.80691885
)

# and some official Earth radii, [m]:
R <- c(
  nominal_zero_tide_equatorial = 6378100.0000,
  nominal_zero_tide_polar      = 6356800.0000,
  equatorial_radius            = 6378137.0000,
  semiminor_axis_b             = 6356752.3141,
  polar_radius_of_curvature    = 6399593.6259,
  mean_radius_R1               = 6371008.7714,
  same_surface_R2              = 6371007.1810,
  same_volume_R3               = 6371000.7900,
  WGS84_ellipsoid_axis_a       = 6378137.0000,
  WGS84_ellipsoid_axis_b       = 6356752.3142,
  WGS84_ellipsoid_curvature_c  = 6399593.6258,
  WGS84_ellipsoid_R1           = 6371008.7714,
  WGS84_ellipsoid_R2           = 6371007.1809,
  WGS84_ellipsoid_R3           = 6371000.7900,
  GRS80_axis_a                 = 6378137.0000,
  GRS80_axis_b                 = 6356752.3141,
  spherical_approx             = 6366707.0195,
  meridional_at_the_equator    = 6335439.0000,
  Chimborazo_maximum           = 6384400.0000,
  Arctic_Ocean_minimum         = 6352800.0000,
  Averaged_center_to_surface   = 6371230.0000
)

# Calculate length of the pipeline segment for different radii:
len <- with(
  pipe, vapply(
    R, geodist, double(1), lat1 = lat1, lon1 = lon1, lat2 = lat2, lon2 = lon2
  )
)

print(range(len))

# [1] 1140.82331483 1152.37564656 #  [m]


# Consider some remarkable objects on Earth, [DD]:
objects <- rbind(
  Mount_Kailash      = c(lat = 31.069831297551982, lon =  81.31215667724196),
  Easter_Island_Moai = c(lat =-27.166873910247862, lon =-109.37092217323053),
  Great_Pyramid      = c(lat = 29.979229451772856, lon =  31.13418110843685),
  Antarctic_Pyramid  = c(lat = -79.97724194984573, lon = -81.96170583068950),
  Stonehendge        = c(lat = 51.179036665131870, lon =-1.8262150017463086)
)

# Consider all combinations of distances between them:
path <- t(combn(rownames(objects), 2))

d <- geodist(
  lat1 = objects[path[, 1], "lat"],
  lon1 = objects[path[, 1], "lon"],
  lat2 = objects[path[, 2], "lat"],
  lon2 = objects[path[, 2], "lon"]
)*1e-3

cat(
  paste(
    sprintf("%s <--- %1.4f km ---> %s", path[, 1], d, path[, 2]),
    collapse = "\n"
)
)

# Consider two areas 
#         * Bermuda triangle     * Polynesian Triangle
lat1 <- c(Miami   =  25.789106,  Hawaii       =   19.820680)
lon1 <- c(Miami   = -80.226529,  Hawaii       = -155.467989)

lat2 <- c(Bermuda =  32.294887,  NewZeland    =  -43.443219)
lon2 <- c(Bermuda = -64.781380,  NewZeland    =  170.271360)

lat3 <- c(SanJuan =  18.466319,  EasterIsland =  -27.112701)
lon3 <- c(SanJuan = -66.105743,  EasterIsland = -109.349668)

# Area provided by manually operated Google Earth:
GETriangleArea <- c(
  Bermuda    =  1147627.48,  # [km^2]
  Polynesian = 28775517.77   # [km^2]
)

# Show the discrepancy in calculations, [km^2]:
print(geoarea(lat1, lon1, lat2, lon2, lat3, lon3)) 
 
 #   Bermuda Polynesian 
 # 0.4673216 11.1030971