Estaba buscando la misma respuesta. El error depende de la distancia, el rumbo entre los dos puntos y la latitud. Así que como no hay nada aquí, decidí hacer una pequeña búsqueda empírica. En lugar de utilizar la latitud media, voy a utilizar la primera latitud - en mi caso de uso estoy calculando muchas distancias pequeñas de algún punto en un dispositivo integrado, y mediante el uso de cost(lat1)
Puedo evitar volver a calcular el coseno, y hacer todo el cálculo en enteros usando aritmética de punto fijo.
Mi prueba empírica consideró un conjunto de latitudes, rumbos y distancias. Para cada cubo, calculo 50K ejemplos aleatorios distribuidos uniformemente. Calculo el error como (great circle distance / approximated distance)
. En las siguientes tablas se muestra el mínimo de dicho factor, el máximo y la desviación estándar:
distance (km) vs lat:
max factor:
[[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. ]
[ 0.01 1. 1. 1. 1. 1. 1. 1. 1. 1.14428]
[ 0.1 1. 1. 1. 1. 1. 1.00001 1.00001 1.00002 1.81028]
[ 1. 1.00001 1.00001 1.00002 1.00003 1.00004 1.00005 1.00008 1.00017 2.69966]
[ 3. 1.00002 1.00003 1.00005 1.00008 1.00011 1.00016 1.00025 1.00051 3.0191 ]
[ 10. 1.00005 1.00011 1.00017 1.00025 1.00036 1.00052 1.00083 1.00171 3.10754]
[ 30. 1.00016 1.00033 1.00052 1.00076 1.00108 1.00157 1.0025 1.00519 3.25157]
[ 100. 1.00054 1.00111 1.00175 1.00255 1.00362 1.00532 1.00841 1.018 3.19962]
[ 1000. 1.00635 1.01235 1.01934 1.02844 1.04142 1.06335 1.11315 1.42766 3.25602]
[ 10000. 2.36138 2.55231 2.70347 2.87681 2.95224 3.10016 3.13947 3.19104 3.15955]
[ 20000. 2.20339 2.08726 1.94497 1.7517 1.5221 1.28392 1.13302 1.06562 1.01239]]
min factor:
[[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. ]
[ 0.01 1. 1. 1. 1. 1. 1. 1. 1. 0.85811]
[ 0.1 1. 1. 1. 1. 1. 0.99999 0.99999 0.99998 0.80262]
[ 1. 0.99999 0.99999 0.99998 0.99997 0.99996 0.99995 0.99992 0.99983 0.79752]
[ 3. 0.99998 0.99997 0.99995 0.99992 0.99989 0.99984 0.99975 0.99949 0.7965 ]
[ 10. 0.99995 0.99989 0.99983 0.99975 0.99964 0.99948 0.99918 0.99831 0.79641]
[ 30. 0.99984 0.99967 0.99948 0.99925 0.99893 0.99844 0.99753 0.99499 0.79633]
[ 100. 0.99948 0.99891 0.99829 0.9975 0.99647 0.99488 0.99198 0.98384 0.79632]
[ 1000. 0.99566 0.99025 0.98425 0.97731 0.96849 0.95569 0.93444 0.88781 0.79632]
[ 10000. 0.98929 0.9609 0.92391 0.88504 0.84837 0.81731 0.80094 0.79732 0.79638]
[ 20000. 0.9893 0.96028 0.91911 0.87388 0.83463 0.81067 0.80169 0.80184 0.84316]]
std deviation
[[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. ]
[ 0.01 0. 0. 0. 0. 0. 0. 0. 0. 0.00006]
[ 0.1 0. 0. 0. 0. 0. 0. 0. 0. 0.00042]
[ 1. 0. 0. 0. 0. 0. 0. 0.00001 0.00001 0.00158]
[ 3. 0. 0. 0. 0.00001 0.00001 0.00001 0.00002 0.00003 0.00282]
[ 10. 0. 0.00001 0.00001 0.00002 0.00003 0.00004 0.00006 0.0001 0.00501]
[ 30. 0.00001 0.00002 0.00004 0.00006 0.00008 0.00012 0.00018 0.00032 0.00895]
[ 100. 0.00003 0.00007 0.00012 0.00019 0.00026 0.00038 0.00057 0.00104 0.01616]
[ 1000. 0.00025 0.00065 0.00111 0.00167 0.00239 0.00345 0.00534 0.01073 0.04507]
[ 10000. 0.01704 0.02979 0.04012 0.04654 0.04911 0.04796 0.04474 0.03961 0.02325]
[ 20000. 0.04231 0.03707 0.02943 0.02307 0.02209 0.02554 0.02726 0.0225 0.01033]]
bearing vs lat, distance in range 0..1000.0000km:
max factor:
[[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. ]
[ 0. 1.00031 1.00061 1.00101 1.00153 1.00242 1.00421 1.01039 1.2449 3.2419 ]
[ 15. 1.00238 1.0048 1.00768 1.01161 1.01839 1.03044 1.0688 1.42566 2.13819]
[ 30. 1.00512 1.00971 1.01585 1.02372 1.03584 1.05781 1.11051 1.39268 1.56511]
[ 45. 1.00634 1.01231 1.01922 1.02827 1.04142 1.06296 1.1115 1.27943 1.29318]
[ 60. 1.00622 1.01229 1.01908 1.02809 1.04071 1.05891 1.0948 1.1373 1.13721]
[ 75. 1.00482 1.00954 1.01415 1.02036 1.02746 1.03688 1.04607 1.04718 1.04708]
[ 90. 1.0016 1.0029 1.00411 1.00482 1.00513 1.00514 1.00515 1.00514 1.00512]
[ 105. 1.00044 0.99979 0.99959 0.99934 0.99908 0.99866 0.99786 0.99664 0.99999]
[ 120. 1.00093 0.99952 0.99903 0.99843 0.99769 0.99679 0.99536 0.99259 0.99998]
[ 135. 1.00101 0.99958 0.9991 0.99857 0.99803 0.99716 0.99602 0.99373 0.99999]
[ 150. 1.00091 0.99981 0.99961 0.99937 0.99905 0.99872 0.99811 0.99702 1. ]
[ 165. 1.00049 0.99997 0.99995 0.99992 0.99988 0.99984 0.99977 0.99963 1. ]
[ 180. 1.00006 1. 1. 1. 1. 1. 1. 1. 1. ]
[ 195. 1.00047 0.99997 0.99995 0.99992 0.99987 0.99982 0.99977 0.99964 1. ]
[ 210. 1.00091 0.99981 0.99959 0.99936 0.9991 0.99875 0.99815 0.99704 1. ]
[ 225. 1.001 0.99957 0.99915 0.99861 0.99803 0.99713 0.99591 0.99385 0.99999]
[ 240. 1.00089 0.99954 0.99899 0.99845 0.99777 0.99684 0.99539 0.99242 0.99998]
[ 255. 1.00044 0.99979 0.99958 0.99935 0.99907 0.99859 0.99807 0.99638 0.99999]
[ 270. 1.00154 1.00291 1.00404 1.00494 1.00514 1.00515 1.00514 1.00515 1.00514]
[ 285. 1.00483 1.0093 1.01428 1.02018 1.02734 1.03678 1.04591 1.04722 1.04724]
[ 300. 1.00631 1.01227 1.01915 1.02791 1.04011 1.05906 1.0939 1.13728 1.13749]
[ 315. 1.00635 1.01235 1.01934 1.02844 1.04115 1.06335 1.11315 1.27722 1.29346]
[ 330. 1.00506 1.01004 1.01581 1.02362 1.03619 1.05676 1.11086 1.38735 1.56542]
[ 345. 1.00245 1.00475 1.00754 1.0118 1.01827 1.03074 1.07095 1.42766 2.13419]
[ 360. 1.00031 1.0006 1.00099 1.00153 1.00234 1.00421 1.01048 1.25661 3.25602]]
min factor:
[[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. ]
[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 0.83243]
[ 15. 1. 1.00003 1.00005 1.0001 1.00013 1.00018 1.00027 1.00046 0.81348]
[ 30. 1.00001 1.00021 1.00042 1.00068 1.00097 1.0014 1.002 1.00329 0.80091]
[ 45. 1.00001 1.00045 1.00093 1.00144 1.00201 1.00311 1.00435 1.0072 0.79633]
[ 60. 1.00001 1.00048 1.00097 1.00154 1.00219 1.00311 1.00471 1.00733 0.79632]
[ 75. 1. 1.0002 1.00041 1.00061 1.00089 1.00122 0.99217 0.93006 0.80115]
[ 90. 0.99823 0.99609 0.99296 0.98919 0.98308 0.97268 0.95135 0.89105 0.81612]
[ 105. 0.99611 0.9914 0.98589 0.9791 0.97051 0.95727 0.93458 0.88781 0.84123]
[ 120. 0.99566 0.99025 0.98425 0.97731 0.96865 0.95571 0.93444 0.8944 0.8751 ]
[ 135. 0.99585 0.99051 0.98483 0.97823 0.97021 0.95887 0.94363 0.92017 0.91384]
[ 150. 0.99708 0.99322 0.98929 0.9848 0.97942 0.9729 0.96374 0.95287 0.95159]
[ 165. 0.99873 0.99705 0.99526 0.99329 0.99112 0.98862 0.9852 0.98181 0.9814 ]
[ 180. 0.99985 0.99964 0.99942 0.9992 0.99895 0.99862 0.99827 0.9979 0.99787]
[ 195. 0.99874 0.99712 0.99526 0.99332 0.9912 0.98853 0.98504 0.9816 0.9814 ]
[ 210. 0.99709 0.99317 0.98914 0.98469 0.97923 0.97237 0.96402 0.95294 0.95155]
[ 225. 0.99584 0.9906 0.98487 0.97819 0.97013 0.95894 0.94337 0.92043 0.91384]
[ 240. 0.99566 0.99028 0.98429 0.97739 0.96849 0.95569 0.93451 0.89383 0.87507]
[ 255. 0.9961 0.99128 0.98605 0.97922 0.97018 0.95665 0.93464 0.88813 0.8412 ]
[ 270. 0.99827 0.99614 0.99306 0.9891 0.98347 0.97246 0.9512 0.89121 0.81606]
[ 285. 1. 1.0002 1.00037 1.00061 1.00087 1.00134 0.9914 0.92805 0.80112]
[ 300. 1.00001 1.0005 1.001 1.00157 1.00223 1.00316 1.00469 1.00706 0.79633]
[ 315. 1.00001 1.00046 1.0009 1.0014 1.00212 1.00293 1.00445 1.00736 0.79632]
[ 330. 1.00001 1.00022 1.00043 1.00066 1.00103 1.00138 1.00207 1.00334 0.80092]
[ 345. 1. 1.00003 1.00005 1.00009 1.00013 1.00018 1.00028 1.00047 0.81335]
[ 360. 1. 1. 1. 1. 1. 1. 1. 1. 0.83229]]
std deviation
[[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. ]
[ 0. 0.00005 0.00012 0.00021 0.00032 0.0005 0.0008 0.00159 0.01138 0.58817]
[ 15. 0.00051 0.00122 0.0021 0.00326 0.00493 0.00782 0.01473 0.06154 0.38418]
[ 30. 0.00142 0.00342 0.00585 0.00897 0.01339 0.02068 0.03651 0.099 0.24159]
[ 45. 0.00213 0.00521 0.00883 0.01332 0.01949 0.02915 0.04742 0.09555 0.15017]
[ 60. 0.0021 0.0052 0.00876 0.01302 0.01852 0.02642 0.03917 0.0612 0.1021 ]
[ 75. 0.00128 0.0032 0.0053 0.00759 0.01039 0.01376 0.01777 0.02134 0.09992]
[ 90. 0.00036 0.00098 0.00174 0.00273 0.00421 0.00686 0.01279 0.03274 0.10824]
[ 105. 0.00108 0.00314 0.00559 0.0086 0.0126 0.01854 0.02911 0.0524 0.10365]
[ 120. 0.00149 0.00443 0.0078 0.01175 0.01669 0.02349 0.03403 0.05398 0.08351]
[ 135. 0.00131 0.00399 0.007 0.01048 0.01454 0.01994 0.02785 0.04104 0.05558]
[ 150. 0.00078 0.00244 0.00427 0.00633 0.00867 0.01174 0.01597 0.02244 0.0282 ]
[ 165. 0.00026 0.00083 0.00145 0.00214 0.00292 0.00391 0.00523 0.0072 0.00865]
[ 180. 0.00003 0.00008 0.00014 0.00021 0.00028 0.00038 0.00051 0.00068 0.00081]
[ 195. 0.00026 0.00083 0.00146 0.00215 0.00292 0.00392 0.00524 0.00717 0.00865]
[ 210. 0.00078 0.00244 0.00428 0.0063 0.00869 0.01176 0.01597 0.02246 0.02813]
[ 225. 0.00131 0.004 0.00705 0.01048 0.01459 0.01998 0.0279 0.04099 0.05564]
[ 240. 0.00149 0.00444 0.00778 0.01175 0.01663 0.02351 0.03409 0.05388 0.08364]
[ 255. 0.00109 0.00313 0.00559 0.00861 0.01259 0.01855 0.029 0.05254 0.10336]
[ 270. 0.00036 0.00099 0.00174 0.00274 0.00422 0.00688 0.01283 0.03262 0.10829]
[ 285. 0.00127 0.00321 0.00532 0.00765 0.01038 0.01377 0.01782 0.02138 0.10016]
[ 300. 0.0021 0.00521 0.00876 0.01299 0.0185 0.0264 0.03923 0.06137 0.10187]
[ 315. 0.00215 0.00521 0.00883 0.01336 0.01951 0.02914 0.0475 0.09555 0.15013]
[ 330. 0.00143 0.00344 0.00586 0.00894 0.01338 0.02072 0.03681 0.09954 0.24286]
[ 345. 0.00051 0.00123 0.00209 0.00324 0.00492 0.00779 0.01474 0.06034 0.38562]
[ 360. 0.00005 0.00012 0.00021 0.00033 0.0005 0.00081 0.00157 0.01179 0.59786]]
distance vs bearing, lat in range 0..80:
max factor:
[[ 0. 0.01 0.1 1. 3. 10. 30. 100. 1000. 10000. 20000. ]
[ 0. 1.00333 1.81028 2.68414 2.61964 3.10754 3.25157 3.19962 3.2419 3.15955 1. ]
[ 15. 1.04825 1.74503 1.92905 2.01209 2.11826 2.12659 2.13008 2.13819 2.12922 1. ]
[ 30. 1.14428 1.42043 1.4745 1.53725 1.55089 1.56228 1.5638 1.56511 1.56296 1. ]
[ 45. 1.12441 1.25933 1.26302 1.28975 1.29248 1.29373 1.29278 1.29318 1.29206 1. ]
[ 60. 1.03106 1.07908 1.12246 1.13287 1.13635 1.1361 1.13742 1.13721 1.12522 1. ]
[ 75. 1.01977 1.03463 1.04647 1.04586 1.04668 1.04726 1.04685 1.04708 1. 1. ]
[ 90. 1.00294 1.00332 1.00456 1.00485 1.0051 1.00512 1.00515 1.00512 0.99999 1. ]
[ 105. 1. 1. 0.99999 0.99994 0.99982 0.99938 0.99922 0.99999 1. 1. ]
[ 120. 1. 1. 0.99998 0.99985 0.99955 0.99964 0.99999 0.99998 1. 1. ]
[ 135. 1. 1. 0.99999 0.99986 0.99959 0.99994 0.9999 0.99999 1. 1. ]
[ 150. 1. 1. 0.99999 0.99994 0.99981 0.99968 0.99997 1. 1. 1. ]
[ 165. 1. 1. 1. 0.99999 0.99998 0.99998 1. 1. 1. 1. ]
[ 180. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.01239]
[ 195. 1. 1. 1. 0.99999 0.99998 0.99996 0.99999 1. 1. 1. ]
[ 210. 1. 1. 0.99999 0.99994 0.99998 0.9999 0.99995 1. 1. 1. ]
[ 225. 1. 1. 0.99999 0.99986 0.99959 0.99993 0.99987 0.99999 1. 1. ]
[ 240. 1. 1. 0.99998 0.99985 0.99975 0.99974 0.99976 0.99998 1. 1. ]
[ 255. 1. 1. 0.99999 0.99994 0.99982 0.99975 0.99963 0.99999 1. 1. ]
[ 270. 1.00301 1.00407 1.00509 1.00489 1.00511 1.00512 1.00515 1.00514 1. 1. ]
[ 285. 1.02476 1.0383 1.04634 1.04468 1.04685 1.0471 1.04713 1.04724 1. 1. ]
[ 300. 1.08744 1.08419 1.12921 1.13597 1.13683 1.13723 1.13714 1.13749 1.12441 1. ]
[ 315. 1.13164 1.19722 1.25881 1.28504 1.29028 1.2933 1.29212 1.29346 1.29252 1. ]
[ 330. 1.02011 1.41233 1.53013 1.52894 1.53543 1.55058 1.56415 1.56542 1.5598 1. ]
[ 345. 1.00512 1.64577 2.08286 1.99041 2.11933 2.12104 2.12578 2.13419 2.11718 1. ]
[ 360. 1.00512 1.30189 2.69966 3.0191 2.86451 3.13177 3.16542 3.25602 3.1336 0.99999]]
min factor:
[[ 0. 0.01 0.1 1. 3. 10. 30. 100. 1000. 10000. 20000. ]
[ 0. 1. 1. 0.86728 0.83539 0.83242 0.83297 0.83241 0.83243 0.83183 0.84798]
[ 15. 1. 0.95975 0.83564 0.81706 0.81466 0.81429 0.81406 0.81348 0.81347 0.8436 ]
[ 30. 1. 1. 0.81755 0.80445 0.80352 0.80082 0.80095 0.80091 0.80091 0.84316]
[ 45. 1. 0.80262 0.79752 0.7965 0.79645 0.79646 0.79638 0.79633 0.79639 0.84981]
[ 60. 1. 0.81827 0.79842 0.79725 0.79654 0.79633 0.79636 0.79632 0.79646 0.85816]
[ 75. 1. 0.9928 0.80578 0.8025 0.80181 0.80125 0.80112 0.80115 0.80125 0.87677]
[ 90. 0.85811 0.86776 0.82413 0.81975 0.81693 0.81656 0.81609 0.81612 0.81631 0.8956 ]
[ 105. 0.97607 0.85708 0.84434 0.84383 0.84192 0.84119 0.84124 0.84123 0.84127 0.90661]
[ 120. 0.92794 0.91415 0.88535 0.87684 0.87534 0.87507 0.87506 0.8751 0.87528 0.90824]
[ 135. 0.95918 0.93877 0.91962 0.91522 0.91452 0.91449 0.91389 0.91384 0.91413 0.908 ]
[ 150. 0.98628 0.95765 0.95465 0.95412 0.95179 0.95223 0.95167 0.95159 0.95181 0.90789]
[ 165. 0.99786 0.9901 0.98337 0.98158 0.98146 0.98144 0.98141 0.9814 0.98146 0.90941]
[ 180. 0.99924 0.99862 0.99814 0.99801 0.9979 0.99789 0.99788 0.99787 0.99788 0.90915]
[ 195. 0.9958 0.98343 0.98243 0.98226 0.98156 0.98143 0.98143 0.9814 0.98146 0.91031]
[ 210. 0.97932 0.95723 0.95413 0.95336 0.9518 0.95178 0.95163 0.95155 0.95187 0.90794]
[ 225. 0.96194 0.91501 0.915 0.91408 0.91438 0.91385 0.91405 0.91384 0.91402 0.90719]
[ 240. 0.98537 0.89014 0.87706 0.87617 0.87585 0.87544 0.8752 0.87507 0.87529 0.90694]
[ 255. 0.99164 0.8504 0.84631 0.84763 0.84162 0.84254 0.84152 0.8412 0.84135 0.91007]
[ 270. 0.98552 0.90239 0.82108 0.8217 0.81688 0.81614 0.81616 0.81606 0.81637 0.89812]
[ 285. 1. 0.80738 0.81018 0.80192 0.80142 0.80168 0.80141 0.80112 0.8014 0.87696]
[ 300. 1. 0.82985 0.80037 0.79683 0.79666 0.79633 0.79632 0.79633 0.7964 0.85947]
[ 315. 1. 0.8075 0.81606 0.79858 0.79641 0.79635 0.79632 0.79632 0.79638 0.84933]
[ 330. 1. 0.88805 0.81244 0.80154 0.80118 0.80152 0.80129 0.80092 0.80091 0.84377]
[ 345. 1. 1. 0.81396 0.82175 0.81609 0.81344 0.81344 0.81335 0.81347 0.84325]
[ 360. 1. 0.84768 0.84086 0.83478 0.83405 0.83252 0.83229 0.83229 0.83181 0.8495 ]]
std deviation
[[ 0. 0.01 0.1 1. 3. 10. 30. 100. 1000. 10000. 20000. ]
[ 0. 0.00001 0.00423 0.01955 0.0304 0.06223 0.10945 0.20447 0.58817 0.22426 0.07565]
[ 15. 0.00026 0.00513 0.01061 0.02421 0.04377 0.07536 0.135 0.38418 0.17084 0.07592]
[ 30. 0.00068 0.00376 0.0075 0.01606 0.02806 0.05129 0.09347 0.24159 0.14146 0.07398]
[ 45. 0.00067 0.00256 0.00635 0.01153 0.02124 0.03731 0.06682 0.15017 0.13303 0.06964]
[ 60. 0.00017 0.00171 0.00453 0.00833 0.015 0.02689 0.04671 0.1021 0.12802 0.0632 ]
[ 75. 0.00011 0.00039 0.00365 0.00644 0.01201 0.021 0.03672 0.09992 0.11649 0.05448]
[ 90. 0.00064 0.00071 0.00373 0.00641 0.01205 0.02111 0.03952 0.10824 0.1 0.04523]
[ 105. 0.00016 0.00145 0.0039 0.00753 0.01386 0.02453 0.0428 0.10365 0.07753 0.03538]
[ 120. 0.00036 0.00094 0.00372 0.00666 0.01329 0.02281 0.03992 0.08351 0.0545 0.02593]
[ 135. 0.00032 0.0008 0.00291 0.00551 0.00981 0.01685 0.0293 0.05558 0.03293 0.0177 ]
[ 150. 0.00009 0.00042 0.0015 0.003 0.00544 0.00933 0.01592 0.0282 0.01572 0.01211]
[ 165. 0.00002 0.00014 0.00051 0.00098 0.0018 0.00304 0.00508 0.00865 0.00469 0.00958]
[ 180. 0. 0.00002 0.00005 0.00009 0.00017 0.00029 0.00048 0.00081 0.00043 0.00892]
[ 195. 0.00004 0.00017 0.00054 0.00093 0.00177 0.00301 0.00512 0.00865 0.00467 0.00953]
[ 210. 0.00012 0.00062 0.00151 0.00302 0.00537 0.00941 0.01587 0.02813 0.01579 0.01217]
[ 225. 0.00025 0.00095 0.00283 0.00564 0.00975 0.01696 0.02936 0.05564 0.03302 0.0178 ]
[ 240. 0.00014 0.00113 0.00395 0.00734 0.01304 0.02252 0.03982 0.08364 0.05428 0.02603]
[ 255. 0.00008 0.00141 0.00388 0.00731 0.01439 0.02429 0.04306 0.10336 0.07795 0.03543]
[ 270. 0.00008 0.00061 0.00373 0.00679 0.01246 0.0222 0.03933 0.10829 0.09986 0.04532]
[ 285. 0.00013 0.0013 0.00308 0.00693 0.01169 0.02043 0.03683 0.10016 0.11724 0.05455]
[ 300. 0.00042 0.00151 0.00441 0.00817 0.01525 0.02679 0.04663 0.10187 0.12759 0.063 ]
[ 315. 0.00062 0.00196 0.00618 0.01184 0.02105 0.03746 0.06605 0.15013 0.13312 0.06947]
[ 330. 0.00011 0.00221 0.00962 0.01654 0.02813 0.05005 0.09079 0.24286 0.14154 0.07392]
[ 345. 0.00003 0.00367 0.00989 0.0223 0.04025 0.07489 0.13359 0.38562 0.17313 0.07584]
[ 360. 0.00002 0.00211 0.0239 0.0396 0.06153 0.11233 0.20075 0.59786 0.2286 0.07568]]
Los errores se comportan como cabría esperar. Cuanto mayor es la latitud, mayor es el error. Las distancias a lo largo de las líneas verticales/horizontales tienen un error 0, los mayores errores se producen a lo largo de las líneas diagonales.
Para mis fines, necesito distancias de menos de 100 km que estén por debajo de los 70 grados de latitud (básicamente distancias dentro de las ciudades existentes). Para ese caso de uso, puedo esperar errores inferiores al 1%.