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@springmeyer
Last active August 22, 2024 14:22
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convert from long/lat to google mercator (or EPSG:4326 to EPSG:900913)
// See https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames for more details.
var degrees2meters = function(lon,lat) {
var x = lon * 20037508.34 / 180;
var y = Math.log(Math.tan((90 + lat) * Math.PI / 360)) / (Math.PI / 180);
y = y * 20037508.34 / 180;
return [x, y]
}
x= -77.035974
y = 38.898717
console.log(degrees2meters(x,y))
// should result in: -8575605.398444, 4707174.018280
@MightyTedKim
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are there ways to convert a geojson 3857 coords to 4326?
doing it with r and java but cant find a way that would solve the propblem
epsg.io is perfect for single data,
function trans2(){

   var test_x = document.getElementById("test_x").value;
   var test_y = document.getElementById("test_y").value;

   var ll2=new OpenLayers.LonLat(test_x, test_y).transform(
			new OpenLayers.Projection("EPSG:900913"), 
			new OpenLayers.Projection("EPSG:4326")
			);
   	      
   $("#test_ll_x").val(ll2.lon);
   $("#test_ll_y").val(ll2.lat);

}

is cool for java

but would like to know ways to do it in R

@tylerlittlefield
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tylerlittlefield commented Aug 16, 2018

@MightyTedKim

Here's my general workflow for transforming a CRS in R.

With some GIS file (shp, gpkg, etc):

library(sf)
library(dplyr)
filepath <- system.file("shape/nc.shp", package="sf")
st_read(filepath) %>%
  st_set_crs(4326) %>% 
  st_transform(3857)

With a CSV:

df <- read.csv("some_csv.csv")
df %>% 
  st_as_sf(coords = c("lat_col", "lng_col")) %>% 
  st_set_crs(4326) %>% 
  st_transform(3857)

@subhasreem
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Thank you so much! It was really helpful.

@rendrom
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rendrom commented Mar 7, 2020

console.log(degrees2meters(-180,-90)) //  [-20037508.34, -Infinity]

@Us77
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Us77 commented Mar 24, 2020

Can you please help with converting meters to lat long?

@kb173
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kb173 commented Apr 14, 2020

@Us77:

The C++ macro posted above works well for converting meters to lat/long degrees: #define DEGREE_TO_METER_REVERSE(Y) (atan(pow(M_E, ((Y)/111319.490778)*M_PI/180.0))*360.0/M_PI-90.0) (Thanks @jiangfeng79!)

Should be trivial to adapt to Python.

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