If the vast majority of your data was within one UTM zone and only a small
amount bled over into the adjacent UTM zone then it'd probably just be ok to
choose the one that contains the majority of your data and accept that there
will be some distortion. Since it's roughly 50/50 though, you should either
split the data into two data sets and perform calculations separately for each
one, or transform them to a common projection (e.g. EPSG:4326 aka WGS84 aka
lat/lon) and then perform the analysis. Most of this has been stated already,
but I figured I'd point you to some tools as well that may help you. Rather
than doing some work arounds that seem to get it to look right it'd be better
to use tools specifically to transform between different projections. Based on
what you wrote it seems like you're working with vector data. For vector data,
the sf package in R is very comprehensive (though terra also has vector
capabilities). The st_crs will let you check or manually set a projection for a
data set, and the st_transform function will let you transform from one
projection to another. Note the difference here is that st_crs will not change
the coordinates, it will just assign the name of the coordinate system whereas
st_transform will convert from one to the other. UTM is convenient in certain
cases because it can make calculations simpler since you can assume the area is
essentially flat over the space considered. That being said, the sf package
many calculations can now be done using spherical
geometry<https://r-spatial.org/book/04-Spherical.html> which will be more
accurate (I'm not sure about google earth engine though as I haven't used it).
Below is some R code to take a dataframe of coordinates (like from your CSV),
define the projection as UTM 11 or UTM 12 in separate objects, and how to
transform them to lat/lon and combine them into a single object.
``` r
library(sf)
#> Linking to GEOS 3.11.1, GDAL 3.6.2, PROJ 9.1.1; sf_use_s2() is TRUE
# Create Data frames of points (Here you would use read.csv)
UTM11<- data.frame(X = c(358334, 571173, 446818, 576082, 331892),
Y = c(5128282, 5105900, 4730385, 4667181, 4811548))
UTM12<- data.frame(X = c(405776, 614821, 428211, 602751, 366593),
Y = c(5132654, 5117139, 4841550, 4704516, 4625745))
#Have 2 separate objects with different crs
UTM11<- st_as_sf(UTM11, coords = c("X", "Y"))
st_crs(UTM11)<- "EPSG:32611" #Set crs (https://epsg.io/32611)
UTM12<- st_as_sf(UTM12, coords = c("X", "Y"))
st_crs(UTM12)<- "EPSG:32612" #Set crs (https://epsg.io/32612)
#Transform to common crs and merge
WGS84<- rbind(st_transform(UTM11, crs = "EPSG:4326"),
st_transform(UTM12, crs = "EPSG:4326"))
```
<sup>Created on 2023-08-08 with [reprex
v2.0.2](https://reprex.tidyverse.org)</sup>
________________________________
From: R-sig-Geo <r-sig-geo-boun...@r-project.org> on behalf of Jason Edelkind
via R-sig-Geo <r-sig-geo@r-project.org>
Sent: Sunday, August 6, 2023 4:23 PM
To: r-sig-geo@r-project.org <r-sig-geo@r-project.org>
Subject: [R-sig-Geo] Did I correctly convert my data from UTM zone 12 to 11?
hello, first time user here and aspiring grad student. I have a set of location
data that I�ve been trying to import into google earth engine from R as a CSV
file. The problem is that about half of my data is from utm zone 12, and the
other half is from utm zone 11. When I import my original data into google
earth engine, the zone 11 data is shifted over to the right because I use utm
zone 12 as the crs in R. After some reading into the definition of a utm zone,
I tried to just subtract 6 from the zone 11 latitude values after first
converting them to a lon/lat format. This appears to have worked as on first
glance all of the zone 11 points are where they should be, however it feels
like too easy a fix for me after struggling with this for several days. So my
question is, is this an acceptable way to convert my data, or am I doing
something wrong that could result in inaccurate location data? Thanks!
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