Recently, I have been working on processing data from multiple sites, which involved calculating the distance to the nearest coastline for the study sites. After exploring various methods in R and ArcGIS to compute the shortest distances, I found that many were complex and tedious. However, by integrating ideas from different sources, I’ve developed a simplest and fastest approach for calculating the shortest distance for each site by using R. This method is capable of processing multiple sites at the same time. I’d happy to share my codes below. Please feel free to let me know if you have any questions 🙂
# Load library
library(ggOceanMaps) # in order to use its function dis2land, the output are in kilometers.
library(rnaturalearth)
library(sf)
# Load coastline data
coast <- ne_coastline(scale=10, returnclass = “sf”)
Your_sites <- read.csv(“Your_sites.csv”)
# For example, I have a csv file called "Global sites", with three columns: sites_codes, Longitude (LON), and latitude (LAT):
# Calculate the shortest distance
ggOceanMaps::dist2land(Your_sites,shapefile =coast)
# The results will look like:ldist is the shortest distance (kilometers).
# Additional explanations:
The usage of ggOceanMaps library: We will use its function called “dis2land” to calculate the shortest distance. The function was originally designed for calculating the distance from a point in the ocean to the land (reference: R/dist2land.R ). But we can replace the shapefile of the ocean by the shapefile of the coastline, so that we can calculate distance to the closest shoreline. The output is “ldist” and it is in kilometers.
The usage of rnaturalearth library: we can get the global coastline shapefile with the available finest resolution (scale =10) directly from this library.
The method presents here is inspired by Mikko’s answer at https://stackoverflow.com/questions/51837454/r-measuring-distance-from-a-coastline.
Your instruction is very detailed and easy to follow. Thanks for sharing.
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This is a really elegant solution — thanks for sharing it! I especially like how you repurposed dist2land() by swapping in the coastline shapefile. That’s a clever workaround and much cleaner than building a custom st_distance() workflow with spatial joins and projections.
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Interesting approach to calculating coastline distances in R. I wonder if this method could be adapted for environmental research applications. Thanks for sharing the code snippets!
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This is honestly a lifesaver! I was totally stuck trying to figure out how to calculate coastline distances without building an absurdly convoluted spatial join workflow. Swapping the ocean shapefile in the dist2land function is such a clever and elegant workaround. Tracking all my messy coordinate data used to feel as frustrating as trying to manage a complicated Fortnite Sprite Tracker, but your clean code snippet fixed everything in seconds. Definitely bookmarking this for my future GIS projects. Thanks!
This is exactly the kind of efficient solution I always look for! I remember struggling with a similar task last year, spending hours manually calculating distances. If only I had this then! For anyone looking to streamline their workflow, Basketball Stars offers great tools for sharing and discovering such innovative code. I’m eager to see your code!
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This was super helpful! I’ve been wrestling with coastline buffers in ArcGIS for days, and your neat trick of swapping the shapefile in `ggOceanMaps::dist2land()` saved me a ton of time. Just ran it over a couple thousand site coordinates, and it handled the batch seamlessly. While working through the script, I had a stream running via the Choicer Voicer free online in the background, which kept me focused. Really appreciate you sharing such a clean workflow!
The dist2land shortcut is much cleaner than the ArcGIS route I had been using, and running multiple sites in one call is the part that actually saves time. Good call flagging that the function was written for ocean points, since that assumption bites people quietly. Estimating distance by eye is hopeless, which is half the fun of Size It Up. Bookmarking this for the next batch of coastline work I have queued.
What a great dive into calculating distances! I recently used R for a coastal project where accuracy was key. Your tips on spatial analysis methods are super helpful— they’ll definitely sharpen my approach. Thanks for sharing such valuable insights!
Replacing the default shapefile in ggOceanMaps::dist2land() is such a clever workaround! Much cleaner than setting up standard spatial joins in R. Thanks for sharing this tip.Heic Converter
Reading these R learning notes on calculating the shortest distance to the coast provides excellent spatial data analysis tips! Mastering GIS workflows in R takes practice, offering a rewarding challenge compared to relaxing with Easy Games during breaks. Thank you for sharing such clear coding tutorials—they are super helpful for learners!
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Using ggOceanMaps with a coastline shapefile from rnaturalearth makes the workflow easy to follow, especially when processing multiple sites together. The note that ldist is in kilometers helps avoid ambiguity when interpreting the results. This gave me an idea for a mapping experiment called only one bed at the inn, comparing locations by their nearest shoreline. Would the scale=10 coastline still be detailed enough for sites close to small bays or islands?
Using ggOceanMaps with a coastline shapefile makes the shortest distance to coast calculation look much more manageable for multiple sites. The note that ldist is in kilometers is particularly useful when checking results against other spatial workflows. I’m collecting practical R examples for a project called nate hand gesture, and this approach caught my attention. Would the scale =10 coastline still be suitable for sites very close to small islands?