I am trying to do nearest neighbor analysis using road distance in R. (For example, looking at nursing homes nationwide and trying to identify the five closest hospitals to each one.) I had used Euclidean distance before using st_nn but I am now interested in trying to replicate the analysis using road distance. I know that if I had any two points I could use a number of packages to define road distance between them (e.g. googleway), but not sure if that is available for k-nearest neighbor approaches.
I was recommended to try spatstat.linnet::distfun.lpp:
https://gis.stackexchange.com/questions/451410/doing-nearest-neighbors-accounting-for-road-distance-in-r/451416?noredirect=1#comment737625_451416)
I am having trouble however trying to figure out how to take a grid like open street maps and coverting it to linnet format, as that program would require. Does anyone have any ideas/alternatives?
Related
I am working on a research project in marine ecology, using R, and I would like to create a map of a small and precise part of the French Mediterranean coast. From this map I would like to add the different fish collection sites in order to calculate the distances between these sites, taking into account the topology of the coast (the sites being very close to the coast). I have used the marmap package to do this, however due to the size of the map I wish to create, the resolution is very poor and the map is unworkable.
data <- getNOAA.bathy(lon1 =2.97,lon2 =3.53,lat1 =41.9,lat2 =42.3,resolution = 1)
I would like to know if there is an alternative, such as using the ggmap package to get a map with a good resolution, then import the GPS points of the sites and calculate the distances between them using marmap ? Are the two packages compatible?
Do you have any other ideas?
I'd recommend using leaflet for mapping and geosphere to find the Haversine (as the crow flies` distance betwen points.
I new on using R with spatial data and I don't understand how to fix my issue.
My goal is to test differents pattern to make soil sample for quantifying soil organic carbon. I have a raster layer which represent the carbon stock with a grid of 1m*1m.
On this raster I want to randomly chose 20 points across the diagonal of the plot (which is rectangular). And I want each point separated by 20 meters.
Then I would like to repeat this operation a lot of times and each times I would like that each points move à litle bit in a certain range around the diagonal.
I'm trying with raster::select function but I don't understand the way it's work.
If you have any help to give me or just some good R package to do this I woul apreciate a lot !
Thank you,
Antoine
I have a list of locations with zipcodes. I have another list of Distribution Centers that serve these locations. Is there anyway to map the nearest DC to each of these locations? I am an extremely green coder, but i have some experience with R
I'd need more information to give you some possible code to solve your problem however, here is one approach to solving your problem.
Convert your zipcodes to longitudes and latitudes.
Not sure what location data you have on your distribution centers, but you should be able to find a way to retrieve the long/lat of each of these.
For each zipcode, compute the the distance to each DC (using their respective longs/lats). To compute the distance, use the haversine formula. Find the minimum of these distances. This is your solution.
I need to create clusters from the distance between clients if the distance between the points is less than certain precise value group them together.
I thought about using Delaunay but I'm not having success
Mine are follow-ups to the question & answer in Approaches for spatial geodesic latitude longitude clustering in R with geodesic or great circle distances.
I would like to better understand:
Question #1: If all the lat / long values are within the same city, is it necessary to use either fossil or distHaversine(...) to first calculate great circle distances ?
or, within a single city, is it OK to run clustering on the lat/long values themselves ?
Question #2: jlhoward suggests that :
It's worth noting that these methods require that all points must go into some cluster. If you just ask which points are close together, and allow that some cities don't go into any cluster, you get very different results.
In my case I would like to ask just ask "which points are close together", without forcing every point into a cluster. How can I do this ?
Question #3: To include one or two factor variables into the clustering (in addition to lat/long), is it as easy as including those factor variables in the df upon which the clustering is run ?
Please confirm.
Thanks!
"within a single city, is it OK to run clustering on the lat/long values themselves ?"
Yes, as long as your city is on the equator, where a degree of longitude is the same distance as a degree of latitude.
I'm standing very close to the north pole. One degree of longitude is 1/360 of the circumference of the circle round the pole from me. Someone ten degrees east of me might only be ten feet away. Someone one degree south of me is miles away. A clustering algorithm based on lat-long would think that guy miles away was closer to me than the guy I can wave to ten degrees east of me.
The solution for small areas to save having to compute great-circle ellipsoid distances is to project to a coordinate system that is near-enough cartesian so that you can use pythagoras' theorem for distance without too much error. Typically you would use a UTM zone transform, which is essentially a coordinate system that puts its equator through your study area.
The spTransform function in sp and rgdal will sort this out for you.