I need to use xarray to write the projection information into the netcdf file so that Iris can directly obtain the projection information when reading it.
We know that there are many projection s in cartopy, such as equal latitude and longitude projection, Lambert projection, etc. How to write this information into netcdf file? Is it similar to an epsg string?
For Iris, you most likely want to encode the grid projection information in a grid_mapping attribute on the data variables according to the Climate and Forecasting netCDF Conventions.
Related
I have the coordinates of Manhattan in a .wkt file and an example of that file content is: LINESTRING (40.619605 -73.9584046, 40.6205286 -73.9585793, 40.6206127 -73.9585952)
ONE simulator does not recognize such coordinates to display the map of Manhattan and instead needs something that looks like this: LINESTRING (4883.179 10736.259, 4924.922 10811.716)
I'm not sure what format that is nor not sure what to search for in google. I need to know how to convert from LINESTRING (40.619605 -73.9584046, ... to something like LINESTRING (4883.179 10736.259, 4924.922 10811.716)
preferred language is python, thanks.
The format ONE uses is subset of the standard WKT format. See details in the WKTReader class and ReadMe.
The coordinates used in the WKT input files are in reference to the simulation world coordinates. You can open your source map file with a WKT editor (e.g., OpenJUMP) and translate the coordinates so that they’re all positive and that could be enough.
I am trying to map a geojson file (A map of Alaska precincts, downloaded from the division of elections and then converted online to geojson) onto a choropleth map using folium, the problem is the coordinates are in 7-digit numbers like this:
[ -16624764.227, 8465801.1497 ]
I read on a similar post that this was most likely a US coordinate system like UTM or State Plane, and recommended using an API to reproject it. Is it also possible to access the coordinates directly such as with geopandas and divide them by 100000?
The data is most likely in a specific cartographic projection. You don't just want to divide by 100k - the data will likely have nonlinear transformations which have a different effect on the position depending on the location. See the GeoPandas docs on working with projections.
If the CRS of the data is correctly encoded, you can re-project the dataframe into lat/lons (e.g. WGS84, which has the EPSG Code 4326) using geopandas.GeoDataFrame.to_crs, e.g.:
df_latlon = df.to_crs("epsg:4326")
I received a .gdb file that I need to do analysis on. This is an ESRI file, but I don't have access to any ESRI products. I opened it in R using the "rgdal" package, but the coordinates it displays seem very off.
For example, (-9288065, 4604652) should map to Kentucky. My initial thought was that there are missing decimals, so it should be -92.88 and -46.04, but this maps to somewhere north of Kentucky, which is wrong.
Do these coordinate units look familiar? How might I convert them to latitude and longitude?
Solved with help from #camille.
For future users:
library(rgdal)
library(raster)
myFeatureClass<-readOGR('your_data_here')
#checks the projection type
sp::proj4string(myFeatureClass)
#convert projection to longlat
final_data <- as.data.frame(spTransform(myFeatureClass, CRS("+proj=longlat +datum=WGS84")))
I am trying to interpolate landscape influences in the coastal/marine environment by inverse distance weighting while accounting for land barrier, and am excited to find the ipdw package (https://cran.r-project.org/web/packages/ipdw/ipdw.pdf). Within ArcGIS, I currently have 1) a cost raster object (.adf file) that sets the study extent and 2) a point object (.csv file with latitude, longitude, and intended metric for interpolation) - and am in the process of trying to make them compatible with R.
Can someone direct me to resources for converting a .csv file to a shapefile that would work within the ipdw package, to be loaded as the spdf (SpatialPointsDataFrame) object?
Does the ArcGIS raster have to be in a certain format to be loaded as the costras (cost raster) object?
I would really appreciate any leads and insights!
This tutorial covers creating an sp SpatialPointsDataFrame object from a csv:
https://www.neonscience.org/dc-csv-to-shapefile-r
This tutorial covers loading raster files in R and combining with vector objects:
https://www.neonscience.org/dc-crop-extract-raster-data-r
I have a spatial dataset of shopping centers that I would like to create buffers around in R.
I think these packages will be useful:
require(maptools)
require(geosphere)
I was able to do so for a set of coordinates, but not for spatial data. The code looks like this:
coordinates(locs) <- c("Longitude", "Latitude") # set spatial coordinates
fivekm <- cbind(coordinates(locs), X=rowSums(distm (coordinates(locs)[,1:2], fun = distHaversine) / 1000 <= 5)) # number of points within 5 km
But I don't know what function/package to use for a set of polygons. Can someone please advise on the function (or code) and I will go from there?
Thanks!
In library rgeos, there is the gBuffer function that works with SpatialPoints or SpatialPolygons.
The width parameter allows to set the distance to which you want to buffer. However, be careful, this distance is in the scale of the coordinates system used. Thus, in degrees and not in meters with non-projected data. As suggested by #Ege Rubak, you will have to project your data with spTransform first (be sure to use the appropriate CRS according to your location).
As for now, rgeos library works with library sp, but not (yet?) with the recent sf.
I think the only option at the moment is to project your longitude and latitude points to a flat map and then do everything there. As far as I know there are no packages for doing polygonal geometry on the sphere yet (I'm working on one, but there is no ETA).
Projection used to be done with spTransform from the sp package, but now it may be more convenient to use the more modern simple features package sf which has the function st_transform. The vignette https://cran.r-project.org/web/packages/sf/vignettes/sf1.html has a section called "Coordinate reference systems and transformations" to help you with this part. The buffering is described in the section "Geometrical operations".
The two previous post have covered the details but I thought it might be helpful to provide a workflow. This is assuming you have you are using points of lat and long. What is your original spatial data format?
Convert your coordinates into a Spatial Points Dataframe SpatialPointsDataFrame and assign it a geographic CRS (proj4) that matches your coordinate data (probably WGS84)
Change the projection to a local projected CRS with preferred units
Apply buffer to spatial point data frame, the width will now be in more usable units