Hey Geo-computing experts. I’m working on a project involving migration of people between locations. We started with German data, where coordinates were already taken care of for me, and now I’ve got data from US Census on the counties of the US. It includes latitude and longitude of the centroids o…

[image] dlakelan: this is about the simplest thing I can think of If you mean “what is a Cartesian/Euclidian” 2D map where the distance between each pair of points is the same as on a sphere (or a part of it), then that does sound simple indeed, but it’s just not possible to find such a map. …

Yes, I’m aware we can’t project the whole earth to a flat map, but I had hoped to project the whole of the continental US to a flat map without too much distortion. There’s two reasons for this. The first is to calculate distances… and there the Haversine function would be good so maybe that’s fine…

@dlakelan the simplest thing you can do to start experimenting with the data is the following: using GeoStats using GeoIO geotable = GeoIO.load("data.format") projected = geotable |> Proj(utmnorth(10)) # zone 10 to 19 cover the US You can then visualize the flat map with: import GLMakie as Mke …

[image] juliohm: Geodesy.jl is not actively developed (>2 years without a non-trivial commit) and is much behind in terms of features compared to new efforts that we are actively working on, i.e., CoordRefSystems.jl Ah this is good to know! The data for the counties isn’t in any kind of geo-…

[image] dlakelan: The data for the counties isn’t in any kind of geo-data special format or anything, it’s just a delimited text file with things like postal code, ANSI code, name, areas, etc. I’m basically starting from a Data Frame with lat,long as columns. If you already have the DataFrame …

I haven’t read GDSJL but it sounds cool. I’m looking to fit the polynomial myself as part of a large bayesian model in Turing (and get posterior distribution of the coefficients / desirabilities). I’ll look at what kind of thing GeoStats does under the hood. Thanks for the hints.

[image] juliohm: GeoStats.jl provides a trend(geotable, names, degree=1) function I didn’t see this function in the GeoStats.jl repository anywhere (I searched GeoStats.jl github for “trend” and also after using GeoStats there was no GeoStats.trend function). Is it in a separate package? Or s…

It is in the GeoStatsTransforms.jl submodule: JuliaEarth/GeoStatsTransforms.jl/blob/d25bc93d674785da6e87b62de267612b7d36e64f/src/detrend.jl#L117-L162 """ trend(data, vars; degree=1) Return the deterministic spatial trend for the va…

Interesting. The quantity I’m interested in is an unobserved parameter so it will take a different approach I guess (obviously I can’t put that thing in a table of observations). This stuff looks really great, but I did see as I was reading the code it seems you use a Vandermonde matrix, but then do…

Simple use of Geodesy.jl to get euclidean distances?

General Usage

rafael.guerra August 27, 2024, 6:50pm 2

For accuracy better than 0.5% there is the Haversine formula - see Julia code here.

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Simple use of Geodesy.jl to get euclidean distances?

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