geospatial | R package for data | Dataset library

Uber has released h3, a framework for efficiently handling big data in the geospatial file. Using h3, I attempted to get the location of a hexagonal grid location as shown in the figure. (https://eng.uber.com/h3/)

I got the location of the hexagonal grid from the following code. Then I plotted it on a two-dimensional map to see if it covered the entire earth. However, I'm not getting valid hexagons on the boundaries(-90°,90°,-180°,180°). And it doesn't seem to cover the entire globe. (hexagonal grid)

I use the newest snapshot of Apache Sedona (1.3.2-SNAPSHOT) to do some geospatial work with my Apache Spark 3.0.1 on a docker cluster.

When trying out the first example in the tutorials section (http://sedona.apache.org/tutorial/sql/), I am suffering a NoClassDefException as a cause of a ClassNotFoundException:

I am trying to include shapefiles as package data in Python, and I'm having a tough time understanding what to do with the stream of data. Pandas' read_excel() and read_csv() work just fine, but when I try to access more complicated filetypes, I don't know what I should do:

My approach to creating a choropleth map via plotly seems pretty straightforward--load in the DataFrame, load in the geojson, assign the necessary features to the custom polygons, and plot.

Obviously there is a missed step somewhere when referencing of the custom polygons as only a blank map appears after a lengthy loading time.

One major thing to note is that about half of the polygons are located within states, but are their own custom polygon within the states. Therefore to my knowledge, choropleth_mapbox is the more suitable solution.

A sample of the image, showing custom polygons within the states:

The code:

I'd like to do an Europe map, so I was trying with this code but I don't really know how it works.

I'd like to download inside Shiny a georeferenced PDF file (geoPDF) and for this a need some steps like convert the plot in ggplot format to tiff, populate the spatial coordinates, create a geo tiff, and finally my geoPDF. But several steps in downloadHandler() function in tempdir() directory results always in the error:

I have a Vue component like this...

I have a set of geospatial data with me that also has the corresponding timestamps in a separate column.

Something like this:

I want to be able to cluster the data based on their timestamps rather than their distances.

So for the above example, I should obtain 2 clusters: 1 from the 1st 3 data points, and 1 from the remaining 3. I would also like to obtain the range of timestamps for each cluster is possible.

From what I've researched so far, I've only gotten either geospatial distance clustering, or time-series clustering, both of which do not sound like what I need. Are there any recommended algorithms for what I am trying to do?

I have 3, two dimensional arrays that represent geospatial data. Each array shape is (721,1440), i.e., 721 latitude values and 1440 longitude values. I want to compute a weighted mean of these 3 arrays. Normally that is simple and would generally be sum(array*weight)/sum(weights). This works great except in cases where you have nans in the data.

In my specific case arr1 should have a weight of 0.7, arr2 0.2, and arr3 0.1. However, anytime there is a nan, the mean obviously becomes nan. In my case the only data with nans is arr3.

What I want though is when there is a nan for the weighted mean to only comprise of the first two arrays, which would be (arr1*0.7 + arr2*0.2)/0.9. I tried using xr.where() to accomplish this but for some reason it goes hog wild on my RAM and crashes my kernel every single time. Are there any other ways to accomplish this task?