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Elina Svitolina, of Ukraine, reacts during a match against Anastasia Pavlyuchenkova, of Russia, during the second round of the U.S. Open tennis championships, Thursday, Aug. 31, 2023, in New York. (AP Photo/Manu Fernandez)
Ons Jabeur, of Tunisia, reacts during a match against Noskova, of the Czech Republic, at the second round of the U.S. Open tennis championships, Thursday, Aug. 31, 2023, in New York. (AP Photo/Frank Franklin II)
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There are a lot of questions here about matching points in polygons efficiently (examples: Here and Here). The primary variables of interest in these are high number of points N, and number of polygon vertices V. These are all good and useful, but I am looking at a high number of points N and polygons G. This also means that my output will be different (I've primarily seen output consisting of the points that fall inside a polygon, but here I'd like to know the polygon attached to a point).
I have a shapefile with a large number of polygons (hundreds of thousands). Polygons can touch, but there is little to no overlap between them (any overlap of interiors would be a result of error - think census block groups). I also have a csv with points (millions), and I would like to categorize those points by which polygon the point falls in, if any. Some may not fall into a polygon (continuing with my example, think points over the ocean). Below I set up a toy example to look at the issue.
I now want to match points with the polygons. So The desired output would be an additional column in gdf_points with an identifier for which polygon the point is associated with (using the gdf_polys['id'] column). My very slow code, which produces the correct result is below:
Beginning benchmarking:With a grid size of 10, point density of 10 (1440 points): took about 180msWith a grid size of 20, point density of 10 (4840 points): took about 2.8sWith a grid size of 30, point density of 10 (10240 points): took about 12.8sWith a grid size of 50, point density of 10 (27040 points): took about 1.5 minsSo we can see this scales poorly.
Rather than thinking about this as a mass point-in-poly, geopandas has a spatial-join method that is useful here. It's actually quite fast, and at least with this toy example doesn't appear to be all that affected by the number of polygons (I can't rule out that this might be due to the simplicity of these polygons though).
This was very fast compared to my initial code. Running the largest size I tested (27k points) took under 60ms (compare to 1.5 mins for the previous code). Scaling up to some of my actual work, 1mil points took just over 13 seconds to match into just under 200k polygons, most of which were much more complex than the geometries used in my toy example. This seems like a manageable method, but I'd be interested in learning ways to improve the efficiency further.
It sounds like you could avoid iterating through all polygons by using the nearest STRtree algorithm, as written in the documentation (along with note above about recovering indices of the polygons) - and checking if the point sits within the nearest polygon. I.e. something like
Using inspiration from other answers here and on other threads, the solution that worked best for me for very large sets of points (billions) and polygons that may span large areas, was a combination of geopandas.sjoin and slicing up the data into sections, borrowing an implementation from the osmnx library.
To use, simply pass a GeoDataFrame containing the polygons and another containing the points to the assign_region function. This will return the points DataFrame with a new column containing an identifier column from the polygon DataFrame.
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It is frustrating when one wants to create symbols with the colors from polylines to polygons you have to create the colors 2 times. Not a big deal when you have a handful of attribute differences to color.
However when you have hundreds of different attributes it is very time cosuming.
I would like to see the ability to use the colors for a an attribute in one feature type used in the second feature that is not the same type.
Example. I have 573 unique line sections. I want to color all lines and points with same color based on the LineSection attribute. This can be quick and simply way to help find errors in data.
I tried testing the approach above and could not get it to work. Tried going from line to polygon and vice versa; doesn't allow for import. Suppose it makes sense because the symbology for lines and polygons are completely different ... but I figured I would give it a shot because like the OP mentioned, it's quite annoying to match colors between layers with 100's of records ...
This is a very legit requirement that specially in utilities it is required to match the symbology of line and point layers to be colored same to have a better analysis. It is very difficult to give a color to a unique ID if range is in thousands and doing the same activity for other layer with different geometry type is quite a challenge.
We have soil polygon features layers for which we have developed symbology. When checking these maps with field-collected point data, we simply wish to apply the same symbology to the field points based on the code for each of the soils as contained in the polygon features.
I have a line layer with an identical field and attributes that exactly match the polygon data. I need all of the same colors, heading groups, and labels in the line file. Image to help describe what I mean:
The only way I have found to work around this issue is to add a common symbology column (i.e. SYMBOL1 etc.) to each feature, fill it with a hex color code (or other recognized codes) where attributes match between the two features, then under Symbology, click the 'burger' button and check 'Allow symbol property connections', then set the various color properties line/fill to this new field. This symbology does not carry over to features published to ArcGIS Online or Portal in my experience though.
I want to match the point cloud data and the vector map data in RVIZ. However, they don't match. There are a huge gap between them in RVIZ. For example, the point cloud map is at x = 120, y = 80 in map frame while the vector map is at x = 77515, y = 11469 in also map frame. When I searched the reason of that at internet, I saw that the vector map is located in MGRS coordinate system while the point cloud map is located in arbitrary system. However, I didn't find any solution to match them automatically. Is there anyone having any idea?
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