close in some ways, though.
regardless, I did not realize that scipy had builtin sparse adjacency matrix graph support
that makes it super easy to build on top of, I'd think both for spaghetti & for bounder/region
anyway, this rapidly deviated from this channel's intent. Sorry to spam. Was just excited about the sparse network support & algos in scipy already.
right now, that's a slow component at scale.
Now I never cast the sparse matrix to a dense one while solving
(I'm looking back at my issue from a year and a half ago, haha... pysal/spaghetti#9)
@hasecbinusr a network object can now be instantiated with a geopandas.GeoDataFrame, but currently it will still produce the same error you were getting before if you would like a simplified, graph-theoretic object. However, we have added an extragraph kwarg that will bypass simplification if set to False.
Thanks for sticking with this issue. It is absolutely still on the radar.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-14-0c31d729ebc0> in <module>()
----> 1 ntw = spgh.Network(in_shp=edges)
AttributeError: module 'spaghetti' has no attribute 'Network'
Just upgraded to the latest dev version with the same error
Gremlins in the system
If anyone is interested, I was able to pull a road network from OSMnx and import it into Spaghetti using this code:
import spaghetti as spgh
import osmnx as ox
%matplotlib inline
place_name = "Travis County, Texas, United States of America"
graph = ox.graph_from_place(place_name, network_type='drive')
fig, ax = ox.plot_graph(graph)
edges = ox.graph_to_gdfs(graph, nodes=False, edges=True)
ntw = spgh.Network(in_shp=edges)
@hasecbinusr this is due to our recent addition of allowing a
geopandas.GeoDataFrame as the value of the in_shp argument. And it is very cool that we now know this works. Have you thought about contributing a notebook to showcase this use-case? We would be very interested in reviewing any contributions you may have!
Hi! I've been playing with 1.5.3 to test the fix for pysal/spaghetti#526. While playing around with differently split networks, I observed how drastically the detected hotspots can change. I was wondering if we could compute the hotspots based on pts per meter instead of the absolute point counts. Since graph edges differ in length, longer edges will get more points if points are distributed uniformly.
Btw, here's a preview of what we've been up to lately regarding spatiotemporal patterns:
above: raw point pattern per time slice, next: hotspots per time slice
finally: preliminary pattern results
If this is up and documented somewhere, maybe we can provide a link for in the Tutorials section of the docs. Maybe update to “Tutorials & Use Cases” or something.
@anitagraser I just enabled the beta version of GitHub Discussions for the
spaghetti repo and opened a discussion where we can better get ideas and keep track of them. Can you post you notes and figures in there?