osmnx | street networks . | Map library

You can do this in the following way.

Import nodes and edges as geodataframes.

It depends on how you parameterized the function. From the nearest_edges function documentation:

Find the nearest edge to a point or to each of several points.

If X and Y are single coordinate values, this will return the nearest edge to that point. If X and Y are lists of coordinate values, this will return the nearest edge to each point.

If interpolate is None, search for the nearest edge to each point, one at a time, using an r-tree and minimizing the euclidean distances from the point to the possible matches. For accuracy, use a projected graph and points. This method is precise and also fastest if searching for few points relative to the graph’s size.

For a faster method if searching for many points relative to the graph’s size, use the interpolate argument to interpolate points along the edges and index them. If the graph is projected, this uses a k-d tree for euclidean nearest neighbor search, which requires that scipy is installed as an optional dependency. If graph is unprojected, this uses a ball tree for haversine nearest neighbor search, which requires that scikit-learn is installed as an optional dependency.

So if you leave interpolate=None (ideally using a projected graph and projected points for accuracy), the function will find the nearest edge(s) to your point(s) based on the point's minimum distance to any part of the edge's geometry. This is geometrically precise and is fastest if only searching for a few points in a big graph.

Alternatively, if you pass an interpolate argument value, the function will interpolate evenly spaced points along the edges, then find the nearest edge(s) to your point(s) based on the point's minimum distance to any interpolated point along the edge's geometry. This is slightly geometrically imprecise (with this imprecision varying with your interpolate value) but is fastest when searching for many points, particularly in a smaller or mid-sized graph.

I've already left this code running for almost six hours and absolutely nothing happens.

A lot has been happening! Don't believe me? You ran ox.config(log_console=True), so look at your terminal and watch what's happening while it runs. You'll see a line like "2021-10-14 13:05:39 Requesting data within polygon from API in 1827 request(s)"... so you are making 1,827 requests to the Overpass server and the server is asking you to pause for rate limiting between many of those requests.

I know that due to the stipulated area the time is long, but what I wanted to know is if there is an alternative to this procedure or if there is a way to optimize so that the creation of the map is a little faster or if there is another way to load maps to route with osmnx and networkx without using queries to servers

Yes. This answer provides more details. There are tradeoffs between 1) model precision vs 2) area size vs 3) memory/speed. For faster modeling, you can load the network data from a .osm XML file instead of having to make numerous calls to the Overpass API. I'd also recommend using a custom_filter as described in the linked answer. OSMnx by default divides your query area into 50km x 50km pieces, then queries Overpass for each piece one a time to not exceed the server's per-query memory limits. You can configure this max_query_area_size parameter, as well as the server memory allocation, if you prefer to use OSMnx's API querying functions rather than its from-file functionality.

There are inherent trade-offs when you want to model a large study area such as an entire region or an entire country: 1) model precision vs 2) area size vs 3) memory/speed. You need to trade off one of these three.

For the first, you can model a coarser-grained network, such as only major roads in the region/country, rather than millions of fine-grained residential streets and paths. For the second, you can study a smaller area. For the third, you can provision a machine with lots of memory and then let the script run for a while to complete the process. What you trade off will be up to your own needs for this analysis.

In the example code below, I chose to trade off #1: I've modeled this region (West Midlands) by its motorways and trunk roads. Given a different analytical goal, you may trade off other things instead. After creating the model, I randomly sample 1000 origin and destination lat-long points, snap them to the nearest nodes in the graph, and solve the shortest paths by travel time (accounting for speed limits) with multiprocessing.

I do not know the reason why it cannot be obtained. When I looked into it, I was able to get geopandas, and from there I was able to get the latitude and longitude information.

so the answer to your question might be because you are not projecting your Network. Take a look at this similar question I posted which was answered by the creator of the library: Node found is too far from my lat, lon coordinates (osmnx)

You need to load all dataframes you need, store them and then concatenate them together.

The module fractions is part of the Python standard library. There used to be a function gcd, which, as the linked documentation says, is:

Deprecated since version 3.5: Use math.gcd() instead.

Since the function gcd was removed from the module fractions in Python 3.9, it seems that the question uses Python 3.9, not Python 3.7.6 as the question notes, because that Python version still had fractions.gcd.

The error is raised by networkx. Upgrading to the latest version of networkx is expected to avoid this issue:

I wasn't able to download buildings data from OSM with a polygon (shapefile) as the bounding box however I was able to using distance from a point with the following code: