Possibility of Error or Misunderstanding of how "Calculate basic street network measures (topological and metric)" works

This is from notebook "06-example-osmnx-networkx.ipynb

"Use OSMnx to analyze a NetworkX street network, including routing""

In the example, the length of the dataframe is 30 columns.

1. Is that the standard?
2. Should other places, if they exist as a polygon, be consistent in polygon length, if I am using the same arguments?

I have ran multiple tests on different cities, and I either get 26 or 28 columns, respectively.

The only two arguments that I called are G and area from these basic stats docs

I am trying to see where the error is because if I have the same argument for every place and I know the shapes exist, why would they have different column lengths?

  `stats = ox.basic_stats(G, area=area)
    for k, count in stats['streets_per_node_counts'].items():
        stats['int_{}_count'.format(k)] = count
    for k, proportion in stats['streets_per_node_proportion'].items():
        stats['int_{}_prop'.format(k)] = proportion`

The only solution I can think of why I may have different columns is how I am writing it to a csv,

        que = pd.DataFrame(pd.Series(stats)).T
        with open(file_name +'.csv', 'w') as f:
            que.to_csv(f, header=False,index=False,encoding='utf-8')

Full code for clarity

for value in df.Name:
        place = value
        gdf = ox.gdf_from_place(place)
        area = ox.project_gdf(gdf).unary_union.area
        G = ox.graph_from_place(place, network_type='drive_service')
        stats = ox.basic_stats(G, area=area)
        for k, count in stats['streets_per_node_counts'].items():
            stats['int_{}_count'.format(k)] = count
        for k, proportion in stats['streets_per_node_proportion'].items():
            stats['int_{}_prop'.format(k)] = proportion
        # delete the no longer needed dict elements
        del stats['streets_per_node_counts']
        del stats['streets_per_node_proportion']
        file_name = str(counter)
        que = pd.DataFrame(pd.Series(stats)).T
        with open(file_name + '.csv', 'w') as f:
            que.to_csv(f, header=False,index=False,encoding='utf-8')
        del stats
        del place
        del area
        del gdf
       time.sleep(1000)

Example 08-example-line-graph seems not to work anymore.

When executing with mybinder.org, I get an error:

Did something change or am I missing something?

Regarding this example: https://github.com/gboeing/osmnx-examples/blob/master/notebooks/13-isolines-isochrones.ipynb

I propose a new method, make_iso_polys, outlined below. This, rather than a convex hull, might best demonstrate coverage area while preserved nuances ("inlets" or areas of inaccessibility not captured when creating a convex hull around a point cloud).

Example of what improved plot looks like:

Ful function defintion.

def make_iso_polys(G, edge_buff=25, node_buff=50):
    isochrone_polys = []
    for trip_time in sorted(trip_times, reverse=True):
        subgraph = nx.ego_graph(G, center_node, radius=trip_time, distance='time')

        node_points = [Point((data['x'], data['y'])) for node, data in subgraph.nodes(data=True)]
        nodes_gdf = gpd.GeoDataFrame({'id': subgraph.nodes()}, geometry=node_points)
        nodes_gdf = nodes_gdf.set_index('id')

        edge_lines = []
        for n_fr, n_to in subgraph.edges():
            f = nodes_gdf.loc[n_fr].geometry
            t = nodes_gdf.loc[n_to].geometry
            edge_lines.append(LineString([f,t]))

        n = nodes_gdf.buffer(node_buff).geometry
        e = gpd.GeoSeries(edge_lines).buffer(edge_buff).geometry
        all_gs = list(n) + list(e)
        new_iso = gpd.GeoSeries(all_gs).unary_union
        isochrone_polys.append(new_iso)
    return isochrone_polys

Further elaboration available here: http://kuanbutts.com/2017/12/16/osmnx-isochrones/

get a graph for some city

G = ox.graph_from_place('Piedmont, California, USA', network_type='drive') fig, ax = ox.plot_graph(G)

Installed per instructions on Mac OS. Terminal output and error output follow.

Any direction would be appreciated. Thanks, Jay

Output from terminal looks good except the last line (notebook not trusted): Posting to http://overpass-api.de/api/interpreter with timeout=180, "{'data': '[out:json][timeout:180];(way["highway"]["area"!~"yes"]["highway"!~"cycleway|footway|path|pedestrian|steps|track|corridor|elevator|escalator|proposed|construction|bridleway|abandoned|platform|raceway|service"]["motor_vehicle"!~"no"]["motorcar"!~"no"]["access"!~"private"]["service"!~"parking|parking_aisle|driveway|private|emergency_access"](poly:"37.823113 -122.255010 37.823199 -122.255027 37.823651 -122.255055 37.824104 -122.255026 37.824552 -122.254940 37.824991 -122.254797 37.825417 -122.254600 37.825824 -122.254350 37.826210 -122.254049 37.826569 -122.253701 37.826799 -122.253455 37.826915 -122.253330 37.827176 -122.253244 37.827377 -122.253178 37.827800 -122.253010 37.828208 -122.252791 37.828597 -122.252523 37.828964 -122.252207 37.829303 -122.251848 37.829613 -122.251448 37.829891 -122.251012 37.830143 -122.250573 37.830803 -122.249429 37.830839 -122.249407 37.831197 -122.249128 37.831286 -122.249052 37.832123 -122.248407 37.832393 -122.248200 37.832408 -122.248188 37.832448 -122.248157 37.832806 -122.247849 37.833139 -122.247498 37.833444 -122.247109 37.833717 -122.246685 37.833957 -122.246229 37.834162 -122.245747 37.834328 -122.245241 37.834455 -122.244718 37.834542 -122.244183 37.834587 -122.243639 37.834589 -122.243372 37.834678 -122.243039 37.834731 -122.242827 37.834764 -122.242685 37.834890 -122.241972 37.834909 -122.241820 37.834955 -122.241306 37.834963 -122.241152 37.834972 -122.240864 37.834984 -122.239854 37.834988 -122.239743 37.835013 -122.239561 37.835040 -122.239289 37.835061 -122.239227 37.835189 -122.238722 37.835278 -122.238205 37.835314 -122.237839 37.835348 -122.237717 37.835448 -122.237227 37.835481 -122.237028 37.835520 -122.236796 37.835555 -122.236603 37.835691 -122.235869 37.835737 -122.235620 37.835738 -122.235611 37.835971 -122.234342 37.836041 -122.233967 37.836454 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-122.205460 37.820904 -122.205174 37.820515 -122.204933 37.820110 -122.204741 37.819692 -122.204599 37.819265 -122.204508 37.818833 -122.204469 37.818400 -122.204482 37.817970 -122.204548 37.817547 -122.204666 37.817135 -122.204834 37.816738 -122.205051 37.816359 -122.205316 37.816002 -122.205625 37.815670 -122.205975 37.815367 -122.206365 37.814959 -122.206941 37.814930 -122.206982 37.814675 -122.207349 37.814481 -122.207381 37.814339 -122.207410 37.814177 -122.207447 37.814030 -122.207484 37.813634 -122.207607 37.813500 -122.207657 37.812918 -122.207931 37.812911 -122.207935 37.812827 -122.207984 37.812691 -122.208066 37.812132 -122.208467 37.811978 -122.208597 37.811966 -122.208603 37.811575 -122.208821 37.811483 -122.208879 37.811239 -122.209045 37.811213 -122.209064 37.811191 -122.209080 37.811159 -122.209104 37.811136 -122.209120 37.810777 -122.209410 37.810730 -122.209452 37.810345 -122.209837 37.810210 -122.209988 37.810035 -122.210195 37.809941 -122.210312 37.809922 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"cache/2e68726eb0121fe5f6f00f39771d3830.json" Got all network data within polygon from API in 1 request(s) and 9.30 seconds Creating networkx graph from downloaded OSM data... Created graph with 5,578 nodes and 10,929 edges in 0.15 seconds Added edge lengths to graph in 0.09 seconds Identifying all nodes that lie outside the polygon... Created r-tree spatial index for 5,578 points in 0.21 seconds [I 05:13:43.513 NotebookApp] Saving file at /osmnx-examples/notebooks/00-osmnx-features-demo.ipynb [W 05:13:43.514 NotebookApp] Notebook osmnx-examples/notebooks/00-osmnx-features-demo.ipynb is not trusted

Error output:

TypeError Traceback (most recent call last) in 1 # get a graph for some city ----> 2 G = ox.graph_from_place('Piedmont, California, USA', network_type='drive') 3 fig, ax = ox.plot_graph(G)

~/anaconda3/envs/ox/lib/python3.8/site-packages/osmnx/core.py in graph_from_place(query, network_type, simplify, retain_all, truncate_by_edge, name, which_result, buffer_dist, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter) 1443 1444 # create graph using this polygon(s) geometry -> 1445 G = graph_from_polygon(polygon, network_type=network_type, simplify=simplify, 1446 retain_all=retain_all, truncate_by_edge=truncate_by_edge, 1447 name=name, timeout=timeout, memory=memory,

~/anaconda3/envs/ox/lib/python3.8/site-packages/osmnx/core.py in graph_from_polygon(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter) 1324 G_buffered = create_graph(response_jsons, name=name, retain_all=True, 1325 bidirectional=network_type in settings.bidirectional_network_types) -> 1326 G_buffered = truncate_graph_polygon(G_buffered, polygon_buffered, retain_all=True, truncate_by_edge=truncate_by_edge) 1327 1328 # simplify the graph topology

~/anaconda3/envs/ox/lib/python3.8/site-packages/osmnx/core.py in truncate_graph_polygon(G, polygon, retain_all, truncate_by_edge, quadrat_width, min_num, buffer_amount) 731 732 # find all the nodes in the graph that lie outside the polygon --> 733 points_within_geometry = intersect_index_quadrats(gdf_nodes, polygon, quadrat_width=quadrat_width, min_num=min_num, buffer_amount=buffer_amount) 734 nodes_outside_polygon = gdf_nodes[~gdf_nodes.index.isin(points_within_geometry.index)] 735

~/anaconda3/envs/ox/lib/python3.8/site-packages/osmnx/core.py in intersect_index_quadrats(gdf, geometry, quadrat_width, min_num, buffer_amount) 678 # drop duplicate points, if buffered poly caused an overlap on point(s) 679 # that lay directly on a quadrat line --> 680 points_within_geometry = points_within_geometry.drop_duplicates(subset='node') 681 else: 682 # after simplifying the graph, and given the requested network type,

~/anaconda3/envs/ox/lib/python3.8/site-packages/pandas/core/frame.py in drop_duplicates(self, subset, keep, inplace, ignore_index) 4806 4807 inplace = validate_bool_kwarg(inplace, "inplace") -> 4808 duplicated = self.duplicated(subset, keep=keep) 4809 4810 if inplace:

~/anaconda3/envs/ox/lib/python3.8/site-packages/pandas/core/frame.py in duplicated(self, subset, keep) 4883 4884 vals = (col.values for name, col in self.items() if name in subset) -> 4885 labels, shape = map(list, zip(*map(f, vals))) 4886 4887 ids = get_group_index(labels, shape, sort=False, xnull=False)

~/anaconda3/envs/ox/lib/python3.8/site-packages/pandas/core/frame.py in f(vals) 4857 4858 def f(vals): -> 4859 labels, shape = algorithms.factorize( 4860 vals, size_hint=min(len(self), _SIZE_HINT_LIMIT) 4861 )

~/anaconda3/envs/ox/lib/python3.8/site-packages/pandas/core/algorithms.py in factorize(values, sort, na_sentinel, size_hint) 627 na_value = None 628 --> 629 codes, uniques = _factorize_array( 630 values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value 631 )

~/anaconda3/envs/ox/lib/python3.8/site-packages/pandas/core/algorithms.py in _factorize_array(values, na_sentinel, size_hint, na_value) 476 477 table = hash_klass(size_hint or len(values)) --> 478 uniques, codes = table.factorize(values, na_sentinel=na_sentinel, na_value=na_value) 479 480 codes = ensure_platform_int(codes)

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.factorize()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique()

TypeError: unhashable type: 'dict'

I am new to this package, and network analysis on python generally. As I was replicating the commands in the first example notebook, I ran in to the following issue:

edge_centrality = nx.closeness_centrality(nx.line_graph(G))

NetworkXNotImplemented                    Traceback (most recent call last)
<ipython-input-14-9e05ced82762> in <module>()
      1 # edge closeness centrality: convert graph to line graph so edges become nodes and vice versa
----> 2 edge_centrality = nx.closeness_centrality(nx.line_graph(G))

<decorator-gen-501> in line_graph(G, create_using)

/usr/local/lib/python2.7/dist-packages/networkx/utils/decorators.pyc in _not_implemented_for(not_implement_for_func, *args, **kwargs)
     69         if match:
     70             msg = 'not implemented for %s type' % ' '.join(graph_types)
---> 71             raise nx.NetworkXNotImplemented(msg)
     72         else:
     73             return not_implement_for_func(*args, **kwargs)

NetworkXNotImplemented: not implemented for multigraph type

I imagine this is something to do with the networkx package, but I was wondering if anyone else has any suggestions to overcome this problem.

Thanks!

Thanks for the super awesome OSMnx package you have put together. I truly enjoyed going through your demos and examples and learned a lot. However, I miss seeing an example relevant to the following use case I am working on.

I want to find the shortest path between two points on the map with the least X attribute (temperature, pollution etc.)! And here by X attribute I mean data points that we gather from sensors on the street. The trick is that these data points not necessarily align on the edges or nodes on the graph or we may have multiple measurements around each node or edge.

The question is what is the best way to estimate X attribute value for the graph nodes and edges from such data? I find the 12-node-elevations-edge-grades.ipynb example the most relevant to this use case, when calculating shortest paths, considering grade impedance. But here these lines using Google elevation API:

# add elevation to each of the nodes, using the google elevation API, then calculate edge grades
G = ox.add_node_elevations(G, api_key=google_elevation_api_key)
G = ox.add_edge_grades(G)

does the trick neatly and one easily access the elevations at node and edge as you showed!

I was thinking of using the average of M nearest neighbors to estimate the node X attribute, maybe also a similar approach for the edge? Edge sounds a bit trickier though. I further come across the Voronoi diagrams as an alternative options to interpolate the X attribute impedance for node and edge on the graph using the measurements. At the same time I do not want to over-complicate the solutions, as it seems there are many features within OSMnx and NetworkX that may do the trick and I am not aware of (not to mention I am new to graphs as well).

Happy to contribute if I get to learn how if you find this a useful demo to have in this repo.

Hello, when I am trying to run your example, I receive the following error:

AttributeError: 'PolarAxesSubplot' object has no attribute 'flat'

I use python 3.6 version

Thanks in advance

Hi Geoff, I have read the documentation, and searched on the internet (https://stackoverflow.com/) for solutions, but until now I didn't find any hint about how administrative boundaries are handled in osmnx. To give an example, I am trying to run this code: ''' tags = {"boundary":"administrative","admin_level":"4" } gdf =ox.geometries.geometries_from_bbox(51.5, 51.0, 11.7, 11.2, tags) gdf.shape ''' The bounding box seems to be used as a polygon to create an intersection with all the boundaries in the OSM database, the first tag is working because only administrative boundaries are returned, but the filter on level is ignored (checking with gdf["admin_level"].head() shows level 6).

I would like to understand what I am doing wrong, and how I can use this package better; it seems like a very useful library, and could submit a working example, if I have made a mistake in my code and have a map with the correct levels. Thanks, Gijs

I am using osmnx version 0.14.1 . While I am making edge bearing histogram and polar plots, I see some difference as they don't seem similar. As an example in the image: As there are 30 bins in the histogram, each bin having 12 degrees, 0-12 degree have the street of (approx) 800. But in the polar rose, it shows more than 1500 in that direction. Why is that? It looks like the full polar plot is shifted 1 bin backwards. Is there any way to fix that? I am attaching the code and image below.

First of all, a quick thanks for creating such a useful and easy-to-use module! I've got a slight improvement to suggest to one of the existing examples.

The improved isochrones method added in #4 by @kuanb provides, for me, a much better way of visualising the isochrones. However, when the input links are curved this is not respected in the output buffers.

If we alter the call to make_iso_polys in cell 13 to have edge_buff=10 and up the output fig_height, we can observe the buffer not "following" the link along the curved edges for example in the two links indicated by the arrows:

This comes about as a result of the way the buffers are defined in the function:

edge_lines = []
for n_fr, n_to in subgraph.edges():
    f = nodes_gdf.loc[n_fr].geometry
    t = nodes_gdf.loc[n_to].geometry
    edge_lines.append(LineString([f,t]))

As the line is defined just by the start and end nodes, curvature is lost. It should be possible to instead look up the geometry of the link in the original graph, and thus keep the curvature.

I'm not sure of the best method to do this, but a slightly messy alternative method I've created is as follows:

edge_lines = []
for n_fr, n_to in subgraph.edges():
    f = nodes_gdf.loc[n_fr].geometry
    t = nodes_gdf.loc[n_to].geometry
    edge_lookup = G.get_edge_data(n_fr, n_to)[0].get('geometry',  LineString([f,t]))
    edge_lines.append(edge_lookup)

It seems as though the straight edges don't have a geometry key, hence the slightly awkward call to .get(). The result of this modification is that the curved edges are indeed followed:

I'm happy to submit a PR including this fix, but I wasn't sure if this was the best method, and I don't want to tread on anyone's toes!

All through the examples while calculating shortest_path it looks like the code is calculating the shortest path without taking into account the actual length. nx.shortest_path(G, origin, destination) calculates the shortest path in terms of number of jumps from one node to another node in the graph, to take into account the actual distance between them it should be nx.shortest_path(G, origin, destination, weight='length') to take into account the length of all the edges. Let me know if I am missing something.

Thanks for this comprehensive library.

I found and old example of yours, which interesting for our organization of field work.

https://github.com/gboeing/network-clustering/blob/main/network-clustering-simple.ipynb

I would love to see this as part of this repo. Adapted to the newer API.

Thanks and best wishes,

Miili