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!

Attempting to implement the google elevation example found in notebook 12 results in the following error:

AttributeError                            Traceback (most recent call last)
<ipython-input-14-977fe0d112ef> in <module>()
      1 # add elevation to each of the nodes, using the google elevation API, then calculate edge grades
----> 2 G = ox.add_node_elevations(G, api_key=google_elevation_api_key)
      3 G = ox.add_edge_grades(G)

AttributeError: module 'osmnx' has no attribute 'add_node_elevations'

Hi, I had install osmnx at python 3.6 anaconda in ubuntu 16.04 and when I try to load the package, the message of error is:

  import osmnx as ox

  ox.graph_from_address('Brasilia, Distrito Federal', network_type='drive')

  Traceback (most recent call last):
File "/home/isaque/Continuum/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2963, in run_code
   exec(code_obj, self.user_global_ns, self.user_ns)
    File "<ipython-input-3-071b4c6fb9a2>", line 1, in <module>
         ox.graph_from_address('Brasilia, Distrito Federal', network_type='drive')
     AttributeError: module 'osmnx' has no attribute 'graph_from_address'

 The strange part is when I try to verify the version, it returns the version is "1.0.0", when in fact your version is 0.8 (look at picture):

I try install by pip, by anaconda, using direct download from github. I don't know to fix it. Any idea?

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.

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.

I got an error when running notebook 08 using the environment from the yml provided in the repository:

In chunk 2
G = ox.graph_from_place('Piedmont, California', network_type='drive')

ValueError: Assigning CRS to a GeoDataFrame without a geometry column is not supported. Use GeoDataFrame.set_geometry to set the active geometry column.

This issue tracker is for proposing new examples or specific changes to existing examples. Please ask "how-to" questions on https://stackoverflow.com/ instead. If you're having trouble with OSMnx, please open an issue at the OSMnx repo: https://github.com/gboeing/osmnx/issues

Before you proceed, please read the contributing guidelines in this repo's CONTRIBUTING.md.

Any issues opened here will be automatically closed unless they propose new examples or specific changes to existing examples.

PRs welcome for new examples!

According to the guidelines above, I think this issue proposes specific changes to existing examples.

Regarding 13-isolines-isochrones.ipynb, the following cell:

# add an edge attribute for time in minutes required to traverse each edge
meters_per_minute = travel_speed * 1000 / 60  # km per hour to m per minute
for _, _, _, data in G.edges(data=True, keys=True):
    data["time"] = data["length"] / meters_per_minute

the cell is not assigning the attribute time to the edges in G.

Then, ego_graph is not taking into account time:

for trip_time, color in zip(sorted(trip_times, reverse=True), iso_colors):
    subgraph = nx.ego_graph(G, center_node, radius=trip_time, distance="time")
    for node in subgraph.nodes():
        node_colors[node] = color

Actually, ego_graph should fail, but it doesn't raise an error.

Consider changing the for-loop cell with the following:

# add an edge attribute for time in minutes required to traverse each edge
meters_per_minute = travel_speed * 1000 / 60  # km per hour to m per minute
time_attr = {(x, y , z): data["length"] / meters_per_minute 
                            for x, y, z, data in G.edges(data=True, keys=True)}
nx.set_edge_attributes(G, time_attr, name="time")

The simple notebook 00-osmnx-features-demo.ipynb is not working due to connection error:

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

ConnectionError: HTTPConnectionPool(host='overpass-api.de', port=80): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd250542d30>: Failed to establish a new connection: [Errno 101] Network is unreachable',))

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!

I try to run this simple code:

import osmnx as ox
ox.config(log_console=True, use_cache=True)

tags = {'amenity': ['restaurant', 'pub', 'hotel'],
        'building': 'hotel',
        'tourism': 'hotel'}
gdf = ox.pois_from_point(point=(53.360701, -2.916342), dist=500, tags=tags)
gdf.shape #(109, 59)

but get:

AttributeError: module 'osmnx' has no attribute 'pois_from_point'

TBH I cannot find it in the doc either. Any ideas? Thanks!

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

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)

Running the second example in section `Get networks other ways' of the zerotn nb gives:
ModuleNotFoundError Traceback (most recent call last)
in ()
1 # add elevation to nodes automatically, calculate edge grades, plot network
----> 2 from keys import google_elevation_api_key
3 G = ox.add_node_elevations(G, api_key=google_elevation_api_key)
4 G = ox.add_edge_grades(G)
5 nc = ox.get_node_colors_by_attr(G, 'elevation', cmap='plasma', num_bins=20)

ModuleNotFoundError: No module named 'keys'

In the readme you explain it's possible to load .osm file.

I'didn't find in the doc or in the exemple how to do.

thanks for this work!

I have used method simplify_graph for network simplification, there are still many intermediate nodes that do not have interactions.

G3 = ox.graph_from_file( osm_simplified_file, bidirectional=False,simplify=False, retain_all=False, name='unnamed')
G3 = ox.simplify_graph(G3, strict=False)
G3 = ox.remove_isolated_nodes(G3);
G3 = ox.get_largest_component(G3,strongly=True);
ox.plot_graph(G3,......)

Is there anything missing in above code?
please check following links for details
https://stackoverflow.com/questions/59184047/osmnx-simplify-graph-does-not-remove-all-nodes-that-are-not-intersections

Am unable to use osmnx to get data for california, texas and some us state but it worked fine for Alabama and new york

plotting in osmnx is on images, but is it possible to plot a trajectory (for example a car) that is dynamically moving ?
if it is possible what is the efficient approach ? thank you

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

https://github.com/gboeing/osmnx-examples/blob/v0.11/notebooks/06-example-osmnx-networkx.ipynb

im following that but is bit out of date:

gdf_from_place is now geocode_to_gdf (gboeing/osmnx@e039471)
but extended_stats is just deprecated and i dont see any new function replacing it, i'll wait for it to be updated, thanks
btw amazing work! :D

@gboeing
I am getting the point geometry of my place, instead of the polygon geometry in ox.gdf_from_places function.

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 -122.232630 37.836562 -122.232282 37.836840 -122.231384 37.837223 -122.230148 37.837363 -122.229621 37.837462 -122.229079 37.837518 -122.228527 37.837531 -122.227970 37.837501 -122.227415 37.837429 -122.226866 37.837313 -122.226329 37.837157 -122.225809 37.836961 -122.225310 37.836727 -122.224838 37.836458 -122.224397 37.836321 -122.224194 37.835909 -122.223581 37.835902 -122.223571 37.835860 -122.223509 37.835655 -122.223085 37.835595 -122.222985 37.835586 -122.222968 37.835578 -122.222952 37.835434 -122.222684 37.835429 -122.222676 37.835381 -122.222587 37.835374 -122.222573 37.835322 -122.222478 37.835298 -122.222434 37.835063 -122.221995 37.835055 -122.221980 37.834974 -122.221830 37.834716 -122.221399 37.834427 -122.221000 37.834110 -122.220638 37.833766 -122.220315 37.833400 -122.220035 37.833015 -122.219801 37.832810 -122.219691 37.832272 -122.219402 37.831977 -122.219183 37.831614 -122.218915 37.830654 -122.218095 37.830647 -122.218088 37.830591 -122.218041 37.830515 -122.217976 37.830496 -122.217959 37.830412 -122.217887 37.830144 -122.217659 37.829807 -122.217396 37.829733 -122.217344 37.829631 -122.217281 37.829503 -122.217182 37.829430 -122.217131 37.828987 -122.216860 37.828701 -122.216709 37.828662 -122.216688 37.828379 -122.216543 37.827516 -122.215453 37.827432 -122.215347 37.827198 -122.215051 37.827190 -122.215041 37.827029 -122.214674 37.826870 -122.214305 37.826798 -122.214155 37.826740 -122.214040 37.826711 -122.213957 37.826644 -122.213786 37.826416 -122.213272 37.826146 -122.212791 37.825839 -122.212347 37.825749 -122.212230 37.825389 -122.211810 37.825007 -122.211454 37.825005 -122.211449 37.824892 -122.211240 37.824758 -122.211006 37.824502 -122.210581 37.823733 -122.209301 37.823648 -122.209175 37.823573 -122.209040 37.823492 -122.208894 37.823215 -122.208389 37.823146 -122.208264 37.823031 -122.208047 37.822619 -122.207266 37.822482 -122.207006 37.822225 -122.206566 37.821937 -122.206159 37.821618 -122.205789 37.821273 -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 -122.210337 37.809674 -122.210537 37.809327 -122.210879 37.809009 -122.211262 37.808722 -122.211682 37.808469 -122.212135 37.808252 -122.212618 37.808073 -122.213125 37.807935 -122.213651 37.807838 -122.214192 37.807783 -122.214742 37.807771 -122.215296 37.807802 -122.215849 37.807875 -122.216396 37.807991 -122.216931 37.808147 -122.217449 37.808240 -122.217718 37.808412 -122.218216 37.808499 -122.218428 37.808535 -122.218647 37.808536 -122.218646 37.808586 -122.218943 37.808619 -122.219109 37.808647 -122.219244 37.808740 -122.219662 37.808776 -122.219850 37.809087 -122.221225 37.809106 -122.221298 37.809181 -122.221649 37.809265 -122.221991 37.809318 -122.222187 37.809322 -122.222205 37.809389 -122.222450 37.809446 -122.222678 37.809566 -122.223440 37.809569 -122.223463 37.809617 -122.223762 37.809705 -122.224211 37.809768 -122.224486 37.809957 -122.225311 37.809961 -122.225329 37.810025 -122.225605 37.810110 -122.225936 37.810180 -122.226181 37.810184 -122.226195 37.810396 -122.226930 37.810412 -122.226982 37.810520 -122.227465 37.810522 -122.227472 37.810980 -122.229494 37.811104 -122.230040 37.811143 -122.230250 37.811185 -122.230460 37.811258 -122.230791 37.811688 -122.232912 37.811690 -122.232921 37.811838 -122.233645 37.812030 -122.234375 37.812098 -122.234585 37.812105 -122.234606 37.812310 -122.235233 37.812335 -122.235308 37.812365 -122.235416 37.812513 -122.235963 37.812569 -122.236157 37.812624 -122.236335 37.812672 -122.236504 37.812695 -122.236590 37.812699 -122.236606 37.812766 -122.236853 37.812871 -122.237407 37.812918 -122.237961 37.812919 -122.237980 37.812939 -122.238210 37.813006 -122.238743 37.813113 -122.239267 37.813198 -122.239611 37.813451 -122.240643 37.813454 -122.240655 37.813499 -122.240838 37.813517 -122.240934 37.813670 -122.241713 37.813721 -122.241973 37.813767 -122.242187 37.813858 -122.242584 37.814131 -122.243775 37.814134 -122.243787 37.814226 -122.244185 37.814278 -122.244395 37.814318 -122.244546 37.814438 -122.244999 37.814453 -122.245050 37.814580 -122.245495 37.814634 -122.245662 37.814641 -122.245685 37.814797 -122.246160 37.814803 -122.246192 37.814849 -122.246421 37.814970 -122.246925 37.815128 -122.247413 37.815321 -122.247881 37.815547 -122.248324 37.815805 -122.248739 37.816092 -122.249121 37.816171 -122.249217 37.816316 -122.249386 37.816481 -122.249570 37.816608 -122.249705 37.816727 -122.249828 37.816858 -122.249983 37.816976 -122.250117 37.817113 -122.250268 37.817343 -122.250506 37.817479 -122.250637 37.818026 -122.251088 37.818110 -122.251147 37.818393 -122.251471 37.818403 -122.251482 37.818668 -122.251851 37.819597 -122.253146 37.819902 -122.253532 37.820234 -122.253880 37.820592 -122.254186 37.820972 -122.254447 37.821369 -122.254661 37.821781 -122.254826 37.822203 -122.254941 37.822633 -122.255004 37.823065 -122.255015 37.823113 -122.255010");>;);out;'}"
Downloaded 792.5KB from overpass-api.de in 8.82 seconds
Saved response to cache file "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'

Hi,

I am having problems to install osmnx package on colab jupyter notebook.

When I use pip I got this error:
!pip install osmnx
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

When I use apt, I got this:
!apt install osmnx
Reading package lists... Done
Building dependency tree
Reading state information... Done
E: Unable to locate package osmnx

Why it is this happening? any advice?,

Thanks in advance, JL

Is there a way to download whole state data?

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?

Getting below issue when tried to execute the code mentioned in 00-osmnx-features-demo.ipynb

I am trying to execute that code in my local notebook & came across below error

I am not sure if this issue belongs to this repo or OSMnx repo's issue tracker.

After upgrading to Pandas 1.1, the last cell of 13-isolines-isochrones.ipynb example fails with a TypeError: "Set type is unordered" (see Traceback below)

This worked as expected with Pandas 1.0.5, but fails with Pandas 1.1 or 1.1.1

Root cause is that this code initializes a data frame with subgraph.nodes():

nodes_gdf = gpd.GeoDataFrame({'id': subgraph.nodes()}, geometry=node_points)

subgraph.nodes() is a NodeView, which behaves both like a dictionary and a set. These are unordered types, but Pandas needs an ordered collection such as a numpy array or list. Pandas 1.1 introduced a type check to catch this in issue 32582.

Traceback

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
 in 
     26     return isochrone_polys
     27 
---> 28 isochrone_polys = make_iso_polys(G, edge_buff=25, node_buff=0, infill=True)
     29 fig, ax = ox.plot_graph(G, show=False, close=False, edge_color='#999999', edge_alpha=0.2, node_size=0)
     30 for polygon, fc in zip(isochrone_polys, iso_colors):

 in make_iso_polys(G, edge_buff, node_buff, infill)
      5 
      6         node_points = [Point((data['x'], data['y'])) for node, data in subgraph.nodes(data=True)]
----> 7         nodes_gdf = gpd.GeoDataFrame({'id': subgraph.nodes()}, geometry=node_points)
      8         nodes_gdf = nodes_gdf.set_index('id')
      9 

~/miniconda3/envs/osmnx-examples/lib/python3.8/site-packages/geopandas/geodataframe.py in __init__(self, *args, **kwargs)
     87         crs = kwargs.pop("crs", None)
     88         geometry = kwargs.pop("geometry", None)
---> 89         super(GeoDataFrame, self).__init__(*args, **kwargs)
     90 
     91         # need to set this before calling self['geometry'], because

~/miniconda3/envs/osmnx-examples/lib/python3.8/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    466 
    467         elif isinstance(data, dict):
--> 468             mgr = init_dict(data, index, columns, dtype=dtype)
    469         elif isinstance(data, ma.MaskedArray):
    470             import numpy.ma.mrecords as mrecords

~/miniconda3/envs/osmnx-examples/lib/python3.8/site-packages/pandas/core/internals/construction.py in init_dict(data, index, columns, dtype)
    281             arr if not is_datetime64tz_dtype(arr) else arr.copy() for arr in arrays
    282         ]
--> 283     return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
    284 
    285 

~/miniconda3/envs/osmnx-examples/lib/python3.8/site-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype, verify_integrity)
     81 
     82         # don't force copy because getting jammed in an ndarray anyway
---> 83         arrays = _homogenize(arrays, index, dtype)
     84 
     85         columns = ensure_index(columns)

~/miniconda3/envs/osmnx-examples/lib/python3.8/site-packages/pandas/core/internals/construction.py in _homogenize(data, index, dtype)
    349                     val = dict(val)
    350                 val = lib.fast_multiget(val, oindex._values, default=np.nan)
--> 351             val = sanitize_array(
    352                 val, index, dtype=dtype, copy=False, raise_cast_failure=False
    353             )

~/miniconda3/envs/osmnx-examples/lib/python3.8/site-packages/pandas/core/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
    450         subarr = _try_cast(arr, dtype, copy, raise_cast_failure)
    451     elif isinstance(data, abc.Set):
--> 452         raise TypeError("Set type is unordered")
    453     elif lib.is_scalar(data) and index is not None and dtype is not None:
    454         data = maybe_cast_to_datetime(data, dtype)

TypeError: Set type is unordered

I'm an absolut newby in code and like to know how can I save the features for future treatment or even web publishing.
Thanks

The README.md has a link to binder which is not working.

It references the branch master which seems to be unsupported now in binder and fails. It helpfully suggests moving from master to main branches, which does in fact work.

If you're having trouble with OSMnx, please open an issue at the OSMnx repo: https://github.com/gboeing/osmnx/issues

Any issues opened here will be automatically closed unless they are proposals for new examples or demos, or suggestions for the existing ones.

PRs welcome for new examples!

When I look at the folium example notebook, none of the IFrame outputs displays anything. Am I missing something? Is there a problem with the html files?

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

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.

The multiprocessing example in notebook 02 doesn't seem to work properly anymore, apparently because multiprocessing doesn't play nicely with interactive interpreters like Jupyter: https://stackoverflow.com/questions/41385708/multiprocessing-example-giving-attributeerror

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/

As discussed in gboeing/osmnx#1110