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amauryval edited this page Sep 3, 2020 · 49 revisions

What is it ?

OsmGt is a geospatial python library to help you to play with OpenStreetMap (OSM) roads data and to do network analysis with Graph-tool.

This library offers some main features :

  • get roads data into a Geodataframe (GeoPandas) : vehicle or pedestrian paths [more coming soon]
  • generate a graph-tool graph linked to the roads GeoDataframe (topolygy checking and fixing)
  • add new nodes on the network to extend you network analysis

Additional features about OpenStreetMap data are available

  • get OSM point of interests

Also, OsmGt can be a tool to do some common network analysis:

  • (a lot of ) shortest paths
  • isochrone based on time and distance

Of course you can use the geodataframe and the graph without this functions according to your specific requirements.

How to install it ?

conda install -c amauryval osmgt

How to use it ?

Before to present code lines, we have to present you some prerequisites informations about functions arguments.

You can load/compute data from:

  1. a location name (ex: Lyon)
  2. a bounding box (min_x=-74.018433, min_y=40.718087, max_x=-73.982749, max_y=40.733356)

You can load a specific network based on the transport mode:

  1. vehicle mode: will create a directed graph, because direction are important
  2. pedestrian mode (freeway are not load here for example): will create an undirected graph
  3. more will comming soon...

How to load data ?

Roads and graph

2 methods are available.

  • Get roads network from a location name :
from osmgt import OsmGt

roads_osmgt_initialized = OsmGt.roads_from_location(
    location_name="Lyon",
    mode="vehicle",  # or "pedestrian"
    additional_nodes=my_additional_nodes_gdf  # eat a GeoDataframe [optional]
)

# You can get the GeoDataframe
roads_gdf = roads_osmgt_initialized.get_gdf()

# You can get the graph-tool graph based on the roads gdf built
roads_graph = roads_osmgt_initialized.get_graph()

As you can see add additionals nodes on your next network:

  1. with a nodes Geodataframe (attributes are preserved)
  • Get roads network from a bounding box:
from osmgt import OsmGt

roads_osmgt_initialized = OsmGt.roads_from_bbox(
    bbox_values=(min_x=-74.018433, min_y=40.718087, max_x=-73.982749, max_y=40.733356),
    mode="vehicle",  # or "pedestrian"
    additional_nodes=my_additional_nodes_gdf  # eat a GeoDataframe [optional]
)

# You can get a roads GeoDataframe
roads_gdf = roads_osmgt_initialized.get_gdf()
# the topology processing generate a column named "topo_uuid" used as unique id for each features, because the network has been fixing (intersection, connection...)
# OSM attributes are present, especially the OSM ID named "id" (not unique after processing)

# You can get the graph-tool graph based on the roads GeoDataframe built above
roads_graph = roads_osmgt_initialized.get_graph()

Of course you are free to use the graph-tool library !

Point of interests

2 methods are available.

  • Get POIs from a location name:
from osmgt import OsmGt

pois_gdf = OsmGt.pois_from_location(
    location_name="Lyon"
)
  • Get POIs from a bounding box:
from osmgt import OsmGt

pois_gdf = OsmGt.pois_from_bbox(
    bbox_values=(min_x=-74.018433, min_y=40.718087, max_x=-73.982749, max_y=40.733356),
)

Now we can use the GeoDataframe output as argument on the OsmGt.roads_from_bbox() method to add all the POIs found on the roads network !

Let's go to play

Now you have load some data. So we can play with the graph-tool library, compute some network analysis.

We are starting with this:

from osmgt import OsmGt

bbox_area = (74.018433, 40.718087, -73.982749, 40.733356)

pois_gdf = OsmGt.pois_from_bbox(
    bbox_values=bbox_area
)

roads_osmgt_initialized = OsmGt.roads_from_bbox(
    bbox_values=bbox_area,
    mode="vehicle",
    additional_nodes=pois_gdf
)

# You have a roads GeoDataframe
roads_gdf = roads_osmgt_initialized.get_gdf()

# You have a graph-tool graph based on the roads GeoDataframe built above
roads_graph = roads_osmgt_initialized.get_graph()

Be careful: if you do some geometry changes on the output roads Geodataframe, the roads graph generated won't match anymore, because the link between them is based on the node wkt geometry value :

  • graph is build with the start and end geom wkt nodes of each road lines
  • graph edge are based on the "topo_uuid" linestring value
  • graph edge wiegth based on the line length

That's why the graph-tool graph have some specified methods to link the roads GeoDataframe:

roads_graph = roads_osmgt_initialized.get_graph()

# find a specific vertex based on the name (means geom wkt)
a_vertex = roads_graph.find_vertex_from_name("POINT (1 0)")  # vertex (or node)
an_edge = roads_graph.find_edge_from_name("10_0")  # edge (meaning linestring on the GeoDataframe)

# find a specific edge/vertex based on a graph-tool edge/vertex
an_edge = roads_graph.edge_names[a_graph_tool_edge]
a_vertex = roads_graphaph.vertex_names[a_graph_tool_vertex]

So if you are doing a shortest path computing, you can link the shortest path graph-tool output with your GeoDataframe with these methods. You can see an example HERE at "By using the hard way" ; hard way because OsmGt can help you to do some network analysis...

How can OsmGt help you ?

OsmGt have other method to help you to compute:

  • shortest paths
  • isochrones

Shortest paths

2 methods are available:

  • Find some shortest paths from a location name
from osmgt import OsmGt

shortest_paths_gdf = OsmGt.shortest_path_from_location(
    location_name="Lyon",
    source_target_points=source_targets_pairs,  # a list of tuples containing a source and target shapely Point,
    mode="pedestrian",  # or "vehicle"
)
  • Find some shortest paths from a bounding box
from osmgt import OsmGt

shortest_paths_gdf = OsmGt.shortest_path_from_bbox(
    bbox_values=(74.018433, 40.718087, -73.982749, 40.733356),
    source_target_points=source_targets_pairs,  # a list of tuples containing a source and target shapely Point,
    mode="pedestrian",  # or "vehicle"
)

Each function returns 1 GeoDataframe containing all the shortest paths computed [TODO describe column generated by OsmGT]

Isochrones

2 methods are available:

  • create isochrone based on times
from osmgt import OsmGt

isochrones_polygons_gdf, isochrones_lines_gdf = OsmGt.isochrone_from_coordinates(
    source_node=source_point,  # a shapely Point
    isochrones_times=[2, 5, 10],  # in minutes, so we'll create 3 isochrones based on time
    trip_speed=3,  # km/h
    mode="pedestrian",  # or "vehicle"
)
  • create isochrone based on distances
from osmgt import OsmGt

isochrones_polygons_gdf, isochrones_lines_gdf = isochrone_distance_from_coordinates(
    source_node=source_point,  # a shapely Point
    distances=[100, 1000],  # in meters, so we'll create 2 isochrones based on distance
    trip_speed=3,  # km/h
    mode="pedestrian",  # or "vehicle"
)

Each function returns 2 GeoDataframe containing:

  1. isochrones polygons
  2. roads network concerned and tagged by each isochrone [TODO describe column generated by OsmGT]
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