Furthemore, the output map contains the shortest path between each cat, tcat pair. Each path consists of several lines. If a line is on the shortest path from a point then the category of this point is assigned to the line. Note that every line may contain more than one category value since a single line may be on the shortest path for more than one from feature. And so the shortest paths can be easily obtained by querying lines with corresponding category number. Alternatively, unique paths can be created with the -l flag where each path will be a separate single line in the output.
The costs of arcs in forward and backward direction are specified by arc_column and arc_backward_column columns respectively. If arc_backward_column is not given, the same cost is used in both directions.
v.net.distance will not work if you are trying to find the nearest neighbors within a group of nodes, i.e. where to and from are the same set of nodes, as the closest node will be the node itself and the result will be zero-length paths. In order to find nearest neighbors within a group of nodes, you can either loop through each node as to and all other nodes as from or create a complete distance matrix with v.net.allpairs and select the lowest non-zero distance for each node.
Streets are grey lines, schools are green circles, hospitals are red crosses, shortest paths are blue lines:
# connect schools to streets as layer 2 v.net input=streets_wake points=schools_wake output=streets_net1 \ operation=connect thresh=400 arc_layer=1 node_layer=2 # connect hospitals to streets as layer 3 v.net input=streets_net1 points=hospitals output=streets_net2 \ operation=connect thresh=400 arc_layer=1 node_layer=3 # inspect the result v.category in=streets_net2 op=report # shortest paths from schools (points in layer 2) to nearest hospitals (points in layer 3) v.net.distance in=streets_net2 out=schools_to_hospitals flayer=2 to_layer=3 # visualization g.region vector=streets_wake d.mon wx0 d.vect streets_wake color=220:220:220 d.vect schools_wake color=green size=10 d.vect map=hospitals icon=basic/cross3 size=15 color=black fcolor=red d.vect schools_to_hospitals
# add coordinates of pollution point source of pollution as vector pollution.txt: 634731.563206905|216390.501834892 v.in.ascii input=pollution.txt output=pollution # add table to vector v.db.addtable map=pollution # add coordinates of sample points as vector samples.txt: 634813.332814905|216333.590706166 634893.462007813|216273.763350851 634918.660011082|216254.949609689 v.in.ascii input=samples.txt output=samples # add table to vector v.db.addtable map=samples # connect samples and pollution to streams v.net -c input=streams points=samples output=streams_samples \ operation=connect node_layer=3 threshold=10 \ v.net -c input=streams_samples points=pollution output=streams_samples_pollution operation=connect \ node_layer=4 threshold=10 # check vector layers v.category input=streams_samples_pollution option=report Layer/table: 1/streams_samples_pollution type count min max point 0 0 0 line 8562 40102 101351 boundary 0 0 0 centroid 0 0 0 area 0 0 0 face 0 0 0 kernel 0 0 0 all 8562 40102 101351 Layer: 3 type count min max point 3 1 3 line 0 0 0 boundary 0 0 0 centroid 0 0 0 area 0 0 0 face 0 0 0 kernel 0 0 0 all 3 1 3 Layer: 4 type count min max point 1 1 1 line 0 0 0 boundary 0 0 0 centroid 0 0 0 area 0 0 0 face 0 0 0 kernel 0 0 0 all 1 1 1 # calculate distance between sample points and pollution point source v.net.distance input=streams_samples_pollution \ output=distance_samples_to_pollution from_layer=3 to_layer=4 # check results v.report map=distance_samples_to_pollution@vnettest option=length cat|tcat|dist|length 1|1|100.0|100.0 2|1|200.0|200.0 3|1|231.446|231.446
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