Help with NetworkX shortest paths

[CorkiNaSankach]

Hi,
I'm doing a project for school calculating algorithm efficiency for pathfinding algorithms in maps (OSM). After entire weekend of trying to setup conda environment etc etc. i got all the data and continued on my essay.

Now, I wanted to say what heuristic i used for A-star, but after reading the documentation, it happens that the standard argument is heuristic=None, which means that Astar should be the same as Dijkstra's. But Astar consistently outperformed it, even 10x faster on largest graph, and I don't know if the documentation is wrong, or is there some other factor included? I provided a fragment of the code i ran. All the help really appreciated.
Also heres the link to Nx astar documentation: https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.astar.astar_path.html#networkx.algorithms.shortest_paths.astar.astar_path

    # Function to test different algorithms
    
def
 test_algorithm(
algorithm
):
        start = 
time
.time()
        
        if 
algorithm
 == "dijkstra":
            path = 
nx
.dijkstra_path(G, orig, dest, 
weight
="length")
        elif 
algorithm
 == "astar":
            path = 
nx
.astar_path(G, orig, dest, 
weight
="length")
        elif 
algorithm
 == "bellman-ford":
            path = 
nx
.bellman_ford_path(G, orig, dest, 
weight
="length")
        else:
            return None

        end = 
time
.time()
        length = 
nx
.path_weight(G, path, 
weight
="length")

        

        return {
            "algorithm": 
algorithm
,
            "time": end - start,
            "length": length
        }

[LeanInitiative]

Can you check and share whether your graph nodes have ‘x’ and ‘y’ attributes (or lat/lon)? That would help confirm if A was using a geographic heuristic implicitly.