Mapping Neo4j spatial nodes with GeoPandas

In the recent post Setting up a graph db using GTFS data & Neo4J, we noted that — unfortunately — Neomap is not an option to visualize spatial nodes anymore.

GeoPandas to the rescue!

But first we need the neo4j Python driver:

pip install neo4j

Then we can connect to our database. The default user name is neo4j and you get to pick the password when creating the database:

from neo4j import GraphDatabase

URI = "neo4j://localhost"
AUTH = ("neo4j", "password")

with GraphDatabase.driver(URI, auth=AUTH) as driver:
    driver.verify_connectivity()

Once we have confirmed that the connection works as expected, we can run a query:

QUERY = "MATCH (p:Stop) RETURN p.name AS name, p.location AS geom"

records, summary, keys = driver.execute_query(
    QUERY, database_="neo4j",
)

for rec in records:
    print(rec)

Nice. There we have our GTFS stops, their names and their locations. But how to put them on a map?

Conveniently, there is a to_db() function in the Neo4j driver:

import geopandas as gpd
import numpy as np

with driver.session(database="neo4j") as session:
    tx = session.begin_transaction()
    results = tx.run(QUERY)
    df = results.to_df(expand=True)
    df = df[df["geom[].0"]>0]
    gdf = gpd.GeoDataFrame(
        df['name'], crs=4326,
        geometry=gpd.points_from_xy(df['geom[].0'], df['geom[].1']))
    print(gdf)

tx.close()

Since some of the nodes lack geometries, I added a quick and dirty hack to get rid of these nodes because — otherwise — gdf.explore() will complain about None geometries.