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get_nearest_node() had [y(lat),x(lon)], now nearest_nodes() has x(lon… #26

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get_nearest_node() had [y(lat),x(lon)], now nearest_nodes() has x(lon… #26

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41 changes: 22 additions & 19 deletions scripts/02.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,25 +33,27 @@
G_carall = G_caralls_simplified[placeid]

for poiid, poitag in poiparameters.items():
gdf = ox.geometries.geometries_from_polygon(location, poitag)
gdf = gdf[gdf['geometry'].type == "Point"] # only consider points, no polygons etc
# Now snap to closest nodes in street network, save the nearest node ids
nnids = set()
for g in gdf['geometry']:
n = ox.distance.get_nearest_node(G_carall, [g.y, g.x])
if not n in nnids and haversine((g.y, g.x), (G_carall.nodes[n]["y"], G_carall.nodes[n]["x"]), unit="m") <= snapthreshold:
nnids.add(n)
# nnids = ox.distance.get_nearest_nodes(G_carall, gdf['geometry'].x, gdf['geometry'].y) # This could be faster but does not seem to work correctly
with open(PATH["data"] + placeid + "/" + placeid + '_' + 'poi_' + poiid + '_nnidscarall.csv', 'w') as f:
for item in nnids:
f.write("%s\n" % item)

gdf = gdf.apply(lambda c: c.astype(str) if c.name != 'geometry' else c, axis=0)
try: # For some cities writing the gdf does not work (i.e. London, Manhattan)
gdf.to_file(PATH["data"] + placeid + "/" + placeid + '_' + 'poi_' + poiid + '.gpkg', driver = 'GPKG')
try:
gdf = ox.geometries.geometries_from_polygon(location, poitag)
gdf = gdf[gdf['geometry'].type == "Point"] # only consider points, no polygons etc
# Now snap to closest nodes in street network, save the nearest node ids
nnids = set()
for g in gdf['geometry']:
n = ox.distance.nearest_nodes(G_carall, g.x, g.y) # !! get_nearest_node() had [y(lat),x(lon)], now nearest_nodes() has x(lon),y(lat)
if not n in nnids and haversine((g.y, g.x), (G_carall.nodes[n]["y"], G_carall.nodes[n]["x"]), unit="m") <= snapthreshold:
nnids.add(n)
with open(PATH["data"] + placeid + "/" + placeid + '_' + 'poi_' + poiid + '_nnidscarall.csv', 'w') as f:
for item in nnids:
f.write("%s\n" % item)

gdf = gdf.apply(lambda c: c.astype(str) if c.name != 'geometry' else c, axis=0)
try: # For some cities writing the gdf does not work (i.e. London, Manhattan)
gdf.to_file(PATH["data"] + placeid + "/" + placeid + '_' + 'poi_' + poiid + '.gpkg', driver = 'GPKG')
except:
print("Notice: Writing the gdf did not work for " + placeid)
if debug: gdf.plot(color = 'red')
except:
print("Notice: Writing the gdf did not work for " + placeid)
if debug: gdf.plot(color = 'red')
print("No stations in " + placeinfo["name"] + ". No POIs created.")


for placeid, placeinfo in tqdm(cities.items(), desc = "Cities"):
Expand All @@ -60,6 +62,7 @@
location = locations[placeid]



# FIRST, determine the most common bearing, for the best grid orientation
G = G_caralls[placeid]
bearings = {}
Expand Down Expand Up @@ -148,7 +151,7 @@
# 3) Snap grid points to map
nnids = set()
for g in gridpoints_cut:
n = ox.distance.get_nearest_node(G, [g[1], g[0]])
n = ox.distance.nearest_nodes(G, g[0], g[1]) # !! get_nearest_node() had [y(lat),x(lon)], now nearest_nodes() has x(lon),y(lat)
if n not in nnids and haversine((g[1], g[0]), (G.nodes[n]["y"], G.nodes[n]["x"]), unit="m") <= snapthreshold:
nnids.add(n)
with open(PATH["data"] + placeid + "/" + placeid + '_poi_grid_nnidscarall.csv', 'w') as f:
Expand Down