diff --git a/modules/08-urban-networks-ii/lecture.ipynb b/modules/08-urban-networks-ii/lecture.ipynb
index 7a07f6e..f1b4ed1 100644
--- a/modules/08-urban-networks-ii/lecture.ipynb
+++ b/modules/08-urban-networks-ii/lecture.ipynb
@@ -55,7 +55,7 @@
    "source": [
     "# create a study site: geocode city hall, convert coords to shapely geometry,\n",
     "# project geometry to UTM, buffer by 5km, project back to lat-lng\n",
-    "latlng_coords = ox.geocode(\"Los Angeles City Hall\")\n",
+    "latlng_coords = ox.geocoder.geocode(\"Los Angeles City Hall\")\n",
     "latlng_point = Point(latlng_coords[1], latlng_coords[0])\n",
     "latlng_point_proj, crs = ox.projection.project_geometry(latlng_point)\n",
     "polygon_proj = latlng_point_proj.buffer(5000)\n",
@@ -71,8 +71,8 @@
    "source": [
     "# model the street network within study site\n",
     "# your parameterization makes assumptions about your interests here\n",
-    "G = ox.graph_from_polygon(polygon, network_type=\"drive\", truncate_by_edge=True)\n",
-    "fig, ax = ox.plot_graph(G, node_size=0, edge_color=\"w\", edge_linewidth=0.3)"
+    "G = ox.graph.graph_from_polygon(polygon, network_type=\"drive\", truncate_by_edge=True)\n",
+    "fig, ax = ox.plot.plot_graph(G, node_size=0, edge_color=\"w\", edge_linewidth=0.3)"
    ]
   },
   {
@@ -82,8 +82,8 @@
    "outputs": [],
    "source": [
     "# add speeds and travel times\n",
-    "G = ox.add_edge_speeds(G)\n",
-    "G = ox.add_edge_travel_times(G)"
+    "G = ox.speed.add_edge_speeds(G)\n",
+    "G = ox.speed.add_edge_travel_times(G)"
    ]
   },
   {
@@ -125,8 +125,8 @@
    "outputs": [],
    "source": [
     "# now clean up the intersections and re-calculate\n",
-    "clean_intersects = ox.consolidate_intersections(\n",
-    "    ox.project_graph(G), rebuild_graph=False, tolerance=10\n",
+    "clean_intersects = ox.simplification.consolidate_intersections(\n",
+    "    ox.projection.project_graph(G), rebuild_graph=False, tolerance=10\n",
     ")\n",
     "clean_intersect_count = len(clean_intersects)\n",
     "clean_intersect_count"
@@ -177,8 +177,8 @@
    "outputs": [],
    "source": [
     "# get home/work network nodes\n",
-    "home_nodes = ox.nearest_nodes(G, X=od[\"home_lng\"], Y=od[\"home_lat\"])\n",
-    "work_nodes = ox.nearest_nodes(G, X=od[\"work_lng\"], Y=od[\"work_lat\"])"
+    "home_nodes = ox.distance.nearest_nodes(G, X=od[\"home_lng\"], Y=od[\"home_lat\"])\n",
+    "work_nodes = ox.distance.nearest_nodes(G, X=od[\"work_lng\"], Y=od[\"work_lat\"])"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "# calculate each shortest path\n",
     "paths = [\n",
-    "    ox.shortest_path(G, orig, dest, weight=\"travel_time\")\n",
+    "    ox.routing.shortest_path(G, orig, dest, weight=\"travel_time\")\n",
     "    for orig, dest in zip(home_nodes, work_nodes)\n",
     "]\n",
     "len(paths)"
@@ -204,7 +204,7 @@
    "outputs": [],
    "source": [
     "# filter out any nulls (ie, not successfully solved)\n",
-    "paths = [path for path in paths if path is not None]\n",
+    "paths = [path for path in paths if path is not None and len(path) > 1]\n",
     "len(paths)"
    ]
   },
@@ -215,7 +215,7 @@
    "outputs": [],
    "source": [
     "# plot 100 routes\n",
-    "fig, ax = ox.plot_graph_routes(\n",
+    "fig, ax = ox.plot.plot_graph_routes(\n",
     "    G,\n",
     "    routes=paths[0:100],\n",
     "    node_size=0,\n",
@@ -255,13 +255,13 @@
    "source": [
     "def calc_efficiency(G, route, attr=\"length\"):\n",
     "    # sum the edge lengths in the route\n",
-    "    trip_distance = sum(ox.utils_graph.get_route_edge_attributes(G, route=route, attribute=attr))\n",
+    "    trip_distance = sum(ox.utils_graph.route_to_gdf(G, route=route)[attr])\n",
     "    # fast vectorized great-circle distance calculator\n",
-    "    gc_distance = ox.distance.great_circle_vec(\n",
+    "    gc_distance = ox.distance.great_circle(\n",
     "        lat1=G.nodes[route[0]][\"y\"],\n",
-    "        lng1=G.nodes[route[0]][\"x\"],\n",
+    "        lon1=G.nodes[route[0]][\"x\"],\n",
     "        lat2=G.nodes[route[-1]][\"y\"],\n",
-    "        lng2=G.nodes[route[-1]][\"x\"],\n",
+    "        lon2=G.nodes[route[-1]][\"x\"],\n",
     "    )\n",
     "    return gc_distance / trip_distance\n",
     "\n",
@@ -331,13 +331,13 @@
    "outputs": [],
    "source": [
     "# get home/work network nodes again, calculate routes, drop nulls\n",
-    "home_nodes_per = ox.nearest_nodes(G_per, X=od[\"home_lng\"], Y=od[\"home_lat\"])\n",
-    "work_nodes_per = ox.nearest_nodes(G_per, X=od[\"work_lng\"], Y=od[\"work_lat\"])\n",
+    "home_nodes_per = ox.distance.nearest_nodes(G_per, X=od[\"home_lng\"], Y=od[\"home_lat\"])\n",
+    "work_nodes_per = ox.distance.nearest_nodes(G_per, X=od[\"work_lng\"], Y=od[\"work_lat\"])\n",
     "paths_per = [\n",
-    "    ox.shortest_path(G_per, orig, dest, weight=\"travel_time\")\n",
+    "    ox.routing.shortest_path(G_per, orig, dest, weight=\"travel_time\")\n",
     "    for orig, dest in zip(home_nodes_per, work_nodes_per)\n",
     "]\n",
-    "paths_per = [path for path in paths_per if path is not None]\n",
+    "paths_per = [path for path in paths_per if path is not None and len(path) > 1]\n",
     "len(paths_per)"
    ]
   },
@@ -393,7 +393,7 @@
     "# shuffle the order, so you don't just plot new atop old\n",
     "paths_colors = pd.DataFrame({\"path\": all_paths, \"color\": colors}).sample(frac=1)\n",
     "\n",
-    "fig, ax = ox.plot_graph_routes(\n",
+    "fig, ax = ox.plot.plot_graph_routes(\n",
     "    G,\n",
     "    routes=paths_colors[\"path\"],\n",
     "    node_size=0,\n",
@@ -445,7 +445,7 @@
    "outputs": [],
    "source": [
     "# study area within 1/2 mile of SF Civic Center\n",
-    "latlng_coords = ox.geocode(\"Civic Center, San Francisco, CA, USA\")\n",
+    "latlng_coords = ox.geocoder.geocode(\"Civic Center, San Francisco, CA, USA\")\n",
     "latlng_point = Point(latlng_coords[1], latlng_coords[0])\n",
     "latlng_point_proj, crs = ox.projection.project_geometry(latlng_point)\n",
     "polygon_proj = latlng_point_proj.buffer(800)\n",
@@ -505,7 +505,7 @@
    "outputs": [],
    "source": [
     "# model the street network across all the study sub-sites\n",
-    "G_all = ox.graph_from_polygon(sf_tracts.unary_union, custom_filter=custom_filter)\n",
+    "G_all = ox.graph.graph_from_polygon(sf_tracts.unary_union, custom_filter=custom_filter)\n",
     "len(G_all.nodes)"
    ]
   },
@@ -519,8 +519,10 @@
     "# calculate clean intersection counts per tract\n",
     "intersect_counts = {}\n",
     "for label, geom in zip(sf_tracts.index, sf_tracts[\"geometry\"]):\n",
-    "    G_tmp = ox.graph_from_polygon(geom, custom_filter=custom_filter)\n",
-    "    clean_intersects = ox.consolidate_intersections(ox.project_graph(G_tmp), rebuild_graph=False)\n",
+    "    G_tmp = ox.graph.graph_from_polygon(geom, custom_filter=custom_filter)\n",
+    "    clean_intersects = ox.simplification.consolidate_intersections(\n",
+    "        ox.projection.project_graph(G_tmp), rebuild_graph=False\n",
+    "    )\n",
     "    intersect_counts[label] = len(clean_intersects)"
    ]
   },
@@ -550,7 +552,7 @@
     "ax = sf_tracts.plot(\n",
     "    ax=ax, column=\"intersect_density\", cmap=\"Reds_r\", legend=True, legend_kwds={\"shrink\": 0.8}\n",
     ")\n",
-    "fig, ax = ox.plot_graph(G_all, ax=ax, node_size=0, edge_color=\"#111111\")\n",
+    "fig, ax = ox.plot.plot_graph(G_all, ax=ax, node_size=0, edge_color=\"#111111\")\n",
     "fig.savefig(\"map.png\", dpi=300, facecolor=\"#111111\", bbox_inches=\"tight\")"
    ]
   },
@@ -603,8 +605,8 @@
    "outputs": [],
    "source": [
     "# model the walkable network within our original study site\n",
-    "G_walk = ox.graph_from_polygon(polygon, network_type=\"walk\")\n",
-    "fig, ax = ox.plot_graph(G_walk, node_size=0, edge_color=\"w\", edge_linewidth=0.3)"
+    "G_walk = ox.graph.graph_from_polygon(polygon, network_type=\"walk\")\n",
+    "fig, ax = ox.plot.plot_graph(G_walk, node_size=0, edge_color=\"w\", edge_linewidth=0.3)"
    ]
   },
   {
@@ -616,7 +618,7 @@
     "# set a uniform walking speed on every edge\n",
     "for u, v, data in G_walk.edges(data=True):\n",
     "    data[\"speed_kph\"] = walk_speed\n",
-    "G_walk = ox.add_edge_travel_times(G_walk)"
+    "G_walk = ox.speed.add_edge_travel_times(G_walk)"
    ]
   },
   {
@@ -626,8 +628,8 @@
    "outputs": [],
    "source": [
     "# extract node/edge GeoDataFrames, retaining only necessary columns (for pandana)\n",
-    "nodes = ox.graph_to_gdfs(G_walk, edges=False)[[\"x\", \"y\"]]\n",
-    "edges = ox.graph_to_gdfs(G_walk, nodes=False).reset_index()[[\"u\", \"v\", \"travel_time\"]]"
+    "nodes = ox.utils_graph.graph_to_gdfs(G_walk, edges=False)[[\"x\", \"y\"]]\n",
+    "edges = ox.utils_graph.graph_to_gdfs(G_walk, nodes=False).reset_index()[[\"u\", \"v\", \"travel_time\"]]"
    ]
   },
   {
@@ -639,7 +641,7 @@
     "# get all the \"fresh food\" stores on OSM within the study site\n",
     "# you could load any amenities DataFrame, but we'll get ours from OSM\n",
     "tags = {\"shop\": [\"grocery\", \"greengrocer\", \"supermarket\"]}\n",
-    "amenities = ox.geometries_from_bbox(\n",
+    "amenities = ox.features.features_from_bbox(\n",
     "    north=nodes[\"y\"].max(),\n",
     "    south=nodes[\"y\"].min(),\n",
     "    east=nodes[\"x\"].min(),\n",
@@ -707,7 +709,7 @@
    "outputs": [],
    "source": [
     "# plot distance to nearest amenity\n",
-    "fig, ax = ox.plot_graph(\n",
+    "fig, ax = ox.plot.plot_graph(\n",
     "    G_walk, node_size=0, edge_linewidth=0.1, edge_color=\"gray\", show=False, close=False\n",
     ")\n",
     "\n",
@@ -750,7 +752,7 @@
    "outputs": [],
    "source": [
     "# plot amenity count within your walking horizon\n",
-    "fig, ax = ox.plot_graph(\n",
+    "fig, ax = ox.plot.plot_graph(\n",
     "    G_walk, node_size=0, edge_linewidth=0.1, edge_color=\"gray\", show=False, close=False\n",
     ")\n",
     "\n",