diff --git a/modules/07-urban-networks-i/lecture.ipynb b/modules/07-urban-networks-i/lecture.ipynb
index 8672e6b..8108786 100644
--- a/modules/07-urban-networks-i/lecture.ipynb
+++ b/modules/07-urban-networks-i/lecture.ipynb
@@ -229,8 +229,8 @@
    "source": [
     "# download/model a street network for some city then visualize it\n",
     "place = \"Piedmont, California, USA\"\n",
-    "G = ox.graph_from_place(place, network_type=\"drive\")\n",
-    "fig, ax = ox.plot_graph(G)"
+    "G = ox.graph.graph_from_place(place, network_type=\"drive\")\n",
+    "fig, ax = ox.plot.plot_graph(G)"
    ]
   },
   {
@@ -287,7 +287,7 @@
    "outputs": [],
    "source": [
     "# convert your graph to node and edge GeoPandas GeoDataFrames\n",
-    "gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)\n",
+    "gdf_nodes, gdf_edges = ox.utils_graph.graph_to_gdfs(G)\n",
     "gdf_nodes.head()"
    ]
   },
@@ -314,7 +314,7 @@
    "outputs": [],
    "source": [
     "# convert node/edge GeoPandas GeoDataFrames to a NetworkX MultiDiGraph\n",
-    "G2 = ox.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=G.graph)\n",
+    "G2 = ox.utils_graph.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=G.graph)\n",
     "print(len(G2.nodes))\n",
     "print(len(G2.edges))"
    ]
@@ -343,8 +343,8 @@
    "outputs": [],
    "source": [
     "# get our study site's geometry\n",
-    "gdf = ox.geocode_to_gdf(place)\n",
-    "gdf_proj = ox.project_gdf(gdf)\n",
+    "gdf = ox.geocoder.geocode_to_gdf(place)\n",
+    "gdf_proj = ox.projection.project_gdf(gdf)\n",
     "geom_proj = gdf_proj[\"geometry\"].iloc[0]\n",
     "geom_proj"
    ]
@@ -367,7 +367,7 @@
    "outputs": [],
    "source": [
     "# project the graph (automatically) then check its new CRS\n",
-    "G_proj = ox.project_graph(G)\n",
+    "G_proj = ox.projection.project_graph(G)\n",
     "G_proj.graph[\"crs\"]"
    ]
   },
@@ -378,7 +378,7 @@
    "outputs": [],
    "source": [
     "# show some basic stats about the (projected) network\n",
-    "ox.basic_stats(G_proj, area=area_m, clean_int_tol=10)"
+    "ox.stats.basic_stats(G_proj, area=area_m, clean_int_tol=10)"
    ]
   },
   {
@@ -395,8 +395,8 @@
    "outputs": [],
    "source": [
     "# save graph to disk as geopackage (for GIS) or GraphML file (for Gephi etc)\n",
-    "ox.save_graph_geopackage(G, filepath=\"./data/mynetwork.gpkg\")\n",
-    "ox.save_graphml(G, filepath=\"./data/mynetwork.graphml\")"
+    "ox.io.save_graph_geopackage(G, filepath=\"./data/mynetwork.gpkg\")\n",
+    "ox.io.save_graphml(G, filepath=\"./data/mynetwork.graphml\")"
    ]
   },
   {
@@ -427,7 +427,7 @@
    "source": [
     "# color edges in original graph with centralities from line graph\n",
     "ec = ox.plot.get_edge_colors_by_attr(G, \"edge_centrality\", cmap=\"inferno\")\n",
-    "fig, ax = ox.plot_graph(G, edge_color=ec, edge_linewidth=2, node_size=0)"
+    "fig, ax = ox.plot.plot_graph(G, edge_color=ec, edge_linewidth=2, node_size=0)"
    ]
   },
   {
@@ -444,8 +444,8 @@
    "outputs": [],
    "source": [
     "# impute missing edge speeds then calculate edge (free-flow) 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)"
    ]
   },
   {
@@ -455,8 +455,8 @@
    "outputs": [],
    "source": [
     "# get the nearest network nodes to two lat/lng points\n",
-    "orig = ox.nearest_nodes(G, -122.245846, 37.828903)\n",
-    "dest = ox.nearest_nodes(G, -122.215006, 37.812303)"
+    "orig = ox.distance.nearest_nodes(G, -122.245846, 37.828903)\n",
+    "dest = ox.distance.nearest_nodes(G, -122.215006, 37.812303)"
    ]
   },
   {
@@ -466,8 +466,8 @@
    "outputs": [],
    "source": [
     "# find the shortest path between these nodes, minimizing travel time, then plot it\n",
-    "route = ox.shortest_path(G, orig, dest, weight=\"travel_time\")\n",
-    "fig, ax = ox.plot_graph_route(G, route, node_size=0)"
+    "route = ox.routing.shortest_path(G, orig, dest, weight=\"travel_time\")\n",
+    "fig, ax = ox.plot.plot_graph_route(G, route, node_size=0)"
    ]
   },
   {
@@ -477,7 +477,7 @@
    "outputs": [],
    "source": [
     "# how long is our route in meters?\n",
-    "edge_lengths = ox.utils_graph.get_route_edge_attributes(G, route, \"length\")\n",
+    "edge_lengths = ox.utils_graph.route_to_gdf(G, route)[\"length\"]\n",
     "sum(edge_lengths)"
    ]
   },
@@ -488,7 +488,7 @@
    "outputs": [],
    "source": [
     "# how far is it between these two nodes as the crow flies (haversine)?\n",
-    "ox.distance.great_circle_vec(\n",
+    "ox.distance.great_circle(\n",
     "    G.nodes[orig][\"y\"], G.nodes[orig][\"x\"], G.nodes[dest][\"y\"], G.nodes[dest][\"x\"]\n",
     ")"
    ]
@@ -521,8 +521,8 @@
    "source": [
     "# you can make query an unambiguous dict to help the geocoder find it\n",
     "place = {\"city\": \"San Francisco\", \"state\": \"California\", \"country\": \"USA\"}\n",
-    "G = ox.graph_from_place(place, network_type=\"drive\", truncate_by_edge=True)\n",
-    "fig, ax = ox.plot_graph(G, figsize=(10, 10), node_size=0, edge_color=\"y\", edge_linewidth=0.2)"
+    "G = ox.graph.graph_from_place(place, network_type=\"drive\", truncate_by_edge=True)\n",
+    "fig, ax = ox.plot.plot_graph(G, figsize=(10, 10), node_size=0, edge_color=\"y\", edge_linewidth=0.2)"
    ]
   },
   {
@@ -532,8 +532,8 @@
    "outputs": [],
    "source": [
     "# you can get networks anywhere in the world\n",
-    "G = ox.graph_from_place(\"Sinalunga, Italy\", network_type=\"all\")\n",
-    "fig, ax = ox.plot_graph(G, node_size=0, edge_linewidth=0.5)"
+    "G = ox.graph.graph_from_place(\"Sinalunga, Italy\", network_type=\"all\")\n",
+    "fig, ax = ox.plot.plot_graph(G, node_size=0, edge_linewidth=0.5)"
    ]
   },
   {
@@ -546,8 +546,15 @@
     "# ...useful when OSM just doesn't already have a polygon for the place you want\n",
     "lewis_hall = (34.019267, -118.283566)\n",
     "one_mile = 1609  # meters\n",
-    "G = ox.graph_from_point(lewis_hall, dist=one_mile, network_type=\"drive\")\n",
-    "fig, ax = ox.plot_graph(G, node_size=0)"
+    "G = ox.graph.graph_from_point(lewis_hall, dist=one_mile, network_type=\"drive\")\n",
+    "fig, ax = ox.plot.plot_graph(G, node_size=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consider study area boundaries and the artificial perimeter problem."
    ]
   },
   {
@@ -577,7 +584,7 @@
    "outputs": [],
    "source": [
     "# get NY subway rail network\n",
-    "G = ox.graph_from_place(\n",
+    "G = ox.graph.graph_from_place(\n",
     "    \"New York City, New York, USA\",\n",
     "    retain_all=False,\n",
     "    truncate_by_edge=True,\n",
@@ -585,7 +592,7 @@
     "    custom_filter='[\"railway\"~\"subway\"]',\n",
     ")\n",
     "\n",
-    "fig, ax = ox.plot_graph(G, node_size=0, edge_color=\"c\", edge_linewidth=0.2)"
+    "fig, ax = ox.plot.plot_graph(G, node_size=0, edge_color=\"c\", edge_linewidth=0.2)"
    ]
   },
   {
@@ -594,7 +601,7 @@
    "source": [
     "## Get any geospatial entities' geometries and attributes\n",
     "\n",
-    "Use the `geometries` module to download entities, such as local amenities, points of interest, or building footprints, and turn them into a GeoDataFrame."
+    "Use the `features` module to download entities, such as local amenities, points of interest, or building footprints, and turn them into a GeoDataFrame."
    ]
   },
   {
@@ -604,9 +611,9 @@
    "outputs": [],
    "source": [
     "# get all building footprints in some neighborhood\n",
-    "place = \"Civic Center, Los Angeles, California\"\n",
+    "place = \"Soho, New York, NY, USA\"\n",
     "tags = {\"building\": True}\n",
-    "gdf = ox.geometries_from_place(place, tags)\n",
+    "gdf = ox.features.features_from_place(place, tags)\n",
     "gdf.shape"
    ]
   },
@@ -616,7 +623,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, ax = ox.plot_footprints(gdf, figsize=(3, 3))"
+    "fig, ax = ox.plot.plot_footprints(gdf, figsize=(3, 3))"
    ]
   },
   {
@@ -627,7 +634,7 @@
    "source": [
     "# get all parks and bus stops in some neighborhood\n",
     "tags = {\"leisure\": \"park\", \"highway\": \"bus_stop\"}\n",
-    "gdf = ox.geometries_from_place(place, tags)\n",
+    "gdf = ox.features.features_from_place(place, tags)\n",
     "gdf.shape"
    ]
   },
@@ -640,7 +647,7 @@
     "# restaurants near the empire state buildings\n",
     "address = \"350 5th Ave, New York, NY 10001\"\n",
     "tags = {\"amenity\": \"restaurant\"}\n",
-    "gdf = ox.geometries_from_address(address, tags=tags, dist=500)\n",
+    "gdf = ox.features.features_from_address(address, tags=tags, dist=500)\n",
     "gdf[[\"name\", \"cuisine\", \"geometry\"]].dropna().head()"
    ]
   },
@@ -662,7 +669,7 @@
    "source": [
     "## In-class workshop\n",
     "\n",
-    "Git clone the [OSMnx examples](https://github.com/gboeing/osmnx-examples) repo to your desktop, then work through the notebooks (skip notebook 00, which covers a similar overview as this lecture)."
+    "Git clone the OSMnx [Examples Gallery](https://github.com/gboeing/osmnx-examples) repository to your desktop, then work through the notebooks (skip notebook 00, which covers a similar overview as this lecture)."
    ]
   },
   {