Spring updates

modules/05-geocoding-apis/lecture.ipynb

@@ -941,7 +941,7 @@
     "ax = vacant.plot(c=\"b\", markersize=1, alpha=0.5)\n",
     "\n",
     "occupied = parking[parking[\"occupancystate\"] == \"OCCUPIED\"]\n",
-    "ax = vacant.plot(ax=ax, c=\"r\", markersize=1, alpha=0.5)"
+    "ax = occupied.plot(ax=ax, c=\"r\", markersize=1, alpha=0.5)"
    ]
   },
   {

modules/06-spatial-data/lecture.ipynb

@@ -28,7 +28,6 @@
     "import folium\n",
     "import geopandas as gpd\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "import rasterio\n",
     "import rasterio.features"
@@ -171,9 +170,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create a geopandas geodataframe from the pandas dataframe\n",
-    "gdf_business = gpd.GeoDataFrame(df)\n",
-    "gdf_business.shape"
+    "# create a geometry array to contain shapely geometry for geopandas to use\n",
+    "# notice the shapely points are lng, lat so that they are equivalent to x, y\n",
+    "geometry = gpd.points_from_xy(x=df[\"lng\"], y=df[\"lat\"])\n",
+    "geometry[:5]"
    ]
   },
   {
@@ -182,11 +182,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create a geometry column to contain shapely geometry for geopandas to use\n",
-    "# notice the shapely points are lng, lat so that they are equivalent to x, y\n",
+    "# create a geopandas geodataframe from the pandas dataframe\n",
     "# also notice that we set the CRS explicitly\n",
-    "gdf_business[\"geometry\"] = gpd.points_from_xy(x=gdf_business[\"lng\"], y=gdf_business[\"lat\"])\n",
-    "gdf_business.crs = \"epsg:4326\"\n",
+    "gdf_business = gpd.GeoDataFrame(df, geometry=geometry, crs=\"epsg:4326\")\n",
     "gdf_business.shape"
    ]
   },
@@ -418,7 +416,7 @@
     "# takes a few seconds...\n",
     "# dissolve lets you aggregate (merge geometries together) by shared attribute values\n",
     "# this is the spatial equivalent of pandas's groupby function\n",
-    "gdf_counties = gdf_tracts.dissolve(by=\"COUNTYFP\", aggfunc=np.sum)"
+    "gdf_counties = gdf_tracts.dissolve(by=\"COUNTYFP\", aggfunc=\"sum\")"
    ]
   },
   {
@@ -831,7 +829,7 @@
     "    legend_name=\"Businesses per square km\",\n",
     ").add_to(m)\n",
     "\n",
-    "# add mouseover tooltip to the countries\n",
+    "# add mouseover tooltip to the tracts\n",
     "c.geojson.add_child(folium.features.GeoJsonTooltip([\"GEOID\", \"density\"]))\n",
     "\n",
     "# save web map to disk\n",

modules/06-spatial-data/raster-crop-bbox.ipynb

@@ -15,7 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import rasterio.mask\n",
+    "import rasterio\n",
+    "from rasterio.mask import mask\n",
     "from shapely.wkt import loads"
    ]
   },
@@ -54,7 +55,7 @@
    "outputs": [],
    "source": [
     "# crop the raster to the bounding box\n",
-    "out_image, out_transform = rasterio.mask.mask(raster, [bbox], crop=True)\n",
+    "out_image, out_transform = mask(raster, [bbox], crop=True)\n",
     "out_meta = raster.meta\n",
     "out_meta.update(\n",
     "    {\n",

modules/07-urban-networks-i/lecture.ipynb

@@ -637,7 +637,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# restaurants near the empire state buildings\n",
+    "# restaurants near the empire state building\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",