Update osm.qmd, fix ci, speed-up

osm.qmd

@@ -364,16 +364,25 @@ if (!"osmnx" %in% py_pkg_names) {
 
 ```{python}
 import osmnx as ox
+import pandas as pd
 import geopandas as gpd
 # Get cycle netework for Poznan
 poznan_polygon = ox.geocode_to_gdf("Poznan, Poland")
 poznan_polygon.plot();
 ```
 
-Download the cycling network for Poznan as follows:
+That is quite a big network, so let's get the area of the polygon and use that to get a smaller network from [GitHub](https://github.com/Robinlovelace/opengeohub2023/raw/main/pois_buffer_simple.geojson):
 
 ```{python}
-G_cycle = ox.graph_from_polygon(poznan_polygon.geometry[0], network_type="bike")
+# Get data from https://github.com/Robinlovelace/opengeohub2023/raw/main/pois_buffer_simple.geojson:
+poznan_small = gpd.read_file("https://github.com/Robinlovelace/opengeohub2023/raw/main/pois_buffer_simple.geojson")
+poznan_small.plot();
+```
+
+Download the cycling network as follows:
+
+```{python}
+G_cycle = ox.graph_from_polygon(poznan_small.geometry[0], network_type="bike")
 ```
 
 Plot the results:
@@ -385,6 +394,7 @@ ox.plot_graph(G_cycle)
 Get basic stats as follows:
 
 ```{python}
+area = ox.project_gdf(poznan_small).unary_union.area
 stats = ox.basic_stats(G_cycle, area=area)
 pd.Series(stats)
 ```
@@ -394,14 +404,15 @@ Calculate the centrality across the network as follows:
 ```{python}
 
 ```
+
 We can convert the object into a 'GeoDataFrame' as follows:
 
 ```{python}
 cycle_gdf = ox.graph_to_gdfs(G_cycle, edges=True)
-
 ```
 
 ```{python}
+```
 
 # Ideas for using OSM data {#sec-ideas}