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Graph-based implementation of mean inter-building distance #11

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Oct 20, 2020
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Graph-based implementation of mean inter-building distance #11

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24 changes: 14 additions & 10 deletions measuring/morphometrics.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,7 @@
"import geopandas\n",
"import libpysal\n",
"import momepy\n",
"import networkx as nx\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pygeos\n",
Expand Down Expand Up @@ -491,22 +492,25 @@
" \n",
" # mean interbuilding distance\n",
" # define adjacency list from lipysal\n",
" adj_list = w.to_adjlist(remove_symmetric=True)\n",
" adj_list[\"distance\"] = (\n",
" adj_list = w.to_adjlist(remove_symmetric=False)\n",
" adj_list[\"weight\"] = (\n",
" df.buildings.iloc[adj_list.focal]\n",
" .reset_index(drop=True)\n",
" .distance(df.buildings.iloc[adj_list.neighbor].reset_index(drop=True)).values\n",
" )\n",
" adj_list = adj_list.set_index(['focal', 'neighbor'])\n",
"\n",
"\n",
" def mean_interbuilding_distance(x):\n",
" neighbours = [x]\n",
" neighbours += w3.neighbors[x]\n",
" return adj_list.distance.loc[neighbours, neighbours].mean()\n",
"\n",
" G = nx.from_pandas_edgelist(\n",
" adj_list, source=\"focal\", target=\"neighbor\", edge_attr=\"weight\"\n",
" )\n",
" ibd = []\n",
" for i in range(len(df)):\n",
" try:\n",
" sub = nx.ego_graph(G, i, radius=3)\n",
" ibd.append(np.nanmean([x[-1] for x in list(sub.edges.data('weight'))]))\n",
" except:\n",
" ibd.append(np.nan)\n",
"\n",
" df['ltbIBD'] = [mean_interbuilding_distance(x) for x in range(len(df))]\n",
" df['ltbIBD'] = ibd\n",
" \n",
" # Reached neighbors and area on 3 topological steps on tessellation\n",
" df['ltcRea'] = [w3.cardinalities[i] for i in range(len(df))]\n",
Expand Down