diff --git a/code/04_generalisation/create_summary.ipynb b/code/04_generalisation/create_summary.ipynb
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+++ b/code/04_generalisation/create_summary.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualise the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_folder = \"/Users/martin/Library/CloudStorage/OneDrive-SharedLibraries-TheAlanTuringInstitute/Daniel Arribas-Bel - demoland_data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_path = f\"{data_folder}/spatial_units_test/meta.json\"\n",
+    "\n",
+    "with open(meta_path, \"r\") as f:\n",
+    "    data = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/2f/fhks6w_d0k556plcv3rfmshw0000gn/T/ipykernel_45861/2928594131.py:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'leeds' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
+      "  df.loc[i, [\"city\", \"geom\", \"model\", \"loop\"] + list(data[city][geom][model][loop].keys())] = [city, geom, model, loop] + list(data[city][geom][model][loop].values())\n",
+      "/var/folders/2f/fhks6w_d0k556plcv3rfmshw0000gn/T/ipykernel_45861/2928594131.py:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'h3' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
+      "  df.loc[i, [\"city\", \"geom\", \"model\", \"loop\"] + list(data[city][geom][model][loop].keys())] = [city, geom, model, loop] + list(data[city][geom][model][loop].values())\n",
+      "/var/folders/2f/fhks6w_d0k556plcv3rfmshw0000gn/T/ipykernel_45861/2928594131.py:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'air' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
+      "  df.loc[i, [\"city\", \"geom\", \"model\", \"loop\"] + list(data[city][geom][model][loop].keys())] = [city, geom, model, loop] + list(data[city][geom][model][loop].values())\n",
+      "/var/folders/2f/fhks6w_d0k556plcv3rfmshw0000gn/T/ipykernel_45861/2928594131.py:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'loop_0' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
+      "  df.loc[i, [\"city\", \"geom\", \"model\", \"loop\"] + list(data[city][geom][model][loop].keys())] = [city, geom, model, loop] + list(data[city][geom][model][loop].values())\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame()\n",
+    "i = 0\n",
+    "for city in data:\n",
+    "    for geom in data[city]:\n",
+    "        for model in data[city][geom]:\n",
+    "            for loop in data[city][geom][model]:\n",
+    "                df.loc[i, [\"city\", \"geom\", \"model\", \"loop\"] + list(data[city][geom][model][loop].keys())] = [city, geom, model, loop] + list(data[city][geom][model][loop].values())\n",
+    "                i += 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"moran_diff\"] = df.moran_obs - df.moran_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'Air Pollution')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, axs = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "sns.boxplot(data=df.query(\"model == 'air'\"), x=\"geom\", y=\"mse\", hue=\"city\", ax=axs[0][0])\n",
+    "sns.boxplot(data=df.query(\"model == 'air'\"), x=\"geom\", y=\"me\", hue=\"city\", ax=axs[0][1])\n",
+    "sns.boxplot(data=df.query(\"model == 'air'\"), x=\"geom\", y=\"r2\", hue=\"city\", ax=axs[1][0])\n",
+    "sns.boxplot(data=df.query(\"model == 'air'\"), x=\"geom\", y=\"moran_diff\", hue=\"city\", ax=axs[1][1])\n",
+    "axs[0][0].set_title(\"mean squared error\")\n",
+    "axs[0][1].set_title(\"mean error\")\n",
+    "axs[1][0].set_title(\"R2\")\n",
+    "axs[1][1].set_title(\"Moran's I difference\")\n",
+    "f.suptitle(\"Air Pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'House price')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, axs = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "sns.boxplot(data=df.query(\"model == 'hp'\"), x=\"geom\", y=\"mse\", hue=\"city\", ax=axs[0][0])\n",
+    "sns.boxplot(data=df.query(\"model == 'hp'\"), x=\"geom\", y=\"me\", hue=\"city\", ax=axs[0][1])\n",
+    "sns.boxplot(data=df.query(\"model == 'hp'\"), x=\"geom\", y=\"r2\", hue=\"city\", ax=axs[1][0])\n",
+    "sns.boxplot(data=df.query(\"model == 'hp'\"), x=\"geom\", y=\"moran_diff\", hue=\"city\", ax=axs[1][1])\n",
+    "axs[0][0].set_title(\"mean squared error\")\n",
+    "axs[0][1].set_title(\"mean error\")\n",
+    "axs[1][0].set_title(\"R2\")\n",
+    "axs[1][1].set_title(\"Moran's I difference\")\n",
+    "f.suptitle(\"House price\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore differences within splits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'Air pollution')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, axs = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "sns.scatterplot(data=df.query(\"(model == 'air')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"mse\", hue=\"loop\", ax=axs[0][0])\n",
+    "sns.scatterplot(data=df.query(\"(model == 'air')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"me\", hue=\"loop\", ax=axs[0][1])\n",
+    "sns.scatterplot(data=df.query(\"(model == 'air')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"r2\", hue=\"loop\", ax=axs[1][0])\n",
+    "sns.scatterplot(data=df.query(\"(model == 'air')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"moran_diff\", hue=\"loop\", ax=axs[1][1])\n",
+    "axs[0][0].set_title(\"mean squared error\")\n",
+    "axs[0][1].set_title(\"mean error\")\n",
+    "axs[1][0].set_title(\"R2\")\n",
+    "axs[1][1].set_title(\"Moran's I difference\")\n",
+    "f.suptitle(\"Air pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'House price')"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, axs = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "sns.scatterplot(data=df.query(\"(model == 'hp')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"mse\", hue=\"loop\", ax=axs[0][0])\n",
+    "sns.scatterplot(data=df.query(\"(model == 'hp')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"me\", hue=\"loop\", ax=axs[0][1])\n",
+    "sns.scatterplot(data=df.query(\"(model == 'hp')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"r2\", hue=\"loop\", ax=axs[1][0])\n",
+    "sns.scatterplot(data=df.query(\"(model == 'hp')\").query(\"(city == 'leeds')\"), x=\"geom\", y=\"moran_diff\", hue=\"loop\", ax=axs[1][1])\n",
+    "axs[0][0].set_title(\"mean squared error\")\n",
+    "axs[0][1].set_title(\"mean error\")\n",
+    "axs[1][0].set_title(\"R2\")\n",
+    "axs[1][1].set_title(\"Moran's I difference\")\n",
+    "f.suptitle(\"House price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "stable",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
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+}