diff --git a/notebooks/statistical_matching.ipynb b/notebooks/statistical_matching.ipynb
index 98c47306..a401a059 100644
--- a/notebooks/statistical_matching.ipynb
+++ b/notebooks/statistical_matching.ipynb
@@ -18,10 +18,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import geopandas as gpd\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -30,6 +31,7 @@
     "from acbm.matching import match_individuals, match_psm\n",
     "from acbm.preprocessing import (\n",
     "    count_per_group,\n",
+    "    match_coverage_col,\n",
     "    nts_filter_by_region,\n",
     "    nts_filter_by_year,\n",
     "    num_adult_child_hh,\n",
@@ -56,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 186,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [
     {
@@ -419,7 +421,7 @@
        "4         1    2         26          1     6.0  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,17 +434,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [],
    "source": [
     "# temporary reduction of the dataset for quick analysis\n",
-    "spc = spc.head(5000)"
+    "spc = spc.head(15000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [
     {
@@ -459,7 +461,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -482,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 190,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -499,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -551,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -599,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -668,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -688,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -840,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -861,12 +863,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -878,14 +880,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Individuals in SPC = 5000\n",
-      "Individuals without reported income = 3029\n",
-      "% of individuals with reported income = 39.4\n",
+      "Individuals in SPC = 15000\n",
+      "Individuals without reported income = 9226\n",
+      "% of individuals with reported income = 38.5\n",
       "Individuals with reported income: 0 = 0\n",
-      "Households in SPC = 2373\n",
-      "Households without reported income = 1577\n",
-      "% of households with reported income = 66.5\n",
-      "Households with reported income: 0 = 1577\n"
+      "Households in SPC = 6725\n",
+      "Households without reported income = 4605\n",
+      "% of households with reported income = 68.5\n",
+      "Households with reported income: 0 = 4605\n"
      ]
     }
    ],
@@ -924,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -958,12 +960,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -973,7 +975,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -1008,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [
     {
@@ -1022,7 +1024,7 @@
        "Name: proportion, dtype: float64"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1041,7 +1043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1061,7 +1063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [
     {
@@ -1093,7 +1095,7 @@
        "  '-10': 'DEAD'})"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1116,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 204,
    "metadata": {},
    "outputs": [
     {
@@ -1219,7 +1221,7 @@
        "9                      0              1"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1245,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [
     {
@@ -1375,7 +1377,7 @@
        "9          5        1              1              0                  3"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,12 +1439,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -1452,7 +1454,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -1486,6 +1488,1064 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Urban Rural Classification\n",
+    "\n",
+    "We use the 2011 rural urban classification to match the SPC to the NTS. The NTS has 2 columns that we can use to match to the SPC: `Settlement2011EW_B03ID` and `Settlement2011EW_B04ID`. The `Settlement2011EW_B03ID` column is more general (urban / rural only), while the `Settlement2011EW_B04ID` column is more specific. We stick to the more general column for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>household</th>\n",
+       "      <th>workplace</th>\n",
+       "      <th>location</th>\n",
+       "      <th>events</th>\n",
+       "      <th>weekday_diaries</th>\n",
+       "      <th>weekend_diaries</th>\n",
+       "      <th>orig_pid</th>\n",
+       "      <th>id_tus_hh</th>\n",
+       "      <th>id_tus_p</th>\n",
+       "      <th>pid_hs</th>\n",
+       "      <th>msoa</th>\n",
+       "      <th>oa</th>\n",
+       "      <th>members</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>has_cardiovascular_disease</th>\n",
+       "      <th>has_diabetes</th>\n",
+       "      <th>has_high_blood_pressure</th>\n",
+       "      <th>number_medications</th>\n",
+       "      <th>self_assessed_health</th>\n",
+       "      <th>life_satisfaction</th>\n",
+       "      <th>sic1d2007</th>\n",
+       "      <th>sic2d2007</th>\n",
+       "      <th>soc2010</th>\n",
+       "      <th>pwkstat</th>\n",
+       "      <th>salary_yearly</th>\n",
+       "      <th>salary_hourly</th>\n",
+       "      <th>hid</th>\n",
+       "      <th>accommodation_type</th>\n",
+       "      <th>communal_type</th>\n",
+       "      <th>num_rooms</th>\n",
+       "      <th>central_heat</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>num_cars</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age_years</th>\n",
+       "      <th>ethnicity</th>\n",
+       "      <th>nssec8</th>\n",
+       "      <th>salary_yearly_hh</th>\n",
+       "      <th>salary_yearly_hh_cat</th>\n",
+       "      <th>is_adult</th>\n",
+       "      <th>num_adults</th>\n",
+       "      <th>is_child</th>\n",
+       "      <th>num_children</th>\n",
+       "      <th>is_pension_age</th>\n",
+       "      <th>num_pension_age</th>\n",
+       "      <th>pwkstat_FT_hh</th>\n",
+       "      <th>pwkstat_PT_hh</th>\n",
+       "      <th>pwkstat_NTS_match</th>\n",
+       "      <th>OA11CD</th>\n",
+       "      <th>RUC11</th>\n",
+       "      <th>RUC11CD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'x': -1.7892179489135742, 'y': 53.91915130615...</td>\n",
+       "      <td>{'concert_f': 1.2791347489984115e-31, 'concert...</td>\n",
+       "      <td>[1583, 13161]</td>\n",
+       "      <td>[1582, 13160]</td>\n",
+       "      <td>E02002183_0001_001</td>\n",
+       "      <td>11291218</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2905399</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053954</td>\n",
+       "      <td>[0]</td>\n",
+       "      <td>24.879356</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>J</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>1115.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0001</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>E00053954</td>\n",
+       "      <td>Urban city and town</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'x': -1.8262380361557007, 'y': 53.92028045654...</td>\n",
+       "      <td>{'concert_f': 9.743248151956307e-21, 'concert_...</td>\n",
+       "      <td>[2900, 4948, 4972, 7424, 10284, 10586, 12199, ...</td>\n",
+       "      <td>[2901, 4949, 4973, 7425, 10285, 10585, 12198, ...</td>\n",
+       "      <td>E02002183_0002_001</td>\n",
+       "      <td>17291219</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2905308</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053953</td>\n",
+       "      <td>[1, 2]</td>\n",
+       "      <td>27.491207</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1121.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0002</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>74</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>E00053953</td>\n",
+       "      <td>Urban city and town</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'x': -1.8262380361557007, 'y': 53.92028045654...</td>\n",
+       "      <td>{'concert_f': 8.46716103992468e-16, 'concert_f...</td>\n",
+       "      <td>[3010, 6389, 9448, 10184, 11598]</td>\n",
+       "      <td>[3011, 6388, 9447, 10183, 11599]</td>\n",
+       "      <td>E02002183_0002_002</td>\n",
+       "      <td>17070713</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2907681</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053953</td>\n",
+       "      <td>[1, 2]</td>\n",
+       "      <td>17.310829</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>P</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>2311.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0002</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>68</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>E00053953</td>\n",
+       "      <td>Urban city and town</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>56126.0</td>\n",
+       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
+       "      <td>{'concert_f': 1.8844366073608398, 'concert_fs'...</td>\n",
+       "      <td>[366, 867, 2096, 3678, 5212, 5450, 8145, 9254,...</td>\n",
+       "      <td>[365, 868, 2097, 3677, 5213, 5451, 8146, 9253,...</td>\n",
+       "      <td>E02002183_0003_001</td>\n",
+       "      <td>20310313</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2902817</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053689</td>\n",
+       "      <td>[3, 4]</td>\n",
+       "      <td>20.852091</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>3422.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32857.859375</td>\n",
+       "      <td>14.360952</td>\n",
+       "      <td>E02002183_0003</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>51020.310547</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>E00053689</td>\n",
+       "      <td>Rural town and fringe</td>\n",
+       "      <td>D1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
+       "      <td>{'concert_f': 4.877435207366943, 'concert_fs':...</td>\n",
+       "      <td>[1289, 12528, 12870]</td>\n",
+       "      <td>[1288, 12529, 12871]</td>\n",
+       "      <td>E02002183_0003_002</td>\n",
+       "      <td>13010909</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2900884</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053689</td>\n",
+       "      <td>[3, 4]</td>\n",
+       "      <td>20.032526</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>J</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>7214.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18162.451172</td>\n",
+       "      <td>9.439944</td>\n",
+       "      <td>E02002183_0003</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>51020.310547</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>E00053689</td>\n",
+       "      <td>Rural town and fringe</td>\n",
+       "      <td>D1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  household  workplace  \\\n",
+       "0   0          0        NaN   \n",
+       "1   1          1        NaN   \n",
+       "2   2          1        NaN   \n",
+       "3   3          2    56126.0   \n",
+       "4   4          2        NaN   \n",
+       "\n",
+       "                                            location  \\\n",
+       "0  {'x': -1.7892179489135742, 'y': 53.91915130615...   \n",
+       "1  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
+       "2  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
+       "3  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
+       "4  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
+       "\n",
+       "                                              events  \\\n",
+       "0  {'concert_f': 1.2791347489984115e-31, 'concert...   \n",
+       "1  {'concert_f': 9.743248151956307e-21, 'concert_...   \n",
+       "2  {'concert_f': 8.46716103992468e-16, 'concert_f...   \n",
+       "3  {'concert_f': 1.8844366073608398, 'concert_fs'...   \n",
+       "4  {'concert_f': 4.877435207366943, 'concert_fs':...   \n",
+       "\n",
+       "                                     weekday_diaries  \\\n",
+       "0                                      [1583, 13161]   \n",
+       "1  [2900, 4948, 4972, 7424, 10284, 10586, 12199, ...   \n",
+       "2                   [3010, 6389, 9448, 10184, 11598]   \n",
+       "3  [366, 867, 2096, 3678, 5212, 5450, 8145, 9254,...   \n",
+       "4                               [1289, 12528, 12870]   \n",
+       "\n",
+       "                                     weekend_diaries            orig_pid  \\\n",
+       "0                                      [1582, 13160]  E02002183_0001_001   \n",
+       "1  [2901, 4949, 4973, 7425, 10285, 10585, 12198, ...  E02002183_0002_001   \n",
+       "2                   [3011, 6388, 9447, 10183, 11599]  E02002183_0002_002   \n",
+       "3  [365, 868, 2097, 3677, 5213, 5451, 8146, 9253,...  E02002183_0003_001   \n",
+       "4                               [1288, 12529, 12871]  E02002183_0003_002   \n",
+       "\n",
+       "   id_tus_hh  id_tus_p   pid_hs       msoa         oa members        bmi  \\\n",
+       "0   11291218         1  2905399  E02002183  E00053954     [0]  24.879356   \n",
+       "1   17291219         1  2905308  E02002183  E00053953  [1, 2]  27.491207   \n",
+       "2   17070713         2  2907681  E02002183  E00053953  [1, 2]  17.310829   \n",
+       "3   20310313         1  2902817  E02002183  E00053689  [3, 4]  20.852091   \n",
+       "4   13010909         3  2900884  E02002183  E00053689  [3, 4]  20.032526   \n",
+       "\n",
+       "   has_cardiovascular_disease  has_diabetes  has_high_blood_pressure  \\\n",
+       "0                       False         False                    False   \n",
+       "1                       False         False                     True   \n",
+       "2                       False          True                     True   \n",
+       "3                       False         False                    False   \n",
+       "4                       False         False                    False   \n",
+       "\n",
+       "   number_medications  self_assessed_health  life_satisfaction sic1d2007  \\\n",
+       "0                 NaN                   3.0                2.0         J   \n",
+       "1                 NaN                   3.0                NaN         C   \n",
+       "2                 NaN                   2.0                4.0         P   \n",
+       "3                 NaN                   2.0                1.0         C   \n",
+       "4                 1.0                   2.0                3.0         J   \n",
+       "\n",
+       "   sic2d2007  soc2010  pwkstat  salary_yearly  salary_hourly             hid  \\\n",
+       "0       58.0   1115.0        6            NaN            NaN  E02002183_0001   \n",
+       "1       25.0   1121.0        6            NaN            NaN  E02002183_0002   \n",
+       "2       85.0   2311.0        6            NaN            NaN  E02002183_0002   \n",
+       "3       31.0   3422.0        1   32857.859375      14.360952  E02002183_0003   \n",
+       "4       62.0   7214.0        1   18162.451172       9.439944  E02002183_0003   \n",
+       "\n",
+       "   accommodation_type  communal_type  num_rooms  central_heat  tenure  \\\n",
+       "0                 1.0            NaN        2.0          True     2.0   \n",
+       "1                 3.0            NaN        6.0          True     2.0   \n",
+       "2                 3.0            NaN        6.0          True     2.0   \n",
+       "3                 3.0            NaN        6.0          True     2.0   \n",
+       "4                 3.0            NaN        6.0          True     2.0   \n",
+       "\n",
+       "   num_cars  sex  age_years  ethnicity  nssec8  salary_yearly_hh  \\\n",
+       "0         2    1         86          1     1.0          0.000000   \n",
+       "1         2    1         74          3     1.0          0.000000   \n",
+       "2         2    2         68          1     2.0          0.000000   \n",
+       "3         1    1         27          1     4.0      51020.310547   \n",
+       "4         1    2         26          1     6.0      51020.310547   \n",
+       "\n",
+       "   salary_yearly_hh_cat  is_adult  num_adults  is_child  num_children  \\\n",
+       "0                     1         1           1         0             0   \n",
+       "1                     1         1           2         0             0   \n",
+       "2                     1         1           2         0             0   \n",
+       "3                     3         1           2         0             0   \n",
+       "4                     3         1           2         0             0   \n",
+       "\n",
+       "   is_pension_age  num_pension_age  pwkstat_FT_hh  pwkstat_PT_hh  \\\n",
+       "0               1                1              0              0   \n",
+       "1               1                2              0              0   \n",
+       "2               1                2              0              0   \n",
+       "3               0                0              2              0   \n",
+       "4               0                0              2              0   \n",
+       "\n",
+       "   pwkstat_NTS_match     OA11CD                  RUC11 RUC11CD  \n",
+       "0                  1  E00053954    Urban city and town      C1  \n",
+       "1                  1  E00053953    Urban city and town      C1  \n",
+       "2                  1  E00053953    Urban city and town      C1  \n",
+       "3                  6  E00053689  Rural town and fringe      D1  \n",
+       "4                  6  E00053689  Rural town and fringe      D1  "
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read the rural urban classification data\n",
+    "rural_urban = pd.read_csv('../data/census_2011_rural_urban.csv', sep=',')\n",
+    "\n",
+    "# merge the rural_urban data with the spc\n",
+    "spc_edited = spc_edited.merge(rural_urban[['OA11CD', 'RUC11', 'RUC11CD']], left_on='oa', right_on='OA11CD')\n",
+    "spc_edited.head(5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create dictionary from the NTS `Settlement2011EW_B03ID` column\n",
+    "Settlement2011EW_B03ID_nts_hh = {\n",
+    "    '1': 'Urban',\n",
+    "    '2': 'Rural',\n",
+    "    '3': 'Scotland',\n",
+    "    '-8': 'NA',\n",
+    "    '-10': 'DEAD'\n",
+    "}\n",
+    "\n",
+    "Settlement2011EW_B04ID_nts_hh = {\n",
+    "    '1': 'Urban Conurbation',\n",
+    "    '2': 'Urban City and Town',\n",
+    "    '3': 'Rural Town and Fringe',\n",
+    "    '4': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    '5': 'Scotland',\n",
+    "    '-8': 'NA',\n",
+    "    '-10': 'DEAD'\n",
+    "}\n",
+    "\n",
+    "\n",
+    "census_2011_to_nts_B03ID = {\n",
+    "    'Urban major conurbation': 'Urban',\n",
+    "    'Urban minor conurbation': 'Urban',\n",
+    "    'Urban city and town': 'Urban',\n",
+    "    'Urban city and town in a sparse setting': 'Urban',\n",
+    "    'Rural town and fringe': 'Rural',\n",
+    "    'Rural town and fringe in a sparse setting': 'Rural',\n",
+    "    'Rural village': 'Rural',\n",
+    "    'Rural village in a sparse setting': 'Rural',\n",
+    "    'Rural hamlets and isolated dwellings': 'Rural',\n",
+    "    'Rural hamlets and isolated dwellings in a sparse setting': 'Rural'\n",
+    "}\n",
+    "\n",
+    "census_2011_to_nts_B04ID = {\n",
+    "    'Urban major conurbation': 'Urban Conurbation',\n",
+    "    'Urban minor conurbation': 'Urban Conurbation',\n",
+    "    'Urban city and town': 'Urban City and Town',\n",
+    "    'Urban city and town in a sparse setting': 'Urban City and Town',\n",
+    "    'Rural town and fringe': 'Rural Town and Fringe',\n",
+    "    'Rural town and fringe in a sparse setting': 'Rural Town and Fringe',\n",
+    "    'Rural village': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    'Rural village in a sparse setting': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    'Rural hamlets and isolated dwellings': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    'Rural hamlets and isolated dwellings in a sparse setting': 'Rural Village, Hamlet and Isolated Dwellings'\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <th></th>\n",
+       "      <th>id</th>\n",
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+       "      <th>workplace</th>\n",
+       "      <th>location</th>\n",
+       "      <th>events</th>\n",
+       "      <th>weekday_diaries</th>\n",
+       "      <th>weekend_diaries</th>\n",
+       "      <th>orig_pid</th>\n",
+       "      <th>id_tus_hh</th>\n",
+       "      <th>id_tus_p</th>\n",
+       "      <th>pid_hs</th>\n",
+       "      <th>msoa</th>\n",
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+       "      <th>members</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>has_cardiovascular_disease</th>\n",
+       "      <th>has_diabetes</th>\n",
+       "      <th>has_high_blood_pressure</th>\n",
+       "      <th>number_medications</th>\n",
+       "      <th>self_assessed_health</th>\n",
+       "      <th>life_satisfaction</th>\n",
+       "      <th>sic1d2007</th>\n",
+       "      <th>sic2d2007</th>\n",
+       "      <th>soc2010</th>\n",
+       "      <th>pwkstat</th>\n",
+       "      <th>salary_yearly</th>\n",
+       "      <th>salary_hourly</th>\n",
+       "      <th>hid</th>\n",
+       "      <th>accommodation_type</th>\n",
+       "      <th>communal_type</th>\n",
+       "      <th>num_rooms</th>\n",
+       "      <th>central_heat</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>num_cars</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age_years</th>\n",
+       "      <th>ethnicity</th>\n",
+       "      <th>nssec8</th>\n",
+       "      <th>salary_yearly_hh</th>\n",
+       "      <th>salary_yearly_hh_cat</th>\n",
+       "      <th>is_adult</th>\n",
+       "      <th>num_adults</th>\n",
+       "      <th>is_child</th>\n",
+       "      <th>num_children</th>\n",
+       "      <th>is_pension_age</th>\n",
+       "      <th>num_pension_age</th>\n",
+       "      <th>pwkstat_FT_hh</th>\n",
+       "      <th>pwkstat_PT_hh</th>\n",
+       "      <th>pwkstat_NTS_match</th>\n",
+       "      <th>OA11CD</th>\n",
+       "      <th>RUC11</th>\n",
+       "      <th>RUC11CD</th>\n",
+       "      <th>Settlement2011EW_B03ID_spc</th>\n",
+       "      <th>Settlement2011EW_B04ID_spc</th>\n",
+       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
+       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
+       "    </tr>\n",
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+       "      <td>E00053954</td>\n",
+       "      <td>[0]</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>True</td>\n",
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+       "      <td>E00053954</td>\n",
+       "      <td>Urban city and town</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Urban City and Town</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
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+       "      <th>1</th>\n",
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+       "      <td>E00053953</td>\n",
+       "      <td>Urban city and town</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Urban City and Town</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "      <td>Urban city and town</td>\n",
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+       "      <td>Urban</td>\n",
+       "      <td>Urban City and Town</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>[3, 4]</td>\n",
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+       "      <td>27</td>\n",
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+       "      <td>4.0</td>\n",
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+       "      <td>6</td>\n",
+       "      <td>E00053689</td>\n",
+       "      <td>Rural town and fringe</td>\n",
+       "      <td>D1</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>Rural Town and Fringe</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
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+       "      <td>E00053689</td>\n",
+       "      <td>Rural town and fringe</td>\n",
+       "      <td>D1</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>Rural Town and Fringe</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  household  workplace  \\\n",
+       "0   0          0        NaN   \n",
+       "1   1          1        NaN   \n",
+       "2   2          1        NaN   \n",
+       "3   3          2    56126.0   \n",
+       "4   4          2        NaN   \n",
+       "\n",
+       "                                            location  \\\n",
+       "0  {'x': -1.7892179489135742, 'y': 53.91915130615...   \n",
+       "1  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
+       "2  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
+       "3  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
+       "4  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
+       "\n",
+       "                                              events  \\\n",
+       "0  {'concert_f': 1.2791347489984115e-31, 'concert...   \n",
+       "1  {'concert_f': 9.743248151956307e-21, 'concert_...   \n",
+       "2  {'concert_f': 8.46716103992468e-16, 'concert_f...   \n",
+       "3  {'concert_f': 1.8844366073608398, 'concert_fs'...   \n",
+       "4  {'concert_f': 4.877435207366943, 'concert_fs':...   \n",
+       "\n",
+       "                                     weekday_diaries  \\\n",
+       "0                                      [1583, 13161]   \n",
+       "1  [2900, 4948, 4972, 7424, 10284, 10586, 12199, ...   \n",
+       "2                   [3010, 6389, 9448, 10184, 11598]   \n",
+       "3  [366, 867, 2096, 3678, 5212, 5450, 8145, 9254,...   \n",
+       "4                               [1289, 12528, 12870]   \n",
+       "\n",
+       "                                     weekend_diaries            orig_pid  \\\n",
+       "0                                      [1582, 13160]  E02002183_0001_001   \n",
+       "1  [2901, 4949, 4973, 7425, 10285, 10585, 12198, ...  E02002183_0002_001   \n",
+       "2                   [3011, 6388, 9447, 10183, 11599]  E02002183_0002_002   \n",
+       "3  [365, 868, 2097, 3677, 5213, 5451, 8146, 9253,...  E02002183_0003_001   \n",
+       "4                               [1288, 12529, 12871]  E02002183_0003_002   \n",
+       "\n",
+       "   id_tus_hh  id_tus_p   pid_hs       msoa         oa members        bmi  \\\n",
+       "0   11291218         1  2905399  E02002183  E00053954     [0]  24.879356   \n",
+       "1   17291219         1  2905308  E02002183  E00053953  [1, 2]  27.491207   \n",
+       "2   17070713         2  2907681  E02002183  E00053953  [1, 2]  17.310829   \n",
+       "3   20310313         1  2902817  E02002183  E00053689  [3, 4]  20.852091   \n",
+       "4   13010909         3  2900884  E02002183  E00053689  [3, 4]  20.032526   \n",
+       "\n",
+       "   has_cardiovascular_disease  has_diabetes  has_high_blood_pressure  \\\n",
+       "0                       False         False                    False   \n",
+       "1                       False         False                     True   \n",
+       "2                       False          True                     True   \n",
+       "3                       False         False                    False   \n",
+       "4                       False         False                    False   \n",
+       "\n",
+       "   number_medications  self_assessed_health  life_satisfaction sic1d2007  \\\n",
+       "0                 NaN                   3.0                2.0         J   \n",
+       "1                 NaN                   3.0                NaN         C   \n",
+       "2                 NaN                   2.0                4.0         P   \n",
+       "3                 NaN                   2.0                1.0         C   \n",
+       "4                 1.0                   2.0                3.0         J   \n",
+       "\n",
+       "   sic2d2007  soc2010  pwkstat  salary_yearly  salary_hourly             hid  \\\n",
+       "0       58.0   1115.0        6            NaN            NaN  E02002183_0001   \n",
+       "1       25.0   1121.0        6            NaN            NaN  E02002183_0002   \n",
+       "2       85.0   2311.0        6            NaN            NaN  E02002183_0002   \n",
+       "3       31.0   3422.0        1   32857.859375      14.360952  E02002183_0003   \n",
+       "4       62.0   7214.0        1   18162.451172       9.439944  E02002183_0003   \n",
+       "\n",
+       "   accommodation_type  communal_type  num_rooms  central_heat  tenure  \\\n",
+       "0                 1.0            NaN        2.0          True     2.0   \n",
+       "1                 3.0            NaN        6.0          True     2.0   \n",
+       "2                 3.0            NaN        6.0          True     2.0   \n",
+       "3                 3.0            NaN        6.0          True     2.0   \n",
+       "4                 3.0            NaN        6.0          True     2.0   \n",
+       "\n",
+       "   num_cars  sex  age_years  ethnicity  nssec8  salary_yearly_hh  \\\n",
+       "0         2    1         86          1     1.0          0.000000   \n",
+       "1         2    1         74          3     1.0          0.000000   \n",
+       "2         2    2         68          1     2.0          0.000000   \n",
+       "3         1    1         27          1     4.0      51020.310547   \n",
+       "4         1    2         26          1     6.0      51020.310547   \n",
+       "\n",
+       "   salary_yearly_hh_cat  is_adult  num_adults  is_child  num_children  \\\n",
+       "0                     1         1           1         0             0   \n",
+       "1                     1         1           2         0             0   \n",
+       "2                     1         1           2         0             0   \n",
+       "3                     3         1           2         0             0   \n",
+       "4                     3         1           2         0             0   \n",
+       "\n",
+       "   is_pension_age  num_pension_age  pwkstat_FT_hh  pwkstat_PT_hh  \\\n",
+       "0               1                1              0              0   \n",
+       "1               1                2              0              0   \n",
+       "2               1                2              0              0   \n",
+       "3               0                0              2              0   \n",
+       "4               0                0              2              0   \n",
+       "\n",
+       "   pwkstat_NTS_match     OA11CD                  RUC11 RUC11CD  \\\n",
+       "0                  1  E00053954    Urban city and town      C1   \n",
+       "1                  1  E00053953    Urban city and town      C1   \n",
+       "2                  1  E00053953    Urban city and town      C1   \n",
+       "3                  6  E00053689  Rural town and fringe      D1   \n",
+       "4                  6  E00053689  Rural town and fringe      D1   \n",
+       "\n",
+       "  Settlement2011EW_B03ID_spc Settlement2011EW_B04ID_spc  \\\n",
+       "0                      Urban        Urban City and Town   \n",
+       "1                      Urban        Urban City and Town   \n",
+       "2                      Urban        Urban City and Town   \n",
+       "3                      Rural      Rural Town and Fringe   \n",
+       "4                      Rural      Rural Town and Fringe   \n",
+       "\n",
+       "   Settlement2011EW_B03ID_spc_CD  Settlement2011EW_B04ID_spc_CD  \n",
+       "0                              1                              2  \n",
+       "1                              1                              2  \n",
+       "2                              1                              2  \n",
+       "3                              2                              3  \n",
+       "4                              2                              3  "
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# add the nts Settlement2011EW_B03ID and Settlement2011EW_B04ID columns to the spc\n",
+    "spc_edited['Settlement2011EW_B03ID_spc'] = spc_edited['RUC11'].map(census_2011_to_nts_B03ID)\n",
+    "spc_edited['Settlement2011EW_B04ID_spc'] = spc_edited['RUC11'].map(census_2011_to_nts_B04ID)\n",
+    "spc_edited.head()\n",
+    "\n",
+    "# add the keys from nts_Settlement2011EW_B03ID and nts_Settlement2011EW_B04ID to the spc based on above mappings\n",
+    "\n",
+    "# reverse the dictionaries\n",
+    "Settlement2011EW_B03ID_nts_rev = {v: k for k, v in Settlement2011EW_B03ID_nts_hh.items()}\n",
+    "# map the values\n",
+    "spc_edited['Settlement2011EW_B03ID_spc_CD'] = spc_edited['Settlement2011EW_B03ID_spc'].map(Settlement2011EW_B03ID_nts_rev).astype('int')\n",
+    "\n",
+    "Settlement2011EW_B04ID_nts_rev = {v: k for k, v in Settlement2011EW_B04ID_nts_hh.items()}\n",
+    "spc_edited['Settlement2011EW_B04ID_spc_CD'] = spc_edited['Settlement2011EW_B04ID_spc'].map(Settlement2011EW_B04ID_nts_rev).astype('int')\n",
+    "spc_edited.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1502,7 +2562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1530,7 +2590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 211,
    "metadata": {},
    "outputs": [
     {
@@ -1687,7 +2747,7 @@
        "142973     3.0               2.0"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1711,7 +2771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 212,
    "metadata": {},
    "outputs": [
     {
@@ -1733,7 +2793,7 @@
        "  '-10': 'DEAD'})"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1751,7 +2811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 213,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1792,7 +2852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 214,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1831,7 +2891,8 @@
     "| Number of children | `HHoldNumChildren` | `num_children` |\n",
     "| Employment status | `HHoldEmploy_B01ID` | `pwkstat_NTS_match` |\n",
     "| Car ownership | `NumCar_SPC_match` | `num_cars` |\n",
-    "| Type of tenancy | `tenure_nts_for_matching` | `tenure_spc_for_matching` |"
+    "| Type of tenancy | `tenure_nts_for_matching` | `tenure_spc_for_matching` |\n",
+    "| Rural/Urban Classification | `Settlement2011EW_B03ID` | `Settlement2011EW_B03ID_spc_CD` |"
    ]
   },
   {
@@ -1843,7 +2904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 215,
    "metadata": {},
    "outputs": [
     {
@@ -1875,6 +2936,8 @@
        "      <th>pwkstat_NTS_match</th>\n",
        "      <th>num_cars</th>\n",
        "      <th>tenure_spc_for_matching</th>\n",
+       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
+       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1888,6 +2951,8 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1899,6 +2964,8 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1910,6 +2977,8 @@
        "      <td>6</td>\n",
        "      <td>1</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1921,6 +2990,8 @@
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1932,6 +3003,8 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -1943,6 +3016,8 @@
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -1954,6 +3029,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -1965,6 +3042,8 @@
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
@@ -1976,6 +3055,8 @@
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
@@ -1987,6 +3068,8 @@
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2005,20 +3088,32 @@
        "14  E02002183_0009                     1           2             0   \n",
        "16  E02002183_0010                     1           2             1   \n",
        "\n",
-       "    num_pension_age  pwkstat_NTS_match  num_cars  tenure_spc_for_matching  \n",
-       "0                 1                  1         2                      1.0  \n",
-       "1                 2                  1         2                      1.0  \n",
-       "3                 0                  6         1                      1.0  \n",
-       "5                 0                  3         1                      1.0  \n",
-       "6                 1                  1         2                      1.0  \n",
-       "9                 0                  3         1                      2.0  \n",
-       "10                2                  1         1                      1.0  \n",
-       "13                0                  3         2                      1.0  \n",
-       "14                0                  3         2                      1.0  \n",
-       "16                0                  2         2                      1.0  "
+       "    num_pension_age  pwkstat_NTS_match  num_cars  tenure_spc_for_matching  \\\n",
+       "0                 1                  1         2                      1.0   \n",
+       "1                 2                  1         2                      1.0   \n",
+       "3                 0                  6         1                      1.0   \n",
+       "5                 0                  3         1                      1.0   \n",
+       "6                 1                  1         2                      1.0   \n",
+       "9                 0                  3         1                      2.0   \n",
+       "10                2                  1         1                      1.0   \n",
+       "13                0                  3         2                      1.0   \n",
+       "14                0                  3         2                      1.0   \n",
+       "16                0                  2         2                      1.0   \n",
+       "\n",
+       "    Settlement2011EW_B03ID_spc_CD  Settlement2011EW_B04ID_spc_CD  \n",
+       "0                               1                              2  \n",
+       "1                               1                              2  \n",
+       "3                               2                              3  \n",
+       "5                               2                              3  \n",
+       "6                               2                              3  \n",
+       "9                               1                              2  \n",
+       "10                              1                              2  \n",
+       "13                              2                              4  \n",
+       "14                              2                              4  \n",
+       "16                              1                              2  "
       ]
      },
-     "execution_count": 59,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2029,7 +3124,8 @@
     "    'hid',\n",
     "    'salary_yearly_hh_cat', 'num_adults',\n",
     "    'num_children', 'num_pension_age', 'pwkstat_NTS_match',\n",
-    "    'num_cars', 'tenure_spc_for_matching']]\n",
+    "    'num_cars', 'tenure_spc_for_matching',\n",
+    "    'Settlement2011EW_B03ID_spc_CD', 'Settlement2011EW_B04ID_spc_CD']]\n",
     "\n",
     "# edit the df so that we have one row per hid\n",
     "spc_matching = spc_matching.drop_duplicates(subset='hid')\n",
@@ -2037,26 +3133,6 @@
     "spc_matching.head(10)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2373, 8)"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spc_matching.shape"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2066,7 +3142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 216,
    "metadata": {},
    "outputs": [
     {
@@ -2098,6 +3174,8 @@
        "      <th>HHoldEmploy_B01ID</th>\n",
        "      <th>NumCar_SPC_match</th>\n",
        "      <th>tenure_nts_for_matching</th>\n",
+       "      <th>Settlement2011EW_B03ID</th>\n",
+       "      <th>Settlement2011EW_B04ID</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2111,6 +3189,8 @@
        "      <td>1</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142955</th>\n",
@@ -2122,6 +3202,8 @@
        "      <td>3</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142956</th>\n",
@@ -2133,6 +3215,8 @@
        "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142957</th>\n",
@@ -2144,6 +3228,8 @@
        "      <td>4</td>\n",
        "      <td>2.0</td>\n",
        "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142958</th>\n",
@@ -2155,6 +3241,8 @@
        "      <td>3</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142959</th>\n",
@@ -2166,6 +3254,8 @@
        "      <td>3</td>\n",
        "      <td>1.0</td>\n",
        "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142960</th>\n",
@@ -2177,6 +3267,8 @@
        "      <td>5</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142961</th>\n",
@@ -2188,6 +3280,8 @@
        "      <td>1</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142962</th>\n",
@@ -2199,6 +3293,8 @@
        "      <td>6</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142963</th>\n",
@@ -2210,6 +3306,8 @@
        "      <td>2</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2240,20 +3338,32 @@
        "142962                    0                  6               2.0   \n",
        "142963                    2                  2               1.0   \n",
        "\n",
-       "        tenure_nts_for_matching  \n",
-       "142954                      1.0  \n",
-       "142955                      1.0  \n",
-       "142956                      2.0  \n",
-       "142957                      2.0  \n",
-       "142958                      1.0  \n",
-       "142959                      2.0  \n",
-       "142960                      1.0  \n",
-       "142961                      1.0  \n",
-       "142962                      1.0  \n",
-       "142963                      1.0  "
+       "        tenure_nts_for_matching  Settlement2011EW_B03ID  \\\n",
+       "142954                      1.0                       1   \n",
+       "142955                      1.0                       1   \n",
+       "142956                      2.0                       1   \n",
+       "142957                      2.0                       1   \n",
+       "142958                      1.0                       1   \n",
+       "142959                      2.0                       1   \n",
+       "142960                      1.0                       1   \n",
+       "142961                      1.0                       1   \n",
+       "142962                      1.0                       1   \n",
+       "142963                      1.0                       1   \n",
+       "\n",
+       "        Settlement2011EW_B04ID  \n",
+       "142954                       1  \n",
+       "142955                       1  \n",
+       "142956                       1  \n",
+       "142957                       2  \n",
+       "142958                       1  \n",
+       "142959                       1  \n",
+       "142960                       1  \n",
+       "142961                       1  \n",
+       "142962                       2  \n",
+       "142963                       2  "
       ]
      },
-     "execution_count": 61,
+     "execution_count": 216,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2263,7 +3373,8 @@
     "    'HouseholdID','HHIncome2002_B02ID',\n",
     "    'HHoldNumAdults', 'HHoldNumChildren', 'num_pension_age_nts',\n",
     "    'HHoldEmploy_B01ID', 'NumCar_SPC_match',\n",
-    "    'tenure_nts_for_matching']]\n",
+    "    'tenure_nts_for_matching',\n",
+    "    'Settlement2011EW_B03ID', 'Settlement2011EW_B04ID']]\n",
     "\n",
     "nts_matching.head(10)"
    ]
@@ -2277,7 +3388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 217,
    "metadata": {},
    "outputs": [
     {
@@ -2290,17 +3401,22 @@
        " 'num_pension_age': ['num_pension_age', 'num_pension_age_nts'],\n",
        " 'employment_status': ['pwkstat_NTS_match', 'HHoldEmploy_B01ID'],\n",
        " 'number_cars': ['num_cars', 'NumCar_SPC_match'],\n",
-       " 'tenure_status': ['tenure_spc_for_matching', 'tenure_nts_for_matching']}"
+       " 'tenure_status': ['tenure_spc_for_matching', 'tenure_nts_for_matching'],\n",
+       " 'rural_urban_2_categories': ['Settlement2011EW_B03ID_spc_CD',\n",
+       "  'Settlement2011EW_B03ID'],\n",
+       " 'rural_urban_4_categories': ['Settlement2011EW_B04ID_spc_CD',\n",
+       "  'Settlement2011EW_B04ID']}"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# column_names (keys) for the dictionary\n",
-    "matching_ids = ['household_id', 'yearly_income', 'number_adults', 'number_children', 'num_pension_age', 'employment_status', 'number_cars', 'tenure_status']\n",
+    "matching_ids = ['household_id', 'yearly_income', 'number_adults', 'number_children', 'num_pension_age',\n",
+    "                'employment_status', 'number_cars', 'tenure_status', 'rural_urban_2_categories', 'rural_urban_4_categories']\n",
     "\n",
     "# i want the value to be a list with spc_matching and nts_matching\n",
     "matching_dfs_dict = {column_name: [spc_value, nts_value] for column_name, spc_value, nts_value in zip(matching_ids, spc_matching, nts_matching)}\n",
@@ -2316,20 +3432,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 218,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "265 households in the SPC had no match\n",
-      "11.2 % of households in the SPC had no match\n"
+      "991 households in the SPC had no match\n",
+      "14.7 % of households in the SPC had no match\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2340,7 +3456,8 @@
    ],
    "source": [
     "# columns for matching\n",
-    "keys = ['yearly_income', 'number_adults', 'number_children', 'num_pension_age', 'employment_status', 'number_cars', 'tenure_status']\n",
+    "keys = ['yearly_income', 'number_adults', 'number_children', 'num_pension_age',\n",
+    "        'employment_status', 'number_cars', 'tenure_status', 'rural_urban_2_categories']\n",
     "\n",
     "\n",
     "spc_cols = [matching_dfs_dict[key][0] for key in keys]\n",
@@ -2368,6 +3485,264 @@
     "print(round((spc_nts_1_hist[spc_nts_1_hist['count'] == 0].shape[0] / spc_matching['hid'].unique().shape[0]) * 100, 1), \"% of households in the SPC had no match\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Matched</th>\n",
+       "      <th>Percentage Matched</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pwkstat_NTS_match</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2408</td>\n",
+       "      <td>1879.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>604</td>\n",
+       "      <td>450.0</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2297</td>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>77.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>460</td>\n",
+       "      <td>439.0</td>\n",
+       "      <td>95.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>900</td>\n",
+       "      <td>888.0</td>\n",
+       "      <td>99.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Total  Matched  Percentage Matched\n",
+       "pwkstat_NTS_match                                    \n",
+       "1                   2408   1879.0                78.0\n",
+       "2                    604    450.0                75.0\n",
+       "3                   2297   2045.0                89.0\n",
+       "4                     31     24.0                77.0\n",
+       "5                    460    439.0                95.0\n",
+       "6                    900    888.0                99.0\n",
+       "7                      3      1.0                33.0\n",
+       "8                      8      4.0                50.0\n",
+       "9                      1      NaN                 NaN\n",
+       "10                     5      4.0                80.0\n",
+       "11                     8      NaN                 NaN"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Matched</th>\n",
+       "      <th>Percentage Matched</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4927</td>\n",
+       "      <td>4310</td>\n",
+       "      <td>87.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1556</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>242</td>\n",
+       "      <td>174</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               Total  Matched  Percentage Matched\n",
+       "Settlement2011EW_B04ID_spc_CD                                    \n",
+       "2                               4927     4310                87.0\n",
+       "3                               1556     1250                80.0\n",
+       "4                                242      174                72.0"
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def matching_coverage_per_column(x: pd.DataFrame,\n",
+    "                                 id_x: str, id_y: str,\n",
+    "                                 column: str) -> pd.DataFrame:\n",
+    "\n",
+    "    x_hist = (x\n",
+    "              .assign(count=(x\n",
+    "                             .groupby(id_x)[id_y]\n",
+    "                             .transform('count')))\n",
+    "              .drop_duplicates(subset=id_x))\n",
+    "\n",
+    "    total = x_hist.groupby(column)['count'].size()\n",
+    "    matched = x_hist[x_hist['count'] >= 1].groupby(column).size()\n",
+    "\n",
+    "    # Calculate percentage of matched rows\n",
+    "    percentage_matched = round(matched / total * 100)\n",
+    "\n",
+    "    # combined total, matched in one df\n",
+    "    total_matched = pd.concat([total, matched, percentage_matched],\n",
+    "                              axis=1,\n",
+    "                              keys=['Total', 'Matched', 'Percentage Matched'])\n",
+    "    return total_matched\n",
+    "\n",
+    "\n",
+    "a = matching_coverage_per_column(spc_nts_1,\n",
+    "                                 'hid',\n",
+    "                                 'HouseholdID',\n",
+    "                                 matching_dfs_dict['rural_urban_4_categories'][0])\n",
+    "\n",
+    "\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Urban city and town', 'Rural town and fringe',\n",
+       "       'Rural hamlets and isolated dwellings'], dtype=object)"
+      ]
+     },
+     "execution_count": 280,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spc_edited['RUC11'].unique()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2377,20 +3752,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 219,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "130 households in the SPC had no match\n",
-      "5.5 % of households in the SPC had no match\n"
+      "674 households in the SPC had no match\n",
+      "10.0 % of households in the SPC had no match\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2401,7 +3776,8 @@
    ],
    "source": [
     "# columns for matching\n",
-    "keys = ['number_adults', 'number_children', 'num_pension_age', 'employment_status', 'number_cars', 'tenure_status']\n",
+    "keys = ['number_adults', 'number_children', 'num_pension_age', 'employment_status',\n",
+    "        'number_cars', 'tenure_status','rural_urban_2_categories']\n",
     "# extract equivalent column names from dictionary\n",
     "spc_cols = [matching_dfs_dict[key][0] for key in keys]\n",
     "nts_cols = [matching_dfs_dict[key][1] for key in keys]\n",
@@ -2438,20 +3814,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 220,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "96 households in the SPC had no match\n",
-      "4.0 % of households in the SPC had no match\n"
+      "547 households in the SPC had no match\n",
+      "8.1 % of households in the SPC had no match\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2463,7 +3839,8 @@
    "source": [
     "\n",
     "# columns for matching\n",
-    "keys = ['number_adults', 'number_children', 'num_pension_age', 'employment_status', 'number_cars']\n",
+    "keys = ['number_adults', 'number_children', 'num_pension_age', 'employment_status',\n",
+    "        'number_cars', 'rural_urban_2_categories']\n",
     "# extract equivalent column names from dictionary\n",
     "spc_cols = [matching_dfs_dict[key][0] for key in keys]\n",
     "nts_cols = [matching_dfs_dict[key][1] for key in keys]\n",
@@ -2500,20 +3877,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 221,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "18 households in the SPC had no match\n",
-      "0.8 % of households in the SPC had no match\n"
+      "266 households in the SPC had no match\n",
+      "4.0 % of households in the SPC had no match\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2525,7 +3902,7 @@
    "source": [
     "\n",
     "# columns for matching\n",
-    "keys = ['number_adults', 'number_children', 'num_pension_age', 'number_cars']\n",
+    "keys = ['number_adults', 'number_children', 'num_pension_age', 'number_cars', 'rural_urban_2_categories']\n",
     "# extract equivalent column names from dictionary\n",
     "spc_cols = [matching_dfs_dict[key][0] for key in keys]\n",
     "nts_cols = [matching_dfs_dict[key][1] for key in keys]\n",
@@ -2562,18 +3939,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 222,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2373 Total households in SPC\n",
-      "265 Unmatched households - matching on all categories\n",
-      "130 Unmatched households - exclusing Salary from matching\n",
-      "96 Unmatched households - exclusing Salary and Tenure from matching\n",
-      "18 Unmatched households - exclusing Salary, Tenure and Employment status from matching\n"
+      "6725 Total households in SPC\n",
+      "991 Unmatched households - matching on all categories\n",
+      "674 Unmatched households - exclusing Salary from matching\n",
+      "547 Unmatched households - exclusing Salary and Tenure from matching\n",
+      "266 Unmatched households - exclusing Salary, Tenure and Employment status from matching\n"
      ]
     }
    ],
@@ -2603,31 +3980,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "106 households in the SPC had no match\n",
-      "4.5 % of households in the SPC had no match\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# match on different subset of column depending on yearly_income value\n",
-    "keys = ['yearly_income', 'number_adults', 'number_children', 'num_pension_age', 'employment_status', 'number_cars', 'tenure_status']\n",
+    "keys = ['yearly_income', 'number_adults', 'number_children', 'num_pension_age',\n",
+    "        'employment_status', 'number_cars', 'tenure_status', 'rural_urban_2_categories']\n",
     "# remove yearly income from the list\n",
     "# new list without yearly income, without modifying the original list\n",
     "keys_no_salary = keys.copy()\n",
diff --git a/notebooks/synthpop.ipynb b/notebooks/synthpop.ipynb
index 05db6167..23cc5a45 100644
--- a/notebooks/synthpop.ipynb
+++ b/notebooks/synthpop.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,12 +50,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Pick a region with SPC output saved\n",
-    "path = \"../data/spc_output/raw/\"\n",
+    "path = \"../data/external/spc_output/raw/\"\n",
     "region = \"west-yorkshire\""
    ]
   },
@@ -68,244 +68,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-       "                                        demographics  \\\n",
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-       "4                               [1289, 12528, 12870]   \n",
-       "\n",
-       "                                     weekend_diaries            orig_pid  \\\n",
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-       "\n",
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-       "\n",
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-       "\n",
-       "[5 rows x 36 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# add people and households\n",
     "spc_people_hh = (\n",
@@ -316,242 +81,14 @@
     "    .build()\n",
     ")\n",
     "\n",
-    "spc_people_hh.head()"
+    "spc_people_hh.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
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-       "   id  household  workplace  \\\n",
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-       "1   1          1        NaN   \n",
-       "2   2          1        NaN   \n",
-       "3   3          2    56126.0   \n",
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-       "\n",
-       "                                            location  \\\n",
-       "0  {'x': -1.7892179489135742, 'y': 53.91915130615...   \n",
-       "1  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
-       "2  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
-       "3  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
-       "4  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
-       "\n",
-       "                                              events  \\\n",
-       "0  {'sport': 0.09000000357627869, 'rugby': 0.1133...   \n",
-       "1  {'sport': 0.23899999260902405, 'rugby': 0.3011...   \n",
-       "2  {'sport': 0.23899999260902405, 'rugby': 0.1768...   \n",
-       "3  {'sport': 0.2329999953508377, 'rugby': 0.14678...   \n",
-       "4  {'sport': 0.2329999953508377, 'rugby': 0.08620...   \n",
-       "\n",
-       "                                     weekday_diaries  \\\n",
-       "0                                      [1583, 13161]   \n",
-       "1  [2900, 4948, 4972, 7424, 10284, 10586, 12199, ...   \n",
-       "2                   [3010, 6389, 9448, 10184, 11598]   \n",
-       "3  [366, 867, 2096, 3678, 5212, 5450, 8145, 9254,...   \n",
-       "4                               [1289, 12528, 12870]   \n",
-       "\n",
-       "                                     weekend_diaries            orig_pid  \\\n",
-       "0                                      [1582, 13160]  E02002183_0001_001   \n",
-       "1  [2901, 4949, 4973, 7425, 10285, 10585, 12198, ...  E02002183_0002_001   \n",
-       "2                   [3011, 6388, 9447, 10183, 11599]  E02002183_0002_002   \n",
-       "3  [365, 868, 2097, 3677, 5213, 5451, 8146, 9253,...  E02002183_0003_001   \n",
-       "4                               [1288, 12529, 12871]  E02002183_0003_002   \n",
-       "\n",
-       "   id_tus_hh  id_tus_p  ...  accommodation_type communal_type num_rooms  \\\n",
-       "0   11291218         1  ...                 1.0           NaN       2.0   \n",
-       "1   17291219         1  ...                 3.0           NaN       6.0   \n",
-       "2   17070713         2  ...                 3.0           NaN       6.0   \n",
-       "3   20310313         1  ...                 3.0           NaN       6.0   \n",
-       "4   13010909         3  ...                 3.0           NaN       6.0   \n",
-       "\n",
-       "  central_heat  tenure  num_cars  sex  age_years  ethnicity  nssec8  \n",
-       "0         True     2.0         2    1         86          1     1.0  \n",
-       "1         True     2.0         2    1         74          3     1.0  \n",
-       "2         True     2.0         2    2         68          1     2.0  \n",
-       "3         True     2.0         1    1         27          1     4.0  \n",
-       "4         True     2.0         1    2         26          1     6.0  \n",
-       "\n",
-       "[5 rows x 38 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# we need to unnest the demographic data. If we do this above\n",
     "# we get an error as there will be two \"nssec8\" columns.\n",
@@ -569,12 +106,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# save the output\n",
-    "spc_people_hh.to_parquet('../data/spc_output/' + region + '_people_hh.parquet')\n"
+    "spc_people_hh.to_parquet('../data/external/spc_output/' + region + '_people_hh.parquet')\n"
    ]
   },
   {
@@ -655,7 +192,7 @@
    "outputs": [],
    "source": [
     "# save the output\n",
-    "spc_people_tu.write_parquet('../data/spc_output/' + region + '_people_tu.parquet')"
+    "spc_people_tu.write_parquet('../data/external/spc_output/' + region + '_people_tu.parquet')"
    ]
   }
  ],
diff --git a/src/acbm/preprocessing.py b/src/acbm/preprocessing.py
index a7aa6a96..77cacebb 100644
--- a/src/acbm/preprocessing.py
+++ b/src/acbm/preprocessing.py
@@ -235,3 +235,66 @@ def truncate_values(x: int, lower: int = None, upper: int = None) -> int:
         if x < lower:
             return lower
     return x
+
+
+def match_coverage_col(
+    data: pd.DataFrame, id_x: str, id_y: str, column: str
+) -> pd.DataFrame:
+    """
+    Calculate the number of matched rows for each unique value in a column
+    e.g.
+
+    Input:
+
+    | hid | HouseholdId | 'num_adults' |
+    |-----|-------------|--------------|
+    | 1   | 2           | 2            |
+    | 2   | 5           | 1            |
+    | 3   | 5           | 1            |
+    | 4   | NA          | 5            |
+    | 5   | NA          | 2            |
+
+    Output:
+
+    num_adults | Total | Matched | Percentage Matched
+    1          | 2     | 2       | 100
+    2          | 2     | 1       | 50
+    5          | 1     | 0       | 0
+
+    Parameters
+    ----------
+    data: pandas DataFrame
+        The df to get matching stats from. It is the output of matching two dfs
+    id_x: str
+        Unique identifier from the first df
+    id_y: str
+        Unique identifier from the second df
+    column: str
+        the column that we want to calculate matching stats for. It is one of the columns
+        that we matched on
+
+    Returns
+    -------
+    pandas DataFrame
+        A DataFrame with the total number of rows, matched rows
+        and the percentage of matched rows for a specific column
+
+    """
+
+    data_hist = data.assign(
+        count=(data.groupby(id_x)[id_y].transform("count"))
+    ).drop_duplicates(subset=id_x)
+
+    total = data_hist.groupby(column)["count"].size()
+    matched = data_hist[data_hist["count"] >= 1].groupby(column).size()
+
+    # Calculate percentage of matched rows
+    percentage_matched = round(matched / total * 100)
+
+    # combined total, matched in one df
+    total_matched = pd.concat(
+        [total, matched, percentage_matched],
+        axis=1,
+        keys=["Total", "Matched", "Percentage Matched"],
+    )
+    return total_matched