Explanatory and Interactive Workbooks/read_shapefile.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "04dc9185",
   "metadata": {},
   "source": [
    "# Create a geodatabase using UK Census data\n",
    "This notebook covers one methodology to utilise data from the UK census for geospatial analysis.\n",
    "\n",
    "## Motivation - Clunky downloading of UK Census data\n",
    "\n",
    "Like many other data covering whole national populations, accessing and using data from the UK census can be very clunky and frustrating. There are different resources designed to help individuals to download this publically-available data, such the [bulk data download](https://www.ons.gov.uk/census/2011census/2011censusdata/bulkdata) option from the Office for National Statistics (ONS) - the public body who carry out the census in the UK. However, my preferred option for downloading census data is the [Nomis](https://www.nomisweb.co.uk) website - an organisation ran by the University of Durham, on behalf of the ONS.\n",
    "\n",
    "Despite being my favoured method of downloading data from the UK Census, there is one major flaw in the functionality in downloading UK Census data from the Nomis website - demographic and spatial data have to be downloaded separately.\n",
    "\n",
    "In other words, this means that you can download a file containing the information on a population living within various Output Areas (OAs - the spatial areas aggregated to present the data and maintain an acceptable amount of anonymity), yet there is no way of knowing where this actually is in the UK. Each OA is given a code, which doesn't directly indicate if it is in London anymore than it does Land's End.\n",
    "\n",
    "This might not seem like such a big deal. But as a geographer, what is so frustrating is this gives you no scope straight off the bat to make a map of the information you are downloading from the census, to spot patterns or begin to unpick what this actually means. Increasingly, there are more detailed tools that can use location and proximity to compute statistics about data and help to build narratives about how it can be understood.\n",
    "\n",
    "Thankfully, spatial information is available, in the form of boundary data which can be downloaded separately. The demographic data and boundary data can then be paired together to undertake geospatial analysis (analysis that utilises location). However, this is a faff and it is useful to see a use-case of it in work."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d652132",
   "metadata": {},
   "source": [
    "## Context - Leicester, UK\n",
    "This workbook will focus on merging census data from Leicester, in the UK, at Output Area (OA) level. It takes two forms of data:\n",
    "* [Demographic data](https://www.nomisweb.co.uk/census/2011/ks201ew) - on the ethic identity of residents of Leicester. In the format of a comma-separated file (csv)\n",
    "* [Boundary data](https://borders.ukdataservice.ac.uk/bds.html) - of the geometry of each Output Area in Leicester. In the format of a shapefile (shp)\n",
    "\n",
    "*Please note that in both websites linked above, it is required to manually filter the required subset of data before you download the csv and/or shp files.*\n",
    "\n",
    "*The demographic data also requires manual cleaning (removal of some rows, and renaming of columns) prior to being read into this workbook. I would recommend you do the same within Excel or alternative csv editor.*\n",
    "\n",
    "### Common attribute - *oa11cd*\n",
    "These common attribute of these two types of data - that allows them to be paired together, in what is commonly referred to as a *merge* - is the code given to each Output Area. In these two sets of data, this is stored within a column called **oa11cd** - which refers to the Output Area (oa) related to the 2011 census (11) code (cd).\n",
    "\n",
    "For example, in Leicester some of the data stored in the oa11cd column looks like this:\n",
    "\n",
    "*E00068657, E00068658, E00068659, E00068660*\n",
    "\n",
    "Identifying this common attribute (normally a shared column) is crucial in the merging of geometry data to other data.\n",
    "\n",
    "So, without further rambling, let's get started creating our merged data!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc527255",
   "metadata": {},
   "source": [
    "## Step 1 - Import the necessary modules\n",
    "Working in Python for this example, we will use tools from 3 different libraries:\n",
    "* Pandas - to read and manipulate csv files\n",
    "* Geopandas - to read and manipulate shapefiles, as well as write to geopackage\n",
    "* Matplotlib.pyplot - to plot shapefiles and confirm that the merge has worked correctly\n",
    "\n",
    "Below you can see them being imported, and given nicknames such that our future code isn't too long-winded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c85b1adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db87e49",
   "metadata": {},
   "source": [
    "## Step 2 - Reading in the data\n",
    "Now we can read in the data, from files in our local computer to the workspace we are using - which for this example is this interactive Python notebook.\n",
    "\n",
    "### 2a - Set file paths\n",
    "We first need to set the locations of the files we are trying to access. While you can do this within 2b, creating objects storing the file paths helps you to keep track of all that you are doing. And also can help with troubleshooting, should you get errors in your code.\n",
    "\n",
    "You can see the file paths have been set below, with *fp_eth* relating to the location of the csv file that has the ethnicity data and *fp_shp* relating to the location of the shapefile that contains the boundary data. These file paths can be followed within the GitHub repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64ea6d2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ethnicity data for the city of Leicester from the 2011 UK census - at Output Area (OA) level\n",
    "fp_eth = \"../Example Data - Leicester, UK/Unmerged Data from the ONS/ethnicity_OA_leicester_2011.csv\"\n",
    "\n",
    "# Boundary data for the city of Leicester - at Output Area (OA) level (apologies for the length of this file path)\n",
    "fp_shp = \"../Example Data - Leicester, UK/Unmerged Data from the ONS/2011_OA_Boundaries_Leicester_only-shp-ONS/Output_Areas_(December_2011)_Boundaries_EW_BFC.shp\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59855f54",
   "metadata": {},
   "source": [
    "### 2b - Read in the data\n",
    "Now the paths to the files have been set, data from the files themselves can then actually be read in.\n",
    "\n",
    "As we said in step 1, we will be using commands from different libraries to read in the different types of data that we have: pd (pandas) for the csv file and gpd (geopandas) for the shapefile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95753f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OA ethnicity data\n",
    "oa_eth_leicester = pd.read_csv(fp_eth)\n",
    "\n",
    "# OA boundary data\n",
    "oa_shp_leicester = gpd.read_file(fp_shp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267388f0",
   "metadata": {},
   "source": [
    "### 2c - Confirm data read correctly\n",
    "Different functions can then be used to get an initial look at what the data you have read in looks like. This can be a good idea, to compare it with what you might expect, or remind yourself exactly what the columns are for future use.\n",
    "\n",
    "Below I have used the `.head` function to see what the first few observations of the data look like, as well as the `.columns` function where appropriate to list the names of all of the columns when they do not all fit on the output of the head function. Comparing both outputs from the `.head` function, you can see that the common attribute columns taht we have identified do indeed contain the same information - which is why we are going use them as the point of reference to combine the two pieces of data.\n",
    "\n",
    "The `.plot` function has also been used to show the data contained within the geometry column of the shapefile. You can see the shapes of each of the OAs in Leicester. As you can see though, no further information is avaiable about the people that live within each OA (the shapes are all the same colour), this is the functionality we are aiming to add once the data has been merged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87ef8197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      oa11cd  all    w   p_w  w_b  p_w_b  w_i  p_w_i  w_g  p_w_g  ...  b_c  \\\n",
      "0  E00068657  413  359  86.9  345   83.5    4    1.0    0    0.0  ...    3   \n",
      "1  E00068658  359  248  69.1  222   61.8    4    1.1    0    0.0  ...    4   \n",
      "2  E00068659  334  273  81.7  241   72.2    2    0.6    2    0.6  ...    0   \n",
      "3  E00068660  280  127  45.4  109   38.9    3    1.1    0    0.0  ...    6   \n",
      "4  E00068661  320  225  70.3  194   60.6    6    1.9    0    0.0  ...    6   \n",
      "\n",
      "   p_b_c  b_o  p_b_o   o  p_o  o_a  p_o_a  o_o  p_o_o  \n",
      "0    0.7    3    0.7   0  0.0    0    0.0    0    0.0  \n",
      "1    1.1    2    0.6   1  0.3    0    0.0    1    0.3  \n",
      "2    0.0    0    0.0  15  4.5    3    0.9   12    3.6  \n",
      "3    2.1    1    0.4   7  2.5    1    0.4    6    2.1  \n",
      "4    1.9    0    0.0   8  2.5    0    0.0    8    2.5  \n",
      "\n",
      "[5 rows x 48 columns]\n",
      "Index(['oa11cd', 'all', 'w', 'p_w', 'w_b', 'p_w_b', 'w_i', 'p_w_i', 'w_g',\n",
      "       'p_w_g', 'w_o', 'p_w_o', 'm', 'p_m', 'm_wbc', 'p_m_wbc', 'm_wba',\n",
      "       'p_m_wba', 'm_w_a', 'p_m_w_a', 'm_o', 'p_m_o', 'a', 'p_a', 'a_i',\n",
      "       'p_a_i', 'a_p', 'p_a_p', 'a_b', 'p_a_b', 'a_c', 'p_a_c', 'a_o', 'p_a_o',\n",
      "       'b', 'p_b', 'b_a', 'p_b_a', 'b_c', 'p_b_c', 'b_o', 'p_b_o', 'o', 'p_o',\n",
      "       'o_a', 'p_o_a', 'o_o', 'p_o_o'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Explore what OA ethnicity data looks like\n",
    "print(oa_eth_leicester.head())\n",
    "\n",
    "# Print the columns of the OA ethnicity data\n",
    "print(oa_eth_leicester.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3637f71a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   OBJECTID     OA11CD    LAD11CD    LAD16CD    LAD16NM     Shape__Are  \\\n",
      "0     66570  E00068657  E06000016  E06000016  Leicester   58692.222687   \n",
      "1     66571  E00068658  E06000016  E06000016  Leicester   51615.237915   \n",
      "2     66572  E00068659  E06000016  E06000016  Leicester   98010.396652   \n",
      "3     66573  E00068660  E06000016  E06000016  Leicester   86993.668503   \n",
      "4     66574  E00068661  E06000016  E06000016  Leicester  281105.237427   \n",
      "\n",
      "    Shape__Len                                           geometry  \n",
      "0  2038.589497  POLYGON ((458598.274 307772.432, 458585.335 30...  \n",
      "1  1577.523005  POLYGON ((458785.675 307076.320, 458783.317 30...  \n",
      "2  1752.264912  POLYGON ((458537.000 307386.000, 458544.063 30...  \n",
      "3  1671.905736  POLYGON ((458253.000 307023.000, 458253.745 30...  \n",
      "4  2431.710951  POLYGON ((459252.996 306971.333, 459257.693 30...  \n"
     ]
    }
   ],
   "source": [
    "# Explore what OA boundary data looks like\n",
    "print(oa_shp_leicester.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c456801f",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot and show the shapefile contained within the geometry column of the OA boundary data\n",
    "oa_shp_leicester.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df1bc0b",
   "metadata": {},
   "source": [
    "## Step 3 - Undertake any data cleaning\n",
    "Step 2c can also be really useful for predicting any areas of potential issues in working with the data. I can see such an example, anc will rectify it now, to avoid an error message later on in my code.\n",
    "\n",
    "I mentioned within the *context* section that identifying the common column was crucial step to merging the data together. For ease, it is easiest to complete this merge when the columns you are using to match up data have the same name attributed to them.\n",
    "\n",
    "The issue seen in step 2c is that the common column we are using to match up the two datasets - the output area's code - is called *oa11cd* in the oa_eth_leicester object, and *OA11CD* in the oa_shp_leicester object. While these two say the same thing, programming can be a cruel with the slightest variation in text. The difference in capitalisation between the two objects will bring about an error.\n",
    "\n",
    "While differences in the names of the column can be accounted for while undertaking the merge command, it is easiest to rename one of the columns now, such that the merge command runs first time in the next step.\n",
    "Below I have done this by renaming the OA code column to *oa11cd* in the shapefile, to match the ethnicity data. I have used the `.columns` function again to show the different from before to after the renaming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f2df091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['OBJECTID', 'OA11CD', 'LAD11CD', 'LAD16CD', 'LAD16NM', 'Shape__Are',\n",
      "       'Shape__Len', 'geometry'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Before renaming\n",
    "print(oa_shp_leicester.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2cade16",
   "metadata": {},
   "outputs": [],
   "source": [
    "oa_shp_leicester.columns = ['OBJECTID', 'oa11cd', 'LAD11CD', 'LAD16CD', 'LAD16NM',\n",
    "                            'Shape__Are', 'Shape__Len', 'geometry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35f3219e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['OBJECTID', 'oa11cd', 'LAD11CD', 'LAD16CD', 'LAD16NM', 'Shape__Are',\n",
      "       'Shape__Len', 'geometry'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# After renaming\n",
    "print(oa_shp_leicester.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebcbc3fc",
   "metadata": {},
   "source": [
    "As well as this, we have seen in step 2c that the ethnicity data has tons of columns. When I downloaded this from the Nomis website, I chose to have columns for each Output Area relating to both the raw number of each group and the percentages of each group represented. The percentages columns can be identified through their *'p_'* suffix.\n",
    "\n",
    "For the intended use of this data, only the raw data is required - therefore, to clean the data up a bit, I can run a single (but long) line of code to 'drop' (remove) the columns that begin with the suffix *p_*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "808f07b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Drop' (remove) the columns beginning with 'p_' - which relate to the percentages\n",
    "oa_eth_leicester = oa_eth_leicester.drop(oa_eth_leicester.columns[oa_eth_leicester.columns.str.contains('^p_')], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f39c7b",
   "metadata": {},
   "source": [
    "## Step 4 - Merge the data\n",
    "We can now finally carry out what we have been building up to - merging the demographic data with the geometry data.\n",
    "\n",
    "This is done using the `.merge` function - with the new object saved as *leicester_oa_eth_shp*. The `on` call within the `.merge` function is used to identify the name of the the common column that we have identified as having the same information in both the demographic and geometry data. Now we have renamed the column in the shapefile, they are both called *oa11cd*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94623441",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the two files together, using the common column of 'oa11cd'\n",
    "leicester_oa_eth_shp = oa_eth_leicester.merge(oa_shp_leicester, on = 'oa11cd')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8617f217",
   "metadata": {},
   "source": [
    "Looking at the output of the `.head` function, you can see that the new object contains the columns from both the demographic and geometry data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10ded41c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>oa11cd</th>\n",
       "      <th>all</th>\n",
       "      <th>w</th>\n",
       "      <th>w_b</th>\n",
       "      <th>w_i</th>\n",
       "      <th>w_g</th>\n",
       "      <th>w_o</th>\n",
       "      <th>m</th>\n",
       "      <th>m_wbc</th>\n",
       "      <th>m_wba</th>\n",
       "      <th>...</th>\n",
       "      <th>o</th>\n",
       "      <th>o_a</th>\n",
       "      <th>o_o</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>LAD11CD</th>\n",
       "      <th>LAD16CD</th>\n",
       "      <th>LAD16NM</th>\n",
       "      <th>Shape__Are</th>\n",
       "      <th>Shape__Len</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>E00068657</td>\n",
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       "      <td>66570</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>58692.222687</td>\n",
       "      <td>2038.589497</td>\n",
       "      <td>POLYGON ((458598.274 307772.432, 458585.335 30...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E00068658</td>\n",
       "      <td>359</td>\n",
       "      <td>248</td>\n",
       "      <td>222</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>66571</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>51615.237915</td>\n",
       "      <td>1577.523005</td>\n",
       "      <td>POLYGON ((458785.675 307076.320, 458783.317 30...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E00068659</td>\n",
       "      <td>334</td>\n",
       "      <td>273</td>\n",
       "      <td>241</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>66572</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>98010.396652</td>\n",
       "      <td>1752.264912</td>\n",
       "      <td>POLYGON ((458537.000 307386.000, 458544.063 30...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E00068660</td>\n",
       "      <td>280</td>\n",
       "      <td>127</td>\n",
       "      <td>109</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>66573</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>86993.668503</td>\n",
       "      <td>1671.905736</td>\n",
       "      <td>POLYGON ((458253.000 307023.000, 458253.745 30...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E00068661</td>\n",
       "      <td>320</td>\n",
       "      <td>225</td>\n",
       "      <td>194</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "      <td>66574</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>E06000016</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>281105.237427</td>\n",
       "      <td>2431.710951</td>\n",
       "      <td>POLYGON ((459252.996 306971.333, 459257.693 30...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      oa11cd  all    w  w_b  w_i  w_g  w_o   m  m_wbc  m_wba  ...   o  o_a  \\\n",
       "0  E00068657  413  359  345    4    0   10  11      9      0  ...   0    0   \n",
       "1  E00068658  359  248  222    4    0   22  25     17      3  ...   1    0   \n",
       "2  E00068659  334  273  241    2    2   28  12      1      6  ...  15    3   \n",
       "3  E00068660  280  127  109    3    0   15   6      6      0  ...   7    1   \n",
       "4  E00068661  320  225  194    6    0   25  27     20      0  ...   8    0   \n",
       "\n",
       "   o_o  OBJECTID    LAD11CD    LAD16CD    LAD16NM     Shape__Are   Shape__Len  \\\n",
       "0    0     66570  E06000016  E06000016  Leicester   58692.222687  2038.589497   \n",
       "1    1     66571  E06000016  E06000016  Leicester   51615.237915  1577.523005   \n",
       "2   12     66572  E06000016  E06000016  Leicester   98010.396652  1752.264912   \n",
       "3    6     66573  E06000016  E06000016  Leicester   86993.668503  1671.905736   \n",
       "4    8     66574  E06000016  E06000016  Leicester  281105.237427  2431.710951   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((458598.274 307772.432, 458585.335 30...  \n",
       "1  POLYGON ((458785.675 307076.320, 458783.317 30...  \n",
       "2  POLYGON ((458537.000 307386.000, 458544.063 30...  \n",
       "3  POLYGON ((458253.000 307023.000, 458253.745 30...  \n",
       "4  POLYGON ((459252.996 306971.333, 459257.693 30...  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring what this new object looks like\n",
    "leicester_oa_eth_shp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c559fe",
   "metadata": {},
   "source": [
    "Unfortunately there is one more line of code we need to run to make this data usable for geospatial analysis. We need to convert this object into a *geodataframe*, such that it is easily readable by other geospatial tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5267cdd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the dataframe to a geodataframe\n",
    "leicester_oa_eth_shp = gpd.GeoDataFrame(leicester_oa_eth_shp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80081277",
   "metadata": {},
   "source": [
    "## Step 5 - Confirm merge completed successfully\n",
    "The easiest way I find to check that a merge, including geometry, has worked as hoped is by plotting the output.\n",
    "\n",
    "As I have merged the geometry information with demographic information on the ethnic identity of residents, this can be done very easily.\n",
    "\n",
    "The `.plot` function can be used again, but this time using the `column` call to select a column to use as reference to fill each OA with.\n",
    "\n",
    "Below I have selected the *a* column to do this - which is the raw number of residents that identify as Asian in Leicester, as part of the 2011 UK Census.\n",
    "\n",
    "You can see this is shown successfully, with the lower numbers shown in the darkest blues and the higher numbers shown in the brightest greens and yellows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca221fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the merged file, of ethnicity data with boundary geometry\n",
    "leicester_oa_eth_shp.plot(column = 'a')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa834ff5",
   "metadata": {},
   "source": [
    "## Step 6 - Save new object as a GeoPackage\n",
    "Now that we have confirmation that this merge has been completed successfully, it is useful to save the newly merged object back to a file such that it can be used within other notebooks, without having to repeat this whole process again.\n",
    "\n",
    "This is done using the `.to_file` function, where you can set the desired file path of where you would like this new file to be saved.\n",
    "\n",
    "I have called it *leicester_ethnicity* and have decided to output the merged data as a [GeoPackage](https://www.geopackage.org) - which is a relatively new, but impressively efficient format for storing geographic data and attributes. In order to do this, I have added a crucial *.gpkg* extension at the end of the file path along with telling the command to use the GeoPackage driver to export the file correctly.\n",
    "\n",
    "\n",
    "*(please note that I could have instead chosen to output the merged data as a shapefile, by using the .shp extension in the place of .gpkg - however, I was recommended the GeoPackage format due to its efficiency)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "56b935bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(leicester_oa_eth_shp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66ecfdd3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Save the new file as a geopackage, titled 'leicester_ethnicity'\n",
    "leicester_oa_eth_shp.to_file(\"../Example Data - Leicester, UK/leicester_ethnicity.gpkg\", driver = \"GPKG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb42d832",
   "metadata": {},
   "source": [
    "That should be everything you need to know to have a crack at reading and merging your own UK census data, ready for any geospatial analysis you could imagine!\n",
    "\n",
    "I hope this notebook helped - to see what I have gone on to use this geopackage to calculate a Kullback–Leibler (KL) divergence profile to measure multiscalar segregation, check out my [kl_divergence](https://github.com/noahbouchier/GSoC-PySAL-21/blob/master/Explanatory%20and%20Interactive%20Workbooks/kl_divergence.ipynb) interactive workbook.\n",
    "\n",
    "All the best,\n",
    "\n",
    "Noah"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "geo_env",
   "language": "python",
   "name": "geo_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}