From 99cce1e0a42ab8d8d30528a0751e5ef7d04e6ab1 Mon Sep 17 00:00:00 2001 From: Vahid Ahmadi Date: Mon, 16 Dec 2024 11:55:06 +0100 Subject: [PATCH] add constituency example part --- docs/constituency_methodology.ipynb | 829 ++++++++++++++++++++++- docs/pictures/parliamentary_earnings.png | Bin 0 -> 987292 bytes 2 files changed, 828 insertions(+), 1 deletion(-) create mode 100644 docs/pictures/parliamentary_earnings.png diff --git a/docs/constituency_methodology.ipynb b/docs/constituency_methodology.ipynb index a480273..4beac59 100644 --- a/docs/constituency_methodology.ipynb +++ b/docs/constituency_methodology.ipynb @@ -22,7 +22,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Income level data\n", + "### Income data\n", "\n", "Income data for UK parliamentary constituencies is obtained from [this government statistics report](https://www.gov.uk/government/statistics/income-and-tax-by-parliamentary-constituency-confidence-intervals). This dataset provides detailed information about income and tax by Parliamentary constituency with confidence intervals, and is stored as `total_income.csv`, including two key variables:\n", "\n", @@ -1168,6 +1168,833 @@ "\n", "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map constituency 2010 to 2024\n", + "\n", + "PolicyEngine uses HMRC household data which aligns with 2010 constituency boundaries; to handle this issue and align it with 2024 constituency boundaries definitions, we follow these processes:\n", + "\n", + "1. Download the mapping data from [this website](https://www.data.gov.uk/dataset/20c4ffe5-7d86-419f-808e-da98e46f4f52/westminster-pcon-may-2010-to-westminster-pcon-july-2024-lookup-in-the-uk-v2) that contains the official lookup table between 2010 and 2024 Westminster Parliamentary Constituencies.\n", + "\n", + "2. Create a mapping matrix (650 x 650) which maps each constituency from 2010 to corresponding constituency in 2024 using the `construct_mapping_matrix.py` script. This is a many-to-many mapping, as 2010 constituencies can be split across multiple 2024 constituencies, and 2024 constituencies can contain parts of multiple 2010 constituencies. The matrix structure has rows representing 2010 constituencies and columns representing 2024 constituencies.\n", + "\n", + "3. For each row in the matrix (representing a 2010 constituency), normalize the weights so they sum to 1. This ensures that when we redistribute data from 2010 boundaries to 2024 boundaries, we maintain the correct proportions.\n", + "\n", + "The following table represents this mapping matrix:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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takes three main input parameters: dataset (defaults to `enhanced_frs_2022_23`), time_period (defaults to 2025), and an optional reform parameter for policy changes.\n", + "\n", + "- It reads three files containing real data: `age.csv`, `total_income.csv`, and `employment_income.csv`.\n", + "\n", + "- It creates a PolicyEngine Microsimulation object using the specified dataset and reform parameters.\n", + "\n", + "- The function creates two main matrices: `matrix` for simulated values from PolicyEngine, and `y` for actual target values from HMRC data.\n", + "\n", + "- It calculates total income metrics by computing both the total amounts and counts of people with income in each constituency.\n", + "\n", + "- It processes age distributions by creating 10-year age bands from 0 to 80, calculating how many people fall into each band.\n", + "\n", + "- For employment income, it processes both counts and amounts for different income bands between £12,570 and £70,000, excluding people under 16.\n", + "\n", + "- The `sim.map_result()` function is used throughout to map individual-level results to household level.\n", + "\n", + "- The function returns both the simulated matrix and the target matrix `(matrix, y)` which can be used for comparing the simulation results against actual data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calibration function\n", + "\n", + "The file `calibrate.py` defines a main `calibrate()` function that performs weight calibration for constituency-level analysis.\n", + "\n", + "* It imports necessary functions and matrices from other files including `create_constituency_target_matrix`, `create_national_target_matrix` from `loss.py`, and `transform_2010_to_2024` for constituency boundary transformations.\n", + "\n", + "* Sets up initial matrices using the `create_constituency_target_matrix` and `create_national_target_matrix` functions for both constituency and national level data.\n", + "\n", + "* Creates a Microsimulation object using the `enhanced_frs_2022_23` dataset.\n", + "\n", + "* Initializes weights for 650 constituencies x 100180 households, starting with the log of household weights divided by constituency count.\n", + "\n", + "* Converts all the matrices and weights into PyTorch tensors to enable optimization.\n", + "\n", + "* Defines a loss function that calculates and combines both constituency-level and national-level mean squared errors into a single loss value.\n", + "\n", + "* Uses Adam optimizer with a learning rate of 0.1 to minimize the loss over 512 epochs.\n", + "\n", + "* Every 100 epochs during optimization, it updates the weights using the mapping matrix from 2010 to 2024 constituencies and saves the current weights to a `weights.h5` file.\n", + "\n", + "* Includes an `update_weights()` function that applies the constituency mapping matrix to transform the weights between different boundary definitions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example code for constituency analysis\n", + "\n", + "The following code demonstrates how to analyze and visualize median earnings across UK parliamentary constituencies using PolicyEngine:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{code-block} python\n", + "# Import required libraries\n", + "from policyengine_uk import Microsimulation\n", + "from policyengine.utils.charts import *\n", + "from policyengine import Simulation\n", + "import plotly.graph_objects as go\n", + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "import os\n", + "\n", + "# Initialize baseline microsimulation for UK\n", + "baseline = Microsimulation()\n", + "# Calculate household net income for 2024\n", + "baseline_income = baseline.calculate(\"real_household_net_income\", period=2024)\n", + "# Calculate number of people per household for 2024\n", + "baseline_people = baseline.calculate(\"people\", map_to = \"household\", period=2024)\n", + "\n", + "# Load constituency-level age distribution data\n", + "age_df = pd.read_csv('../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv')\n", + "# Load pre-calculated weights for geographic distribution\n", + "with h5py.File(\"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/weights.h5\", \"r\") as f:\n", + " weights = f[\"weight\"][:]\n", + "\n", + "# Calculate weighted income for 2024 using matrix multiplication\n", + "income_2024 = np.dot(weights, baseline_income.values)\n", + "# Calculate weighted population for 2024\n", + "population_2024 = np.dot(weights, baseline_people.values) \n", + "# Calculate per capita income\n", + "per_capita_2024 = income_2024 / population_2024\n", + "\n", + "# Create DataFrame with constituency data\n", + "df = pd.DataFrame({\n", + " 'code': age_df['code'],\n", + " 'name': age_df['name'],\n", + " 'income': per_capita_2024\n", + "})\n", + "# Rename code column to match expected format\n", + "df = df.rename(columns={'code': 'LAD24CD'})\n", + "\n", + "# Initialize simulation for visualization\n", + "sim = Simulation(\n", + " country=\"uk\",\n", + " scope=\"macro\",\n", + " time_period=\"2025\",\n", + " options={\n", + " \"include_constituencies\": True, # Enable constituency-level analysis\n", + " }\n", + ")\n", + "\n", + "# Add fonts for visualization\n", + "add_fonts()\n", + "\n", + "# Define function to calculate median earnings for adults\n", + "def adult_earnings_median(sim):\n", + " # Filter for working age adults (18-65)\n", + " adult = sim.calculate(\"age\").between(18, 65)\n", + " # Get employment income\n", + " earnings = sim.calculate(\"employment_income\")\n", + " # Return median of positive earnings for adults\n", + " return earnings[earnings > 0][adult].quantile(0.5)\n", + "\n", + "# Create and display visualization of median earnings by constituency\n", + "sim.calculate(\n", + " \"macro/gov/local_areas/parliamentary_constituencies\",\n", + " metric=adult_earnings_median,\n", + " chart=True\n", + ").update_layout(\n", + " title=\"Median earnings of adults in parliamentary constituencies\",\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code demonstrates how to:\n", + "\n", + "1. Load and process constituency-level data using PolicyEngine's microsimulation capabilities\n", + "\n", + "2. Calculate real household income and population metrics\n", + "\n", + "3. Apply constituency weights to generate accurate geographic distributions\n", + "\n", + "4. Create constituency-level visualizations of median earnings\n", + "\n", + "5. Filter for working-age adults (18-65) and positive earnings\n", + "\n", + "6. Generate an interactive visualization showing median earnings across parliamentary constituencies\n", + "\n", + "The figure below shows the simulated results, displaying median earnings data across UK parliamentary constituencies in a geographic representation. The color intensity indicates earnings levels, providing an intuitive visualization of how earnings vary across different regions of the UK." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from policyengine_uk import Microsimulation\n", + "from policyengine.utils.charts import *\n", + "from policyengine import Simulation\n", + "import plotly.graph_objects as go\n", + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "import os\n", + "\n", + "os.environ[\"HUGGING_FACE_TOKEN\"] = \"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\"\n", + "\n", + "baseline = Microsimulation()\n", + "# reformed = Microsimulation(reform=reform)\n", + "baseline_income = baseline.calculate(\"real_household_net_income\", period=2024)\n", + "# reformed_income = reformed.calculate(\"real_household_net_income\", period=2029)\n", + "baseline_people = baseline.calculate(\"people\", map_to = \"household\", period=2024)\n", + "# reformed_people = baseline.calculate(\"people\", map_to = \"household\", period=2029)\n", + "\n", + "# hex_locations = pd.read_csv('hex_map_LA.csv')\n", + "age_df = pd.read_csv('../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv')\n", + "with h5py.File(\"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/weights.h5\", \"r\") as f:\n", + " weights = f[\"weight\"][:]\n", + "\n", + "\n", + "income_2024 = np.dot(weights, baseline_income.values)\n", + "population_2024 = np.dot(weights, baseline_people.values) \n", + "per_capita_2024 = income_2024 / population_2024\n", + "\n", + "df = pd.DataFrame({\n", + " 'code': age_df['code'],\n", + " 'name': age_df['name'],\n", + " 'income': per_capita_2024\n", + "})\n", + "df = df.rename(columns={'code': 'LAD24CD'})\n", + "\n", + "\n", + "sim = Simulation(\n", + " country=\"uk\",\n", + " scope=\"macro\",\n", + " time_period=\"2025\",\n", + " options={\n", + " \"include_constituencies\": True,\n", + " }\n", + ")\n", + "\n", + "add_fonts()\n", + "\n", + "def adult_earnings_median(sim):\n", + " adult = sim.calculate(\"age\").between(18, 65)\n", + " earnings = sim.calculate(\"employment_income\")\n", + " return earnings[earnings > 0][adult].quantile(0.5)\n", + "\n", + "sim.calculate(\n", + " \"macro/gov/local_areas/parliamentary_constituencies\",\n", + " metric=adult_earnings_median,\n", + " chart=True\n", + ").update_layout(\n", + " title=\"Median earnings of adults in parliamentary constituencies\",\n", + ").write_image(\"pictures/parliamentary_earnings.png\", scale=6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](pictures/parliamentary_earnings.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] } ], "metadata": { diff --git 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