From 5747469bde787cd532aa5721ed3d4ebc36738515 Mon Sep 17 00:00:00 2001 From: Vahid Ahmadi Date: Thu, 12 Dec 2024 16:50:10 +0100 Subject: [PATCH 1/8] write data structure part of the documentation --- docs/_toc.yml | 1 + docs/constituency_methodology.ipynb | 1194 +++++++++++++++++++++++++++ docs/pictures/earning_dist.png | Bin 0 -> 185811 bytes docs/pictures/nomis_screenshot1.png | Bin 0 -> 304724 bytes 4 files changed, 1195 insertions(+) create mode 100644 docs/constituency_methodology.ipynb create mode 100644 docs/pictures/earning_dist.png create mode 100644 docs/pictures/nomis_screenshot1.png diff --git a/docs/_toc.yml b/docs/_toc.yml index 5c0c742..9f3e365 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -8,3 +8,4 @@ chapters: - file: validation/constituencies.ipynb - file: validation/local_authorities.ipynb - file: pension_contributions.ipynb +- file: constituency_methodology.ipynb \ No newline at end of file diff --git a/docs/constituency_methodology.ipynb b/docs/constituency_methodology.ipynb new file mode 100644 index 0000000..a480273 --- /dev/null +++ b/docs/constituency_methodology.ipynb @@ -0,0 +1,1194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Constituency methodology" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Earning and jobs data\n", + "\n", + "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK parliamentary constituencies from [this website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as `nomis_earning_jobs_data.xlsx`. To download the data, follow the variable selection process shown in the image below:\n", + "\n", + "![](pictures/nomis_screenshot1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Income level 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", + "- `total_income_count`: the total number of taxpayers in each constituency\n", + "\n", + "- `total_income_amount`: the total amount of income for all taxpayers in each constituency\n", + "\n", + "These measures are used to identify similar constituencies when employment distribution data is missing - constituencies with similar income patterns (both in terms of number of taxpayers and total income) are assumed to have similar earnings distribution patterns. The following table shows the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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zB6R6(Z<{qCE@$Sdzb>xc^L4zu!pKg&Kv! zq}*24qAfx!MYKdoeO2X%`*>qQxt;AP{LF@>-P#OuR7eD_sQg?~>7nH3`L>P(1=9yK zsccj;@`{ Date: Mon, 16 Dec 2024 11:55:06 +0100 Subject: [PATCH 2/8] 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", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\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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a/docs/constituency_methodology.ipynb b/docs/constituency_methodology.ipynb index 4beac59..d9f68d5 100644 --- a/docs/constituency_methodology.ipynb +++ b/docs/constituency_methodology.ipynb @@ -11,6 +11,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Introduction\n", + "\n", + "When policy changes in the UK - taxes, benefits, or public spending - it affects places and people differently. PolicyEngine UK builds tools to analyze incomes, jobs, and population patterns in each constituency. This documentation explains how we create a microsimulation model that works at the constituency level. The system combines workplace surveys of jobs and earnings, HMRC tax records, and population statistics. We map data between 2010 and 2024 constituency boundaries, estimate income distributions, and optimize geographic weights.\n", + "\n", + "This guide shows how to use PolicyEngine UK for constituency analysis. We start with data collection, transform it for modeling, and build tools to examine policies. The guide provides examples and code to implement these methods. Users can measure changes in household budgets, track employment, and understand economic patterns on different constituencies. This document starts with data collection from workplace surveys, tax records, and population counts, then explains how we convert this data into usable forms through income brackets and boundary mapping. It concludes with technical details about accuracy measurement and calibration, plus example code for analysis and visualization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", "### Earning and jobs data\n", "\n", "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK parliamentary constituencies from [this website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as `nomis_earning_jobs_data.xlsx`. To download the data, follow the variable selection process shown in the image below:\n", @@ -813,10 +826,228 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Convert earning percentiles to brackets\n", + "## Preprocessing\n", "\n", - "The next step is to convert earning percentiles to earning brackets. To do this, the distribution of earnings needs to be estimated. Based on the ratio of different percentile incomes from [this government statistics report](https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax#:~:text=Details,in%20the%20Background%20Quality%20Report), earnings for percentiles from 90 to 99 for each constituency are estimated. Also, the earnings distribution starts from 0. The following image shows the earnings distribution for an example constituency.\n", + "### Convert earning percentiles to brackets\n", "\n", + "The next step is to convert earning percentiles to earning brackets. To do this, the distribution of earnings needs to be estimated. Based on the ratio of different percentile incomes from [this government statistics report](https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax#:~:text=Details,in%20the%20Background%20Quality%20Report), earnings for percentiles from 90 to 99 for each constituency are estimated. The following code and image show the earnings distribution for an example constituency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{code-block} python\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Sample data for Darlington\n", + "income_data = {\n", + " 'parliamentary constituency 2010': ['Darlington'],\n", + " 'constituency_code': ['E14000658'],\n", + " 'Number of jobs': ['31000'],\n", + " '10 percentile': [13298.0],\n", + " '20 percentile': [16723.0],\n", + " '30 percentile': [20778.0],\n", + " '40 percentile': [23407.0],\n", + " '50 percentile': [27158.0],\n", + " '60 percentile': [30471.0],\n", + " '70 percentile': [33812.0],\n", + " '80 percentile': [40717.0],\n", + " '90 percentile': [55762.0],\n", + " '91 percentile': [58878.0],\n", + " '92 percentile': [62394.4],\n", + " '93 percentile': [66722.3],\n", + " '94 percentile': [71952.0],\n", + " '95 percentile': [78804.5],\n", + " '96 percentile': [87640.7],\n", + " '97 percentile': [100083.5],\n", + " '98 percentile': [123526.5],\n", + " '100 percentile': [179429.0]\n", + "}\n", + "\n", + "income_sample = pd.DataFrame(income_data)\n", + "\n", + "# Excel Data Method\n", + "def load_real_data():\n", + " # Read Excel data\n", + " income_real = pd.read_excel(\"nomis_earning_jobs_data.xlsx\", skiprows=7)\n", + " income_real.columns = income_real.iloc[0]\n", + " income_real = income_real.drop(index=0).reset_index(drop=True)\n", + " \n", + " # Select and rename columns\n", + " columns_to_keep = [\n", + " 'parliamentary constituency 2010',\n", + " 'constituency_code',\n", + " 'Number of jobs',\n", + " 'Median',\n", + " '10 percentile',\n", + " '20 percentile',\n", + " '30 percentile',\n", + " '40 percentile',\n", + " '60 percentile',\n", + " '70 percentile',\n", + " '80 percentile',\n", + " '90 percentile'\n", + " ]\n", + " income_real = income_real[columns_to_keep]\n", + " income_real = income_real.rename(columns={'Median': '50 percentile'})\n", + " return income_real\n", + "\n", + "# Plotting function\n", + "def plot_constituency_distribution(income_df, constituency_name, detailed=True):\n", + " constituency_data = income_df[income_df['parliamentary constituency 2010'] == constituency_name].iloc[0]\n", + " \n", + " percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100]\n", + " income_values = [\n", + " 0,\n", + " constituency_data['10 percentile'],\n", + " constituency_data['20 percentile'],\n", + " constituency_data['30 percentile'],\n", + " constituency_data['40 percentile'],\n", + " constituency_data['50 percentile'],\n", + " constituency_data['60 percentile'],\n", + " constituency_data['70 percentile'],\n", + " constituency_data['80 percentile'],\n", + " constituency_data['90 percentile'],\n", + " constituency_data['91 percentile'],\n", + " constituency_data['92 percentile'],\n", + " constituency_data['93 percentile'],\n", + " constituency_data['94 percentile'],\n", + " constituency_data['95 percentile'],\n", + " constituency_data['96 percentile'],\n", + " constituency_data['97 percentile'],\n", + " constituency_data['98 percentile'],\n", + " constituency_data['100 percentile']\n", + " ]\n", + " \n", + " valid_data = [(p, v) for p, v in zip(percentiles, income_values) if pd.notna(v)]\n", + " filtered_percentiles, filtered_income = zip(*valid_data)\n", + " \n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(filtered_percentiles, filtered_income, marker='o')\n", + " plt.xlabel('Percentiles')\n", + " plt.ylabel('Income')\n", + " plt.title(f'Income Distribution for {constituency_name}')\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + "# Plot sample data (Darlington with detailed percentiles)\n", + "plot_constituency_distribution(income_sample, 'Darlington', detailed=True) \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Sample data for Darlington\n", + "income_data = {\n", + " 'parliamentary constituency 2010': ['Darlington'],\n", + " 'constituency_code': ['E14000658'],\n", + " 'Number of jobs': ['31000'],\n", + " '10 percentile': [13298.0],\n", + " '20 percentile': [16723.0],\n", + " '30 percentile': [20778.0],\n", + " '40 percentile': [23407.0],\n", + " '50 percentile': [27158.0],\n", + " '60 percentile': [30471.0],\n", + " '70 percentile': [33812.0],\n", + " '80 percentile': [40717.0],\n", + " '90 percentile': [55762.0],\n", + " '91 percentile': [58878.0],\n", + " '92 percentile': [62394.4],\n", + " '93 percentile': [66722.3],\n", + " '94 percentile': [71952.0],\n", + " '95 percentile': [78804.5],\n", + " '96 percentile': [87640.7],\n", + " '97 percentile': [100083.5],\n", + " '98 percentile': [123526.5],\n", + " '100 percentile': [179429.0]\n", + "}\n", + "\n", + "income_sample = pd.DataFrame(income_data)\n", + "\n", + "# Excel Data Method\n", + "def load_real_data():\n", + " # Read Excel data\n", + " income_real = pd.read_excel(\"nomis_earning_jobs_data.xlsx\", skiprows=7)\n", + " income_real.columns = income_real.iloc[0]\n", + " income_real = income_real.drop(index=0).reset_index(drop=True)\n", + " \n", + " # Select and rename columns\n", + " columns_to_keep = [\n", + " 'parliamentary constituency 2010',\n", + " 'constituency_code',\n", + " 'Number of jobs',\n", + " 'Median',\n", + " '10 percentile',\n", + " '20 percentile',\n", + " '30 percentile',\n", + " '40 percentile',\n", + " '60 percentile',\n", + " '70 percentile',\n", + " '80 percentile',\n", + " '90 percentile'\n", + " ]\n", + " income_real = income_real[columns_to_keep]\n", + " income_real = income_real.rename(columns={'Median': '50 percentile'})\n", + " return income_real\n", + "\n", + "# Plotting function\n", + "def plot_constituency_distribution(income_df, constituency_name, detailed=True):\n", + " constituency_data = income_df[income_df['parliamentary constituency 2010'] == constituency_name].iloc[0]\n", + " \n", + " percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100]\n", + " income_values = [\n", + " 0,\n", + " constituency_data['10 percentile'],\n", + " constituency_data['20 percentile'],\n", + " constituency_data['30 percentile'],\n", + " constituency_data['40 percentile'],\n", + " constituency_data['50 percentile'],\n", + " constituency_data['60 percentile'],\n", + " constituency_data['70 percentile'],\n", + " constituency_data['80 percentile'],\n", + " constituency_data['90 percentile'],\n", + " constituency_data['91 percentile'],\n", + " constituency_data['92 percentile'],\n", + " constituency_data['93 percentile'],\n", + " constituency_data['94 percentile'],\n", + " constituency_data['95 percentile'],\n", + " constituency_data['96 percentile'],\n", + " constituency_data['97 percentile'],\n", + " constituency_data['98 percentile'],\n", + " constituency_data['100 percentile']\n", + " ]\n", + " \n", + " valid_data = [(p, v) for p, v in zip(percentiles, income_values) if pd.notna(v)]\n", + " filtered_percentiles, filtered_income = zip(*valid_data)\n", + " \n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(filtered_percentiles, filtered_income, marker='o')\n", + " plt.xlabel('Percentiles')\n", + " plt.ylabel('Income')\n", + " plt.title(f'Income distribution for {constituency_name}')\n", + " plt.grid(True)\n", + " plt.savefig(\"pictures/earning_dist.png\", dpi=300, bbox_inches='tight')\n", + " plt.close()\n", + "\n", + "# Plot sample data (Darlington with detailed percentiles)\n", + "plot_constituency_distribution(income_sample, 'Darlington', detailed=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "![](pictures/earning_dist.png)\n", "\n", "After estimating the full earnings distribution, the data is converted into income brackets. For each constituency and income bracket, the number of jobs and total earnings are calculated based on the estimated earnings distribution. For constituencies with missing data, the earnings distribution pattern is estimated using data from constituencies with similar total number of taxpayers and total income levels. The Python script `create_employment_incomes.py` generates `employment_income.csv` containing number of jobs (`employment_income_count`) and total earnings (`employment_income_amount`) for each constituency and income bracket. The following table shows employment and income across different brackets for constituencies:" @@ -1744,27 +1975,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Methodology\n", + "\n", "### Loss function\n", "\n", "The file `loss.py` defines a function `create_constituency_target_matrix` that creates target matrices for comparing simulated data against actual constituency-level data. \n", "\n", - "- The function 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", + "1. The function 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", + "2. 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", + "3. 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", + "4. 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", + "5. 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", + "6. 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", + "7. 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", + "8. 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." + "9. 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." ] }, { @@ -1775,30 +2008,30 @@ "\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", + "1. 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", + "2. 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", + "3. 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", + "4. 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", + "5. 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", + "6. 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", + "7. 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", + "8. 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." + "9. 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", + "## Example\n", "\n", "The following code demonstrates how to analyze and visualize median earnings across UK parliamentary constituencies using PolicyEngine:" ] @@ -1809,43 +2042,8 @@ "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", @@ -1903,7 +2101,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -1921,40 +2119,8 @@ } ], "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", diff --git a/docs/pictures/earning_dist.png b/docs/pictures/earning_dist.png index 10eab49ad4a37cdc474c6281e4b4175a02ff43ca..cde5b6b5d3d70d96b1075073e5664146d4d56a73 100644 GIT binary patch literal 107532 zcmd>mXH=Bu);5VJnrJMjh*BdKLz$T9qE0D z5f}yOW#}kFM?lKZVZOabBjkK*ee3)8{&-#MOWVdc++04Me zuvPAYj0yw8k9G_U8x4Nk2!Erw@jy5HpP1b_O*>Vq8+MM@Y>XHbui4$Qu(Go-HTcEB z$i~*x>ZTCC82|BOznIwB-Le%I5U~9A0e&kRV}bpbHnhP*e!6wxsx1Qp{{j5pcTNe? z4h-KhFv!WAx#AT0smsw$r@27%gGMMH^E*5$8TH8Pyc%8Pmac)H~I~;Gyl91elwGgxq5CB=jOPlk7O-R zghm_l5kj&CKiQlJKX7llv1rRqZy7Fl7y6&KtYca(@Csl49NabZ&yR0~zGwgEXXo?J zHhlg0ms*^eFMs;>-P!i_$J-JI>i&7kL*xI5|MQV4ztfw({(L%`@qg9B-yR6lv60)o zd-uVWstT#4PeDz_G48Xb-S={jD@m`|RgzbR6JzwOO79c(h)u^I$DWlhZ|Gtm~ME3K^T#DPN)zOlt?*F{+aZ~PGx2rK#;}gSakHSxxRlUa2 zOIH;H4DW`G&&6Ndyk$$AYT~8(B-O+te0=^y_oa#tukRYRzPgst< zz`*cJ&rkGiP<<1|s&v5~J7U%=&CN@~UI^H#$?BH5!S=Km71R#=FB2 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zB6R6(Z<{qCE@$Sdzb>xc^L4zu!pKg&Kv! zq}*24qAfx!MYKdoeO2X%`*>qQxt;AP{LF@>-P#OuR7eD_sQg?~>7nH3`L>P(1=9yK zsccj;@`{ Date: Tue, 17 Dec 2024 13:20:34 +0100 Subject: [PATCH 4/8] add math part --- docs/constituency_methodology.ipynb | 102 ++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 22 deletions(-) diff --git a/docs/constituency_methodology.ipynb b/docs/constituency_methodology.ipynb index d9f68d5..86b7375 100644 --- a/docs/constituency_methodology.ipynb +++ b/docs/constituency_methodology.ipynb @@ -26,7 +26,7 @@ "\n", "### Earning and jobs data\n", "\n", - "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK parliamentary constituencies from [this website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as `nomis_earning_jobs_data.xlsx`. To download the data, follow the variable selection process shown in the image below:\n", + "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK parliamentary constituencies from [this website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as [`nomis_earning_jobs_data.xlsx`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/nomis_earning_jobs_data.xlsx). To download the data, follow the variable selection process shown in the image below:\n", "\n", "![](pictures/nomis_screenshot1.png)" ] @@ -37,13 +37,13 @@ "source": [ "### 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", + "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`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv), including two key variables:\n", "\n", - "- `total_income_count`: the total number of taxpayers in each constituency\n", + "- [`total_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv#L1): the total number of taxpayers in each constituency\n", "\n", - "- `total_income_amount`: the total amount of income for all taxpayers in each constituency\n", + "- [`total_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv#L1): the total amount of income for all taxpayers in each constituency\n", "\n", - "These measures are used to identify similar constituencies when employment distribution data is missing - constituencies with similar income patterns (both in terms of number of taxpayers and total income) are assumed to have similar earnings distribution patterns. The following table shows the dataset:" + "We use these measures to identify similar constituencies when employment distribution data is missing. Our approach assumes that constituencies with similar income patterns (measured by both taxpayer counts and total income) will have similar earnings distributions. The following table shows the dataset:" ] }, { @@ -384,7 +384,7 @@ "source": [ "### Population data by age\n", "\n", - "Population data by age groups for UK parliamentary constituencies can be downloaded from the [House of Commons Library data dashboard](https://commonslibrary.parliament.uk/constituency-statistics-population-by-age/). The dataset provides detailed age breakdowns for each UK constituency, containing population counts for every age from 0 to 90+ years old across all parliamentary constituencies in England, Wales, Northern Ireland, and Scotland. The data is stored as `age.csv`. The following table shows the dataset:" + "Population data by age groups for UK parliamentary constituencies can be downloaded from the [House of Commons Library data dashboard](https://commonslibrary.parliament.uk/constituency-statistics-population-by-age/). The dataset provides detailed age breakdowns for each UK constituency, containing population counts for every age from 0 to 90+ years old across all parliamentary constituencies in England, Wales, Northern Ireland, and Scotland. The data is stored as [`age.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv). The following table shows the dataset:" ] }, { @@ -830,7 +830,15 @@ "\n", "### Convert earning percentiles to brackets\n", "\n", - "The next step is to convert earning percentiles to earning brackets. To do this, the distribution of earnings needs to be estimated. Based on the ratio of different percentile incomes from [this government statistics report](https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax#:~:text=Details,in%20the%20Background%20Quality%20Report), earnings for percentiles from 90 to 99 for each constituency are estimated. The following code and image show the earnings distribution for an example constituency." + "To analyze earnings data effectively, we convert earning percentiles into earning brackets through the following process:\n", + "\n", + "1. First, we estimate the full distribution of earnings by:\n", + " - Using known percentile data (10th to 90th) from the ASHE dataset\n", + " - Extending this to estimate the 90th-99th percentiles using ratios derived from [this government statistics report](https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax#:~:text=Details,in%20the%20Background%20Quality%20Report)\n", + "\n", + "2. This estimation allows us to map earnings data into brackets that align with policy thresholds.\n", + "\n", + "The following code and visualization demonstrate this process using an example constituency:" ] }, { @@ -1050,7 +1058,9 @@ "source": [ "![](pictures/earning_dist.png)\n", "\n", - "After estimating the full earnings distribution, the data is converted into income brackets. For each constituency and income bracket, the number of jobs and total earnings are calculated based on the estimated earnings distribution. For constituencies with missing data, the earnings distribution pattern is estimated using data from constituencies with similar total number of taxpayers and total income levels. The Python script `create_employment_incomes.py` generates `employment_income.csv` containing number of jobs (`employment_income_count`) and total earnings (`employment_income_amount`) for each constituency and income bracket. The following table shows employment and income across different brackets for constituencies:" + "After estimating the full earnings distribution, we convert the data into income brackets. We calculate the number of jobs and total earnings for each constituency and income bracket based on the estimated earnings distribution. When we encounter constituencies with missing data, we estimate their earnings distribution pattern using data from constituencies with similar total number of taxpayers and total income levels. \n", + "\n", + "The Python script [`create_employment_incomes.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/create_employment_incomes.py) generates [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv) containing number of jobs ([`employment_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv#L1)) and total earnings ([`employment_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv#L1)) for each constituency and income bracket. The following table shows employment and income across different brackets for constituencies:" ] }, { @@ -1410,7 +1420,7 @@ "\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", + "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`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/mapping_2010_to_2024/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", @@ -1979,25 +1989,25 @@ "\n", "### Loss function\n", "\n", - "The file `loss.py` defines a function `create_constituency_target_matrix` that creates target matrices for comparing simulated data against actual constituency-level data. \n", + "The file [`loss.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py) defines a function [`create_constituency_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L18) that creates target matrices for comparing simulated data against actual constituency-level data. \n", "\n", - "1. The function 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", + "1. The function takes three main input parameters: dataset (defaults to [`enhanced_frs_2022_23`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L19)), time_period (defaults to 2025), and an optional reform parameter for policy changes.\n", "\n", - "2. It reads three files containing real data: `age.csv`, `total_income.csv`, and `employment_income.csv`.\n", + "2. It reads three files containing real data: [`age.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv), [`total_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv), and [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv).\n", "\n", "3. It creates a PolicyEngine Microsimulation object using the specified dataset and reform parameters.\n", "\n", - "4. The function creates two main matrices: `matrix` for simulated values from PolicyEngine, and `y` for actual target values from HMRC data.\n", + "4. The function creates two main matrices: [`matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L33) for simulated values from PolicyEngine, and [`y`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L34) for actual target values from both HMRC (income data) and ONS (age data).\n", "\n", - "5. It calculates total income metrics by computing both the total amounts and counts of people with income in each constituency.\n", + "5. It calculates total income metrics at the national level, computing both total amounts and counts of people with income.\n", "\n", "6. It processes age distributions by creating 10-year age bands from 0 to 80, calculating how many people fall into each band.\n", "\n", "7. 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", - "8. The `sim.map_result()` function is used throughout to map individual-level results to household level.\n", + "8. The [`sim.map_result()`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L88) function is used throughout to map individual-level results to household level.\n", "\n", - "9. 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." + "9. The function returns both the simulated matrix and the target matrix [`(matrix, y)`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L34) which can be used for comparing the simulation results against actual data." ] }, { @@ -2006,13 +2016,13 @@ "source": [ "### Calibration function\n", "\n", - "The file `calibrate.py` defines a main `calibrate()` function that performs weight calibration for constituency-level analysis.\n", + "The file [`calibrate.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py) defines a main [`calibrate()`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py#L25) function that performs weight calibration for constituency-level analysis.\n", "\n", - "1. 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", + "1. It imports necessary functions and matrices from other files including [`create_constituency_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py#L16), [`create_national_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py#L17) from [`loss.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py), and [`transform_2010_to_2024`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/transform_constituencies.py#L7) for constituency boundary transformations.\n", "\n", - "2. Sets up initial matrices using the `create_constituency_target_matrix` and `create_national_target_matrix` functions for both constituency and national level data.\n", + "2. Sets up initial matrices using the [`create_constituency_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py#L26) and [`create_national_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py#L28) functions for both constituency and national level data.\n", "\n", - "3. Creates a Microsimulation object using the `enhanced_frs_2022_23` dataset.\n", + "3. Creates a Microsimulation object using the [`enhanced_frs_2022_23`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py#L32) dataset.\n", "\n", "4. Initializes weights for 650 constituencies x 100180 households, starting with the log of household weights divided by constituency count.\n", "\n", @@ -2024,7 +2034,55 @@ "\n", "8. 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", - "9. Includes an `update_weights()` function that applies the constituency mapping matrix to transform the weights between different boundary definitions." + "9. Includes an [`update_weights()`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/calibrate.py#L95) function that applies the constituency mapping matrix to transform the weights between different boundary definitions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization mathematics\n", + "\n", + "In this part, we explain the mathematics behind the calibration process that we discussed above. The optimization uses a two-part loss function that balances constituency-level and national-level accuracy, combining both local and national targets into a single optimization problem. The mathematical formulation can be expressed as follows:\n", + "\n", + "For the constituency-level component, we have:\n", + "- A set of households ($j$) with known characteristics ($metrics_j$) like income, age, etc.\n", + "- A set of constituencies ($i$) with known target values ($y_c$) from official statistics\n", + "- Weights ($w_{ij}$) that we need to optimize for each household in each constituency \n", + "\n", + "Using these components, we calculate predicted constituency-level statistics. For each constituency metric (e.g. total income), the predicted value is:\n", + "\n", + "$$ \\text{pred}_c = \\sum_j (\\exp(w_{ij}) \\times \\text{metrics}_j) $$\n", + "\n", + "where $\\text{metrics}_j$ represents the household-level characteristics for that specific metric (e.g. household income). We use exponential of weights to ensure they stay positive.\n", + "\n", + "To measure how well our predictions match the real constituency data, we calculate the constituency mean squared error:\n", + "\n", + "$$ \\text{MSE}_c = \\text{mean}((\\text{pred}_c / (1 + y_c) - 1)^2) $$\n", + "\n", + "where $y_c$ are the actual target values from official statistics for each constituency. We use relative error (dividing by $1 + y_c$) to make errors comparable across different scales of metrics.\n", + "\n", + "For the national component, we need to ensure our constituency-level adjustments don't distort national-level statistics. We aggregate across all constituencies:\n", + "\n", + "$$ \\text{pred}_n = \\sum_i (\\sum_j \\exp(w_{ij})) \\times \\text{metrics}_\\text{national} $$\n", + "\n", + "with corresponding mean squared error to measure deviation from national targets:\n", + "\n", + "$$ \\text{MSE}_n = \\text{mean}((\\text{pred}_n / (1 + y_n) - 1)^2) $$\n", + "\n", + "The total loss combines both constituency and national errors:\n", + "\n", + "$$ L = \\text{MSE}_c + \\text{MSE}_n $$\n", + "\n", + "We initialize the weights using the original household weights from the survey data:\n", + "\n", + "$$ w_{\\text{initial}} = \\ln(\\text{household}_{weight}/650) $$\n", + "\n", + "where 650 is the number of constituencies. These weights are then iteratively optimized using the Adam (Adaptive Moment Estimation) optimizer with a learning rate of 0.1. The optimization process runs for 512 epochs, with the weights being updated in each iteration:\n", + "\n", + "$$ w_{t+1} = w_t - 0.1 \\times \\nabla L(w_t) $$\n", + "\n", + "This formulation ensures that the optimized weights maintain both local consistency at the constituency level and global accuracy for national-level statistics. The Adam optimizer adaptively adjusts the weights to minimize both constituency-level and national-level errors simultaneously, providing efficient convergence through adaptive learning rates and momentum. The resulting optimized weights allow us to accurately reweight household survey data to match both constituency-level and national statistics to obtain accurate estimates of income distributions, demographics, and policy impacts for each parliamentary constituency while maintaining consistency with national totals." ] }, { @@ -2101,7 +2159,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, "outputs": [ { From 62ad23135cee78aa83cdebaa54f0d449d80f7ac1 Mon Sep 17 00:00:00 2001 From: Vahid Ahmadi Date: Tue, 17 Dec 2024 14:17:30 +0100 Subject: [PATCH 5/8] edit the text of methodology --- docs/constituency_methodology.ipynb | 256 ++++++++++++++-------------- 1 file changed, 131 insertions(+), 125 deletions(-) diff --git a/docs/constituency_methodology.ipynb b/docs/constituency_methodology.ipynb index 86b7375..aba38e2 100644 --- a/docs/constituency_methodology.ipynb +++ b/docs/constituency_methodology.ipynb @@ -24,9 +24,11 @@ "source": [ "## Data\n", "\n", + "In this section, we describe three main data sources that form the foundation of our constituency-level analysis: earning and jobs data from NOMIS ASHE, income statistics from HMRC, and population age distributions from the House of Commons Library.\n", + "\n", "### Earning and jobs data\n", "\n", - "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK parliamentary constituencies from [this website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as [`nomis_earning_jobs_data.xlsx`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/nomis_earning_jobs_data.xlsx). To download the data, follow the variable selection process shown in the image below:\n", + "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK parliamentary constituencies from the [NOMIS website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as [`nomis_earning_jobs_data.xlsx`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/nomis_earning_jobs_data.xlsx). To download the data, follow the variable selection process shown in the image below:\n", "\n", "![](pictures/nomis_screenshot1.png)" ] @@ -37,7 +39,7 @@ "source": [ "### 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`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv), including two key variables:\n", + "Income data for UK parliamentary constituencies is obtained from [HMRC](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`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv), including two key variables:\n", "\n", "- [`total_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv#L1): the total number of taxpayers in each constituency\n", "\n", @@ -828,6 +830,8 @@ "source": [ "## Preprocessing\n", "\n", + "In this section, we detail two key preprocessing steps necessary for our constituency-level analysis: converting earnings percentiles into practical income brackets, and mapping between different constituency boundary definitions (2010 to 2024). \n", + "\n", "### Convert earning percentiles to brackets\n", "\n", "To analyze earnings data effectively, we convert earning percentiles into earning brackets through the following process:\n", @@ -952,104 +956,104 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Sample data for Darlington\n", - "income_data = {\n", - " 'parliamentary constituency 2010': ['Darlington'],\n", - " 'constituency_code': ['E14000658'],\n", - " 'Number of jobs': ['31000'],\n", - " '10 percentile': [13298.0],\n", - " '20 percentile': [16723.0],\n", - " '30 percentile': [20778.0],\n", - " '40 percentile': [23407.0],\n", - " '50 percentile': [27158.0],\n", - " '60 percentile': [30471.0],\n", - " '70 percentile': [33812.0],\n", - " '80 percentile': [40717.0],\n", - " '90 percentile': [55762.0],\n", - " '91 percentile': [58878.0],\n", - " '92 percentile': [62394.4],\n", - " '93 percentile': [66722.3],\n", - " '94 percentile': [71952.0],\n", - " '95 percentile': [78804.5],\n", - " '96 percentile': [87640.7],\n", - " '97 percentile': [100083.5],\n", - " '98 percentile': [123526.5],\n", - " '100 percentile': [179429.0]\n", - "}\n", - "\n", - "income_sample = pd.DataFrame(income_data)\n", - "\n", - "# Excel Data Method\n", - "def load_real_data():\n", - " # Read Excel data\n", - " income_real = pd.read_excel(\"nomis_earning_jobs_data.xlsx\", skiprows=7)\n", - " income_real.columns = income_real.iloc[0]\n", - " income_real = income_real.drop(index=0).reset_index(drop=True)\n", + "# import pandas as pd\n", + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", + "\n", + "# # Sample data for Darlington\n", + "# income_data = {\n", + "# 'parliamentary constituency 2010': ['Darlington'],\n", + "# 'constituency_code': ['E14000658'],\n", + "# 'Number of jobs': ['31000'],\n", + "# '10 percentile': [13298.0],\n", + "# '20 percentile': [16723.0],\n", + "# '30 percentile': [20778.0],\n", + "# '40 percentile': [23407.0],\n", + "# '50 percentile': [27158.0],\n", + "# '60 percentile': [30471.0],\n", + "# '70 percentile': [33812.0],\n", + "# '80 percentile': [40717.0],\n", + "# '90 percentile': [55762.0],\n", + "# '91 percentile': [58878.0],\n", + "# '92 percentile': [62394.4],\n", + "# '93 percentile': [66722.3],\n", + "# '94 percentile': [71952.0],\n", + "# '95 percentile': [78804.5],\n", + "# '96 percentile': [87640.7],\n", + "# '97 percentile': [100083.5],\n", + "# '98 percentile': [123526.5],\n", + "# '100 percentile': [179429.0]\n", + "# }\n", + "\n", + "# income_sample = pd.DataFrame(income_data)\n", + "\n", + "# # Excel Data Method\n", + "# def load_real_data():\n", + "# # Read Excel data\n", + "# income_real = pd.read_excel(\"nomis_earning_jobs_data.xlsx\", skiprows=7)\n", + "# income_real.columns = income_real.iloc[0]\n", + "# income_real = income_real.drop(index=0).reset_index(drop=True)\n", " \n", - " # Select and rename columns\n", - " columns_to_keep = [\n", - " 'parliamentary constituency 2010',\n", - " 'constituency_code',\n", - " 'Number of jobs',\n", - " 'Median',\n", - " '10 percentile',\n", - " '20 percentile',\n", - " '30 percentile',\n", - " '40 percentile',\n", - " '60 percentile',\n", - " '70 percentile',\n", - " '80 percentile',\n", - " '90 percentile'\n", - " ]\n", - " income_real = income_real[columns_to_keep]\n", - " income_real = income_real.rename(columns={'Median': '50 percentile'})\n", - " return income_real\n", - "\n", - "# Plotting function\n", - "def plot_constituency_distribution(income_df, constituency_name, detailed=True):\n", - " constituency_data = income_df[income_df['parliamentary constituency 2010'] == constituency_name].iloc[0]\n", + "# # Select and rename columns\n", + "# columns_to_keep = [\n", + "# 'parliamentary constituency 2010',\n", + "# 'constituency_code',\n", + "# 'Number of jobs',\n", + "# 'Median',\n", + "# '10 percentile',\n", + "# '20 percentile',\n", + "# '30 percentile',\n", + "# '40 percentile',\n", + "# '60 percentile',\n", + "# '70 percentile',\n", + "# '80 percentile',\n", + "# '90 percentile'\n", + "# ]\n", + "# income_real = income_real[columns_to_keep]\n", + "# income_real = income_real.rename(columns={'Median': '50 percentile'})\n", + "# return income_real\n", + "\n", + "# # Plotting function\n", + "# def plot_constituency_distribution(income_df, constituency_name, detailed=True):\n", + "# constituency_data = income_df[income_df['parliamentary constituency 2010'] == constituency_name].iloc[0]\n", " \n", - " percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100]\n", - " income_values = [\n", - " 0,\n", - " constituency_data['10 percentile'],\n", - " constituency_data['20 percentile'],\n", - " constituency_data['30 percentile'],\n", - " constituency_data['40 percentile'],\n", - " constituency_data['50 percentile'],\n", - " constituency_data['60 percentile'],\n", - " constituency_data['70 percentile'],\n", - " constituency_data['80 percentile'],\n", - " constituency_data['90 percentile'],\n", - " constituency_data['91 percentile'],\n", - " constituency_data['92 percentile'],\n", - " constituency_data['93 percentile'],\n", - " constituency_data['94 percentile'],\n", - " constituency_data['95 percentile'],\n", - " constituency_data['96 percentile'],\n", - " constituency_data['97 percentile'],\n", - " constituency_data['98 percentile'],\n", - " constituency_data['100 percentile']\n", - " ]\n", + "# percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100]\n", + "# income_values = [\n", + "# 0,\n", + "# constituency_data['10 percentile'],\n", + "# constituency_data['20 percentile'],\n", + "# constituency_data['30 percentile'],\n", + "# constituency_data['40 percentile'],\n", + "# constituency_data['50 percentile'],\n", + "# constituency_data['60 percentile'],\n", + "# constituency_data['70 percentile'],\n", + "# constituency_data['80 percentile'],\n", + "# constituency_data['90 percentile'],\n", + "# constituency_data['91 percentile'],\n", + "# constituency_data['92 percentile'],\n", + "# constituency_data['93 percentile'],\n", + "# constituency_data['94 percentile'],\n", + "# constituency_data['95 percentile'],\n", + "# constituency_data['96 percentile'],\n", + "# constituency_data['97 percentile'],\n", + "# constituency_data['98 percentile'],\n", + "# constituency_data['100 percentile']\n", + "# ]\n", " \n", - " valid_data = [(p, v) for p, v in zip(percentiles, income_values) if pd.notna(v)]\n", - " filtered_percentiles, filtered_income = zip(*valid_data)\n", + "# valid_data = [(p, v) for p, v in zip(percentiles, income_values) if pd.notna(v)]\n", + "# filtered_percentiles, filtered_income = zip(*valid_data)\n", " \n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(filtered_percentiles, filtered_income, marker='o')\n", - " plt.xlabel('Percentiles')\n", - " plt.ylabel('Income')\n", - " plt.title(f'Income distribution for {constituency_name}')\n", - " plt.grid(True)\n", - " plt.savefig(\"pictures/earning_dist.png\", dpi=300, bbox_inches='tight')\n", - " plt.close()\n", - "\n", - "# Plot sample data (Darlington with detailed percentiles)\n", - "plot_constituency_distribution(income_sample, 'Darlington', detailed=True)\n" + "# plt.figure(figsize=(8, 6))\n", + "# plt.plot(filtered_percentiles, filtered_income, marker='o')\n", + "# plt.xlabel('Percentiles')\n", + "# plt.ylabel('Income')\n", + "# plt.title(f'Income distribution for {constituency_name}')\n", + "# plt.grid(True)\n", + "# plt.savefig(\"pictures/earning_dist.png\", dpi=300, bbox_inches='tight')\n", + "# plt.close()\n", + "\n", + "# # Plot sample data (Darlington with detailed percentiles)\n", + "# plot_constituency_distribution(income_sample, 'Darlington', detailed=True)\n" ] }, { @@ -1418,7 +1422,7 @@ "\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", + "1. Download the mapping data from the [ONS 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`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/mapping_2010_to_2024/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", @@ -1987,6 +1991,8 @@ "source": [ "## Methodology\n", "\n", + "This section describes our approach to creating accurate constituency-level estimates through three key components: a loss function for evaluating accuracy, a calibration process for optimizing weights, and the mathematical framework behind the optimization. To see how well this methodology performs in practice, you can check our detailed [validation results page](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/docs/validation/constituencies.ipynb) comparing our estimates against actual data at both constituency and national levels.\n", + "\n", "### Loss function\n", "\n", "The file [`loss.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py) defines a function [`create_constituency_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L18) that creates target matrices for comparing simulated data against actual constituency-level data. \n", @@ -2048,7 +2054,7 @@ "For the constituency-level component, we have:\n", "- A set of households ($j$) with known characteristics ($metrics_j$) like income, age, etc.\n", "- A set of constituencies ($i$) with known target values ($y_c$) from official statistics\n", - "- Weights ($w_{ij}$) that we need to optimize for each household in each constituency \n", + "- Weights in log space ($w_{ij}$) that we need to optimize for each household in each constituency\n", "\n", "Using these components, we calculate predicted constituency-level statistics. For each constituency metric (e.g. total income), the predicted value is:\n", "\n", @@ -2177,33 +2183,33 @@ } ], "source": [ - "from policyengine.utils.charts import *\n", - "from policyengine import Simulation\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)" + "# from policyengine.utils.charts import *\n", + "# from policyengine import Simulation\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)" ] }, { From 12e18a7fb809dba08bf1558b0f6cbea26d00a7c6 Mon Sep 17 00:00:00 2001 From: Vahid Ahmadi Date: Wed, 18 Dec 2024 11:05:48 +0100 Subject: [PATCH 6/8] edit titles and example code --- Makefile | 2 +- docs/constituency_methodology.ipynb | 3750 +++++++++++++++++++++++++-- 2 files changed, 3599 insertions(+), 153 deletions(-) diff --git a/Makefile b/Makefile index f7b2c55..1fcf77b 100644 --- a/Makefile +++ b/Makefile @@ -22,7 +22,7 @@ docker: documentation: jb clean docs && jb build docs - python docs/add_plotly_to_book.py docs/book + python docs/add_plotly_to_book.py docs data: python policyengine_uk_data/datasets/frs/dwp_frs.py diff --git a/docs/constituency_methodology.ipynb b/docs/constituency_methodology.ipynb index aba38e2..6df86b7 100644 --- a/docs/constituency_methodology.ipynb +++ b/docs/constituency_methodology.ipynb @@ -950,112 +950,6 @@ "```" ] }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# import pandas as pd\n", - "# import numpy as np\n", - "# import matplotlib.pyplot as plt\n", - "\n", - "# # Sample data for Darlington\n", - "# income_data = {\n", - "# 'parliamentary constituency 2010': ['Darlington'],\n", - "# 'constituency_code': ['E14000658'],\n", - "# 'Number of jobs': ['31000'],\n", - "# '10 percentile': [13298.0],\n", - "# '20 percentile': [16723.0],\n", - "# '30 percentile': [20778.0],\n", - "# '40 percentile': [23407.0],\n", - "# '50 percentile': [27158.0],\n", - "# '60 percentile': [30471.0],\n", - "# '70 percentile': [33812.0],\n", - "# '80 percentile': [40717.0],\n", - "# '90 percentile': [55762.0],\n", - "# '91 percentile': [58878.0],\n", - "# '92 percentile': [62394.4],\n", - "# '93 percentile': [66722.3],\n", - "# '94 percentile': [71952.0],\n", - "# '95 percentile': [78804.5],\n", - "# '96 percentile': [87640.7],\n", - "# '97 percentile': [100083.5],\n", - "# '98 percentile': [123526.5],\n", - "# '100 percentile': [179429.0]\n", - "# }\n", - "\n", - "# income_sample = pd.DataFrame(income_data)\n", - "\n", - "# # Excel Data Method\n", - "# def load_real_data():\n", - "# # Read Excel data\n", - "# income_real = pd.read_excel(\"nomis_earning_jobs_data.xlsx\", skiprows=7)\n", - "# income_real.columns = income_real.iloc[0]\n", - "# income_real = income_real.drop(index=0).reset_index(drop=True)\n", - " \n", - "# # Select and rename columns\n", - "# columns_to_keep = [\n", - "# 'parliamentary constituency 2010',\n", - "# 'constituency_code',\n", - "# 'Number of jobs',\n", - "# 'Median',\n", - "# '10 percentile',\n", - "# '20 percentile',\n", - "# '30 percentile',\n", - "# '40 percentile',\n", - "# '60 percentile',\n", - "# '70 percentile',\n", - "# '80 percentile',\n", - "# '90 percentile'\n", - "# ]\n", - "# income_real = income_real[columns_to_keep]\n", - "# income_real = income_real.rename(columns={'Median': '50 percentile'})\n", - "# return income_real\n", - "\n", - "# # Plotting function\n", - "# def plot_constituency_distribution(income_df, constituency_name, detailed=True):\n", - "# constituency_data = income_df[income_df['parliamentary constituency 2010'] == constituency_name].iloc[0]\n", - " \n", - "# percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100]\n", - "# income_values = [\n", - "# 0,\n", - "# constituency_data['10 percentile'],\n", - "# constituency_data['20 percentile'],\n", - "# constituency_data['30 percentile'],\n", - "# constituency_data['40 percentile'],\n", - "# constituency_data['50 percentile'],\n", - "# constituency_data['60 percentile'],\n", - "# constituency_data['70 percentile'],\n", - "# constituency_data['80 percentile'],\n", - "# constituency_data['90 percentile'],\n", - "# constituency_data['91 percentile'],\n", - "# constituency_data['92 percentile'],\n", - "# constituency_data['93 percentile'],\n", - "# constituency_data['94 percentile'],\n", - "# constituency_data['95 percentile'],\n", - "# constituency_data['96 percentile'],\n", - "# constituency_data['97 percentile'],\n", - "# constituency_data['98 percentile'],\n", - "# constituency_data['100 percentile']\n", - "# ]\n", - " \n", - "# valid_data = [(p, v) for p, v in zip(percentiles, income_values) if pd.notna(v)]\n", - "# filtered_percentiles, filtered_income = zip(*valid_data)\n", - " \n", - "# plt.figure(figsize=(8, 6))\n", - "# plt.plot(filtered_percentiles, filtered_income, marker='o')\n", - "# plt.xlabel('Percentiles')\n", - "# plt.ylabel('Income')\n", - "# plt.title(f'Income distribution for {constituency_name}')\n", - "# plt.grid(True)\n", - "# plt.savefig(\"pictures/earning_dist.png\", dpi=300, bbox_inches='tight')\n", - "# plt.close()\n", - "\n", - "# # Plot sample data (Darlington with detailed percentiles)\n", - "# plot_constituency_distribution(income_sample, 'Darlington', detailed=True)\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1418,9 +1312,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Map constituency 2010 to 2024\n", + "### Mapping constituencies from 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", + "PolicyEngine uses HMRC income 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 the [ONS 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", @@ -1995,23 +1889,23 @@ "\n", "### Loss function\n", "\n", - "The file [`loss.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py) defines a function [`create_constituency_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L18) that creates target matrices for comparing simulated data against actual constituency-level data. \n", + "The file [`loss.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py) defines a function [`create_constituency_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L18) that creates target matrices for comparing simulated data against actual constituency-level data. The following process outlines how the function processes:\n", "\n", - "1. The function takes three main input parameters: dataset (defaults to [`enhanced_frs_2022_23`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L19)), time_period (defaults to 2025), and an optional reform parameter for policy changes.\n", + "1. Takes three main input parameters: dataset (defaults to [`enhanced_frs_2022_23`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L19)), time_period (defaults to 2025), and an optional reform parameter for policy changes.\n", "\n", - "2. It reads three files containing real data: [`age.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv), [`total_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv), and [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv).\n", + "2. Reads three files containing real data: [`age.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv), [`total_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv), and [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv).\n", "\n", - "3. It creates a PolicyEngine Microsimulation object using the specified dataset and reform parameters.\n", + "3. Creates a PolicyEngine Microsimulation object using the specified dataset and reform parameters.\n", "\n", - "4. The function creates two main matrices: [`matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L33) for simulated values from PolicyEngine, and [`y`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L34) for actual target values from both HMRC (income data) and ONS (age data).\n", + "4. Creates two main matrices: [`matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L33) for simulated values from PolicyEngine, and [`y`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L34) for actual target values from both HMRC (income data) and ONS (age data).\n", "\n", - "5. It calculates total income metrics at the national level, computing both total amounts and counts of people with income.\n", + "5. Calculates total income metrics at the national level, computing both total amounts and counts of people with income.\n", "\n", - "6. It processes age distributions by creating 10-year age bands from 0 to 80, calculating how many people fall into each band.\n", + "6. Processes age distributions by creating 10-year age bands from 0 to 80, calculating how many people fall into each band.\n", "\n", - "7. For employment income, it processes both counts and amounts for different income bands between £12,570 and £70,000, excluding people under 16.\n", + "7. Processes both counts and amounts for different income bands between £12,570 and £70,000, excluding people under 16 for employment income.\n", "\n", - "8. The [`sim.map_result()`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L88) function is used throughout to map individual-level results to household level.\n", + "8. Maps individual-level results to household level throughout the [`sim.map_result()`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L88) function.\n", "\n", "9. The function returns both the simulated matrix and the target matrix [`(matrix, y)`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/constituencies/loss.py#L34) which can be used for comparing the simulation results against actual data." ] @@ -2165,7 +2059,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -2180,43 +2074,3595 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "%{hovertext}

x=%{x}
y=%{y}
Value=%{marker.color}", + "hovertext": [ + "Aldershot", + "Aldridge-Brownhills", + "Altrincham and Sale West", + "Amber Valley", + "Arundel and South Downs", + "Ashfield", + "Ashford", + "Ashton-under-Lyne", + "Aylesbury", + "Banbury", + "Barking", + "Barnsley North", + "Barnsley South", + "Barrow and Furness", + "Basildon and Billericay", + "Basingstoke", + "Bassetlaw", + "Bath", + "Battersea", + "Beaconsfield", + "Beckenham and Penge", + "Bedford", + "Bermondsey and Old Southwark", + "Bethnal Green and Stepney", + "Beverley and Holderness", + "Bexhill and Battle", + "Bexleyheath and Crayford", + "Bicester and Woodstock", + "Birkenhead", + "Birmingham Edgbaston", + "Birmingham Erdington", + "Birmingham Hall Green and Moseley", + "Birmingham Hodge Hill and Solihull North", + "Birmingham Ladywood", + "Birmingham Northfield", + "Birmingham Perry Barr", + "Birmingham Selly Oak", + "Birmingham Yardley", + "Bishop Auckland", + "Blackburn", + "Blackley and Middleton South", + "Blackpool North and Fleetwood", + "Blackpool South", + "Blaydon and Consett", + "Blyth and Ashington", + "Bognor Regis and Littlehampton", + "Bolsover", + "Bolton North East", + "Bolton South and Walkden", + "Bolton West", + "Bootle", + "Boston and Skegness", + "Bournemouth East", + "Bournemouth West", + "Bracknell", + "Bradford East", + "Bradford South", + "Bradford West", + "Braintree", + "Brent East", + "Brent West", + "Brentford and Isleworth", + "Brentwood and Ongar", + "Bridgwater", + "Bridlington and The Wolds", + "Brigg and Immingham", + "Brighton Kemptown and Peacehaven", + "Brighton Pavilion", + "Bristol Central", + "Bristol East", + "Bristol North East", + "Bristol North West", + "Bristol South", + "Broadland and Fakenham", + "Bromley and Biggin Hill", + "Bromsgrove", + "Broxbourne", + "Broxtowe", + "Buckingham and Bletchley", + "Burnley", + "Burton and Uttoxeter", + "Bury North", + "Bury South", + "Bury St Edmunds and Stowmarket", + "Calder Valley", + "Camborne and Redruth", + "Cambridge", + "Cannock Chase", + "Canterbury", + "Carlisle", + "Carshalton and Wallington", + "Castle Point", + "Central Devon", + "Central Suffolk and North Ipswich", + "Chatham and Aylesford", + "Cheadle", + "Chelmsford", + "Chelsea and Fulham", + "Cheltenham", + "Chesham and Amersham", + "Chester North and Neston", + "Chester South and Eddisbury", + "Chesterfield", + "Chichester", + "Chingford and Woodford Green", + "Chippenham", + "Chipping Barnet", + "Chorley", + "Christchurch", + "Cities of London and Westminster", + "City of Durham", + "Clacton", + "Clapham and Brixton Hill", + "Colchester", + "Colne Valley", + "Congleton", + "Corby and East Northamptonshire", + "Coventry East", + "Coventry North West", + "Coventry South", + "Cramlington and Killingworth", + "Crawley", + "Crewe and Nantwich", + "Croydon East", + "Croydon South", + "Croydon West", + "Dagenham and Rainham", + "Darlington", + "Dartford", + "Daventry", + "Derby North", + "Derby South", + "Derbyshire Dales", + "Dewsbury and Batley", + "Didcot and Wantage", + "Doncaster Central", + "Doncaster East and the Isle of Axholme", + "Doncaster North", + "Dorking and Horley", + "Dover and Deal", + "Droitwich and Evesham", + "Dudley", + "Dulwich and West Norwood", + "Dunstable and Leighton Buzzard", + "Ealing Central and Acton", + "Ealing North", + "Ealing Southall", + "Earley and Woodley", + "Easington", + "East Grinstead and Uckfield", + "East Ham", + "East Hampshire", + "East Surrey", + "East Thanet", + "East Wiltshire", + "East Worthing and Shoreham", + "Eastbourne", + "Eastleigh", + "Edmonton and Winchmore Hill", + "Ellesmere Port and Bromborough", + "Eltham and Chislehurst", + "Ely and East Cambridgeshire", + "Enfield North", + "Epping Forest", + "Epsom and Ewell", + "Erewash", + "Erith and Thamesmead", + "Esher and Walton", + "Exeter", + "Exmouth and Exeter East", + "Fareham and Waterlooville", + "Farnham and Bordon", + "Faversham and Mid Kent", + "Feltham and Heston", + "Filton and Bradley Stoke", + "Finchley and Golders Green", + "Folkestone and Hythe", + "Forest of Dean", + "Frome and East Somerset", + "Fylde", + "Gainsborough", + "Gateshead Central and Whickham", + "Gedling", + "Gillingham and Rainham", + "Glastonbury and Somerton", + "Gloucester", + "Godalming and Ash", + "Goole and Pocklington", + "Gorton and Denton", + "Gosport", + "Grantham and Bourne", + "Gravesham", + "Great Grimsby and Cleethorpes", + "Great Yarmouth", + "Greenwich and Woolwich", + "Guildford", + "Hackney North and Stoke Newington", + "Hackney South and Shoreditch", + "Halesowen", + "Halifax", + "Hamble Valley", + "Hammersmith and Chiswick", + "Hampstead and Highgate", + "Harborough, Oadby and Wigston", + "Harlow", + "Harpenden and Berkhamsted", + "Harrogate and Knaresborough", + "Harrow East", + "Harrow West", + "Hartlepool", + "Harwich and North Essex", + "Hastings and Rye", + "Havant", + "Hayes and Harlington", + "Hazel Grove", + "Hemel Hempstead", + "Hendon", + "Henley and Thame", + "Hereford and South Herefordshire", + "Herne Bay and Sandwich", + "Hertford and Stortford", + "Hertsmere", + "Hexham", + "Heywood and Middleton North", + "High Peak", + "Hinckley and Bosworth", + "Hitchin", + "Holborn and St Pancras", + "Honiton and Sidmouth", + "Hornchurch and Upminster", + "Hornsey and Friern Barnet", + "Horsham", + "Houghton and Sunderland South", + "Hove and Portslade", + "Huddersfield", + "Huntingdon", + "Hyndburn", + "Ilford North", + "Ilford South", + "Ipswich", + "Isle of Wight East", + "Isle of Wight West", + "Islington North", + "Islington South and Finsbury", + "Jarrow and Gateshead East", + "Keighley and Ilkley", + "Kenilworth and Southam", + "Kensington and Bayswater", + "Kettering", + "Kingston and Surbiton", + "Kingston upon Hull East", + "Kingston upon Hull North and Cottingham", + "Kingston upon Hull West and Haltemprice", + "Kingswinford and South Staffordshire", + "Knowsley", + "Lancaster and Wyre", + "Leeds Central and Headingley", + "Leeds East", + "Leeds North East", + "Leeds North West", + "Leeds South", + "Leeds South West and Morley", + "Leeds West and Pudsey", + "Leicester East", + "Leicester South", + "Leicester West", + "Leigh and Atherton", + "Lewes", + "Lewisham East", + "Lewisham North", + "Lewisham West and East Dulwich", + "Leyton and Wanstead", + "Lichfield", + "Lincoln", + "Liverpool Garston", + "Liverpool Riverside", + "Liverpool Walton", + "Liverpool Wavertree", + "Liverpool West Derby", + "Loughborough", + "Louth and Horncastle", + "Lowestoft", + "Luton North", + "Luton South and South Bedfordshire", + "Macclesfield", + "Maidenhead", + "Maidstone and Malling", + "Makerfield", + "Maldon", + "Manchester Central", + "Manchester Rusholme", + "Manchester Withington", + "Mansfield", + "Melksham and Devizes", + "Melton and Syston", + "Meriden and Solihull East", + "Mid Bedfordshire", + "Mid Buckinghamshire", + "Mid Cheshire", + "Mid Derbyshire", + "Mid Dorset and North Poole", + "Mid Leicestershire", + "Mid Norfolk", + "Mid Sussex", + "Middlesbrough and Thornaby East", + "Middlesbrough South and East Cleveland", + "Milton Keynes Central", + "Milton Keynes North", + "Mitcham and Morden", + "Morecambe and Lunesdale", + "New Forest East", + "New Forest West", + "Newark", + "Newbury", + "Newcastle upon Tyne Central and West", + "Newcastle upon Tyne East and Wallsend", + "Newcastle upon Tyne North", + "Newcastle-under-Lyme", + "Newton Abbot", + "Newton Aycliffe and Spennymoor", + "Normanton and Hemsworth", + "North Bedfordshire", + "North Cornwall", + "North Cotswolds", + "North Devon", + "North Dorset", + "North Durham", + "North East Cambridgeshire", + "North East Derbyshire", + "North East Hampshire", + "North East Hertfordshire", + "North East Somerset and Hanham", + "North Herefordshire", + "North Norfolk", + "North Northumberland", + "North Shropshire", + "North Somerset", + "North Warwickshire and Bedworth", + "North West Cambridgeshire", + "North West Essex", + "North West Hampshire", + "North West Leicestershire", + "North West Norfolk", + "Northampton North", + "Northampton South", + "Norwich North", + "Norwich South", + "Nottingham East", + "Nottingham North and Kimberley", + "Nottingham South", + "Nuneaton", + "Old Bexley and Sidcup", + "Oldham East and Saddleworth", + "Oldham West, Chadderton and Royton", + "Orpington", + "Ossett and Denby Dale", + "Oxford East", + "Oxford West and Abingdon", + "Peckham", + "Pendle and Clitheroe", + "Penistone and Stocksbridge", + "Penrith and Solway", + "Peterborough", + "Plymouth Moor View", + "Plymouth Sutton and Devonport", + "Pontefract, Castleford and Knottingley", + "Poole", + "Poplar and Limehouse", + "Portsmouth North", + "Portsmouth South", + "Preston", + "Putney", + "Queen's Park and Maida Vale", + "Rawmarsh and Conisbrough", + "Rayleigh and Wickford", + "Reading Central", + "Reading West and Mid Berkshire", + "Redcar", + "Redditch", + "Reigate", + "Ribble Valley", + "Richmond and Northallerton", + "Richmond Park", + "Rochdale", + "Rochester and Strood", + "Romford", + "Romsey and Southampton North", + "Rossendale and Darwen", + "Rother Valley", + "Rotherham", + "Rugby", + "Ruislip, Northwood and Pinner", + "Runcorn and Helsby", + "Runnymede and Weybridge", + "Rushcliffe", + "Rutland and Stamford", + "Salford", + "Salisbury", + "Scarborough and Whitby", + "Scunthorpe", + "Sefton Central", + "Selby", + "Sevenoaks", + "Sheffield Brightside and Hillsborough", + "Sheffield Central", + "Sheffield Hallam", + "Sheffield Heeley", + "Sheffield South East", + "Sherwood Forest", + "Shipley", + "Shrewsbury", + "Sittingbourne and Sheppey", + "Skipton and Ripon", + "Sleaford and North Hykeham", + "Slough", + "Smethwick", + "Solihull West and Shirley", + "South Basildon and East Thurrock", + "South Cambridgeshire", + "South Cotswolds", + "South Derbyshire", + "South Devon", + "South Dorset", + "South East Cornwall", + "South Holland and The Deepings", + "South Leicestershire", + "South Norfolk", + "South Northamptonshire", + "South Ribble", + "South Shields", + "South Shropshire", + "South Suffolk", + "South West Devon", + "South West Hertfordshire", + "South West Norfolk", + "South West Wiltshire", + "Southampton Itchen", + "Southampton Test", + "Southend East and Rochford", + "Southend West and Leigh", + "Southgate and Wood Green", + "Southport", + "Spelthorne", + "Spen Valley", + "St Albans", + "St Austell and Newquay", + "St Helens North", + "St Helens South and Whiston", + "St Ives", + "St Neots and Mid Cambridgeshire", + "Stafford", + "Staffordshire Moorlands", + "Stalybridge and Hyde", + "Stevenage", + "Stockport", + "Stockton North", + "Stockton West", + "Stoke-on-Trent Central", + "Stoke-on-Trent North", + "Stoke-on-Trent South", + "Stone, Great Wyrley and Penkridge", + "Stourbridge", + "Stratford and Bow", + "Stratford-on-Avon", + "Streatham and Croydon North", + "Stretford and Urmston", + "Stroud", + "Suffolk Coastal", + "Sunderland Central", + "Surrey Heath", + "Sussex Weald", + "Sutton and Cheam", + "Sutton Coldfield", + "Swindon North", + "Swindon South", + "Tamworth", + "Tatton", + "Taunton and Wellington", + "Telford", + "Tewkesbury", + "The Wrekin", + "Thirsk and Malton", + "Thornbury and Yate", + "Thurrock", + "Tipton and Wednesbury", + "Tiverton and Minehead", + "Tonbridge", + "Tooting", + "Torbay", + "Torridge and Tavistock", + "Tottenham", + "Truro and Falmouth", + "Tunbridge Wells", + "Twickenham", + "Tynemouth", + "Uxbridge and South Ruislip", + "Vauxhall and Camberwell Green", + "Wakefield and Rothwell", + "Wallasey", + "Walsall and Bloxwich", + "Walthamstow", + "Warrington North", + "Warrington South", + "Warwick and Leamington", + "Washington and Gateshead South", + "Watford", + "Waveney Valley", + "Weald of Kent", + "Wellingborough and Rushden", + "Wells and Mendip Hills", + "Welwyn Hatfield", + "West Bromwich", + "West Dorset", + "West Ham and Beckton", + "West Lancashire", + "West Suffolk", + "West Worcestershire", + "Westmorland and Lonsdale", + "Weston-super-Mare", + "Wetherby and Easingwold", + "Whitehaven and Workington", + "Widnes and Halewood", + "Wigan", + "Wimbledon", + "Winchester", + "Windsor", + "Wirral West", + "Witham", + "Witney", + "Woking", + "Wokingham", + "Wolverhampton North East", + "Wolverhampton South East", + "Wolverhampton West", + "Worcester", + "Worsley and Eccles", + "Worthing West", + "Wycombe", + "Wyre Forest", + "Wythenshawe and Sale East", + "Yeovil", + "York Central", + "York Outer", + "Belfast East", + "Belfast North", + "Belfast South and Mid Down", + "Belfast West", + "East Antrim", + "East Londonderry", + "Fermanagh and South Tyrone", + "Foyle", + "Lagan Valley", + "Mid Ulster", + "Newry and Armagh", + "North Antrim", + "North Down", + "South Antrim", + "South Down", + "Strangford", + "Upper Bann", + "West Tyrone", + "East Renfrewshire", + "Na h-Eileanan an Iar", + "Midlothian", + "North Ayrshire and Arran", + "Orkney and Shetland", + "Aberdeen North", + "Aberdeen South", + "Aberdeenshire North and Moray East", + "Airdrie and Shotts", + "Alloa and Grangemouth", + "Angus and Perthshire Glens", + "Arbroath and Broughty Ferry", + "Argyll, Bute and South Lochaber", + "Bathgate and Linlithgow", + "Caithness, Sutherland and Easter Ross", + "Coatbridge and Bellshill", + "Cowdenbeath and Kirkcaldy", + "Cumbernauld and Kirkintilloch", + "Dumfries and Galloway", + "Dumfriesshire, Clydesdale and Tweeddale", + "Dundee Central", + "Dunfermline and Dollar", + "East Kilbride and Strathaven", + "Edinburgh East and Musselburgh", + "Edinburgh North and Leith", + "Edinburgh South", + "Edinburgh South West", + "Edinburgh West", + "Falkirk", + "Glasgow East", + "Glasgow North", + "Glasgow North East", + "Glasgow South", + "Glasgow South West", + "Glasgow West", + "Glenrothes and Mid Fife", + "Gordon and Buchan", + "Hamilton and Clyde Valley", + "Inverclyde and Renfrewshire West", + "Inverness, Skye and West Ross-shire", + "Livingston", + "Lothian East", + "Mid Dunbartonshire", + "Moray West, Nairn and Strathspey", + "Motherwell, Wishaw and Carluke", + "North East Fife", + "Paisley and Renfrewshire North", + "Paisley and Renfrewshire South", + "Perth and Kinross-shire", + "Rutherglen", + "Stirling and Strathallan", + "West Dunbartonshire", + "Ayr, Carrick and Cumnock", + "Berwickshire, Roxburgh and Selkirk", + "Central Ayrshire", + "Kilmarnock and Loudoun", + "West Aberdeenshire and Kincardine", + "Aberafan Maesteg", + "Alyn and Deeside", + "Bangor Aberconwy", + "Blaenau Gwent and Rhymney", + "Brecon, Radnor and Cwm Tawe", + "Bridgend", + "Caerfyrddin", + "Caerphilly", + "Cardiff East", + "Cardiff North", + "Cardiff South and Penarth", + "Cardiff West", + "Ceredigion Preseli", + "Clwyd East", + "Clwyd North", + "Dwyfor Meirionnydd", + "Gower", + "Llanelli", + "Merthyr Tydfil and Aberdare", + "Mid and South Pembrokeshire", + "Monmouthshire", + "Montgomeryshire and Glyndwr", + "Neath and Swansea East", + "Newport East", + "Newport West and Islwyn", + "Pontypridd", + "Rhondda and Ogmore", + "Swansea West", + "Torfaen", + "Vale of Glamorgan", + "Wrexham", + "Ynys Môn" + ], + "legendgroup": "", + "marker": { + "color": [ + 41423.47265625, + 34819.73046875, + 50368.54296875, + 35540.13653859061, + 38036.82334936957, + 33378.91015625, + 38001.53515625, + 34819.73046875, + 40823.1328125, + 43764.80078125, + 34519.5625, + 34519.5625, + 34519.5625, + 35420.0703125, + 40703.06640625, + 40823.1328125, + 36980.95703125, + 40823.1328125, + 60454.25390625, + 49948.3046875, + 47546.93792127848, + 44185.0390625, + 48807.65625, + 52109.52734375, + 36860.88671875, + 35420.0703125, + 41843.7109375, + 44004.938457836084, + 38001.53515625, + 38341.571060303155, + 33378.9140625, + 34519.5625, + 36860.88671875, + 34819.73046875, + 35239.96875, + 35720.2421875, + 34519.5625, + 34519.5625, + 34819.73046875, + 34579.59375, + 34519.5625, + 38421.7734375, + 33979.25390625, + 35420.0703125, + 35420.0703125, + 37060.44420507146, + 34519.55859375, + 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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)" + "from policyengine.utils.charts import *\n", + "from policyengine import Simulation\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", + ")" ] }, { From fbd104286d9e3027435bf9f61b550213e1b171b2 Mon Sep 17 00:00:00 2001 From: Vahid Ahmadi Date: Wed, 18 Dec 2024 15:39:02 +0100 Subject: [PATCH 7/8] write data and preprocessing --- docs/LA_methodology.ipynb | 872 ++++++++++++++++++ docs/pictures/nomis_screenshot2.png | Bin 0 -> 303191 bytes .../targets/nomis_earning_jobs_data.xlsx | Bin 0 -> 47741 bytes 3 files changed, 872 insertions(+) create mode 100644 docs/LA_methodology.ipynb create mode 100644 docs/pictures/nomis_screenshot2.png create mode 100644 policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/nomis_earning_jobs_data.xlsx diff --git a/docs/LA_methodology.ipynb b/docs/LA_methodology.ipynb new file mode 100644 index 0000000..6c81703 --- /dev/null +++ b/docs/LA_methodology.ipynb @@ -0,0 +1,872 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Local authority methodology" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "When policy changes in the UK - taxes, benefits, or public spending - it affects places and people differently. PolicyEngine UK builds tools to analyze incomes, jobs, and population patterns in each local authoirity district. This documentation explains how we create a microsimulation model that works at the local authoirity level. The system combines workplace surveys of jobs and earnings, HMRC tax records, and population statistics. We estimate income distributions, and optimize geographic weights.\n", + "\n", + "This guide shows how to use PolicyEngine UK for local authoirity analysis. We start with data collection, transform it for modeling, and build tools to examine policies. The guide provides examples and code to implement these methods. Users can measure changes in household budgets, track employment, and understand economic patterns on different local authoirity districts. This document starts with data collection from workplace surveys, tax records, and population counts, then explains how we convert this data into usable forms through income brackets. It concludes with technical details about accuracy measurement and calibration, plus example code for analysis and visualization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "In this section, we describe three main data sources that form the foundation of our local authority level analysis: earning and jobs data from NOMIS ASHE, income statistics from HMRC, and population age distributions from the House of Commons Library.\n", + "\n", + "### Earning and jobs data\n", + "\n", + "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK local authority districts from the [NOMIS website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as [`nomis_earning_jobs_data.xlsx`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/nomis_earning_jobs_data.xlsx). To download the data, follow the variable selection process shown in the image below:\n", + "\n", + "![](pictures/nomis_screenshot2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Income data\n", + "\n", + "Income data for UK local authorities is obtained from [HMRC](https://www.gov.uk/government/statistics/income-and-tax-by-borough-and-district-or-unitary-authority-confidence-intervals). This dataset provides detailed information about income and tax by local authorities with confidence intervals, and is stored as [`total_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv), including two key variables:\n", + "\n", + "- [`total_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv#L1): the total number of taxpayers in each local authority\n", + "\n", + "- [`total_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv#L1): the total amount of income for all taxpayers in each local authority\n", + "\n", + "We use these measures to identify similar local authorities when employment distribution data is missing. Our approach assumes that local authorities with similar income patterns (measured by both taxpayer counts and total income) will have similar earnings distributions. The following table shows the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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First, we estimate the full distribution of earnings by:\n", + " - Using known percentile data (10th to 90th) from the ASHE dataset\n", + " - Extending this to estimate the 90th-99th percentiles using ratios derived from [this government statistics report](https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax#:~:text=Details,in%20the%20Background%20Quality%20Report)\n", + "\n", + "2. This estimation allows us to map earnings data into brackets that align with policy thresholds.\n", + "\n", + "After estimating the full earnings distribution, we convert the data into income brackets. We calculate the number of jobs and total earnings for each local authority and income bracket based on the estimated earnings distribution. When we encounter local authorities with missing data, we estimate their earnings distribution pattern using data from local authorities with similar total number of taxpayers and total income levels. \n", + "\n", + "The Python script [`create_employment_incomes.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/create_employment_incomes.py) generates [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv) containing number of jobs ([`employment_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) and total earnings ([`employment_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) for each constituency and income bracket. 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Date: Fri, 20 Dec 2024 12:46:48 +0100 Subject: [PATCH 8/8] complete methodology and example parts --- docs/LA_methodology.ipynb | 2955 ++++++++++++++++++++++++++++++++++++- docs/_toc.yml | 3 +- 2 files changed, 2956 insertions(+), 2 deletions(-) diff --git a/docs/LA_methodology.ipynb b/docs/LA_methodology.ipynb index 6c81703..369cf06 100644 --- a/docs/LA_methodology.ipynb +++ b/docs/LA_methodology.ipynb @@ -844,7 +844,2960 @@ "\n", "After estimating the full earnings distribution, we convert the data into income brackets. We calculate the number of jobs and total earnings for each local authority and income bracket based on the estimated earnings distribution. When we encounter local authorities with missing data, we estimate their earnings distribution pattern using data from local authorities with similar total number of taxpayers and total income levels. \n", "\n", - "The Python script [`create_employment_incomes.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/create_employment_incomes.py) generates [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv) containing number of jobs ([`employment_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) and total earnings ([`employment_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) for each constituency and income bracket. The following table shows employment and income across different brackets for constituencies:" + "The Python script [`create_employment_incomes.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/create_employment_incomes.py) generates [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv) containing number of jobs ([`employment_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) and total earnings ([`employment_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) for each local authority and income bracket. The following table shows employment and income across different brackets for local authorities:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "

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codenameemployment_income_lower_boundemployment_income_upper_boundemployment_income_countemployment_income_amount
\n", + "\n", + "
\n", + "Loading ITables v2.2.3 from the init_notebook_mode cell...\n", + "(need help?)
\n", + "\n" + ], + "text/plain": [ + " code name employment_income_lower_bound \\\n", + "0 E06000001 Hartlepool 0 \n", + "1 E06000001 Hartlepool 50000 \n", + "2 E06000001 Hartlepool 12570 \n", + "3 E06000001 Hartlepool 15000 \n", + "4 E06000001 Hartlepool 30000 \n", + "... ... ... ... \n", + "4675 W06000024 Merthyr Tydfil 200000 \n", + "4676 W06000024 Merthyr Tydfil 300000 \n", + "4677 W06000024 Merthyr Tydfil 50000 \n", + "4678 W06000024 Merthyr Tydfil 20000 \n", + "4679 W06000024 Merthyr Tydfil 500000 \n", + "\n", + " employment_income_upper_bound employment_income_count \\\n", + "0 12570.0 758.154411 \n", + "1 70000.0 4042.906639 \n", + "2 15000.0 405.049313 \n", + "3 20000.0 1609.951463 \n", + "4 40000.0 5209.259896 \n", + "... ... ... \n", + "4675 300000.0 74.985344 \n", + "4676 500000.0 0.000000 \n", + "4677 70000.0 4111.661887 \n", + "4678 30000.0 5329.828771 \n", + "4679 inf 0.000000 \n", + "\n", + " employment_income_amount \n", + "0 4.765000e+06 \n", + "1 2.425744e+08 \n", + "2 5.583605e+06 \n", + "3 2.817415e+07 \n", + "4 1.823241e+08 \n", + "... ... \n", + "4675 1.874634e+07 \n", + "4676 0.000000e+00 \n", + "4677 2.466997e+08 \n", + "4678 1.332457e+08 \n", + "4679 0.000000e+00 \n", + "\n", + "[4680 rows x 6 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from itables import init_notebook_mode, show\n", + "import itables.options as opt\n", + "opt.maxBytes = \"1MB\"\n", + "init_notebook_mode(all_interactive=True)\n", + "\n", + "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Methodology\n", + "\n", + "This section describes our approach to creating accurate local authority level estimates through three key components: a loss function for evaluating accuracy, a calibration process for optimizing weights, and the mathematical framework behind the optimization. To see how well this methodology performs in practice, you can check our detailed [validation results page](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/docs/validation/local_authorities.ipynb) comparing our estimates against actual data at both local authority and national levels.\n", + "\n", + "### Loss function\n", + "\n", + "The file [`loss.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py) defines a function [`create_local_authority_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py#L8) that creates target matrices for comparing simulated data against actual local authority level data. The following process outlines how the function processes:\n", + "\n", + "1. Takes three main input parameters: dataset (defaults to [`enhanced_frs_2022_23`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py#L15)), time_period (defaults to 2025), and an optional reform parameter for policy changes.\n", + "\n", + "2. Reads three files containing real data: [`age.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/age.csv), [`total_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv), and [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv).\n", + "\n", + "3. Creates a PolicyEngine Microsimulation object using the specified dataset and reform parameters.\n", + "\n", + "4. Creates two main matrices: [`matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py#L29) for simulated values from PolicyEngine, and [`y`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py#L30) for actual target values from both HMRC (income data) and ONS (age data).\n", + "\n", + "5. Calculates total income metrics at the national level, computing both total amounts and counts of people with income.\n", + "\n", + "6. Processes age distributions by creating 10-year age bands from 0 to 80, calculating how many people fall into each band.\n", + "\n", + "7. Processes both counts and amounts for different income bands between £12,570 and £70,000, excluding people under 16 for employment income.\n", + "\n", + "8. Maps individual-level results to household level throughout the [`sim.map_result()`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py#L33) function.\n", + "\n", + "9. The function returns both the simulated matrix and the target matrix [`(matrix, y)`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py#L33) 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`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/calibrate.py) defines a main [`calibrate()`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/calibrate.py#L16) function that performs weight calibration for local authority level analysis.\n", + "\n", + "1. It imports necessary functions and matrices from other files including [`create_local_authority_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/calibrate.py#L17), [`create_national_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/calibrate.py#L21) from [`loss.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/loss.py).\n", + "\n", + "2. Sets up initial matrices using the [`create_local_authority_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/calibrate.py#L17) and [`create_national_target_matrix`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/calibrate.py#L21) functions for both local authority and national level data.\n", + "\n", + "3. Creates a Microsimulation object using the [`enhanced_frs_2022_23`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/calibrate.py#L25) dataset.\n", + "\n", + "4. Initializes weights for 360 local authority districts x 100180 households, starting with the log of household weights divided by local authority district count.\n", + "\n", + "5. Converts all the matrices and weights into PyTorch tensors to enable optimization.\n", + "\n", + "6. Defines a loss function that calculates and combines both local authority level and national-level mean squared errors into a single loss value.\n", + "\n", + "7. Uses Adam optimizer with a learning rate of 0.1 to minimize the loss over 512 epochs.\n", + "\n", + "8. Every 100 epochs during optimization, it updates the weights and saves the current weights to a `weights.h5` file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization mathematics\n", + "\n", + "In this part, we explain the mathematics behind the calibration process that we discussed above. The optimization uses a two-part loss function that balances local authority level and national-level accuracy, combining both local and national targets into a single optimization problem. The mathematical formulation can be expressed as follows:\n", + "\n", + "For the local authority level component, we have:\n", + "- A set of households ($j$) with known characteristics ($metrics_j$) like income, age, etc.\n", + "- A set of local authorities ($i$) with known target values ($y_c$) from official statistics\n", + "- Weights in log space ($w_{ij}$) that we need to optimize for each household in each local authority\n", + "\n", + "Using these components, we calculate predicted local authority level statistics. For each local authority metric (e.g. total income), the predicted value is:\n", + "\n", + "$$ \\text{pred}_c = \\sum_j (\\exp(w_{ij}) \\times \\text{metrics}_j) $$\n", + "\n", + "where $\\text{metrics}_j$ represents the household-level characteristics for that specific metric (e.g. household income). We use exponential of weights to ensure they stay positive.\n", + "\n", + "To measure how well our predictions match the real local authority data, we calculate the local authority mean squared error:\n", + "\n", + "$$ \\text{MSE}_c = \\text{mean}((\\text{pred}_c / (1 + y_c) - 1)^2) $$\n", + "\n", + "where $y_c$ are the actual target values from official statistics for each local authority. We use relative error (dividing by $1 + y_c$) to make errors comparable across different scales of metrics.\n", + "\n", + "For the national component, we need to ensure our local authority level adjustments don't distort national-level statistics. We aggregate across all local authorities:\n", + "\n", + "$$ \\text{pred}_n = \\sum_i (\\sum_j \\exp(w_{ij})) \\times \\text{metrics}_\\text{national} $$\n", + "\n", + "with corresponding mean squared error to measure deviation from national targets:\n", + "\n", + "$$ \\text{MSE}_n = \\text{mean}((\\text{pred}_n / (1 + y_n) - 1)^2) $$\n", + "\n", + "The total loss combines both local authority and national errors:\n", + "\n", + "$$ L = \\text{MSE}_c + \\text{MSE}_n $$\n", + "\n", + "We initialize the weights using the original household weights from the survey data:\n", + "\n", + "$$ w_{\\text{initial}} = \\ln(\\text{household}_{weight}/650) $$\n", + "\n", + "where 650 is the number of local authorities. These weights are then iteratively optimized using the Adam (Adaptive Moment Estimation) optimizer with a learning rate of 0.1. The optimization process runs for 512 epochs, with the weights being updated in each iteration:\n", + "\n", + "$$ w_{t+1} = w_t - 0.1 \\times \\nabla L(w_t) $$\n", + "\n", + "This formulation ensures that the optimized weights maintain both local local authority at the local authority level and global accuracy for national-level statistics. The Adam optimizer adaptively adjusts the weights to minimize both local authority level and national-level errors simultaneously, providing efficient convergence through adaptive learning rates and momentum. The resulting optimized weights allow us to accurately reweight household survey data to match both local authority level and national statistics to obtain accurate estimates of income distributions, demographics, and policy impacts for each parliamentary local authority while maintaining local authority with national totals." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example\n", + "\n", + "The following code demonstrates how to analyze and visualize median earnings across UK local authority districts using PolicyEngine:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{code-block} python\n", + "# Import required libraries\n", + "from policyengine.utils.charts import *\n", + "from policyengine import Simulation\n", + "\n", + "# Initialize simulation for visualization\n", + "sim = Simulation(\n", + " country=\"uk\",\n", + " scope=\"macro\",\n", + " time_period=\"2025\",\n", + " options={\n", + " \"include_local_authorities\": True, # Enable local authority 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 local authority\n", + "sim.calculate(\n", + " \"macro/gov/local_areas/local_authorities\",\n", + " metric=adult_earnings_median,\n", + " chart=True\n", + ").update_layout(\n", + " title=\"Median earnings of adults in parliamentary local authorities\",\n", + ")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "%{hovertext}

x=%{x}
y=%{y}
Value=%{marker.color}", + "hovertext": [ + "Hartlepool", + "Middlesbrough", + "Redcar and Cleveland", + "Stockton-on-Tees", + "Darlington", + "Halton", + "Warrington", + "Blackburn with Darwen", + "Blackpool", + "Kingston upon Hull, City of", + "East Riding of Yorkshire", + "North East Lincolnshire", + "North Lincolnshire", + "York", + "Derby", + "Leicester", + "Rutland", + "Nottingham", + "Herefordshire, County of", + "Telford and Wrekin", + "Stoke-on-Trent", + "Bath and North East Somerset", + "Bristol, City of", + "North Somerset", + "South Gloucestershire", + "Plymouth", + "Torbay", + "Swindon", + "Peterborough", + "Luton", + "Southend-on-Sea", + "Thurrock", + "Medway", + "Bracknell Forest", + "West Berkshire", + "Reading", + "Slough", + "Windsor and Maidenhead", + "Wokingham", + "Milton Keynes", + "Brighton and Hove", + "Portsmouth", + "Southampton", + "Isle of Wight", + "County Durham", + "Cheshire East", + "Cheshire West and Chester", + "Shropshire", + "Cornwall", + "Isles of Scilly", + "Wiltshire", + "Bedford", + "Central Bedfordshire", + "Northumberland", + "Bournemouth, Christchurch and Poole", + "Dorset", + "Buckinghamshire", + "North Northamptonshire", + "West Northamptonshire", + "Cumberland", + "Westmorland and Furness", + "North Yorkshire", + "Somerset", + "Cambridge", + "East Cambridgeshire", + "Fenland", + "Huntingdonshire", + "South Cambridgeshire", + "Amber Valley", + "Bolsover", + "Chesterfield", + "Derbyshire Dales", + "Erewash", + "High Peak", + "North East Derbyshire", + "South Derbyshire", + "East Devon", + "Exeter", + "Mid Devon", + "North Devon", + "South Hams", + "Teignbridge", + "Torridge", + "West Devon", + "Eastbourne", + "Hastings", + "Lewes", + "Rother", + "Wealden", + "Basildon", + "Braintree", + "Brentwood", + "Castle Point", + "Chelmsford", + "Colchester", + "Epping Forest", + "Harlow", + "Maldon", + "Rochford", + "Tendring", + "Uttlesford", + "Cheltenham", + "Cotswold", + "Forest of Dean", + "Gloucester", + "Stroud", + "Tewkesbury", + "Basingstoke and Deane", + "East Hampshire", + "Eastleigh", + "Fareham", + "Gosport", + "Hart", + "Havant", + "New Forest", + "Rushmoor", + "Test Valley", + "Winchester", + "Broxbourne", + "Dacorum", + "Hertsmere", + "North Hertfordshire", + "Three Rivers", + "Watford", + "Ashford", + "Canterbury", + "Dartford", + "Dover", + "Gravesham", + "Maidstone", + "Sevenoaks", + "Folkestone and Hythe", + "Swale", + "Thanet", + "Tonbridge and Malling", + "Tunbridge Wells", + "Burnley", + "Chorley", + "Fylde", + "Hyndburn", + "Lancaster", + "Pendle", + "Preston", + "Ribble Valley", + "Rossendale", + "South Ribble", + "West Lancashire", + "Wyre", + "Blaby", + "Charnwood", + "Harborough", + "Hinckley and Bosworth", + "Melton", + "North West Leicestershire", + "Oadby and Wigston", + "Boston", + "East Lindsey", + "Lincoln", + "North Kesteven", + "South Holland", + "South Kesteven", + "West Lindsey", + "Breckland", + "Broadland", + "Great Yarmouth", + "King's Lynn and West Norfolk", + "North Norfolk", + "Norwich", + "South Norfolk", + "Ashfield", + "Bassetlaw", + "Broxtowe", + "Gedling", + "Mansfield", + "Newark and Sherwood", + "Rushcliffe", + "Cherwell", + "Oxford", + "South Oxfordshire", + "Vale of White Horse", + "West Oxfordshire", + "Cannock Chase", + "East Staffordshire", + "Lichfield", + "Newcastle-under-Lyme", + "South Staffordshire", + "Stafford", + "Staffordshire Moorlands", + "Tamworth", + "Babergh", + "Ipswich", + "Mid Suffolk", + "Elmbridge", + "Epsom and Ewell", + "Guildford", + "Mole Valley", + "Reigate and Banstead", + "Runnymede", + "Spelthorne", + "Surrey Heath", + "Tandridge", + "Waverley", + "Woking", + "North Warwickshire", + "Nuneaton and Bedworth", + "Rugby", + "Stratford-on-Avon", + "Warwick", + "Adur", + "Arun", + "Chichester", + "Crawley", + "Horsham", + "Mid Sussex", + "Worthing", + "Bromsgrove", + "Malvern Hills", + "Redditch", + "Worcester", + "Wychavon", + "Wyre Forest", + "St Albans", + "Welwyn Hatfield", + "East Hertfordshire", + "Stevenage", + "East Suffolk", + "West Suffolk", + "Bolton", + "Bury", + "Manchester", + "Oldham", + "Rochdale", + "Salford", + "Stockport", + "Tameside", + "Trafford", + "Wigan", + "Knowsley", + "Liverpool", + "St. Helens", + "Sefton", + "Wirral", + "Barnsley", + "Doncaster", + "Rotherham", + "Sheffield", + "Newcastle upon Tyne", + "North Tyneside", + "South Tyneside", + "Sunderland", + "Birmingham", + "Coventry", + "Dudley", + "Sandwell", + "Solihull", + "Walsall", + "Wolverhampton", + "Bradford", + "Calderdale", + "Kirklees", + "Leeds", + "Wakefield", + "Gateshead", + "City of London", + "Barking and Dagenham", + "Barnet", + "Bexley", + "Brent", + "Bromley", + "Camden", + "Croydon", + "Ealing", + "Enfield", + "Greenwich", + "Hackney", + "Hammersmith and Fulham", + "Haringey", + "Harrow", + "Havering", + "Hillingdon", + "Hounslow", + "Islington", + "Kensington and Chelsea", + "Kingston upon Thames", + "Lambeth", + "Lewisham", + "Merton", + "Newham", + "Redbridge", + "Richmond upon Thames", + "Southwark", + "Sutton", + "Tower Hamlets", + "Waltham Forest", + "Wandsworth", + "Westminster", + "Antrim and Newtownabbey", + "Armagh City, Banbridge and Craigavon", + "Belfast", + "Causeway Coast and Glens", + "Derry City and Strabane", + "Fermanagh and Omagh", + "Lisburn and Castlereagh", + "Mid and East Antrim", + "Mid Ulster", + "Newry, Mourne and Down", + "Clackmannanshire", + "Dumfries and Galloway", + "East Ayrshire", + "East Lothian", + "East Renfrewshire", + "Na h-Eileanan Siar", + "Falkirk", + "Highland", + "Inverclyde", + "Midlothian", + "Moray", + "North Ayrshire", + "Orkney Islands", + "Scottish Borders", + "Shetland Islands", + "South Ayrshire", + "South Lanarkshire", + "Stirling", + "Aberdeen City", + "Aberdeenshire", + "Argyll and Bute", + "City of Edinburgh", + "Renfrewshire", + "West Dunbartonshire", + "West Lothian", + "Angus", + "Dundee City", + "East Dunbartonshire", + "Fife", + "Perth and Kinross", + "Glasgow City", + "North Lanarkshire", + "Isle of 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{ + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Median earnings of adults in local authorities" + }, + "width": 800, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "gridcolor": "#F4F4F4", + "range": [ + -20, + 25 + ], + "tickvals": [], + "title": { + "text": "" + }, + "zerolinecolor": "#F4F4F4" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "gridcolor": "#F4F4F4", + "range": [ + -10, + 35 + ], + "tickvals": [], + "title": { + "text": "" + }, + "zerolinecolor": "#F4F4F4" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from policyengine.utils.charts import *\n", + "from policyengine import Simulation\n", + "\n", + "\n", + "sim = Simulation(\n", + " country=\"uk\",\n", + " scope=\"macro\",\n", + " time_period=\"2025\",\n", + " options={\n", + " \"include_local_authorities\": 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/local_authorities\",\n", + " metric=adult_earnings_median,\n", + " chart=True\n", + ").update_layout(\n", + " title=\"Median earnings of adults in local authorities\",\n", + ")" ] } ], diff --git a/docs/_toc.yml b/docs/_toc.yml index 9f3e365..2e7dd86 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -8,4 +8,5 @@ chapters: - file: validation/constituencies.ipynb - file: validation/local_authorities.ipynb - file: pension_contributions.ipynb -- file: constituency_methodology.ipynb \ No newline at end of file +- file: constituency_methodology.ipynb +- file: LA_methodology.ipynb \ No newline at end of file
\n", + "\n", + "\n", + "

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