{ "cells": [ { "cell_type": "markdown", "id": "ec2bdd49-2e83-4dd9-96ce-1b9a73f1ad45", "metadata": {}, "source": [ "# Introduction to weighting scores\n", "\n", "A common task or goal in verification is to understand how the accuracy of a model varies, or account for it.\n", "\n", "One of the most common factors to take into account is that most geospatial data arrays don't represent equal amounts of physical area at each coordinate, particularly for one of the most common representations, the \"LLXY\" representation whereby each part of the array represents some even subdivision of latitude and longitude. Without going into the details on why this happens, what's important to know is that those apparently equal subdivisions of latitude do not represent an equal area. Lines of longitude are physically closer together nearer the poles, and are further apart where latitude equals zero. This is taken into account by reducing the values towards the poles by 'weighting' the results. In this case, all the weights are less than or equal to 1.\n", "\n", "Another common weighting is to account for the effect of accuracy on people, and so the results may be weighted by population density. In this case, the weightings could be greater than one, and increase the values in some places, depending on the expression of density. Any normalisation is the responsibility of the user when creating the weightings array. One approach could be to divide the weightings array by its maximum weight.\n", "\n", "Weighting in this context means multiplying. Internally to the scores package, the process is as follows:\n", "\n", "1. Calculate the underlying error metric for the score (e.g. absolute error)\n", "2. Multiply those errors by a factor supplied to the algorithm (for example, latitude weighting or population density)\n", "3. Perform dimensionality reduction (e.g. calculate the mean) of the weighted value\n", "\n", "It is important to realise that this factor can greatly distort the intuitive meaning of the scores. Latitude weighting apply a maximum weighting of 1 at the equator (so no change), and reduce the errors significantly towards the poles, as the area represented by each region also reduces significatly (going to zero in the extreme). Latitude weighting by cosine (the method implemented in this package) is inherently normalised between zero and one.\n", "\n", "Population density, by contrast, may not be normalised naturally. It could be expressed as a number of people per kilometer. In this case, perhaps it's appropriate to weight by both population density AND latitude/area. Perhaps it would also be useful to mask out oceans and lakes, since those areas don't impact the population in the same way.\n", "\n", "Sometimes, it's useful to calculate a few different perspectives at once. A more complex example might be to compare the latitude-weighted score to a population-weighted one, meaning both things need to be collected.\n", "\n", "This notebook will go through some examples from the simple to the complex, showing both the importance and significance of considering weighting when calculating verification scores.\n", "\n", "**Note:** In this tutorial we use the forecast and analysis grids that are downloaded or derived in `First_Data_Fetching.ipynb`. Please run through this tutorial first to fetch data." ] }, { "cell_type": "code", "execution_count": 1, "id": "bebeb555-b58a-40dc-87ab-d5572faf9fb5", "metadata": {}, "outputs": [], "source": [ "import io\n", "import pandas\n", "import scores\n", "import xarray\n", "import zipfile\n", "\n", "# Note - while not imported, xarray depends on rasterio and rioxarray being installed to load the geotiffs\n", "# for exploring population density in the latter part of the notebook" ] }, { "cell_type": "code", "execution_count": 2, "id": "4d4b2938-73a4-43e7-93ea-accb49fbf413", "metadata": {}, "outputs": [], "source": [ "# Here we consider the errors at 4 days lead time into the prediction, at a specific hour, compared to the analysis for that time step.\n", "forecast = xarray.open_dataset('forecast_grid.nc')\n", "analysis = xarray.open_dataset('analysis_grid.nc')\n", "time_step_of_interest = forecast.temp_scrn[24*4-1]" ] }, { "cell_type": "code", "execution_count": 3, "id": "60207ad5-1da3-42ea-b197-308e5999b281", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The maximum weighting in the array is 0.9999994770895914. This has an insignificant floating point rounding error.\n" ] } ], "source": [ "# The standard latitude weight array has a magnitude of around 1 at the equator, and reduce to zero approaching the poles\n", "weights = scores.functions.create_latitude_weights(analysis.lat)\n", "print(f\"The maximum weighting in the array is {weights.max().values}. This has an insignificant floating point rounding error.\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "0a664c02-163f-4dbe-bdeb-2069ccd9a4eb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'lat' (lat: 1536)>\n",
       "array([0.00102266, 0.00306796, 0.00511325, ..., 0.00511325, 0.00306796,\n",
       "       0.00102266])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 89.94 89.82 89.71 89.59 ... -89.71 -89.82 -89.94\n",
       "Attributes:\n",
       "    long_name:  latitudes\n",
       "    type:       uniform\n",
       "    units:      degrees_north\n",
       "    valid_min:  -90.0\n",
       "    valid_max:  90.0\n",
       "    axis:       Y
" ], "text/plain": [ "\n", "array([0.00102266, 0.00306796, 0.00511325, ..., 0.00511325, 0.00306796,\n", " 0.00102266])\n", "Coordinates:\n", " * lat (lat) float64 89.94 89.82 89.71 89.59 ... -89.71 -89.82 -89.94\n", "Attributes:\n", " long_name: latitudes\n", " type: uniform\n", " units: degrees_north\n", " valid_min: -90.0\n", " valid_max: 90.0\n", " axis: Y" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights" ] }, { "cell_type": "code", "execution_count": 5, "id": "b035c295-f6a8-4ceb-8834-4f7cf2ed7641", "metadata": {}, "outputs": [], "source": [ "# We will use a neutral weighting to confirm that nothing strange is happening. You could experiment with\n", "# creating a population density weights array to contrast different weighting strategies\n", "ones = weights.where(weights == 1, other=1) " ] }, { "cell_type": "code", "execution_count": 6, "id": "aeae28eb-d8af-4ffc-ac76-624c052eb1fb", "metadata": {}, "outputs": [], "source": [ "unweighted_mae = scores.continuous.mae(time_step_of_interest, analysis.temp_scrn, reduce_dims='lon')\n", "neutral_mae = scores.continuous.mae(time_step_of_interest, analysis.temp_scrn, weights=ones, reduce_dims='lon')\n", "weighted_mae = scores.continuous.mae(time_step_of_interest, analysis.temp_scrn, weights=weights, reduce_dims='lon')" ] }, { "cell_type": "code", "execution_count": 7, "id": "9b825ec0-b017-4902-ba25-0b804aca0c05", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Figure One - Side by Side Comparison of alternative weighting strategies\n", "names = pandas.Index(['Unweighted', 'Neutral', 'By Latitude'], name=\"Method\")\n", "xarray.concat([unweighted_mae, neutral_mae, weighted_mae], dim=names).plot(col='Method')" ] }, { "cell_type": "markdown", "id": "2cb0c5d3-733c-4b81-9b70-b14e66d7b452", "metadata": {}, "source": [ "The effect of weighting by area is quite dramatic. Let's download population density data and see the impact of that. Unfortunately, population density information is not quite so simple to work with as latitude weighting. There are a large number of different sources, in a variety of different structures, ranging from average density per country as a simple listing through to 1km global data. Many of these data sets have some level of restriction or signup required to access them. \n", "\n", "For the same of demonstration in a tutorial, we will be working Australian population density data, provided as a direct download by the Australian Bureau of Statistics (see https://www.abs.gov.au/statistics/people/population/regional-population/2021-22). We will then examine the impact of weightings considered over the Australian region." ] }, { "cell_type": "code", "execution_count": 8, "id": "2e678651-1447-4f51-a118-4cd7f2cae299", "metadata": {}, "outputs": [], "source": [ "# This is a basic file fetch only. Do not rely on it for untrusted data. A basic check has been included to make sure the\n", "# downloaded file matches what was expected.\n", "\n", "import hashlib\n", "import os\n", "import requests\n", "\n", "def basic_fetch(url, filename, expected_hash):\n", " if os.path.exists(filename):\n", " print(\"File already exists, skipping download\")\n", " else:\n", " response = requests.get(url, allow_redirects=True)\n", " if response.ok:\n", " outfile = open(filename, 'wb') # This will write the file out to the current directory, typically where this notebook is being run\n", " outfile.write(response.content) \n", " \n", " content = open(filename, 'rb').read()\n", " found_hash = hashlib.sha256(content).hexdigest()\n", " if found_hash != expected_hash:\n", " os.remove(filename)\n", " print(\"File has unexpected contents. The file has been removed - please download manually and check the data carefully\")\n", " " ] }, { "cell_type": "code", "execution_count": 9, "id": "905e0288-149c-418f-a580-4387c7ffc546", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File already exists, skipping download\n" ] } ], "source": [ "# Fetch the data for the tutorial as a zip file\n", "\n", "data_url = 'https://www.abs.gov.au/statistics/people/population/regional-population/2021-22/Australian_Population_Grid_2022_in_GEOTIFF_format.zip'\n", "filename = 'auspopn.zip'\n", "data_hash = 'a14172681cd343f69150de7f2939f1102aba1e7f6fe1aebde0e87c45069032a2' # Taken 23/8/23\n", "basic_fetch(data_url, filename, data_hash) " ] }, { "cell_type": "code", "execution_count": 10, "id": "ee763eb8-83d3-44a0-8acf-6ed34b049aa1", "metadata": {}, "outputs": [], "source": [ "zf = zipfile.ZipFile('auspopn.zip', 'r') # Open the zip file\n", "popntiff = zf.read('apg22r_1_0_0.tif') # Read the geotiff data file from the archive into memory\n", "bytes = io.BytesIO(popntiff) # Create an in-memory file-like object from the data\n", "auspop = xarray.open_dataset(bytes, engine='rasterio', band_as_variable=True) # Open as an xarray, with hints for rasterio\n", "auspop = auspop.rio.reproject(\"EPSG:4326\") # Move from x/y coords to lat/lon values\n", "auspop = auspop.rename({'x': 'lon', 'y': 'lat'}) # Rename axes for consistency\n", "auspop = auspop.band_1 # Focus on the variable of interest\n", "auspop_lr = auspop.coarsen({'lat': 4, 'lon': 4}, boundary='trim').mean() # Reduce the resolution to closer to the data" ] }, { "cell_type": "code", "execution_count": 11, "id": "42817797-f012-466f-95e6-568c74bd31db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The population density is -1 over the ocean and 0 for much of Australia\n", "# It is best to mask to positive values and leave NaN elsewhere, but this could vary by situation\n", "auspop_normalised = auspop_lr.where(auspop_lr > 0) / auspop_lr.max() # Obtain the population density between 0 and 1\n", "auspop_normalised.plot()" ] }, { "cell_type": "code", "execution_count": 12, "id": "415ba42f-67fc-4493-a5e7-24fde48a22ae", "metadata": {}, "outputs": [], "source": [ "# Crop all data to the Australian Region\n", "\n", "min_lon = 100 \n", "max_lon = 160\n", "min_lat = -5\n", "max_lat = -45\n", "\n", "\n", "fcst_aus = time_step_of_interest.sel(lat=slice(min_lat,max_lat), lon=slice(min_lon,max_lon))\n", "anal_aus = analysis.sel(lat=slice(min_lat,max_lat), lon=slice(min_lon,max_lon))\n", "lat_weight_aus = weights.sel(lat=slice(min_lat,max_lat)) # no lons included, uses broadcasting\n", "popn_density_aus = auspop_normalised.sel(lat=slice(min_lat,max_lat), lon=slice(min_lon,max_lon))\n", "popn_density_aus = popn_density_aus.interp_like(anal_aus)\n", "aus_popn_neutrual = (popn_density_aus / popn_density_aus)" ] }, { "cell_type": "code", "execution_count": 13, "id": "e02c9d1c-8f7d-4f78-8f40-d659f1bf733f", "metadata": {}, "outputs": [], "source": [ "aus_unweighted_mae = scores.continuous.mae(fcst_aus, anal_aus.temp_scrn, reduce_dims='lon')\n", "aus_lat_weighted_mae = scores.continuous.mae(fcst_aus, anal_aus.temp_scrn, weights=lat_weight_aus, reduce_dims='lon')\n", "aus_popn_weighted_mae = scores.continuous.mae(fcst_aus, anal_aus.temp_scrn, weights=popn_density_aus, reduce_dims='lon')\n", "aus_popn_neutral_mae = scores.continuous.mae(fcst_aus, anal_aus.temp_scrn, weights=aus_popn_neutrual, reduce_dims='lon')" ] }, { "cell_type": "code", "execution_count": 14, "id": "979c0053-4fe7-4e56-8c65-c3b0baa06634", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Figure One - Side by Side Comparison of alternative weighting strategies\n", "names = pandas.Index(['Unweighted', 'By Latitude', 'By Density', 'Where Population'], name=\"Method\")\n", "xarray.concat([aus_unweighted_mae, aus_lat_weighted_mae, aus_popn_weighted_mae, aus_popn_neutral_mae], dim=names).plot(col='Method')" ] }, { "cell_type": "markdown", "id": "83e7cde3-0883-4153-8e7c-7c366af809c2", "metadata": {}, "source": [ "A few interesting things can be seen here.\n", "\n", "1. Weighting by latitude has relatively low impact over just Australia, despite its large land mass\n", "2. Weighting by population density greatly reduces the scores in most of Australia, due to the huge peaks in cities\n", "3. Effectively masking to populated areas increases the score, showing the forecast accuracy is worse than the aggregate indicator would suggest - for almost everyone\n", "\n", "A more sophisticated normalisation strategy for calculating the population density error weightings, which also weights by latitude would probably give a more realistic picture of the impact of forecast error on the world population than any of the approximations listed here." ] }, { "cell_type": "code", "execution_count": null, "id": "9aae0750-5ae9-48c7-aa2d-b01aab66768e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }