2d:null 2e:["$","title",null,{"children":"Modelled estimates of recent births | Greater London Authority | Observable"}] 33:T896,md_intro = md`# Modelled estimates of recent births ## Modelled estimates of annual live births by year ending date. Official birth estimates from ONS are considered very accurate, but the lag between the end of the period covered and the publication of estimates is typically 9-12 months. To gain a more timely indication of birth trends, the GLA Demography team produces modelled estimates of annual births based on counts of infants registered with GP practices. Modelled birth estimates can be produced with the same frequency and latency that [NHS Digital](https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice) publishes patient count data - currently this is monthly and with a lag of 1-2 weeks. The approach used to generate the modelled birth estimates was originally described in this [2016 technical note](https://data.london.gov.uk/dataset/estimating-births-using-gp-registration-data). The methodology relies on the correlation between the count of persons age 0 (i.e. yet to reach their first birthday) on the patient register and resident in an area with the number of births that have taken place in that area over the preceding year. The data used to create these modelled birth estimates are 1. [Modelled estimates of patient counts by age, sex, and local authority of residence](https://data.london.gov.uk/dataset/patients-registered-at-a-gp-practice) 2. [Official estimates of annual live births by local authority of residence](https://data.london.gov.uk/dataset/birth-estimates) A respository of the code used to generate the modelled birth estimates is [available on GitHub](https://github.com/Greater-London-Authority/nowcast-birth-estimates). The repository's ReadMe provides more information about the methodology used to produce the estimates as well as a summary analysis of their past accuracy. The outputs cover a range of geographies in England. ### Data visualisations The graph below shows data from the *actual\_and\_predicted\_births.csv* file. Use the selection options to change the geographical area and back-series length. The graph can be downloaded by clicking on the 'Save as PNG' button twice.`34:Tf43,BirthsGraph = Plot.plot({ style: {...ldnTheme.defaultStyle}, ...ldnTheme.defaultSize, marginTop: 30, height: 400, // DISABLING THE DEFAULT LEGEND MAINTAINS PURE SVG OUTPUT // Setting legend to false keeps everything inside a pure
Source: GLA modelled estimates based on ONS births estimates and NHS Digital patient count data
N.B. Data for City of London are not included`, marks: [ Plot.geo(boroughs, Plot.centroid({ fill: d => dataForMap.get(d.properties.gss_code), // tip: true, // channels: { // Borough: d => d.properties.name, // GSS_CODE: d => d.properties.gss_code // } })), Plot.geo(boroughsMesh, {stroke: currentTheme.color.canvas.background[0]}), // styled tooltip Plot.tip(boroughs, Plot.pointer(Plot.geoCentroid({...ldnTheme.defaultTip, title: (d) => d.properties.name + '\n' + d3.format(".0%")(dataForMap.get(d.properties.gss_code))}))), ] });36:T55a,borough_perc_diffs = { const matchKeys = ["gss_code", "gss_name"]; const calcKey = "annual_births"; // 1. Create a composite key string helper const makeKey = (item) => matchKeys.map(k => item[k]).join('|'); // 2. Index the NEW array into a Map for instant lookup const newMap = new Map(latest_gla_prediction.map(item => [makeKey(item), item])); // 3. Map over the OLD array to join data and calculate differences //return latest_official_estimate.map(itemOld => { const joined = latest_official_estimate.map(itemOld => { const key = makeKey(itemOld); const itemNew = newMap.get(key) || {}; // Fallback to empty object if no match // Base object containing all matching columns const result = {}; matchKeys.forEach(k => result[k] = itemOld[k]); // Calculate percentage difference for each specified value column const oldCalcVal = itemOld[calcKey] || 0; const newCalcVal = itemNew[calcKey] || 0; const pctDiff = oldCalcVal !== 0 ? ((newCalcVal - oldCalcVal) / oldCalcVal) : 0; result[`old_${calcKey}`] = oldCalcVal; result[`new_${calcKey}`] = newCalcVal; result[`diff_pct_${calcKey}`] = parseFloat(pctDiff.toFixed(2)); return result; }); // 4. Filter out city of London as it skews the range for plotting on the map return joined.filter(d => d.gss_code != "E09000001"); }37:[{"id":"6c7b9f3bf356251a9513afcab17b410cfea50fb8fe7076df2ac5738b847e4462b6ad4dc6827af0c60a6662889fe45e4aedf929986cbe4111a811c379618dff38","href":"https://static.observableusercontent.com/files/6c7b9f3bf356251a9513afcab17b410cfea50fb8fe7076df2ac5738b847e4462b6ad4dc6827af0c60a6662889fe45e4aedf929986cbe4111a811c379618dff38","name":"birth_gp_ratios.csv","type":"text/csv","lastModified":1784218084348.012,"size":4853473},{"id":"eb8c2d2f0ca4b9e333fea2faf6a94e5bcab735c25910fd3dcca14d9158ca3ed2367020b4dcd2fa676830ac25a46a793e26f50720e040ffed94a2dede80c1a5ec","href":"https://static.observableusercontent.com/files/eb8c2d2f0ca4b9e333fea2faf6a94e5bcab735c25910fd3dcca14d9158ca3ed2367020b4dcd2fa676830ac25a46a793e26f50720e040ffed94a2dede80c1a5ec","name":"birth_gp_ratios@1.csv","type":"text/csv","lastModified":1788266003789.531,"size":5170206},{"id":"98d819a59443f1f487acb3f16e194506754cfb1507d6d1aa7b01eb85975a3ce10e84b1c87268d49d87c8af0e057d6d87376748ebead82481a21b8b92b0986db3","href":"https://static.observableusercontent.com/files/98d819a59443f1f487acb3f16e194506754cfb1507d6d1aa7b01eb85975a3ce10e84b1c87268d49d87c8af0e057d6d87376748ebead82481a21b8b92b0986db3","name":"actual_and_predicted_births@1.csv","type":"text/csv","lastModified":1788265992215.644,"size":3286245},{"id":"16bfc4a45c21389f14d1a3575ecbe2409c126d44f874380ae4b963af5241867eb53700a7095d4f17b5af6b5e1cf6c3345b7264d5e611494786134322324b64ed","href":"https://static.observableusercontent.com/files/16bfc4a45c21389f14d1a3575ecbe2409c126d44f874380ae4b963af5241867eb53700a7095d4f17b5af6b5e1cf6c3345b7264d5e611494786134322324b64ed","name":"boroughs-simplified-topo.json","type":"application/json","lastModified":1783611594080.833,"size":46482},{"id":"fdd0240b9e7c52a43b7dddecaf09804827584363c26f3dc5e56a38f38722b3fecdb709765c1035ac91d73ce1d290347d1775266233de5cd442b74c7aa241c25e","href":"https://static.observableusercontent.com/files/fdd0240b9e7c52a43b7dddecaf09804827584363c26f3dc5e56a38f38722b3fecdb709765c1035ac91d73ce1d290347d1775266233de5cd442b74c7aa241c25e","name":"actual_and_predicted_births.csv","type":"text/csv","lastModified":1783612075770.881,"size":3325494}] 31:{"id":"471988370cbaf7c5","slug":"modelled-estimates-of-recent-births","title":"Modelled estimates of recent births","thumbnail":"ba3cee3b1a898093775f8fe5fb77a796ccc94eec8d12ab8956268ae67e9f6a86","access_level":"public","create_time":"$D2026-07-09T15:39:54.080Z","update_time":"$D2026-09-16T11:08:28.807Z","view_time":null,"first_public_time":"$D2026-09-15T11:57:20.088Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"4bf49e4e36c4436e","login":"ldn","name":"Greater London Authority","avatar_url":"https://avatars-next.observableusercontent.com/f28604c35c26f7073878cf94cfaaf04f1c723bf492c79692ceaa60dc08d07244","tier":"pro"},"version":3778,"publish_version":3778,"role":null,"authored":false,"authors":[{"type":"individual","id":"752d2e1cf1a7e2f8","login":"lapsel","name":"Marta","avatar_url":"https://avatars-next.observableusercontent.com/b19a25c5507aa366ea8101bfa1f144b5640fdfef2d509e5b515e42562470b652","tier":null}],"members":[{"type":"team","id":"4bf49e4e36c4436e","login":"ldn","name":"Greater London Authority","avatar_url":"https://avatars-next.observableusercontent.com/f28604c35c26f7073878cf94cfaaf04f1c723bf492c79692ceaa60dc08d07244","tier":"pro","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Modelled estimates of recent births","cells":[{"id":1349,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2796,"value":"viewof selected_geography_level = Inputs.select(\n [\"Local Authority\", \"Region\", \"ITL2\", \"Country\"],\n { label: \"Select geography level\", value: \"Region\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2798,"value":"viewof selected_geography = {\n\n let options;\n let defaultValue;\n\n if (selected_geography_level == \"Region\") {\n options = regions;\n defaultValue = regions.find(i => i.gss_name == \"London\");\n } else {\n defaultValue = undefined;\n \n options = selected_geography_level == \"Local Authority\" ? lads \n : selected_geography_level == \"Country\" ? countries \n : selected_geography_level == \"ITL2\" ? ITL2s \n : lads;\n }\n \n return Inputs.select(options, {\n value: defaultValue, \n label: \"Select geography\", \n format: x => x.gss_name \n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2884,"value":"viewof selected_start_year = Inputs.range(births_actual_years_extent, {label: \"Earliest year shown\", step: 1, value: births_actual_years_extent[1]-4})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3291,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3103,"value":"downloadButton = DOM.download(() => rasterize(BirthsGraph), \"BirthsGraph\", \"Save as PNG\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3379,"value":"md_map_caption = md`The map below gives a quick overview of how the expected changes in births since the last official estimates vary across London boroughs`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3354,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3166,"value":"md_file_contents = md`### File contents\n\nFiles containing the outputs from this modelling process are available for download below.\n\nThe downloads contain official ONS annual births estimates between ${d3.timeFormat(\"%B %Y\")(earliest_official_estimate_date)} and ${d3.timeFormat(\"%B %Y\")(latest_official_estimate_date)}, interpolated estimates at monthly intervals between official estimates, and predictions of annual births up to ${d3.timeFormat(\"%B %Y\")(latest_gla_prediction_date)}. Predicted data are given alongside the upper and lower bounds of the 95% prediction interval.\n\nThe data are given for local authority districts, regions, country, and international territorial levels ITL2 in England.\n\nThese data are not currently split by sex.\n\nThe following are example rows to show the format of the csv data file downloads.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3212,"value":"md_actual_predicted_title = md`#### actual_and_predicted_births.csv`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3208,"value":"viewof table_births = Inputs.table(data_births.slice(-3))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3217,"value":"md_birth_gp_ratios_title = md`#### birth_gp_ratios.csv`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3226,"value":"viewof table_birth_gp_ratios = Inputs.table(data_birth_gp_ratios.slice(-3))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1188,"value":"----\n## CODE & DATA WRANGLING","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":263,"value":"### Import Data","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":142,"value":"import {demoMonthlyTimeseriesLong, demoAggregatedByYearTimeseriesLong} from \"@ldn/viz-tools-imports\"","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2792,"value":"import_births_data = FileAttachment(\"actual_and_predicted_births@1.csv\").csv({typed:true})","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3220,"value":"import_birth_gp_ratios_data = FileAttachment(\"birth_gp_ratios@1.csv\").csv({typed:true})","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1194,"value":"### Wrangle Data","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2800,"value":"data_births = import_births_data.filter(d => d.sex == \"persons\")","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3222,"value":"data_birth_gp_ratios = import_birth_gp_ratios_data.filter(d => d.sex == \"persons\")","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2807,"value":"data_births_lad = {\n const pattern = /^LAD/;\n return data_births.filter(d => pattern.test(d.geography))\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2810,"value":"data_births_region = {\n const pattern = /^RGN/;\n return data_births.filter(d => pattern.test(d.geography))\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2818,"value":"data_births_country = {\n const pattern = /^CTRY/;\n return data_births.filter(d => pattern.test(d.geography))\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2820,"value":"data_births_ITL2 = {\n const pattern = /^ITL2/;\n return data_births.filter(d => pattern.test(d.geography))\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2860,"value":"births_actual_geography = data_births.filter(d => d.gss_code == selected_geography.gss_code & d.type == \"actual\")","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2907,"value":"births_actual_geography_plot = births_actual_geography.filter(d => d.date >= new Date(selected_start_year + \"-01-01\"))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2871,"value":"births_modelled_geography = data_births.filter(d => d.gss_code == selected_geography.gss_code & d.type != \"actual\")","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2915,"value":"births_modelled_geography_plot = births_modelled_geography.filter(d => d.date >= new Date(selected_start_year + \"-01-01\"))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3079,"value":"births_CI_geography_plot = births_modelled_geography_plot.filter(d => d.type == \"predicted\")","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2931,"value":"births_actual_london = data_births.filter(d => d.gss_code.startsWith(\"E09\") & d.type == \"actual\")","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2982,"value":"latest_official_estimate_date = d3.max(births_actual_london, d => new Date(d.date))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2929,"value":"latest_official_estimate = {\n const latestData = births_actual_london.filter(d => new Date(d.date).getTime() === latest_official_estimate_date.getTime())\n\n const cleanData = latestData.map(({ gss_code, gss_name, date, annual_births}) => ({ gss_code, gss_name, date, annual_births }))\n\n return cleanData\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3178,"value":"earliest_official_estimate_date = d3.min(births_actual_london, d => new Date(d.date))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2938,"value":"births_predicted_london = data_births.filter(d => d.gss_code.startsWith(\"E09\") & d.type == \"predicted\")","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3001,"value":"latest_gla_prediction_date = d3.max(births_predicted_london, d => new Date(d.date))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2942,"value":"latest_gla_prediction = {\n const latestData = births_predicted_london.filter(d => new Date(d.date).getTime() === latest_gla_prediction_date.getTime())\n\n const cleanData = latestData.map(({ gss_code, gss_name, date, annual_births}) => ({ gss_code, gss_name, date, annual_births }))\n\n return cleanData\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2968,"value":"$36","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1380,"value":"### Application Variables","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1785,"value":"lads = {\n const temp = new Map();\n data_births_lad.forEach(d => {\n if (!temp.has(d.gss_code)) temp.set(d.gss_code, d.gss_name);\n });\n\n return Array.from(temp, ([gss_code, gss_name]) => ({ gss_code, gss_name }))\n .sort((a, b) => a.gss_name.localeCompare(b.gss_name));\n}\n","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1939,"value":"regions = {\n const temp = new Map();\n data_births_region.forEach(d => {\n if (!temp.has(d.gss_code)) temp.set(d.gss_code, d.gss_name);\n });\n\n return Array.from(temp, ([gss_code, gss_name]) => ({ gss_code, gss_name }))\n .sort((a, b) => a.gss_name.localeCompare(b.gss_name));\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1951,"value":"countries = {\n const temp = new Map();\n data_births_country.forEach(d => {\n if (!temp.has(d.gss_code)) temp.set(d.gss_code, d.gss_name);\n });\n\n return Array.from(temp, ([gss_code, gss_name]) => ({ gss_code, gss_name }))\n .sort((a, b) => a.gss_name.localeCompare(b.gss_name));\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2824,"value":"ITL2s = {\n const temp = new Map();\n data_births_ITL2.forEach(d => {\n if (!temp.has(d.gss_code)) temp.set(d.gss_code, d.gss_name);\n });\n\n return Array.from(temp, ([gss_code, gss_name]) => ({ gss_code, gss_name }))\n .sort((a, b) => a.gss_name.localeCompare(b.gss_name));\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2887,"value":"births_actual_years_extent = {\n const extent = d3.extent(births_actual_geography, d => d.date.getFullYear())\n // reduce the upper bound year by one or the graph series/legend messes up as the interpolated data is no longer in it\n extent[1] -= 1;\n return(extent)\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1514,"value":"----\n## Map Functions","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1516,"value":"### Map Geo","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1518,"value":"ldnTopo = FileAttachment(\"boroughs-simplified-topo.json\").json();","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1520,"value":"boroughs = topojson.feature(ldnTopo, ldnTopo.objects.boroughs)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1522,"value":"boroughsMesh = topojson.mesh(ldnTopo, ldnTopo.objects.boroughs, (a, b) => a !== b)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1524,"value":"mapLayoutConfig = ({ \n width: width,\n height: (width)*0.35,\n zoom: 180, // usually 220\n xOffset: 0,\n yOffset: 0\n})","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1527,"value":"mapTranslationFromConfig = [((mapLayoutConfig.width / 2)-(mapLayoutConfig.width*mapLayoutConfig.xOffset)),((mapLayoutConfig.height/2)-(mapLayoutConfig.height*mapLayoutConfig.yOffset))];","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1530,"value":"mapScaleFromConfig = Math.pow(mapLayoutConfig.zoom,2);","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1536,"value":"### Map Data","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2165,"value":"dataForMap = new Map((borough_perc_diffs).map(d => [d.gss_code, (d.diff_pct_annual_births).toFixed(3)]))\n","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1554,"value":"### Colors","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1556,"value":"ldnBlue10 = ldnVizUtils.getColorRamp({colors:[\n currentTheme.color.palette.blue[100],\n currentTheme.color.palette.blue[500],\n currentTheme.color.palette.blue[1000]\n], count:10, even:true})","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1562,"value":"asRamp(ldnBlue10)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1559,"value":"colorRampForMap = ldnBlue10;","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":258,"value":"----\n## Imports, Plot Defaults & Themes","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":244,"value":"**Import functions from @ldn/plot-fragments**","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":271,"value":"Dynamic theme & defaults + static dark & light defaults","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"import {getCurrentTheme, getDefaultPlotStyles, darkDefaults, lightDefaults, fontStack, ldnVizUtils} from \"@ldn/viz-tools-imports\"","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"**Get currentTheme** - based on notebook theme selection. Useful to get dynamic colors (like categorical purple)","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":206,"value":"currentTheme = getCurrentTheme(notebookTheme)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":254,"value":"**Get LDN Theme Plot Defaults**","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"ldnTheme = getDefaultPlotStyles(notebookTheme);","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1297,"value":"**Other Imports**","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1321,"value":"TidyJS is a good library to help reshape your data:","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1313,"value":"import {tidy, pivotLonger, groupBy, mutate, arrange, desc, sum, summarize, max} from \"@pbeshai/tidyjs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1324,"value":"Vega lite is a useful alternative to Observable Plot, but we only really use it for donut charts","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1308,"value":"import{vl} from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1330,"value":"If SVG export from a cell doesn't work as you hoped, you can wrap your plot code in toSVG() and this is sometimes a better export.","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1300,"value":"import {toSVG} from \"@fil/plot-figure-to-svg\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3261,"value":"Mike Bostock's helpful \"Serialize\" and \"Rasterize\" functions, which you can attach to a button to generate and download a SVG/PNG","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3263,"value":"import {serialize, rasterize} from \"@mbostock/saving-svg\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"----","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":267,"value":"## Helper functions","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":172,"value":"formatLow = d3.format(',.0f'); // for lower than 10000, format commas and not dp","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"formatHigh = d3.format(',.4~s'); // for 10000 and above, format commas and SI numbering (M & K)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1568,"value":"swatch = (color) => {\n return html`

`;\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1565,"value":"asRamp = (colors) => {\n const canvas = colors.map(c => swatch(c));\n const dark = d3.lab(colors[0]).l < 50;\n const label = document.createElement(\"DIV\");\n const ramp = html`

${canvas}

${label}

`;\n label.textContent = '';\n label.style.position = \"absolute\";\n label.style.top = \"0px\";\n label.style.padding = \"4px\";\n label.style.fontFamily = \"Arial, sans-serif\";\n label.style.color = dark ? `#fff` : `#000`;\n ramp.onclick = () => {\n label.textContent = \"Copied!\";\n navigator.clipboard.writeText(JSON.stringify(colors));\n setTimeout(() => label.textContent = '', 1000);\n };\n return ramp\n}","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2046,"value":"arrayRange = (start, stop, step) =>\n Array.from(\n { length: (stop - start) / step + 1 },\n (value, index) => start + index * step);","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":292,"value":"----","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":297,"value":"## Notebook HTML styling","pinned":false,"hidden":true,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":49,"value":"viewof notebookTheme = Inputs.select([\"light\", \"dark\"], {value:\"light\", label: \"Notebook default:\"})","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":281,"value":"html``","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 32:"$31:body"