2e:null 2f:["$","title",null,{"children":"Data analysis for normalized U.S. hurricane losses, 1900-2017 | Climate Lab | Observable"}] 34:T617,// Generate annual data function generateAnnualData(decennialData, endYear) { let data = []; // Interpolate between decennial years, but stop at 2010 (do not go beyond 2010) for (let i = 0; i < decennialData.length - 1; i++) { const startYear = decennialData[i].year; const startPopulation = decennialData[i].population; const endYearDecade = decennialData[i + 1].year; const endPopulation = decennialData[i + 1].population; // Only interpolate up to 2010 (exclude 2011-2015) for (let year = startYear; year < Math.min(endYearDecade, 2011); year++) { const population = P(startYear, startPopulation, endYearDecade, endPopulation, year); data.push({ year, population }); } } // Interpolate between 2010 and 2016 (for 2011 to 2015) const year2010 = decennialData.find(d => d.year === 2010); const year2016 = { year: 2016, population: decennialData[decennialData.length - 1].population }; for (let year = 2011; year < year2016.year; year++) { const population = P(year2010.year, year2010.population, year2016.year, year2016.population, year); data.push({ year, population }); } // Extrapolate from 2016 to 2017 using the growth rate from 2010-2016 const growthRate2010to2016 = Math.log(year2016.population / year2010.population) / (year2016.year - year2010.year); for (let year = year2016.year; year <= endYear; year++) { const population = year2016.population * Math.exp(growthRate2010to2016 * (year - year2016.year)); data.push({ year, population }); } return data; } 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32:{"id":"ad943fb33e1502e8","slug":"data-analysis-normalized-us-hurricane-losses","title":"Data analysis for normalized U.S. hurricane losses, 1900-2017","thumbnail":"49e3b8e7b5c25c76853ac31b4ed41e895e2facb91573182f4e68e995d0ccb899","access_level":"public","create_time":"$D2024-09-24T23:52:33.576Z","update_time":"$D2024-10-24T13:41:03.322Z","view_time":null,"first_public_time":"$D2024-10-24T13:24:25.011Z","has_unpublished_edits":null,"fork":false,"stars":3,"starred":false,"owner":{"type":"team","id":"650e68a1afc4786b","login":"climatelab","name":"Climate Lab","avatar_url":"https://avatars-next.observableusercontent.com/b06f29c9ebba5f82bb96583af4e514bf21938607389bae332427fac99185246f","tier":"starter_2024"},"version":535,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"61e2153e05789733","login":"harrystevens","name":"Harry Stevens","avatar_url":"https://avatars-next.observableusercontent.com/bc9bae4b7e30062b343ae978a13454e29cd3f2daf3a800d0024043686ddf094b","tier":null}],"members":[{"type":"team","id":"650e68a1afc4786b","login":"climatelab","name":"Climate Lab","avatar_url":"https://avatars-next.observableusercontent.com/b06f29c9ebba5f82bb96583af4e514bf21938607389bae332427fac99185246f","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"Data analysis for normalized U.S. hurricane losses, 1900-2017","cells":[{"id":0,"value":"# Data analysis for normalized U.S. hurricane losses, 1900-2017","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":473,"value":"This notebook contains the data and code behind the charts on normalized hurricanes losses and their influences, as seen in [“The real reason billion-dollar disasters like Hurricane Helene are growing more common”](https://www.washingtonpost.com/climate-environment/interactive/2024/billion-dollar-disasters-climate-change-economy/), published in The Washington Post on Oct. 24, 2024.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":524,"value":"To use the data in your notebook:\n\n~~~js\nimport { data_hurricanes } from \"@climatelab/data-analysis-normalized-us-hurricane-losses\"\n~~~","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":216,"value":"## Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":475,"value":"The sources of data here are:\n- [Weinkle, J., Landsea, C., Collins, D. et al. Normalized hurricane damage in the continental United States 1900–2017. Nat Sustain 1, 808–813 (2018)](https://www.nature.com/articles/s41893-018-0165-2#Sec8)\n- [U.S. Census Bureau, List of coastline counties and their 2016 populations](https://www2.census.gov/library/stories/2018/08/coastline-counties-list.xlsx)\n- [Andrew J. Van Leueven, U.S. Census county populations, 1900-2010](https://andrewvanleuven.com/post/countypopulations/)\n- [National Oceanic and Atmospheric Administration, American Community Survey for US Coastal Shoreline Counties](https://coast.noaa.gov/htdata/SocioEconomic/AmericanCommunitySurvey/ShorelineCounties.zip)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":488,"value":"The basic method is:\n1. Get a list of contiguous U.S. counties on the coasts of the Gulf of Mexico and the Atlantic Ocean.\n- Collect their populations in each decade from 1900 through 2010 as well as in 2016.\n- Use an exponential growth function to interpolate their annual populations between the known years and extrapolate their 2017 populations based on the 2010-2016 growth rate.\n- Take Weinkle et. al.'s annual per capita wealth data, which represents fixed reproducible tangible wealth in the United States, and multiply it by the population in the coastal counties to estimate annual wealth in coastal counties.\n- Use Weinkle et. al.'s data on number of hurricanes, number of Category 3-5 hurricanes, annual inflation-adjusted damage, and annual normalized damage.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":308,"value":"// Set a start year for our analysis\nstart_year = 1900","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"We'll get a unique list of coastline county FIPS codes using the [Census Bureau's list](https://www2.census.gov/library/stories/2018/08/coastline-counties-list.xlsx). This also contains the population data for 2016. That will help us calculate the 2010-2016 growth rate, which we will use to extrapolate to 2017.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"// https://www2.census.gov/library/stories/2018/08/coastline-counties-list.xlsx\ncoastline_counties = (await FileAttachment(\"census-coastline-counties-2016@1.json\").json())\n .map(d => (d.fips = d.fips.toString(), d.year = 2016, d.population = d.population_est_2016, d)) // Ensure FIPS codes are strings","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":202,"value":"hurricane_counties = coastline_counties\n .filter(d => d.coastline_region !== \"Pacific\") // We only want the Atlantic and Gulf of Mexico","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"hurricane_counties_fips = hurricane_counties\n .map(d => d.fips) // Just unique FIPS codes","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":318,"value":"For 1900 to 1960, Census data has been cleaned and organized by [Andrew J. Van Leuven](https://andrewvanleuven.com/post/countypopulations/), assistant professor at the University of Vermont.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":320,"value":"counties_1900_1990 = (await FileAttachment(\"cencounts.csv\").csv())\n .map(d => (d.fips = d.fips === \"12025\" ? \"12086\" : d.fips, d)) // Dade County (FIPS code 12025) was renamed Miami–Dade County and changed its FIPS code to 12086 in 1997. https://www.hamiltonproject.org/assets/files/PBP_Framing_TechAppendix.pdf","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":354,"value":"// Make a tall version of the data (each row is a year-county)\ncounties_1900_1990_tall = d3\n .range(1900, 1970, 10)\n .map(year => {\n return counties_1900_1990.map(county => {\n return {\n year,\n fips: county.fips,\n population: +county[`pop${year}`]\n }\n })\n })\n .flat()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"For 1970 to 2010, we get the decadal population data on shoreline counties from [NOAA](https://coast.noaa.gov/digitalcoast/data/).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"// via https://coast.noaa.gov/digitalcoast/data/\nshoreline_counties = (await FileAttachment(\"DemographicTrends_ShorelineCounties@1.csv\").csv())\n .map(d => (d.fips = d.fips.padStart(5, 0), d.year = +d.year, d.population = +d.c_popul, d))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":207,"value":"// Combine the data\ncounties = [\n ...counties_1900_1990_tall\n .filter(f => hurricane_counties_fips.includes(f.fips)) // only use the Atlantic and Gulf of Mexico counties\n .map(d => {\n const l = shoreline_counties.find(f => f.fips === d.fips);\n return {\n fips: d.fips,\n year: d.year,\n population: d.population,\n ctystate: l.ctystate,\n st_abbr: l.st_abbr\n }\n }),\n ...shoreline_counties\n .filter(f => hurricane_counties_fips.includes(f.fips)) // only use the Atlantic and Gulf of Mexico counties\n .filter(f => f.year >= start_year)\n , \n ...hurricane_counties.map(d => {\n const l = shoreline_counties.find(f => f.fips === d.fips);\n return {\n fips: d.fips,\n year: d.year,\n population: d.population,\n ctystate: l.ctystate,\n st_abbr: l.st_abbr\n }\n })\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"After summing the county populations, we'll use an exponential growth function to:\n

Interpolate between the known years from 1900 through 2016.
Extrapolate to 2017 based on the growth rate between 2010 and 2016.

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":66,"value":"The formula is:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":60,"value":"$${tex.block`P(t) = P_0 \\cdot \\left( \\frac{P_1}{P_0} \\right)^{\\frac{t - t_0}{t_1 - t_0}}`}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":68,"value":"Where:\n- ${tex`P(t)`} is the interpolated population at year ${tex`t`}.\n- ${tex`P_0`} is the population at the starting year ${tex`t_0`}.\n- ${tex`P_1`} is the population at the end year ${tex`t_1`}.\n- ${tex`t`} is the year for which you want the population estimate.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":102,"value":"You can also express this as a function of the growth rate.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":85,"value":"Growth Rate Calculation: The growth rate ${tex`r`} is calculated as:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":81,"value":"$${tex.block`r = \\frac{\\ln\\left(\\frac{P_1}{P_0}\\right)}{t_1 - t_0}`}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":89,"value":"This is the continuous growth rate based on the change from ${tex`P_0`} to ${tex`P_1`} over the time interval from ${tex`t_0`} to ${tex`t_1`}.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":93,"value":"Interpolation Formula with Growth Rate: Once you have the growth rate ${tex`r`}, you can use it to interpolate the population for any year ${tex`t`}:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":96,"value":"$${tex.block`P(t) = P_0 \\cdot e^{r \\cdot (t - t_0)}`}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":99,"value":"This is the same exponential growth model as the first formula, just expressed with the growth rate ${tex`r`}.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":79,"value":"// Function to interpolate between two known points using exponential interpolation\nfunction P(t0, P0, t1, P1, t) {\n const r = Math.log(P1 / P0) / (t1 - t0);\n return P0 * Math.exp(r * (t - t0));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"$34","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"// Sum the coastline data in each year\ncoastline_decadal = d3\n .groups(counties, d => d.year)\n .map(([year, entries]) => ({year, population: d3.sum(entries, d => d.population)}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":258,"value":"Plot.plot({\n height: 200,\n marginLeft: 70,\n x: {\n ticks: [...d3.range(start_year, 2020, 10), 2016]\n },\n marks: [\n Plot.barY(coastline_decadal, {x: \"year\", y: \"population\", tip: true }),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":413,"value":"Create the annual data of coastline counties.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":437,"value":"coastline_annual = generateAnnualData(coastline_decadal, 2017)\n .map(d => {\n // Calculate wealth using Weinkle et. al.'s annual per capita wealth\n d.wealth = d.population * weinkle_inflation_wealth.find(f => f.year === d.year).real_wealth_per_cap;\n return d;\n });","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":248,"value":"Plot.plot({\n height: 200,\n marginLeft: 110,\n x: {\n ticks: d3.range(start_year, 2020, 10)\n },\n marks: [\n Plot.barY(coastline_annual, {x: \"year\", y: \"population\", tip: true }),\n // Plot.linearRegressionY(coastline_annual, {x: \"year\", y: \"population\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"Plot.plot({\n height: 200,\n marginLeft: 110,\n x: {\n ticks: d3.range(start_year, 2020, 10)\n },\n marks: [\n Plot.barY(coastline_annual, {x: \"year\", y: \"wealth\", tip: true }),\n // Plot.linearRegressionY(coastline_annual, {x: \"year\", y: \"wealth\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":408,"value":"We use [data](https://www.nature.com/articles/s41893-018-0165-2#Sec8) from Weinkle et. al. 2018 for data on storm damages.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"// From the \"Storm Damages\" tab in the Excel file\nweinkle_damages = FileAttachment(\"weinkle_damages.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":264,"value":"// From the \"Inflation & Wealth\" tab in the Excel file\nweinkle_inflation_wealth = FileAttachment(\"weinkle_inflation_wealth.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":275,"value":"damages_annual = d3\n .range(start_year, 2018)\n .map(year => {\n const entries = weinkle_damages.filter(f => f.year === year);\n return {\n year,\n count: entries.length,\n count_major: entries.filter(f => f.category >= 3).length,\n inflation: d3.sum(entries, d => d.base_damage) * weinkle_inflation_wealth.find(f => f.year === year).multiplier,\n normalized: d3.sum(entries, d => d.normalized_pl)\n }\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":278,"value":"Plot.plot({\n height: 200,\n marginLeft: 110,\n x: {\n ticks: d3.range(start_year, 2020, 10)\n },\n marks: [\n Plot.barY(damages_annual, {x: \"year\", y: \"count\", tip: true }),\n Plot.linearRegressionY(damages_annual, {x: \"year\", y: \"count\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":464,"value":"Plot.plot({\n height: 200,\n marginLeft: 110,\n x: {\n ticks: d3.range(start_year, 2020, 10)\n },\n marks: [\n Plot.barY(damages_annual, {x: \"year\", y: \"count_major\", tip: true }),\n Plot.linearRegressionY(damages_annual, {x: \"year\", y: \"count_major\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":302,"value":"Plot.plot({\n height: 200,\n marginLeft: 110,\n x: {\n ticks: d3.range(start_year, 2020, 10)\n },\n marks: [\n Plot.barY(damages_annual, {x: \"year\", y: \"inflation\", tip: true }),\n Plot.linearRegressionY(damages_annual, {x: \"year\", y: \"inflation\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":304,"value":"Plot.plot({\n height: 200,\n marginLeft: 110,\n x: {\n ticks: d3.range(start_year, 2020, 10)\n },\n marks: [\n Plot.barY(damages_annual, {x: \"year\", y: \"normalized\", tip: true }),\n Plot.linearRegressionY(damages_annual, {x: \"year\", y: \"normalized\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":425,"value":"Finally, we combine all the datasets into a single annualized array.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":427,"value":"data_hurricanes = d3\n .range(start_year, 2018)\n .map(year => {\n const { count, count_major, inflation, normalized } = damages_annual.find(d => d.year === year);\n const { population, wealth } = coastline_annual.find(d => d.year === year);\n\n return {\n year,\n count,\n count_major,\n inflation,\n normalized,\n population,\n wealth\n }\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 33:"$32:body"