Our platform provides access to normalized hurricane dama data spanning from 1900 to 2024, allowing researchers, policymakers, insurance professional, and the public to better understand how hurricane costs have changed over time.
The core datasets used in this app are based on research by Muller et al. (2025) and the expanded analysis by Mooney et al. (2025), published in the Bulletin of the American Meteorlogical Society and ****JOURNAL**** respectively. These studies update and refine hurricane damage normalization methodologies to provide a more accurate picture of how historical hurricanes would impact today\'s society.
Our platform incoroprates several methodological innovations over previously used cost normalization formulas:
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' ) ``` ```{r} HTML( 'Our platform provides access to normalized hurricane dama data spanning from 1900 to 2024, allowing researchers, policymakers, insurance professional, and the public to better understand how hurricane costs have changed over time.
The core datasets used in this app are based on research by Muller et al. (2025) published in the Bulletin of the American Meteorlogical Society. This study updates and refines hurricane damage normalization methodologies to provide a more accurate picture of how historical hurricanes would impact today\'s society.
Our platform incoroprates several methodological innovations over previously used cost normalization formulas:
CONTACT INFO?
' ) ``` Col {data-width=500} ---------------------------------------------- ### Storm Selector {data-height=500} ```{r} h5("Select Storm Name and Year") dbStormYears <- dbGetQuery(con, "SELECT DISTINCT storm_year, storm_name FROM econ.storm_base_loss ORDER BY storm_year") fluidRow( column(6, selectInput("stormYear", "Select Year", choices = dbStormYears$storm_year) ), column(6, selectInput("stormName", "Select Storm", choices = NULL) ) ) fluidRow( column(12, actionButton("selectStorm", "Submit", class = "btn-primary")) ) #h5("Search") #textInput("stormSearch", "Search by hurricane name, year") observeEvent(input$stormYear, { stormsByChosenYear <- dbStormYears[dbStormYears$storm_year == input$stormYear, ] updateSelectInput(session, "stormName", choices = stormsByChosenYear$storm_name, selected = NULL) }) observeEvent(input$selectStorm, { storm_selection$storm_year <- input$stormYear storm_selection$storm_name <- input$stormName storm_selection$storm_basin <- "AL" storm_selection$is_selected <- TRUE showNotification("Storm selection updated!", type = "message") }) ``` ### All Storms {data-height=500 .no-padding} ```{r} output$allStorms <- renderDT({ datatable( normalized2024, rownames = F, options = list( pageLength = 1000, order = list(2, 'desc'), searching = F, paging = F, info = F, lengthChange = F, server = T ) ) %>% formatCurrency(c("MMH24", "MMP24"), "$", digits = 0) }) DTOutput("allStorms") ``` Storm Overview {data-navmenu="Storm Details"} ==================================== Col {data-width=500} ------------------------------------ ### Storm Details {data-height=200} ```{r} HTML('Hurricane Katrina was a powerful, devestating and historic tropical cyclone that caused 1,392 fatalities and damages estimated at $125 billion in late August 2005.
') ``` ### Normalization Cost Index {data-height=800} ```{r} ##BY LANDFALL### ##MULTIPLE LINES### dbNormalizedLandfalls <- reactive({ req(storm_selection$is_selected) query <- paste0(" SELECT *, CONCAT(lf_type, lf_id) as lfid FROM econ.normalized_landfalls WHERE storm_basin = '", storm_selection$storm_basin, "' AND storm_year = ", storm_selection$storm_year, " AND storm_name = '", storm_selection$storm_name, "'") result <- dbGetQuery(con, query) #cat(str(result)) showNotification(nrow(result)) return(result) }) dbUniq <- reactive({ req(dbNormalizedLandfalls(), nrow(dbNormalizedLandfalls()) > 0) dbNormalizedLandfalls() %>% distinct(lfid) #re <- unique(res$lfid) #cat(str(res)) #cat(str(re)) #cat("hi2") #cat(str(res)) #return(res) }) lfNormalized <- reactive({ req(storm_selection$lf_id) lf <- dbNormalizedLandfalls() %>% filter(lfid == storm_selection$lf_id) %>% mutate( mmh = gdp_deflator * rwhu * affected_housing, mmp = gdp_deflator * rwpc * affected_population, years = as.Date(paste0(normalization_year, "-01-01")) ) %>% select( years, mmh, mmp ) lf_ts <- lf %>% select(-years) %>% xts(order.by = lf$years) cat(storm_selection$lf_id) cat(str(lf_ts)) return(lf_ts) }) fillCol( flex = c(.2, .8), fluidRow( column(4, selectInput("lfSelect", "Landfall Select", choices = NULL) ), column(4, #checkboxInput("mmhSelect", "Display MMH", value = T) ), column(4, #checkboxInput("mmpSelect", "Display MMP", value = T) ) ), #dygraphOutput("katrinaCostIndex") dygraphOutput("costIndex") ) observe({ req(dbUniq(), nrow(dbUniq()) > 0) #cat("hi") #cat(nrow(dbUniq())) #cat(str(dbUniq())) updateSelectInput( session, "lfSelect", choices = dbUniq()$lfid, selected = dbUniq()$lfid[1] ) updateSelectInput( session, "growthTrendLfSelect", choices = dbUniq()$lfid, selected = dbUniq()$lfid[1] ) storm_selection$lf_id = dbUniq()$lfid[1] }) observeEvent(input$lfSelect, { storm_selection$lf_id = input$lfSelect showNotification("Landfall updated", type = "message") }) output$costIndex <- renderDygraph({ req(lfNormalized()) dygraph(lfNormalized(), main = "Cost Index") %>% dySeries("mmh", label = "MMH") %>% dySeries("mmp", label = "MMP") %>% dyRangeSelector(height = 30) }) ``` Col {data-width=500} ------------------------------------ ### Fatalities {data-height=200} ```{r} fluidRow( column(6, HTML('Surge: 387
') ), column(6, actionLink("fatalitiesLink", tagList(icon("arrow-right"), "Fatalities dashboard"), class = "btn btn-outline") ) ) ``` ### Landfalls {data-height=300 .no-padding} ```{r} #selectLandfalls <- hurdat.best_track %>% # filter( # storm_name == storm_selection$storm_name & # storm_year == storm_selection$storm_year & # record_identifier == "L" # ) landfallsdata <- reactive({ req(storm_selection$is_selected) df <- selected.best_track() %>% filter(record_identifier == "L") %>% select( Date = datetime, Latitude = lat, Longitude = lon, Pressure = pressure, Windspeed = windspeed, RMW = rmw ) }) output$landfalls <- renderDT({ datatable( landfallsdata(), rownames = F, options = list( order = list(0, 'asc'), paging = F, searching = F, info = F, lengthChange = F, server = T ) ) %>% formatDate(columns = "Date", method = "toUTCString") }) DTOutput("landfalls") ``` ### Storm Track {data-height=500 .no-padding} ```{r} output$trackMap <- renderLeaflet({ leaflet() %>% addProviderTiles("CartoDB.Positron", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% setView(lng = -80, lat = 32, zoom = 4) %>% addCircleMarkers( #data = katrinaTrack, #lng = ~Longitude, #lat = ~Latitude, data = selected.best_track(), lng = ~lon, lat = ~lat, radius = 5, color = ~ifelse(record_identifier == "L", "red", "blue"), fillColor = ~ifelse(record_identifier == "L", "red", "blue") ) }) leafletOutput("trackMap", height="100%") ``` Growth Trends {data-navmenu="Storm Details"} ================================ Column {data-width=550} ------------------------------- ### {data-height=1000 .no-padding} ```{r} dbStormCounties <- reactive({ req(storm_selection$is_selected) sel_storm_basin <- storm_selection$storm_basin sel_storm_year <- storm_selection$storm_year sel_storm_name <- storm_selection$storm_name sel_lfid <- growth_trends$lf_id affectedCounties <- gis.affected_area_landfalls %>% mutate( fips = paste0(state_fips, county_fips), lfid = paste0(lf_type, lf_id) ) %>% filter( storm_basin == sel_storm_basin, storm_year == sel_storm_year, storm_name == sel_storm_name, lfid == sel_lfid ) %>% left_join( public.counties %>% mutate(geom_wkt = sql("ST_AsText(ST_Transform(geom, 4326))")) %>% rename(state_fips = statefp, county_fips = countyfp), by = c("state_fips", "county_fips") ) %>% collect() affectedCounties_sf <- affectedCounties %>% st_as_sf(wkt = "geom_wkt") cat(str(affectedCounties_sf)) return(affectedCounties_sf) }) output$popMapPoly <- renderLeaflet({ leaflet(dbStormCounties()) %>% addProviderTiles("CartoDB.Positron", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% setView(lng = -89.8, lat = 29.6, zoom = 8) %>% addPolygons( fillColor = "red", fillOpacity = 0.3, color = "black", weight = 2 ) }) output$housingMapPoly <- renderLeaflet({ leaflet() %>% addProviderTiles("CartoDB.Positron", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% setView(lng = -89.8, lat = 29.6, zoom = 8) %>% addPolygons( data = dbStormCounties(), fillColor = "blue", fillOpacity = 0.3, color = "black", weight = 2 ) }) fillCol( flex = c(1, 1), leafletOutput("popMapPoly"), leafletOutput("housingMapPoly") ) ``` Column {data-width=450} ---------------------------------- ### Landfall Selection {data-height=150} ```{r} fluidRow( column(4, selectInput("growthTrendLfSelect", "Landfall", choices = NULL) ), column(4, ), column(4, ) ) observeEvent(input$growthTrendLfSelect, { growth_trends$lf_id = input$growthTrendLfSelect showNotification("Landfall updated", type = "message") }) ``` ### Time Series {data-height=400 .no-padding} ```{r} katrinaLfTwoLong <- katrinaLfTwoCountyMetrics %>% pivot_longer( cols = -c(fips, metric), names_to = "year", values_to = "value" ) %>% group_by( metric, year ) %>% summarize( aggregateValue = sum(value, na.rm = T), .groups = "drop" ) %>% pivot_wider( names_from = metric, values_from = aggregateValue ) normalizedKatrinaLfTwo <- xlallLandfallsNormalized %>% filter(hurdatId == "AL122005" & lfId == "LF2") %>% select(normalizationYear, affectedPop, affectedHousing) normalizedKatrinaLfTwoTs <- normalizedKatrinaLfTwo %>% select(-normalizationYear) %>% as.matrix() %>% xts(order.by = as.Date(paste0(normalizedKatrinaLfTwo$normalizationYear, "-01-01"))) katrinaLfTwoLongTs <- katrinaLfTwoLong %>% select(-year) %>% as.matrix() %>% xts(order.by = as.Date(paste0(katrinaLfTwoLong$year, "-01-01"))) output$popHu <- renderDygraph({ dygraph(normalizedKatrinaLfTwoTs, main = "Normalized LF2 Aggregate Growth") %>% dySeries("affectedPop", label = "Population") %>% dySeries("affectedHousing", label = "Housing Units") %>% dyRangeSelector() }) dygraphOutput("popHu") ``` ### County Data {data-height=450 .no-padding} ```{r} output$katrinaCounties <- renderDT({ datatable( katrinaLfTwoCountyMetrics, rownames = F, #extensions = "RowGroup", options = list( pageLength = 1000, #rowGroup = list(dataSrc = 1), paging = F, searching = F, info = F, lengthChange = F, server = T ) ) }) DTOutput("katrinaCounties") ``` Fatalities {data-navmenu="Fatalities"} === Column {data-width=500} --- ### {data-height=500} ### {data-height=500} Column {data-width=500} --- ### {data-height=500} ```{r} ``` ### {data-height=500} ```{r} ``` Storm Comparison {data-navmenu="Compute"} === Sandbox {data-navmenu="Compute"} === Data {data-navmenu="Compute"} === Top 50 Storms === About ===