DOI: 10.5061/dryad.76hdr7t30
title: Replication data for global population profile of tropical cyclone exposure during 2002 and 2019

description: Tropical cyclones have far-reaching impacts on livelihoods and population
 health that often persist years after the event. Characterizing the
 demographic and socioeconomic profile and the vulnerabilities of the
 exposed populations is essential to assess health and other risks
 associated with future tropical cyclone events. Estimates of exposure to
 tropical cyclones are often regional rather than global and do not
 consider population vulnerabilities. Here, we combine spatially resolved
 annual demographic estimates with tropical cyclone wind fields estimates
 to construct a global profile of the populations exposed to tropical
 cyclones between 2002 and 2019. We find that approximately 560 million
 people are exposed yearly and that the number of people exposed has
 increased across all cyclone intensities over the study period. The age
 distribution of those exposed has shifted away from children (under-5) and
 towards older people (over-60) in recent years compared to the early
 2000s. Populations exposed to tropical cyclones are more socioeconomically
 deprived than those unexposed within the same country, and this
 relationship is more pronounced for people exposed to higher intensity
 storms. By characterizing the patterns and vulnerabilities of populations
 exposed to tropical cyclones, our results can help identify mitigation
 strategies and assess the global burden and future risks of tropical
 cyclones.
description: The extent of the wind exposure associated with tropical cyclones
 are estimated using a parametric tropical cyclone wind model, as described
 in Chavas et al. (2015).
description: # Replication data for Global Population Profile of Tropical Cyclone
 Exposure during 2002 and 2019" Replication materials for Jing, R.,
 Heft-Neal, S., Chavas, D.R. *et al.* Global population profile of tropical
 cyclone exposure from 2002 to 2019. *Nature* (2023).
 The materials in this
 repository reproduce the figures, tables, and calculations appearing in
 the main text and extended data of the paper. If you find meaningful
 errors in the code or have questions or suggestions, please contact Renzhi
 Jing at ## Organization of repository *
 **scripts**: scripts for replication of figures, tables, and calculations.
 * **figures/published**: published versions of the figures. *
 **figures/raw**: scripts plot_Figure*.R will generate pdf figures in this
 directory. * **data/tc**: gridded high-resolution tropical cyclone
 exposure data * **data/worldpop**: high-resolution gridded population data
 * **data/misc**: uncategorized data that is needed for the paper *
 **results**: folder to save intermediate results ### Data * \**data/tc
 **(All files under this path can be generated by unzipping the provided
 **{year}.zip** files.) * ***duration***  tropical cyclone exposure
 assuming up to 6 hours, 12 hours and no limit on duration of overland
 sustained wind. The temporal resolution is 3 hour. The files follow a
 structured naming convention:
 **duration_{year}*{wind_level}*{landfall}.tif** For instance, a file named
 duration_2002_cat1_12h.tif contains gridded data representing the duration
 that each grid endured the impact of Category 1 tropical cyclones,
 assuming up to 12 hours of sustained winds over land. With a temporal
 resolution of 3 hours, a duration value of 2 indicates that in 2002 the
 grid was exposed for a total of 6 hours. * \**data/worldpop **(We note
 that all files under this path can be downloaded using the links provided
 below. We provide this path to align with subsequent analysis and
 visualization) * ***worldpop_total***  high-resolution gridded population
 data from WorldPop, downloaded at
 . The datasets are
 available for each year from 2000 to 2019 with a resolution of 30 arc
 seconds. * ***worldpop_age_gender***  high-resolution gridded age and sex
 distributions data from Worldpop, downloaded at
 . Similarly, the
 datasets are available for each year from 2000 to 2019 with a resolution
 of 30 arc seconds. * \**data/misc **(We note that all files under this
 path can be downloaded using the links provided below. We provide this
 path to align with subsequent analysis and visualization) *
 ***gadm_410.gpkg***  GADM data version 4.1, providing maps and spatial
 data for all countries and their sub-divisions. The data is downloaded at:
 . * ***world_countries_2020***  country
 boundaries from IPUMS, downloaded at:
 . *
 ***world_continent***  continent boundaries downloaded from ArcGIS Hub,
 downloaded at:
 . *
 ***global_mask.tif***   2D array with the same resolution as the worldpop
 data, which is only used to generate a dictionary, saving the indices of
 the map for each country and continent. *
 ***povmap-grdi-v1_high_res_global.tif***   high-resolution gridded
 relative deprivation index. The raw data is downloaded at SEDAC
 with a
 resolution of ~1 km. We adjust the map's resolution to align with
 that of the Worldpop data. * ***supplementary_table1.csv***   data used to
 generate supplementary table 1. The columns are: * country: Country name *
 avg_person_day: Averaged person-days exposures during 2002-2019 for each
 country * share: Share of global person-days exposure during the study
 period for each country * avg_rdi: Population-averaged relative
 deprivation index for each country * rdi_rate: The ratio of
 population-averaged relative deprivation index between exposed and
 unexposed population for each country 'NA' means not applicable.
 * ***ibtracs_data_1989_2019.csv***   historical tropical cyclone tracks,
 derived from IBTrACS dataset. The tracks are used in Figure 1. Missing
 values are filled with 'NA'. The columns are: * sid: Storm
 identifier * basin: Basins include: NA - North Atlantic EP - Eastern North
 Pacific WP - Western North Pacific NI - North Indian SI - South Indian SP
 - Southern Pacific SA - South Atlantic * name: Storm name provided by the
 agency * date_time, year, month, day, hour: Time in Universal Time
 Coordinates (UTC). * tclat, tclon: Storm latitude and longitude *
 max_wind: Maximum sustained wind speed for the current location, in knots
 * rmax: Radius of max winds for the current location, in nmile * tspd:
 Storm translation speed, in m/s ### Scripts Python files calculate
 intermediate data that is used to generate the figures. The scripts are
 maintained at:
 * Script helper_functions.py includes global variables and self-defined functions. * Script script_Figure*.py replicate the calculations reported in the paper. ### Figures R files (plot_Figure*.R) generate the figures in the paper and write them to figures/raw. The figures produced by these scripts will be slightly visually different than the published figures because post-processing was done in Adobe Illustrator. Published versions of the figures are available in figures/clean. The scripts are maintained at: * Script plot_Figure1.R generates Figure 1. * Script plot_Figure2.R generates Figure 2, Figure ED2, Figure ED3 and Figure ED8. * Script plot_Figure3.R generates Figure 3, Figure ED4. * Script plot_Figure4.R generates Figure 4 and Figure ED6. * Script plot_Figure5.R generates Figure 5 and Figure ED7. * Script plot_FigureED1.R generates Figure ED1. * Script plot_FigureED5.R generates Figure ED5. ## Code/Software Scripts were written in Python 3.6.1 and R 4.2.3. \[Link to GitHub repo with all the replication data and code] () ### Python packages required * **numpy** * **math** * **pandas** * **os** * **time** * **pickle** * **geopandas** * **rasterio** * **itertools** * **scipy** * **osgeo** * **shapely** * **global_land_mask** * **multiprocessing** for parallel computing ### R packages required * **ggplot2** * **pracma** * **scales** * **dplyr** * **tidyr** * **tidyverse** * **reshape2** * **sf** * **sp** * **MetBrewer**