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Prepared by: Helena M. Garcia, University of North Carolina at Chapel Hill and Kathryn Foster, Cornell University  

Date: June 2026

Original Authors: Helena M. Garcia, Antonia Sebastian, Kieran P. Fitzmaurice, Miyuki Hino, Elyssa L. Collins, Gregory W. Characklis

Specific purpose of code:

The code provides an example of how to generate flood extent data, modeled after the process used to create the North Carolina Flood Extent Archive dataset. Due to privacy restrictions from the National Flood Insurance Program (NFIP), the address-level records used to create NC-FLDEX, a full code cannot be shared. However, an example code is shared using randomly generated NFIP claims and policy locations from the NC Building Footprint 2010 data. The event dates for the code align with Hurricane Florence (2018). The purpose of the example code is 1) to represent how NC-FLDEX was developed, and 2) to provide a guide for replicating similar products for different geographies.

The North Carolina Flood Extent Archive (NC-FLDEX) example code creates flood exent raster data. The rasters represent binary flood extents (1= likely flooded) and are created using random forest models trained on high-resolution geospatial predictors and address-level National Flood Insurance Program (NFIP) claims and policy data. NFIP claims locations are labeled as flood presence points and policy locations without claims are labeled as flood absence points. The flood presence and absence points are then used to estimate flood probabilities at every 30-meter cell in the study area. The NC-FLDEX example code includes a comparison of model outputs with publicly available physics-based and remote sensing-based model outputs for Hurricane Florence. The archive example code also includes North Carolina building footprint data to support building-level exposure summaries.

General Application:

This code can be adapted to estimate flood extent in other locations and different time frames that are relevant to aging populations. It can also be combined with other relevant spatial datasets to examine historical flooding events to compare exposure across events.

How does or could this code allow researchers to assess research questions related  to aging or life course?:

The spatial 30m NC-FLDEX raster data can be spatially aggregated to demographic units (e.g., census tracts, block groups) and linked with datasets containing age or health-related variables.

Data sets used: 

• Population, socioeconomic, or health data: US Census Bureau Primary and Secondary Roads, US Census Bureau Census Tracts 2010, US Zip Code Tabulation Areas (ZCTAs) 2000, and North Carolina Building Footprints
• Climate, weather, disaster or environment data: Address-level NFIP Claims, NFIP Redacted Claims and Policies, USGS 30m Elevation, NLCD Fractional Impervious Surface, ERA5 Hourly Precipitation, NHD Coastline, NC OneMap Major Hydrography, Height Above Nearest Drainage (HAND), Soil Hydraulic Conductivity (ksat), and FEMA Special Flood Hazard Area (SFHA)

Are all the data publicly available or are some restricted-access?

The address-level NFIP claims are restricted-access data.

Links to data: 

US Census Bureau Primary and Secondary Roads:

https://www.fema.gov/openfema-data-page/fima-nfip-redacted-policies-v2

USGS 30m Elevation: https://data.usgs.gov/datacatalog/data/USGS:35f9c4d4-b113-4c8d-8691-47c428c29a5b

NLCD Fractional Impervious Surface: https://www.sciencebase.gov/catalog/item/655ceb8ad34ee4b6e05cc51a

ERA5 Hourly Precipitation: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview

NHD Coastline: https://www.usgs.gov/national-hydrography/national-hydrography-dataset

NC OneMap Major Hydrography: https://www.nconemap.gov/datasets/nconemap::major-hydrography-streams-rivers/about

Height Above Nearest Drainage (HAND): https://www.hydroshare.org/resource/73aaa3efcda2465ba6227f535400f36b/

Soil Hydraulic Conductivity (ksat): https://websoilsurvey.nrcs.usda.gov/app/

FEMA Special Flood Hazard Area (SFHA): https://www.nconemap.gov/maps/a178aae74ee347d786e853e5a442eea2/explore?location=35.121157%2C-79.918650%2C7.86

Coding Language: R was used to create these data, more information and example codes are available in the dataverse repository.

Tools and Packages used: Dplyr, sp, raster, tigris, ggplot2, sf, readxl, writexl, nngeo, lubridate, stringr, tidyr, raster, ggplot2, reshape2, data.table, randomForest, stats,ranger, caret, tuneRanger, mlr, stringr, pROC, ROCR, dismo

Output(s): Dataset and mapping

Spatial extent: Flood extent rasters span the coastal draining USGS HUC-6 watersheds within North Carolina (030101, 030102, 030201, 030202, 030203, 030300, 030401, 030402). The study area overlaps with 78 of North Carolina’s 100 counties. A shapefile of the study area is included in repository.

Temporal extent: The 78 flood events included in NC-FLDEX are based on National Flood Insurance Program damage records from 1/1/1996 to 9/30/2020.

Published papers that use this code:

Garcia, H. M., Sebastian, A., Fitzmaurice, K. P., Hino, M., Collins, E. L., & Characklis, G. W. (2025). Reconstructing repetitive flood exposure across 78 events from 1996 to 2020 in North Carolina, USA. Earth’s Future, 13, e2025EF006026. https://doi.org/10.1029/2025EF006026

Joyce Pak, Bradford E. Jackson, Shabbar I. Ranapurwala, Miyuki Hino, Lawrence S. Engel, Katherine E. Reeder-Hayes, Jillian L. Evans-Strong, Jennifer L. Lund; Impacts of Hurricane-Related Flooding on Time to Initial Cancer Directed Treatment in North Carolina. Cancer Epidemiol Biomarkers Prev 2026; https://doi.org/10.1158/1055-9965.EPI-25-1664

Graphic reproduced from Garcia et al (2025) published in Earth’s Future.

Link to code

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Date: November 2025

Authors/Creators/ Team Members: Sara R. Ronnkvist, Zoe Haskell-Craig, Risto Conte Keviabu, Abbie Robinson, Mathew E. Hauer, Domenico Bovienzo, Emilio Zagheni

Specific purpose of code: This GitHub repository contains a collection of scripts that generate datasets quantifying extreme temperature exposure in Europe using a variety of metrics at two sub-national spatial scales (NUTS 2 and NUTS 3) and three temporal scales (daily, extreme temperature wave, and yearly) from 1980-2024. These datasets capture the breadth of temperature metrics used in epidemiology, demography and environmental literature with 67 different metrics: including regionally-unusual temperature events (defined as temperatures above/below the 95th/5th percentile of historical temperatures) and periods of sustained (consecutive day) exposure to extreme temperatures. Additionally, these scripts can be adapted to construct temperature extremes for other geographic regions or scales with a few minor revisions. 

General Application: The TEE datasets can be linked to any data that contains NUTS identifiers(e.g.Eurostat)using a simplemergeto study the impacts of extreme temperatures on populations.

How does or could this code allow researchers to assess research questions related  to aging or life course?: The TEE datasets provide temperature data in a user-friendly format which can easily be linked to EuroStat or other datasets with NUTS identifiers. Additionally, our code is reproducible and easily adaptable to other geographic regions and/or time frames. Researchers who wish to study other regions may adapt our code to construct extreme temperature measures.

Data sets used: 

• Population, socioeconomic, or health data:
  • Earth Science Data Systems, N. Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11 | NASA Earthdata. (Earth Science Data Systems, NASA,2024,5), https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-gpwv4-popcount-r11-4.11
• Climate, weather, disaster or environment data:
  • Muñoz-Sabater, J. et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021 (2021).
  • Di Napoli, C., Barnard, C., Prudhomme, C., Cloke, H. & Pappenberger, F. ERA5-HEAT: a global gridded historical dataset of human thermal comfort indices from climate reanalysis. Geoscience Data Journal 8, 2–10, https://doi.org/10.1002/gdj3.102 (2021).
  • NUTS shapefiles from Eurostat: https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics 

Are all the data publicly available or are some restricted-access? Publicly available.

Links to data: TEE dataset are available on FigShare: https://springernature.figshare.com/articles/dataset/Temperature_Extremes_in_Europe_TEE_/28063226

Coding Language: We use Python to download the extreme temperature data from Copernicus and aggregate the hourly data to daily temperature measures. We use R for everything else.

Tools and Packages used:

R: terra, exactextractr, sf, tidyverse, foreign, zoo 

Python: os, glob, shutil, zipfile, cdsapi,  xarray, numpy 

Output(s):

16 datasets: 

• Annual measures of extreme temperature 

• tee_yearly_nuts2.csv 

• tee_yearly_nuts3.csv 

• Extreme temperature waves (consecutive days of temperature extremes) 

• tee_wave_nuts2.csv 

• tee_wave_nuts3.csv 

• Daily temperature measure datasets split into 10-year files to reduce file size 

• tee_daily_nuts2_[START YEAR]_[END YEAR].csv 

• tee_daily_nuts3_[START YEAR]_[END YEAR].csv 

Spatial extent: European Union countries

Temporal extent: 1980-2024 

Comments: Ronnkvist et al (2025) contains detailed information on how the datasets were constructed and the included metrics. We provide replication instructions in the TEE-dataset GitHub repository (https://github.com/haskellcraigz/TEE-dataset/tree/main). 

Citation: 

Ronnkvist, S.R., Haskell-Craig, Z., Robinson, A., Conte Keivabu, R., Hauer, M.E., Bovienzo, D., & Zagheni, E. (2025) What’s the TEE: Metrics of Temperature Extremes in Europe NUTS Regions (1980-2024). Scientific Data. https://doi.org/10.1038/s41597-025-05352-7 

Published papers that use this code:

Scientific Data publication associated with the TEE datasets: 

Ronnkvist, S.R., Haskell-Craig, Z., Robinson, A., Conte Keivabu, R., Hauer, M.E., Bovienzo, D., & Zagheni, E. (2025) What’s the TEE: Metrics of Temperature Extremes in Europe NUTS Regions (1980-2024). Scientific Data. https://doi.org/10.1038/s41597-025-05352-7 

Link to code

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Prepared by: Alex Mikulas, PhD, CACHE postdoctoral associate 

Date: April 2026

Original Authors:

Benjamin Jones, Northwestern University 

Jacob Moscona, Massachusetts Institute of Technology 

Benjamin A. Olken, Massachusetts Institute of Technology  

Cristine von Dessauer, Massachusetts Institute of Technology 

Specific purpose of code: This Stata code and program take temperature data at fine temporal and spatial resolution (ie: tract-day) and transform it to an aggregated panel dataset with temperature binned to year-place or month-place specificity. The program calculates both realized and expected number of days in each temperature bin. The data are then ready for use in regression and similar analysis using standard temperature bin specifications. The data are useful in better capturing the role of extreme temperatures by identifying extreme temperature days outside of what would be expected in a given area and are useful for assessing the causal role of increasing extreme temperature exposure.

General Application: Extreme temperature exposure is often operationalized in research using a binning procedure, wherein a researcher aggregates the number of extreme temperature days into binned temperature ranges. A common and serious bias can occur when using binned temperature data over time if the outcome variable of interest is associated with the baseline temperature, producing what are often called “U-shaped” results. When studying the impacts of climate change and increasing extreme temperature exposure, common binning procedures neglect the baseline temperature of a given area.

Let’s say that over a 20-year span, the average temperature increase is uniform across space. A place like Phoenix will see a large increase in extreme heat days, (say, 90-degree+ days) while a place like Boston will see a smaller increase in extreme heat days. This is because the baseline temperature for Phoenix 20 years ago was much warmer than the baseline temperature in Boston.

The bias arises when a given outcome is associated with both extreme heat days, and also the baseline temperature of a given area. If a baseline temperature of an area is closer to the bin thresholds for “extreme heat”, the outcome may be associated with the baseline temperature as well as the increase in extreme heat days, introducing statistical bias into an analysis.

This code addresses the issue by providing the “expected” number of days in each temperature bin, as well as the observed number of days in each temperature bin. These data can then control for different baseline temperatures and trends in warming for different areas. These expected and observed temperature bins allow the researcher to avoid regressing “trends on trends”, which estimate the biased U-shaped results. The estimated area-year and area-month temperature bin data can be used in a wide variety of extreme temperature exposure studies.

How does or could this code allow researchers to assess research questions related  to aging or life course?: This code can be applied to spatially and temporally specific temperature datasets to bin temperature exposure data while also accounting for varying baseline temperatures and varying trends in extreme temperature over time. Health and aging scholars can then integrate these data as a weather exposure variable and more accurately predict the impact of extreme temperature on age and health related outcomes.

Data sets used: 

• Population, socioeconomic, or health data: code generates binned temperature data that can be integrated with any data that has spatial and temporal specificity (such as lat/long, geographic administrative identifiers, panel data, or data with observation dates).
• Climate, weather, disaster or environment data: code is applicable to any temperature data with detailed place/day resolution.

Are all the data publicly available or are some restricted-access? NA

Links to data: NA

Coding Language: Stata 

Tools and Packages used: cftemp, a custom Stata command in an ado. file.

Output(s): Dataset of observed and counterfactual data of binned counts of extreme temperature days at the place/year or place/month level. Output comes from any user supplied, hyper-specific daily temperature datasets. 

Spatial extent: Flexible. Depends on user supplied data.

Temporal extent: Flexible. Depends on user supplied data.

Published papers that use this code: Benjamin Jones, Jacob Moscona, Benjamin A. Olken, and Cristine von Dessauer, “With or Without U? Binning Bias and the Causal Effects of Temperature Extremes,” NBER Working Paper 34671 (2026), https://doi.org/10.3386/w34671. 

Link to code

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Date: April 2026

Authors/Creators/ Team Members: Victoria D. Lynch, Jonathan A. Sullivan, Aaron B. Flores, Xicheng Xie, Sarika Aggarwal, Rachel C. Nethery, Marianthi-Anna Kioumourtzoglou, Anne E. Nigra, and Robbie M. Parks

Specific purpose of code: This code links National Center for Health Statistics (NCHS) mortality data with Global Flood Database (GFD) flood event data from 2001 – 2018 by US county. We used the NCHS data to identify monthly total and cause-specific deaths by age group, sex, and county and used Global Human Settlement Layer (GHSL) population data to calculate county-level flood exposure and mortality rates. We used a Bayesian formulation of the conditional quasi-Poisson model to analyze the county-level association between the number of flood events per month and monthly death rates, accounting for overdispersion in the mortality data. The conditional approach examines differences within matched strata (here, county-months) like a case-crossover study design, which removes confounding bias due to factors that vary across strata. Bayesian inference enables the ‘borrowing of information’ across county units and for the full distributional estimation of the parameters of interest.
All-cause and cause-specific mortality associated with flood events is likely differential by flood cause and severity; therefore, we conducted analyses separately by all-cause and cause-specific mortality (cancers, cardiovascular diseases, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases), flood cause (all floods, heavy rain, tropical cyclone, snowmelt, and ice jam or dam break), and flood severity (mild, moderate, severe, and very severe). Because very severe flood events were most strongly associated with increased mortality across all flood causes and mortality groups, we further analyzed associations stratified by age group (0-64 and 65+ years) and sex (female and male) for very severe floods only.

General Application: This code links county-level flood exposure, categorized by flood type, with county-level mortality rates for the six primary causes of death in the US: cancers, cardiovascular disease, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases. The code could be used with any county-level health outcome and, with modification, with other county-level environmental exposures. The code specifically categorized exposure by flood type and severity, which would not apply to other exposures.

How does or could this code allow researchers to assess research questions related  to aging or life course?: The code is written to assess the association between flood exposure and mortality by age category; in our paper, we specifically stratified by age category (0-64 years old and 65+ years old) to examine flood exposure-related mortality among older adults. The NCHS data include individual-level age at death and would enable analyses with any subset of age groups.

Data sets used: 

• Population, socioeconomic, or health data:
  National Center for Health Statistics (NCHS) mortality data; Global Human Settlement Layer (GHSL) population exposure.
• Climate, weather, disaster or environment data:
  Global Flood Database (GFD) flood event data; Dartmouth Flood Observatory (DFO) flood classification data; Parameter-elevation Regression on Independent Slopes Model (PRISM) temperature data.
  

Are all the data publicly available or are some restricted-access? Data on flood exposure are available without restrictions for individual flooding events. Temperature data and population data are also publicly available.

NCHS mortality data are restricted. To access the NCHS mortality data, applicants must submit a project review form:(https://www.cdc.gov/nchs/data/nvss/nchs-research-review-application.pdf) to nvssrestricteddata@cdc.gov and allow four to six weeks for processing.

Links to data:

1. National Center for Health Statistics, Mortality Data
2. Global Flood Database and Global Human Settlement Layer (downloaded together)
3. Parameter-elevation Regression on Independent Slopes Mode

Coding Language:  R 

Tools and Packages used:

R: acs, BiocManager, dlnm, dplyr, ecm, Epi, fiftystater, foreign, fst, ggpubr, ggplot2, graph, graticule, haven, here, janitor, lubridate, mapproj, maptools, mapview, MetBrewer, pipeR, raster, RColorBrewer, readxl, rgdal, rgeos, rnaturalearth, rnaturalearthdata, scales, sf, sp, sqldf, survival, splines, table1, tidycensus, tidyverse, totalcensus, usmap, zipcodeR, zoo, INLA, Rgraphviz, fmesher

Output(s): Exploratory data analysis of flood and mortality data (maps, figures, tables), and output of statistical analysis (figures, tables)

Spatial extent: United States

Temporal extent: 2001-2018

Published papers that use this code: Lynch, Victoria D., et al. “Large floods drive changes in cause-specific mortality in the United States.” Nature Medicine 31.2 (2025): 663-671. doi: https://doi.org/10.1038/s41591-024-03358-z