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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.18034670 |
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Table of Contents:
- <p><strong><span lang="EN-US">1. Overview</span></strong></p> <p><span lang="EN-US">This project consists of 11 files covering an end-to-end analytical pipeline from climate exposure data processing and health effect modeling to population projection. The study is conducted at the prefecture-city level in China, combining historical observations and future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) to systematically assess both the short-term effects of temperature on fertility and long-term demographic trends. The files are categorized into four types: Jupyter Notebooks (spatial data processing and exposure calculation), R scripts (statistical modeling and population projection), Excel files (future scenario temperature exposure), and a ZIP archive (baseline population data).</span></p> <p><strong><span lang="EN-US">2. File Description by Category</span></strong></p> <p><strong><span lang="EN-US">2.1 Jupyter Notebook Files (Spatial Data Processing & Exposure Calculation)</span></strong></p> <p><strong><span lang="EN-US">(1) IDW_Combain_shp_and_nc.ipynb<span> </span></span></strong></p> <p><span lang="EN-US">Implements Inverse Distance Weighting to spatially align ERA5 climate grid data (NetCDF format) with Chinese prefecture-level administrative boundaries (Shapefile). Computes daily temperature exposure metrics for each city, providing foundational exposure data for subsequent health effect analyses.</span></p> <p><strong><span lang="EN-US">(2) SSP_daily_exposure_calculate.ipynb<span> </span></span></strong></p> <p><span lang="EN-US">Calculates future daily temperature exposure variables based on climate model outputs under different SSP scenarios. Supports multi-scenario, long-term exposure assessment and serves as input for risk projection.</span></p> <p><strong><span lang="EN-US">2.2 R Script Files (Statistical Modeling & Population Projection)</span></strong></p> <p><strong><span lang="EN-US">(1) discrete_time_cox.R<span> </span></span></strong></p> <p><span lang="EN-US">Implements a discrete-time Cox model to analyze the association between preconception temperature exposure and conception risk. The model supports 3-month and 6-month exposure windows and outputs city-specific hazard ratios. It further integrates projected temperature changes to estimate conception risk and the probability of conception within 12 months under different SSP pathways.</span></p> <p><strong><span lang="EN-US">(2) fertility_rate.R<span> </span></span></strong></p> <p><span lang="EN-US">Builds a multi-regional cohort-component population projection model. Based on baseline population structure and age-specific fertility, mortality, and migration rates, it simulates the dynamics of population size and age-sex structure for Chinese cities from 2011 to 2100, supporting population trend analysis under multiple SSP scenarios.</span></p> <p><strong><span lang="EN-US">2.3 Microsoft Excel Files (Future Scenario Temperature Data Summary)</span></strong></p> <p><span lang="EN-US">All Excel files summarize quarterly and annual mean temperature exposure values calculated under different SSP scenarios from 2015 to 2100, covering three representative climate pathways: SSP1-2.6 (low warming scenario), SSP2-4.5 (medium warming scenario), SSP5-8.5 (high warming scenario)<span> </span></span></p> <p><strong><span lang="EN-US">2.4 ZIP Archive (Baseline Data)</span></strong></p> <p><strong><span lang="EN-US">(1) baseline_population_data.zip<span> </span></span></strong></p> <p><span lang="EN-US">Contains foundational population data required for the project, such as age structure, baseline fertility rates, and migration rates. It serves as the starting point for the population projection model.</span></p>