Flood-Map Updates, Insurance Premiums, and the Formalization of Disaster Risk

# Flood map paper replication package This package reproduces the tables, figures, and checked in-text numbers for the IJDRR manuscript on FEMA flood-map updates, NFIP insurance outcomes, and mortgage-market responses. ## Repository structure - `code/run_cleaned.py`: reproduces all manuscript outputs from frozen cleaned data. - `code/run_raw.py`: rebuilds the cleaned data from uploaded public raw files and then reproduces outputs. - `data/cleaned/`: frozen cleaned tract-year panel and simplified map geometry (77.4 MiB). - `docs/raw_files_to_upload.csv`: required raw-upload checklist with file sizes, checksums where available, and source/download method. - `docs/table_figure_crosswalk.csv`: maps manuscript items to generated output files. - `docs/manuscript_number_crosswalk.csv`: checked in-text/table numbers. - `reference_outputs/`: expected tables and figures used for the submitted manuscript. ## Computational environment Use either conda: ```bash conda env create -f environment.yml conda activate fema-ijdrr-replication ``` or venv/pip: ```bash python -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` ## One-line reproduction from frozen cleaned data ```bash python code/run_cleaned.py --analysis-data data/cleaned/fema_auxiliary_tract_year_panel.parquet --map-data data/cleaned/treated_tract_map_geometry.parquet --output outputs_from_cleaned && python code/check_outputs.py --outputs outputs_from_cleaned ``` ## One-line reproduction from uploaded raw data Upload every file listed in `docs/raw_files_to_upload.csv` to the exact `required_upload_path` location inside this package. The required raw-upload files total approximately 32.5 GiB. Then run: ```bash python code/run_raw.py --output outputs_from_raw && python code/check_outputs.py --outputs outputs_from_raw ``` The raw route first checks that all required files are present. If files are missing, it writes `docs/missing_raw_files.csv` and stops. The cleaned-data route has been run end to end. The raw-data route is provided for full reconstruction and requires the 96 public raw files listed in `docs/raw_files_to_upload.csv`. ## Expected outputs Generated outputs are written to the chosen output directory with `tables/`, `figures/`, and `sha256_outputs.csv` subfiles. The `check_outputs.py` script verifies that all manuscript-table and figure files exist and that the checked manuscript numbers match `docs/manuscript_number_crosswalk.csv`.# 洪水地图研究论文复现包 本套件可复现《International Journal of Disaster Risk Reduction》（IJDRR）期刊上关于美国联邦紧急事务管理局（Federal Emergency Management Agency, FEMA）洪水地图更新、国家洪水保险计划（National Flood Insurance Program, NFIP）保险赔付结果以及抵押贷款市场响应的论文中的表格、插图及经校验的正文数值。 ## 仓库结构 - `code/run_cleaned.py`：基于已固化的清理后数据复现论文所有输出结果。 - `code/run_raw.py`：从上传的公开原始文件重建清理后的数据，随后复现论文输出结果。 - `data/cleaned/`：已固化的普查区-年度面板数据与简化地图几何文件（77.4 MiB）。 - `docs/raw_files_to_upload.csv`：必填原始文件上传清单，包含文件大小、可用的校验和以及文件来源与下载方式。 - `docs/table_figure_crosswalk.csv`：用于将论文内容映射至生成输出文件的对照表。 - `docs/manuscript_number_crosswalk.csv`：经校验的正文/表格数值对照表。 - `reference_outputs/`：提交论文时使用的预期输出表格与插图。 ## 计算环境 可通过conda配置运行环境： bash conda env create -f environment.yml conda activate fema-ijdrr-replication 或通过venv/pip配置运行环境： bash python -m venv .venv source .venv/bin/activate pip install -r requirements.txt ## 基于固化清理后数据的一键复现 执行如下命令即可基于已固化的清理后数据完成全流程复现： bash python code/run_cleaned.py --analysis-data data/cleaned/fema_auxiliary_tract_year_panel.parquet --map-data data/cleaned/treated_tract_map_geometry.parquet --output outputs_from_cleaned && python code/check_outputs.py --outputs outputs_from_cleaned ## 基于上传原始数据的一键复现 将`docs/raw_files_to_upload.csv`中列出的所有文件上传至本套件内指定的`required_upload_path`路径。所需上传的原始文件总大小约为32.5 GiB。随后执行如下命令： bash python code/run_raw.py --output outputs_from_raw && python code/check_outputs.py --outputs outputs_from_raw 原始数据复现流程会首先校验所有必填文件是否齐全。若存在缺失文件，脚本将生成`docs/missing_raw_files.csv`并终止运行。 清理数据复现流程已完成端到端全链路测试；原始数据复现流程用于实现完整数据重构，需用到`docs/raw_files_to_upload.csv`中列出的96个公开原始文件。 ## 预期输出 生成的输出结果将写入指定的输出目录，包含`tables/`、`figures/`子目录与`sha256_outputs.csv`校验文件。`check_outputs.py`脚本可用于校验所有论文表格与插图文件是否存在，并验证正文校验数值是否与`docs/manuscript_number_crosswalk.csv`中的记录一致。