Flood vulnerability framework for deprived urban areas in Africa (Data and Code repository)

This repository contains the processed datasets and analysis scripts supporting the manuscript submitted to Nature Communications, which presents a co-developed flood vulnerability framework for deprived urban areas (DUAs) across six Sub-Saharan African cities. The materials enable full reproducibility of all descriptive statistics, analyses, and figures presented in the manuscript. All input and output data are provided and properly described in the medatata files (.txt) in their corresponding folders. This repository includes: 1. Empirical Workshop Data (01_Empirical_Data folder) Digitized vulnerability factors emerged from participatory workshops conducted in Nairobi, Kisumu, Accra, Tema, Beira and Chimoio Coding process, outputs and rationale Consent form template and memo summaries are also included for ethical considerations 2. Scoping Review Dataset (02_Scoping_Review folder) Screening log and exclusion criteria process Coding decisions Extracted vulnerability domains from 45 peer-reviewed publications Alignment matrix comparing literature-derived domains with empirically derived domains 3. Stakeholder validation (03_Validation folder) Online questionnaire Responses summarized Framework revision documentation 4. Scripts (04_Code_Availability folder) Three Jupyter Notebooks that generate descriptive statistics and all manuscript figures derived from processed datasets. All scripts are deterministic and executable using the provided CSV inputs. Ethical Considerations The empirical data originate from participatory workshops involving community members and institutional stakeholders. To protect participant confidentiality, no personally identifiable information is included. Only anonymized, processed analytical outputs are made available. Ethical approval and informed consent procedures are also described in the manuscript. Usage The scripts are fully automated. For replicability, after unzipping the folder: 1. Install the required Python packages.2. Open the notebooks in Jupyter.3. Ensure input CSV files are located in the same directory.4. Run all cells sequentially.