electricsheepafrica/african-election-infrastructure

--- license: cc-by-4.0 pretty_name: African Election Infrastructure tags: - governance - elections - electoral-integrity - sub-saharan-africa - synthetic - lmic - democracy size_categories: - 10K<n<100K configs: - config_name: baseline data_files: data/baseline.csv - config_name: modernized_elections data_files: data/modernized_elections.csv - config_name: contested_elections data_files: data/contested_elections.csv --- # African Election Infrastructure Dataset ## Overview Synthetic dataset modeling electoral infrastructure across **12 Sub-Saharan African (SSA) countries** under **3 scenarios**. Each scenario contains **10,008 records** (834 per country). Parameters are grounded in empirical literature on election technology, observer ratings, cost structures, and dispute resolution in SSA. ## Scenarios | Scenario | Description | |---|---| | `baseline` | Current-state elections reflecting observed patterns in recent cycles (2017–2025) | | `modernized_elections` | Hypothetical digital investment and institutional reform pathway | | `contested_elections` | Disputed outcomes with technology failures and elevated disputes | ## Countries Nigeria, Kenya, Ghana, Rwanda, Uganda, Tanzania, South Africa, Ethiopia, Senegal, DR Congo, Cameroon, Zambia ## Variables (22 columns) | Variable | Type | Description | |---|---|---| | `country` | str | Country name | | `scenario` | str | Scenario identifier | | `population_millions` | float | Total population (millions) | | `registered_voters_millions` | float | Registered voters (millions) | | `polling_stations_total` | int | Total polling stations | | `polling_stations_per_100k` | float | Polling stations per 100k registered voters | | `voter_turnout_rate` | float | Voter turnout rate (%) | | `biometric_verification_pct` | float | % of polling stations with biometric verification | | `electronic_voting_pct` | float | % of votes cast via electronic means | | `observer_rating_score` | float | International observer rating (1–10 scale) | | `result_transmission_speed_hours` | float | Hours to transmit results from polling station to tally centre | | `election_disputes_count` | int | Number of formal election disputes filed | | `dispute_resolution_days` | float | Days to resolve election disputes | | `election_cost_per_voter_usd` | float | Election administration cost per registered voter (USD) | | `election_compliance_score` | float | Composite electoral compliance score (0–100) | | `election_quality_class` | str | Quality classification: well-managed / partially_managable / poorly_managable | | `n_political_parties` | int | Number of contesting political parties | | `international_observer_missions` | int | Number of international observer missions deployed | | `internet_penetration_pct` | float | Internet penetration at time of election (%) | | `security_incidents` | int | Security incidents at or near polling stations | | `voter_education_index` | float | Voter education coverage index (0–100) | | `female_candidate_pct` | float | Female candidate share (%) | ## Literature-Informed Parameter Sources Country-level priors are drawn from: - **Biometric systems**: Nigeria BVAS (INEC, 2023), Kenya KIEMS (IEBC), Uganda BVVKs (AU COMESA-IGAD 2026), Rwanda (>98% biometric coverage) - **Voter turnout**: Nigeria 2023 at ~35% (INEC), Rwanda consistently >90%, SSA average 50–70% (IDEA) - **Cost per voter**: Ghana $2.13–2.19 (2024), Kenya $25.4 (2017), Rwanda $1.05, Nigeria $8.61, SSA average $11.30 (van der Straaten, 2018) - **Electronic voting/transmission**: Nigeria IReV/e-transmission, SSA digitalisation trends (GIGA Focus 2025), Ghana BVRV systems - **Observer ratings**: AU election observer missions, NDI/IRI missions, Commonwealth observers - **Dispute resolution**: Kenya Supreme Court nullification (2017), Nigeria election tribunals, DR Congo contested outcomes ## Data Generation ```bash # Install dependencies pip install -r requirements.txt # Generate all 3 scenarios (~10K records each) python generate_dataset.py --n 834 --seed 42 # Run validation + diagnostic plots python validate_dataset.py ``` ## Diagnostic Plots The validation script produces an 8-panel diagnostic covering: - A: Mean voter turnout by country and scenario - B: Biometric verification distribution - C: Election cost per voter (box plots) - D: Observer rating vs compliance score - E: Disputes vs resolution time - F: Election quality class distribution - G: Result transmission speed - H: E-voting vs internet penetration ## Limitations - This is a **synthetic** dataset; it does not represent actual election results for any specific election cycle. - Country priors reflect literature estimates and may not capture sub-national variation. - Scenario modifiers are illustrative — real-world modernization or contestation dynamics involve complex institutional and political factors not fully captured here. - Quality classifications are based on a simplified composite score and should not be used as authoritative assessments of any country's elections. ## Citation If you use this dataset, please cite: ``` @dataset{african_election_infrastructure_2026, title={African Election Infrastructure Dataset}, year={2026}, license={cc-by-4.0}, note={Synthetic dataset of electoral infrastructure across 12 SSA countries} } ``` ## License CC-BY-4.0许可证：CC-BY-4.0 友好名称：非洲选举基础设施 标签： - 治理 - 选举 - 选举完整性 - 撒哈拉以南非洲（Sub-Saharan African） - 合成数据集 - 低收入和中等收入国家（Low- and Middle-Income Countries, LMIC） - 民主 规模类别：10K<n<100K 配置项： - 配置名称：baseline 数据文件：data/baseline.csv - 配置名称：modernized_elections 数据文件：data/modernized_elections.csv - 配置名称：contested_elections 数据文件：data/contested_elections.csv # 非洲选举基础设施数据集 ## 概览 本合成数据集对**12个撒哈拉以南非洲（Sub-Saharan African, SSA）国家**的选举基础设施进行建模，涵盖**3种场景**。每种场景共计10008条记录（每个国家834条）。数据集参数基于撒哈拉以南非洲地区选举技术、观察员评级、成本结构与争议解决的实证研究成果设定。 ## 场景 | 场景 | 描述 | |---|---| | `baseline` | 反映2017-2025年近期选举周期的现有状态选举模式 | | `modernized_elections` | 假设性的数字投入与制度改革路径 | | `contested_elections` | 存在技术故障与争议升级的选举结果争议场景 | ## 涉及国家 尼日利亚、肯尼亚、加纳、卢旺达、乌干达、坦桑尼亚、南非、埃塞俄比亚、塞内加尔、刚果民主共和国、喀麦隆、赞比亚 ## 变量（共22列） | 变量 | 类型 | 描述 | |---|---|---| | `country` | 字符串型 | 国家名称 | | `scenario` | 字符串型 | 场景标识符 | | `population_millions` | 浮点型 | 总人口（单位：百万） | | `registered_voters_millions` | 浮点型 | 登记选民人数（单位：百万） | | `polling_stations_total` | 整型 | 投票站总数 | | `polling_stations_per_100k` | 浮点型 | 每10万登记选民对应的投票站数量 | | `voter_turnout_rate` | 浮点型 | 选民投票率（%） | | `biometric_verification_pct` | 浮点型 | 采用生物特征验证的投票站占比（%） | | `electronic_voting_pct` | 浮点型 | 电子投票占总投票数的比例（%） | | `observer_rating_score` | 浮点型 | 国际观察员评级（1-10分制） | | `result_transmission_speed_hours` | 浮点型 | 从投票站向计票中心传输选举结果所需时长（单位：小时） | | `election_disputes_count` | 整型 | 正式提交的选举争议数量 | | `dispute_resolution_days` | 浮点型 | 选举争议解决所需时长（单位：天） | | `election_cost_per_voter_usd` | 浮点型 | 每位登记选民的选举管理成本（单位：美元） | | `election_compliance_score` | 浮点型 | 综合选举合规得分（0-100分） | | `election_quality_class` | 字符串型 | 选举质量分类：well-managed / partially_managable / poorly_managable | | `n_political_parties` | 整型 | 参选政党数量 | | `international_observer_missions` | 整型 | 部署的国际观察员使团数量 | | `internet_penetration_pct` | 浮点型 | 选举时期的互联网普及率（%） | | `security_incidents` | 整型 | 投票站及周边发生的安全事件数量 | | `voter_education_index` | 浮点型 | 选民教育覆盖指数（0-100分） | | `female_candidate_pct` | 浮点型 | 女性候选人占比（%） | ## 基于文献的参数来源 各国的先验参数取自以下文献与实践： - **生物特征识别系统**：尼日利亚BVAS（INEC，2023）、肯尼亚KIEMS（IEBC）、乌干达BVVKs（非盟COMESA-IGAD，2026）、卢旺达（生物特征覆盖率超98%） - **选民投票率**：尼日利亚2023年投票率约35%（INEC），卢旺达投票率常年超90%，撒哈拉以南非洲地区平均投票率为50%-70%（IDEA） - **每位选民选举成本**：加纳2024年为2.13-2.19美元，肯尼亚2017年为25.4美元，卢旺达为1.05美元，尼日利亚为8.61美元，撒哈拉以南非洲地区平均成本为11.30美元（van der Straaten，2018） - **电子投票与结果传输**：尼日利亚IReV/电子传输系统、撒哈拉以南非洲地区数字化趋势（GIGA Focus 2025）、加纳BVRV系统 - **观察员评级**：非盟选举观察员使团、NDI/IRI使团、英联邦观察员 - **争议解决**：肯尼亚最高法院2017年选举结果无效案、尼日利亚选举法庭、刚果民主共和国选举争议案例 ## 数据生成 bash # 安装依赖项 pip install -r requirements.txt # 生成全部3种场景（每种场景约10000条记录） python generate_dataset.py --n 834 --seed 42 # 运行验证与诊断绘图 python validate_dataset.py ## 诊断绘图 验证脚本将生成8面板诊断图，涵盖以下内容： - A：按国家与场景划分的平均选民投票率 - B：生物特征验证分布情况 - C：每位选民选举成本（箱线图） - D：观察员评级与合规得分的相关性 - E：选举争议数量与争议解决时长的关系 - F：选举质量分类分布 - G：选举结果传输时长 - H：电子投票占比与互联网普及率 ## 局限性 - 本数据集为**合成数据集**，不代表任何特定选举周期的实际选举结果。 - 各国先验参数基于文献估算，未涵盖次国家层面的差异。 - 场景修改仅为示例：现实中的现代化或争议动态涉及复杂的制度与政治因素，本数据集未完全覆盖。 - 选举质量分类基于简化的综合得分，不应作为任何国家选举的权威评估依据。 ## 引用说明 若您使用本数据集，请引用以下文献： bibtex @dataset{african_election_infrastructure_2026, title={African Election Infrastructure Dataset}, year={2026}, license={cc-by-4.0}, note={Synthetic dataset of electoral infrastructure across 12 SSA countries} } ## 许可证 CC-BY-4.0