Dataset Overview
| Dataset Name | Land Expropriation Compensation Project Dataset |
| Total Data Volume | 8,400 project cases (covering house demolition and relocation + the full land expropriation process) |
| Type of project | Expropriation of houses on state-owned land / collectivization of collectively owned land / urban village improvement / shantytown improvement / old town renovation |
| Time Span | 2012 - 2026 |
| CoverageDimension | Project profile, compensation plan, contract execution progress, funds management, judicial procedures, petition and correspondence security |
| Data Source | Government office systems, natural resources sector, housing sector public data, network of adjudicatory documents |
| Format Output | JSON / CSV / Excel / Knowledge Graph |
Detailed Description of Data Dimensions
| Field Name | Data Type | Value Range / Description |
|---|---|---|
| project_id | String(32) | Item _UNIQUE_TECH_Evil _ID_UNIQUE_TECH__ |
| project_name | Text | Project name (de-identified) |
| project_type | Enum | HOUSE_EXPROPRIATION | LAND_ACQUISITION | URBAN_RENEWAL | SHANTYTOWN |
| region | String | Province-City-District |
| total_area_sqm | Float | Total area acquired (sq m) |
| household_count | Integer | Number of households involved |
| total_budget | Float | Total project budget (thousands of dollars) |
| compensation_mode | Enum | MONETARY | PROPERTY_SWAP | VOUCHER | HYBRID |
| signing_rate | Float | Signing rate 0.0 - 1.0 |
| project_duration_days | Integer | Project duration (days) |
| litigation_count | Integer | Number of cases involving the judiciary |
| milestones | Array | [{phase, date, status, remark}] |
De-identified JSON Data Sample
[{ "project_id": "LAC-2024-GD-SZ-0012", "project_name": "[Shenzhen][[]]urban renewal[]", "project_type": "URBAN_RENEWAL", "region": "[]-[Shenzhen]-[]", "total_area_sqm": 48500.0, "household_count": 312, "total_budget": 82500.0, "compensation_mode": "VOUCHER", "signing_rate": 0.93, "project_duration_days": 540, "litigation_count": 3, "milestones": [ {"phase": "[]", "date": "2024-01-15", "status": "COMPLETED"}, {"phase": "[]", "date": "2024-03-20", "status": "COMPLETED"}, {"phase": "[]", "date": "2024-05-01", "status": "COMPLETED"}, {"phase": "[]", "date": "2025-01-15", "status": "IN_PROGRESS"} ] }, { "project_id": "LAC-2023-ZJ-HZ-0045", "project_name": "Hangzhou[]", "project_type": "LAND_ACQUISITION", "region": "[Zhejiang]-[Hangzhou]-[]", "total_area_sqm": 123000.0, "household_count": 186, "total_budget": 45200.0, "compensation_mode": "PROPERTY_SWAP", "signing_rate": 0.97, "project_duration_days": 420, "litigation_count": 0, "milestones": [ {"phase": "[]", "date": "2023-06-01", "status": "COMPLETED"}, {"phase": "[]", "date": "2023-08-15", "status": "COMPLETED"}, {"phase": "[]", "date": "2023-10-01", "status": "COMPLETED"} ] }, { "project_id": "LAC-2024-HN-CS-0078", "project_name": "Changsha[][[]][]", "project_type": "URBAN_RENEWAL", "region": "[]-Changsha[]-[]", "total_area_sqm": 28700.0, "household_count": 205, "total_budget": 36800.0, "compensation_mode": "HYBRID", "signing_rate": 0.89, "project_duration_days": 365, "litigation_count": 5, "milestones": [ {"phase": "[]", "date": "2024-02-10", "status": "COMPLETED"}, {"phase": "[]", "date": "2024-04-20", "status": "COMPLETED"}, {"phase": "[]", "date": "2024-06-01", "status": "IN_PROGRESS"} ] }, { "project_id": "LAC-2022-JS-NJ-0091", "project_name": "[Nanjing][[]][]", "project_type": "SHANTYTOWN", "region": "[]-[Nanjing]-[]", "total_area_sqm": 15200.0, "household_count": 478, "total_budget": 62300.0, "compensation_mode": "MONETARY", "signing_rate": 0.95, "project_duration_days": 480, "litigation_count": 2, "milestones": [ {"phase": "[]", "date": "2022-09-01", "status": "COMPLETED"}, {"phase": "[]", "date": "2023-01-10", "status": "COMPLETED"}, {"phase": "[]", "date": "2023-09-30", "status": "COMPLETED"} ] }, { "project_id": "LAC-2025-SC-CD-0015", "project_name": "Chengdu[]housing expropriation", "project_type": "HOUSE_EXPROPRIATION", "region": "[]-[Chengdu]-[]", "total_area_sqm": 35200.0, "household_count": 156, "total_budget": 51300.0, "compensation_mode": "HYBRID", "signing_rate": 0.91, "project_duration_days": 390, "litigation_count": 1, "milestones": [ {"phase": "[]", "date": "2025-01-05", "status": "COMPLETED"}, {"phase": "[]", "date": "2025-03-15", "status": "COMPLETED"}, {"phase": "[]", "date": "2025-05-01", "status": "IN_PROGRESS"} ] }
]
AI Training Use Cases
Projected Compensation Amount
Models applied: XGBoost / LightGBM / CatBoost. A compensation amount projection model based on multi-dimensional training of historical project data, supporting budgeting.
Contract Signing Rate Forecasting and Risk Warning
Models applied: Gradient Boosting + SHAP. Forecasts the project signing rate, identifies the key factors affecting contract signing, and provides early risk warnings.
Project Milestone Schedule Collection
Models applied: CCM/PERT + Reinforcement Learning (DQN). A lead-time strategy model trained on historical project critical path data.
Forecast of Administrative Litigation Outcomes
Applied model: Legal-BERT + Multi-Coder. Based on the training model for training in recovery justice cases, supporting response strategy development.
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