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---
license: gpl
dataset_name: nigerian_transport_and_logistics_blockchain_logistics
pretty_name: Nigeria Transport & Logistics – Blockchain Logistics Records
size_categories: [10K<n<1M]
task_categories: [time-series-forecasting, tabular-regression, tabular-classification, other]
tags: [nigeria, transport, logistics, mobility, fleet, supply-chain]
language: [en]
created: 2025-10-12
---
# Nigeria Transport & Logistics – Blockchain Logistics Records
On-chain logistics events with actors, custody, network, and validity.
- **[category]** Emerging & Advanced
- **[rows]** ~120,000
- **[formats]** CSV + Parquet (snappy)
- **[geography]** Nigeria (major cities, corridors, ports, airports)
## Schema
| column | dtype |
|---|---|
| timestamp | object |
| tx_hash | object |
| shipment_id | object |
| action | object |
| actor_wallet | object |
| chain | object |
| custody | object |
| valid | bool |
| note | object |
## Usage
```python
import pandas as pd
df = pd.read_parquet('data/nigerian_transport_and_logistics_blockchain_logistics/nigerian_transport_and_logistics_blockchain_logistics.parquet')
df.head()
```
```python
from datasets import load_dataset
ds = load_dataset('electricsheepafrica/nigerian_transport_and_logistics_blockchain_logistics')
ds
```
## Notes
- Nigeria-specific parameters (fleets, roads, traffic, fuel prices)
- Time-of-day traffic effects and seasonal impacts where applicable
- Physical plausibility checks embedded during generation
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