README.md

---
license: other
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: multilingual
size_categories:
- 1K<n<10K
tags:
- "tabular"
- "africa"
- "open-data"
- "official-statistics"
- "mozambique"
- "demovis"
- "transport"
- "verbetes-inquerito-mensal-aos-comandos-da-policia"
- "novo-verbete-comandosprm03092020-1-xlsx"
- "2020"
- "survey"
- "document"
- "questionnaire-doc"
- "qst"
- "questionarios"
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-00000-of-00001.parquet
pretty_name: "Inquerito Crime E Justica 2020 | Africa (DEMOVIS)"
---

# Inquerito Crime E Justica 2020 | Africa (DEMOVIS)

**1,150 rows** - **1 Africa country/area** - **2020** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*

![rows](https://img.shields.io/badge/rows-1150-blue)
![countries](https://img.shields.io/badge/countries-1-green)
![period](https://img.shields.io/badge/period-2020-orange)
![indicators](https://img.shields.io/badge/indicators-0-purple)
![license](https://img.shields.io/badge/license-other-lightgrey)

## TL;DR

This dataset contains **1,150 rows** from **DEMOVIS**, covering **Inquerito Crime E Justica 2020**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

## What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

Source-provided context: Document, Questionnaire [doc/qst]

## How To Read This Dataset

- **One row means:** one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
- **Primary geography column:** `country_iso3`.
- **Best time column:** `year`.
- **Time coverage basis:** year.
- **Recommended join keys:** `country_iso3` where available plus source-specific keys.

## Coverage

| Dimension | Value |
|---|---:|
| Rows | 1,150 |
| Countries/areas | 1 |
| First period | 2020 |
| Last period | 2020 |
| Indicators | 0 |
| Columns | 60 |
| Source format | XLSX |

## Geographic Coverage

Top areas shown below, sorted by row count when available:

| Area | Rows | First year | Last year | Name |
|------|-----:|-----------:|----------:|------|
| `MOZ` | 1,150 | 2020 | 2020 | `Mozambique` |

## Indicators, Variables, Or Resource Contents

- This repo preserves one source tabular resource with its usable columns kept together.

## Schema

| Column | Type | Description | Example |
|--------|------|-------------|---------|
| `source_record_id` | `string` | Stable row identifier assigned during Electric Sheep Africa engineering. | `moz-ine-nada-60-337:policia:0` |
| `country_iso3` | `dictionary<values=string, indices=int8, ordered=0>` | ISO3 country or area code. | `MOZ` |
| `country_name` | `dictionary<values=string, indices=int8, ordered=0>` | Country or area name. | `Mozambique` |
| `source_sheet` | `string` | Source column from the original resource. | `Policia` |
| `year` | `int64` | Observation year. | `2020` |
| `tipos_de_crimes` | `string` | Source column from the original resource. | `Titulo 1- CRIMES CONTRA PESSOAS` |
| `conhecidos` | `string` | Source column from the original resource. | `` |
| `column_3` | `string` | Source column from the original resource. | `` |
| `column_4` | `string` | Source column from the original resource. | `` |
| `column_5` | `string` | Source column from the original resource. | `` |
| `column_6` | `string` | Source column from the original resource. | `` |
| `column_7` | `string` | Source column from the original resource. | `` |
| `column_8` | `string` | Source column from the original resource. | `` |
| `column_9` | `string` | Source column from the original resource. | `` |
| `esclarecidos` | `string` | Source column from the original resource. | `` |
| `column_11` | `string` | Source column from the original resource. | `` |
| `column_12` | `string` | Source column from the original resource. | `` |
| `column_13` | `string` | Source column from the original resource. | `` |
| `column_14` | `string` | Source column from the original resource. | `` |
| `column_15` | `string` | Source column from the original resource. | `` |
| `column_16` | `string` | Source column from the original resource. | `` |
| `nao_esclarecidos` | `string` | Source column from the original resource. | `` |
| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2020` |
| `source_period_end_year` | `int64` | End year inferred from source metadata. | `2020` |
| `source_period_label` | `dictionary<values=string, indices=int8, ordered=0>` | Source column from the original resource. | `2020` |
| `source_provider` | `dictionary<values=string, indices=int8, ordered=0>` | Publishing organization. | `DEMOVIS` |
| `source_dataset` | `dictionary<values=string, indices=int8, ordered=0>` | Source dataset or package title. | `Inquerito Crime e Justica 2020` |
| `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `Verbetes - Inquérito Mensal aos Comandos da Polícia` |
| `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `MZ-JC-2020` |
| `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `moz-ine-nada-60-337` |
| `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://mozdata.ine.gov.mz/index.php/catalog/60/download/337` |
| `license_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source license identifier. | `other-open` |
| `retrieved_at` | `dictionary<values=string, indices=int8, ordered=0>` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-08-07T22:10:37Z` |
| `column_10` | `string` | Source column from the original resource. | `` |
| `column_17` | `string` | Source column from the original resource. | `` |
| `column_1` | `string` | Source column from the original resource. | `` |
| `h` | `string` | Source column from the original resource. | `` |
| `m` | `string` | Source column from the original resource. | `` |
| `h_2` | `string` | Source column from the original resource. | `` |
| `m_2` | `string` | Source column from the original resource. | `` |
| `h_3` | `string` | Source column from the original resource. | `` |
| `m_3` | `string` | Source column from the original resource. | `` |
| `h_4` | `string` | Source column from the original resource. | `` |
| `m_4` | `string` | Source column from the original resource. | `` |
| `h_5` | `string` | Source column from the original resource. | `` |
| `m_5` | `string` | Source column from the original resource. | `` |
| `h_6` | `string` | Source column from the original resource. | `` |
| `m_6` | `string` | Source column from the original resource. | `` |
| `h_7` | `string` | Source column from the original resource. | `` |
| `m_7` | `string` | Source column from the original resource. | `` |
| `h_8` | `string` | Source column from the original resource. | `` |
| `m_8` | `string` | Source column from the original resource. | `` |
| `h_9` | `string` | Source column from the original resource. | `` |
| `m_9` | `string` | Source column from the original resource. | `` |
| `h_10` | `string` | Source column from the original resource. | `` |
| `m_10` | `string` | Source column from the original resource. | `` |
| `h_11` | `string` | Source column from the original resource. | `` |
| `m_11` | `string` | Source column from the original resource. | `` |
| `h_12` | `string` | Source column from the original resource. | `` |
| `m_12` | `string` | Source column from the original resource. | `` |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mozambique-inquerito-crime-e-justica-2020-0a8badad")
df = ds["train"].to_pandas()
print(df.head())
```

### Inspect Columns

```python
print(df.info())
print(df.head())
```

### Filter By Geography

```python
if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MOZ"]
```

### Time-Series Pattern

```python
if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")
```

### Pivot For Analysis

```python
if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())
```

## Data Quality Notes

- Canonical time field: `year`.
- Missing values are preserved rather than silently imputed.
- Column names are standardized for machine use; source meanings are preserved where known.
- Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

## Source And Provenance

- **Source:** [DEMOVIS](https://mozdata.ine.gov.mz/index.php/catalog/60/related-materials)
- **Publisher:** DEMOVIS
- **Portal:** [https://mozdata.ine.gov.mz](https://mozdata.ine.gov.mz)
- **Resource:** [Verbetes - Inquérito Mensal aos Comandos da Polícia](https://mozdata.ine.gov.mz/index.php/catalog/60/download/337)
- **License:** other-open
- **Retrieved/generated:** `2026-08-07T22:11:08Z`
- **Hugging Face repo:** [electricsheepafrica/africa-mozambique-inquerito-crime-e-justica-2020-0a8badad](https://huggingface.co/datasets/electricsheepafrica/africa-mozambique-inquerito-crime-e-justica-2020-0a8badad)

## Transformations Applied

- Converted the source table to Parquet for efficient analytics and ML workflows.
- Added or preserved source provenance columns where available.
- Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
- Preserved source-reported values without analytical imputation.

## Suggested Analyses

- Track mobility over time
- Compare routes or geographies
- Join with economic and population data
- Build time-series views and period-over-period comparisons
- Check missingness before modeling
- Use `country_iso3` as the safest geography join key when present

## Citation

```bibtex
@misc{electric_sheep_africa_africa_mozambique_inquerito_crime_e_justica_2020_0a8badad_2020,
  title        = {Inquerito Crime E Justica 2020 | Africa (DEMOVIS)},
  author       = {DEMOVIS},
  year         = {2020},
  url          = {https://mozdata.ine.gov.mz/index.php/catalog/60/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mozambique-inquerito-crime-e-justica-2020-0a8badad}}
}
```

## License

Released under other-open.

Original data is published by DEMOVIS. Electric Sheep Africa
engineering standardizes the data for discovery, loading, and analysis on
Hugging Face. Cite both the original source and this ML-ready dataset when used.

## About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

---

Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://mozdata.ine.gov.mz/index.php/catalog/60/related-materials