File size: 9,901 Bytes
2549f40
 
 
 
 
 
 
8d34cdd
2549f40
 
 
8d34cdd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2549f40
 
 
 
 
8d34cdd
2549f40
 
8d34cdd
2549f40
8d34cdd
2549f40
 
 
8d34cdd
2549f40
 
 
 
 
8d34cdd
2549f40
8d34cdd
2549f40
8d34cdd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2549f40
8d34cdd
2549f40
8d34cdd
2549f40
8d34cdd
 
2549f40
 
8d34cdd
2549f40
8d34cdd
2549f40
 
 
 
 
8d34cdd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2549f40
 
 
 
 
 
 
 
 
 
 
8d34cdd
2549f40
 
8d34cdd
 
2549f40
 
8d34cdd
2549f40
 
8d34cdd
 
2549f40
 
8d34cdd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2549f40
 
 
 
8d34cdd
2549f40
 
 
8d34cdd
2549f40
 
 
 
 
 
8d34cdd
2549f40
8d34cdd
 
 
2549f40
8d34cdd
2549f40
8d34cdd
2549f40
 
 
8d34cdd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
---
license: other
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: multilingual
size_categories:
- n<1K
tags:
- "tabular"
- "africa"
- "open-data"
- "official-statistics"
- "mozambique"
- "demovis"
- "transport"
- "verbete-inquerito-mensal-aos-tribunais"
- "novo-verbete-tribunais12052021-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)

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

![rows](https://img.shields.io/badge/rows-459-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 **459 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 | 459 |
| Countries/areas | 1 |
| First period | 2020 |
| Last period | 2020 |
| Indicators | 0 |
| Columns | 40 |
| Source format | XLSX |

## Geographic Coverage

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

| Area | Rows | First year | Last year | Name |
|------|-----:|-----------:|----------:|------|
| `MOZ` | 459 | 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-60-338:tribunais: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. | `Tribunais` |
| `year` | `int64` | Observation year. | `2020` |
| `column_1` | `string` | Source column from the original resource. | `Querela` |
| `reus_presos` | `string` | Source column from the original resource. | `` |
| `column_3` | `string` | Source column from the original resource. | `` |
| `reus_nao_presos` | `string` | Source column from the original resource. | `` |
| `column_5` | `string` | Source column from the original resource. | `` |
| `reus_presos_2` | `string` | Source column from the original resource. | `` |
| `column_7` | `string` | Source column from the original resource. | `` |
| `reus_nao_presos_2` | `string` | Source column from the original resource. | `` |
| `column_9` | `string` | Source column from the original resource. | `` |
| `reus_presos_3` | `string` | Source column from the original resource. | `` |
| `column_11` | `string` | Source column from the original resource. | `` |
| `reus_nao_presos_3` | `string` | Source column from the original resource. | `` |
| `column_13` | `string` | Source column from the original resource. | `` |
| `reus_presos_4` | `string` | Source column from the original resource. | `` |
| `column_15` | `string` | Source column from the original resource. | `` |
| `reus_nao_presos_4` | `string` | Source column from the original resource. | `` |
| `column_17` | `string` | Source column from the original resource. | `` |
| `reus_presos_5` | `string` | Source column from the original resource. | `0` |
| `column_19` | `string` | Source column from the original resource. | `` |
| `column_20` | `string` | Source column from the original resource. | `` |
| `reus_nao_presos_5` | `string` | Source column from the original resource. | `0` |
| `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. | `Verbete - Inquérito Mensal aos Tribunais` |
| `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-60-338` |
| `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/338` |
| `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` |
| `caracteristicas` | `string` | Source column from the original resource. | `` |
| `verbete_vigente` | `string` | Source column from the original resource. | `` |
| `verbete_actualizado` | `string` | Source column from the original resource. | `` |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mozambique-inquerito-crime-e-justica-2020-7acd4028")
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:** [Verbete - Inquérito Mensal aos Tribunais](https://mozdata.ine.gov.mz/index.php/catalog/60/download/338)
- **License:** other-open
- **Retrieved/generated:** `2026-08-07T22:11:07Z`
- **Hugging Face repo:** [electricsheepafrica/africa-mozambique-inquerito-crime-e-justica-2020-7acd4028](https://huggingface.co/datasets/electricsheepafrica/africa-mozambique-inquerito-crime-e-justica-2020-7acd4028)

## 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_7acd4028_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-2020-7acd4028}}
}
```

## 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