electricsheepafrica/africa-population-density-of-kenya-in-1999

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- baseline-population
- geodata
- ken
pretty_name: "Population density of Kenya in 1999."
dataset_info:
splits:
- name: train
num_examples: 5297
- name: test
num_examples: 1324
---

# Population density of Kenya in 1999.

**Publisher:** World Resources Institute (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/population-density-of-kenya-in-1999) · **License:** `other-pd-nr` · **Updated:** 2022-07-21

---

## Abstract

Population density, or the number of people per square kilometer, in Kenya in 1999.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2022-07-21. Geographic scope: **KEN**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Demographics and population |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 6,622 |
| **Columns** | 21 (13 numeric, 8 categorical, 0 datetime) |
| **Train split** | 5,297 rows |
| **Test split** | 1,324 rows |
| **Geographic scope** | KEN |
| **Publisher** | World Resources Institute (inactive) |
| **HDX last updated** | 2022-07-21 |

---

## Variables

**Demographic** — `sum_males` (range 0.0–56464.0), `sum_female` (range 0.0–50905.0).

**Outcome / Measurement** — `sum_total` (range 0.0–107369.0).

**Identifier / Metadata** — `slid` (range 101010101.0–808040703.0), `slname` (TOWNSHIP, MAJENGO, MILIMANI), `locid` (range 1010101.0–70109020.0), `locname` (TOWNSHIP, MUKARO, IRIA-INI), `divid` (range 10101.0–80804.0) and 8 others.

**Other** — `constituen` (TINDERET, BARINGO CENTRAL, KAPENGURIA), `sum_househ` (range 0.0–35098.0), `sum_grtota` (range 0.0–109956.0), `area_km2` (range 0.0605–10715.4623), `grtot_dens` (range 0.0–90059.5238).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-population-density-of-kenya-in-1999")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `slid` | int64 | 0.0% | 101010101.0 – 808040703.0 (mean 542266385.3667) |
| `slname` | object | 0.0% | TOWNSHIP, MAJENGO, MILIMANI |
| `locid` | int64 | 0.0% | 1010101.0 – 70109020.0 (mean 5432196.9481) |
| `locname` | object | 0.0% | TOWNSHIP, MUKARO, IRIA-INI |
| `divid` | int64 | 0.0% | 10101.0 – 80804.0 (mean 54226.6037) |
| `divname` | object | 0.0% | CENTRAL, KILIBWONI, CHEPARERIA |
| `distid` | int64 | 0.0% | 101.0 – 808.0 (mean 542.2212) |
| `distname` | object | 0.0% | NANDI, MACHAKOS, WEST POKOT |
| `provid` | int64 | 0.0% | 1.0 – 8.0 (mean 5.357) |
| `provname` | object | 0.0% | RIFT VALLEY, EASTERN, NYANZA |
| `constituen` | object | 0.0% | TINDERET, BARINGO CENTRAL, KAPENGURIA |
| `const_code` | int64 | 0.0% | 1.0 – 210.0 (mean 109.5574) |
| `sum_househ` | float64 | 0.2% | 0.0 – 35098.0 (mean 967.9225) |
| `sum_males` | float64 | 0.2% | 0.0 – 56464.0 (mean 2105.0098) |
| `sum_female` | float64 | 0.2% | 0.0 – 50905.0 (mean 2172.144) |
| `sum_total` | float64 | 0.2% | 0.0 – 107369.0 (mean 4277.1539) |
| `sum_grtota` | float64 | 0.2% | 0.0 – 109956.0 (mean 4300.021) |
| `area_km2` | float64 | 0.0% | 0.0605 – 10715.4623 (mean 88.0783) |
| `grtot_dens` | float64 | 0.2% | 0.0 – 90059.5238 (mean 653.1239) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `slid` | 101010101.0 | 808040703.0 | 542266385.3667 | 606050501.5 |
| `locid` | 1010101.0 | 70109020.0 | 5432196.9481 | 6060504.0 |
| `divid` | 10101.0 | 80804.0 | 54226.6037 | 60605.0 |
| `distid` | 101.0 | 808.0 | 542.2212 | 606.0 |
| `provid` | 1.0 | 8.0 | 5.357 | 6.0 |
| `const_code` | 1.0 | 210.0 | 109.5574 | 112.0 |
| `sum_househ` | 0.0 | 35098.0 | 967.9225 | 645.0 |
| `sum_males` | 0.0 | 56464.0 | 2105.0098 | 1503.0 |
| `sum_female` | 0.0 | 50905.0 | 2172.144 | 1602.0 |
| `sum_total` | 0.0 | 107369.0 | 4277.1539 | 3123.0 |
| `sum_grtota` | 0.0 | 109956.0 | 4300.021 | 3128.0 |
| `area_km2` | 0.0605 | 10715.4623 | 88.0783 | 16.3649 |
| `grtot_dens` | 0.0 | 90059.5238 | 653.1239 | 206.312 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from World Resources Institute (inactive) and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/population-density-of-kenya-in-1999) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_population_density_of_kenya_in_1999,
title = {Population density of Kenya in 1999.},
author = {World Resources Institute (inactive)},
year = {2022},
url = {https://data.humdata.org/dataset/population-density-of-kenya-in-1999},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*