electricsheepasia/asia-population-unosat-tropical-cyclone-mocha-23-populat

UNOSAT - Tropical Cyclone MOCHA 23 - Population Exposure Analysis in Bangladesh and Myanmar -13 May 2023

Publisher: United Nations Satellite Centre (UNOSAT) · Source: HDX · License: cc-by-sa · Updated: 2025-08-26

Abstract

UNOSAT code TC20230510BGD, GDACS Id: 1000970 Tropical Cyclone MOCHA formed over the southern Bay of Bengal on 11 May 2023 and has since then continued to move towards western Myanmar and southern Bangladesh. On 13 May 2023 06:00 UTC, the centre of the cyclone was located over the sea close to Sittwe City (the capital of Rakhine State, western Myanmar, south-eastern Bangladesh), with maximum sustained winds of 231 km/h. According to the forecast by GDACS, tropical cyclone MOCHA can have a high humanitarian impact based on the maximum sustained wind speed, exposed population, and vulnerability.

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-08-26. Geographic scope: BGD.

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

Dataset Characteristics

Variables

Geographic — population (range 44576.7681–23035460.7679), total_population_exposed (range 44576.7681–24351706.6431).

Identifier / Metadata — unnamed_2 (Barguna, Barishal, Bhola), unnamed_4 (range 0.0–1103487.0), unnamed_5 (range 0.0–212758.8752), esa_source (HDX), esa_processed (2026-05-05).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-population-unosat-tropical-cyclone-mocha-23-populat")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 2 column(s) with >80% missing values were removed: `divisiondistrict, unnamed_1`. 1 exact duplicate rows were removed. 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 United Nations Satellite Centre (UNOSAT) and has not been independently validated by ESA.
• —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• —The following columns have >20% missing values and should be treated with caution in modelling: unnamed_2, unnamed_4, unnamed_5.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_population_unosat_tropical_cyclone_mocha_23_populat,
  title     = {UNOSAT - Tropical Cyclone MOCHA 23 - Population Exposure Analysis  in Bangladesh and Myanmar -13 May 2023},
  author    = {United Nations Satellite Centre (UNOSAT)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/unosat-tropical-cyclone-mocha-23-population-exposure-analysis-in-bangladesh-and-myanmar-13},
  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.