electricsheepasia/asia-iraq-fsmt-sites

Formal Sites Monitoring Tool (FSMT)

Publisher: CCCM Cluster · Source: HDX · License: cc-by · Updated: 2023-10-18

Abstract

The Formal Sites Monitoring Tool (FSMT) is a camp management monitoring tool, designed to provide a synopsis of the main demographic information at site level as well key humanitarian indicators for all formal sites across Iraq.

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2023-10-18. Geographic scope: IRQ.

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

Dataset Characteristics

Variables

Geographic — coordinates_latitude (range 31.8858–37.1982), coordinates_longitude (range 42.4453–47.1678), site_typology (camp, collectivecentre), `populationtracking, extensiontotalplots` (range 23.0–6000.0) and 32 others.

Temporal — month (november, december, october).

Demographic — camp_mngmt_male_staff (range 0.0–38.0), camp_mngmt_female_staff (range 0.0–6.0), total_hh_total_families (range 28.0–6054.0), ind_ages_ind_age_groups_males_0_4 (range 11.0–2816.0), ind_ages_ind_age_groups_females_0_4 (range 7.0–2927.0) and 16 others.

Outcome / Measurement — governorate (Anbar, Dahuk, Ninewa).

Identifier / Metadata — camp_name (caravans camp, Al-Hijra, Al-Zera'aa), enum_name (Mahdi Ahmed, Ahmed kareem mohammed, Omar Mohammed), site_name (IQ0102-0019, IQ0102-0001, IQ0102-0033), wash_wash_source, esa_source and 1 others.

Other — enum_nub (610, 11, 5), area (AAF, aaf, htc-aq), sub_site (IQ0102-0033-001, IQ0102-0001-019, IQ0102-0001-011), camp_mngt (yes, no), camp_mngmt_office and 46 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-iraq-fsmt-sites")
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`. 3 column(s) with >80% missing values were removed: `sitenew, nfiitemsotherspecify`, `whowhatpartnerslivelihood`. 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 CCCM Cluster 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: area, sub_site, health_health_femaleworkers, education_boys_primary, education_boys_secondary, education_girls_primary, education_girls_secondary, education_non_formal_participation....
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_iraq_fsmt_sites,
  title     = {Formal Sites Monitoring Tool (FSMT)},
  author    = {CCCM Cluster},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/iraq-fsmt-sites},
  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.