electricsheepasia/asia-risk-assessment-site-priority-rasp

Risk Assessment Site Priority (RASP)

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

Abstract

The RASP is the technical tool of the CCCM Cluster which captures data from informal settlements and provides location specific information about the population, their living conditions and humanitarian needs.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the date, background_date_first_occupied column(s). Geographic scope: IRQ.

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

Dataset Characteristics

Variables

Geographic — district (Tikrit, Kerbala, Najaf), coordinates_latitude (range 30.4525–34.6616), coordinates_longitude (range 43.4979–47.8558), site_typology (collective shelter, dispersed settlements, self settled camp), shelter_types_tent (range 0.0–8.0) and 150 others.

Temporal — date, background_date_first_occupied, nfi_shelter_winter_season, nfi_shelter_summer_season.

Demographic — hh_total_families (range 5.0–1750.0), hh_total_families_confirm (Yes), background_males_0_4 (range 0.0–450.0), background_females_0_4 (range 0.0–485.0), background_males_5_17 (range 1.0–1343.0) and 6 others.

Outcome / Measurement — governorate (Salah al-Din, Kerbala, Najaf), wash_water_amount, livelihoods_income.

Identifier / Metadata — camp_name (Unfinished house, Khan dari Hay Al Shuhdaa, Al Bassam -Qasam Al Sheyahan village ), site_ssid (IQ1202-0430-036, IQ1202-0430-033, IQ1202-0430-034), vulnerabilities_groups_widow, sanitation_drinking_natural_source, esa_source and 1 others.

Other — site_active (Yes), vulnerabilities_groups_f_hoh, vulnerabilities_groups_child_hoh, vulnerabilities_groups_unaccompanied, vulnerabilities_groups_ppl_mental_dis and 57 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-risk-assessment-site-priority-rasp")
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`. 23 column(s) with >80% missing values were removed: `othersheltertype`, `socialcohesionevictwhen, socialcohesiongovernoratemove`, `socialcohesionleadershipstructureother`, `physicalenvironmentelecconcernsothers`, `physicalenvironmentfireequipment`.... 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 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: site_ssid, site_active, social_cohesion_whatform, social_cohesion_whatarrangement, social_cohesion_leadership_structure_who, social_cohesion_leadership_structure_who_elder_religious_leaders, social_cohesion_leadership_structure_who_camp_committee, social_cohesion_leadership_structure_who_local_auth....
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_risk_assessment_site_priority_rasp,
  title     = {Risk Assessment Site Priority (RASP)},
  author    = {CCCM Cluster},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/risk-assessment-site-priority-rasp},
  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.