electricsheepasia/asia-community-perception-in-earthquake-affected-nepal-round-5

Community Perception in Earthquake Affected Nepal Round 5

Publisher: UN in Nepal · Source: HDX · License: cc-by-sa · Updated: 2023-05-24

Abstract

Round 5 of monthly Community Perception Survey of 1400 respondents in 14 priority affected districts of Nepal, post-earthquake. The data is collected by Accountability Lab and Local Interventions Group, as part of the Inter Agency Common Feedback Project, which aims to capture and represent the perceptions of earthquake affected communities within the response and recovery effort.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2023-05-24. Geographic scope: NPL, NEPAL-EARTHQUAKE.

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

Dataset Characteristics

Variables

Geographic — district (Dhading, Kavrepalanchok, Kathmandu), ward (range 1.0–17.0), caste_ethnicity (Brahmin, Chhetri, Newar), do_you_have_any_health_problem (nodifficulty, yessomediff, yesalotof), `1areyourmainproblemsbeingaddressed` (1notatall, 2notverymuch, 4somewhat_yes) and 18 others.

Demographic — age (40-54, 25-39, 55+), gender (male, female, other).

Identifier / Metadata — vdc_name (madhyapurnp, bhaktapurnp, changurayan), esa_source, esa_processed.

Other — occupation (farmerlaborer, other, ngoworkerbus), `7overallisthepostearthquakereliefeffortmakingprogress`.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-community-perception-in-earthquake-affected-nepal-round-5")
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`. 16 column(s) with >80% missing values were removed: `ifotherwhatisyourcaste, ifotheroccupa, forthefollowingquestionsi, 1aifotherwhatisyourbiggestproblem`, `1bifotherwhatisyoursecondbiggestproblem`, `1cifotherwhatisyourthirdbiggest_problem`.... 10 exact duplicate rows were removed. 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 UN in Nepal 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: 1a_what_is_your_biggest_problem, 1b_what_is_your_second_biggest_problem, 1c_what_is_your_third_biggest_problem, 2a_what_is_top_reason_you_are_not_satisfied_with_government, 2b_what_is_second_reason_you_are_not_satisfied_with_government, 3a_what_is_the_top_things_you_need_information_about, 3b_what_is_the_second_things_you_need_information_about, 4a_why_are_you_not_satisfied_with_ngo_support....
• —This dataset spans 2 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_community_perception_in_earthquake_affected_nepal_round_5,
  title     = {Community Perception in Earthquake Affected Nepal Round 5},
  author    = {UN in Nepal},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/community-perception-in-earthquake-affected-nepal-round-5},
  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.