electricsheepasia/asia-cfp-reconstruction-032017

Nepal -CFP-reconstruction survey

Publisher: Inter Agency Common Feedback Project Nepal (inactive) · Source: HDX · License: cc-by · Updated: 2023-03-02

Abstract

This data is collected from the survey conducted in 14 earthquake affected district in Nepal in May 2017. Total of 2100 respondent were interviewed.

All VDCs in the 14 priority affected districts in which 60 percent or more of the households are eligible for the housing reconstruction grant will be considered part of the survey’s operating area, and eligible for random selection. The population of each district will be considered the total population of all eligible VDCs, as per the 2011 census. The first 2000 samples of the survey will then be distributed by district proportionally. The remaining 100 surveys will be allocated to districts where the total proportional sample size is under 100 respondents, in order to boost the population for an adequate district level analysis of the findings. The number of VDCs selected in each district will vary, depending upon the number of samples allocated to each district. Each VDC will have a minimum of two wards sampled, and each ward a minimum of 10 surveys completed. Both VDCs and wards will be randomly selected from the list of eligible VDCs. Twenty-ve percent of the total sample will be allocated for municipalities, and municipalities will be randomly selected where there is more than one municipality in a district. In municipalities a minimum of three wards will be sampled, with a minimum of 10 surveys collected per ward

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2023-03-02. 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, Sindhupalchowk), ward (range 1.0–15.0), caste_ethnicity (Tamang, Brahmin, Chhetri), what_is_your_mother_tongue (Nepali, tamang, Newari), do_you_have_any_health_problems_that_have_caused_you_difficulty_to_move_see_speak_understand_learn_or_communicate (No difficulty, Yes, some difficulty, Yes, a lot of difficulty) and 37 others.

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

Identifier / Metadata — esa_source, esa_processed.

Other — rural_urban (Rural, Urban), occupation (Agriculture, Home maker/ Housewife, Labourer), direct_cash_transfer, government_housing_grant_nra, self_labour and 6 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-cfp-reconstruction-032017")
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`. 73 column(s) with >80% missing values were removed: `ifotherpleasespecify, ifotherwhatisyourcasteethnicity, ifotherpleasespecifyyourmothertongue, ifotherpleasespecify1, ifotherpleasespecify2, ifotherpleasespecify3`.... 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 Inter Agency Common Feedback Project Nepal (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.
• —The following columns have >20% missing values and should be treated with caution in modelling: 1a_what_is_your_top_most_reconstruction_issues, 1b_what_is_your_second_top_most_reconstruction_issues, 2a_what_is_your_top_most_important_information_you_need, 2b_what_is_your_second_top_most_important_information_you_need, 3a_from_which_source_the_engineer_was_available_for_your_consultation, 3b_what_was_the_suggestions_or_advice_given_by_the_engineer_regarding_your_house, 3c_why_have_you_not_consulted_an_engineer, 5a_what_types_of_housing_reconstruction_support_you_have_received....
• —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_cfp_reconstruction_032017,
  title     = {Nepal -CFP-reconstruction survey},
  author    = {Inter Agency Common Feedback Project Nepal  (inactive)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/cfp-reconstruction-032017},
  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.