Dataset: Earthquake Magnitude, Damage And Impact

About this Dataset

Earthquake Magnitude, Damage And Impact

Context

In April of 2015 the 7.8 magnitude Gorkha earthquake occured near the Gorkha district of Gandaki Pradesh, Nepal. Almost 9,000 lives were lost, millions of people were instantly made homeless, and $10 billion in damages––about half of Nepal's nominal GDP––were incurred. In the years since, the Nepalese government has worked intensely to help rebuild the affected districts' infrastructures. Throughout this process, the National Planning Commission, along with Kathmandu Living Labs and the Central Bureau of Statistics, has generated

One of the largest post-disaster datasets ever collected, containing valuable information on earthquake impacts, household conditions, and socio-economic-demographic statistics.

Content

The dataset mainly consists of information on the buildings' structure and their legal ownership. Each row in the dataset represents a specific building in the region that was hit by Gorkha earthquake.

There are 39 columns in this dataset, where the building_id column is a unique and random identifier. The remaining 38 features are described in the section below. Categorical variables have been obfuscated random lowercase ascii characters. The appearance of the same character in distinct columns does not imply the same original value.

Acknowledgements

The National Planning Commission (NPC) is the apex advisory body of the Government of Nepal for formulating a national vision, periodic plans and policies for development. It is headed by the Right Honorable Prime Minister. The NPC assesses resource needs, identifies sources of funding, and allocates budget for socio-economic development. It serves as a central agency for monitoring and evaluating development plans, policies and programs. The NPC also serves as an intellectual hub for the exchange of new development ideas and proposals from scholars, private sector, civil society, and development partners.

KLL is a leading civic-tech company based out of Nepal. Founded in 2013, primarily, to advance the Open Mapping movement, we have trained and engaged thousands of people from Nepal and other Asian countries in mapping their local communities in OpenStreetMap (OSM). The massive 7.8Mw Nepal earthquake in 2015 was our testimony which we successfully fared. We coordinated both the global and local mapping work, and connected it to the lifesaving on-the-ground needs. It is recognized as one of the most successful use cases of Open Mapping in disaster response so far.

Central Bureau of Statistics (CBS) was established in 1959 under Statistics Act, 2015 BS as the central agency for the collection, consolidation, processing, analysis, publication and dissemination of statistics. It is under the National Planning Commission Secretariat (NPCS) of Nepal and serves as a national statistical organization of Government of Nepal. It generates timely and reliable socio-economic statistics mainly through the operation of censuses and surveys.

Inspiration

Formalize several predictive modeling problems e.g. predicting damage grade
Lots of data analysis and EDA e.g impacts on buildings and households

Tables

N, Building Damage Assessment

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.n__building_damage_assessment

11.25 MB
762106 rows
79 columns


CREATE TABLE n__building_damage_assessment (
  "building_id" BIGINT,
  "district_id" BIGINT,
  "vdcmun_id" BIGINT,
  "ward_id" BIGINT,
  "damage_overall_collapse" VARCHAR,
  "damage_overall_leaning" VARCHAR,
  "damage_overall_adjacent_building_risk" VARCHAR,
  "damage_foundation_severe" VARCHAR,
  "damage_foundation_moderate" VARCHAR,
  "damage_foundation_insignificant" VARCHAR,
  "damage_roof_severe" VARCHAR,
  "damage_roof_moderate" VARCHAR,
  "damage_roof_insignificant" VARCHAR,
  "damage_corner_separation_severe" VARCHAR,
  "damage_corner_separation_moderate" VARCHAR,
  "damage_corner_separation_insignificant" VARCHAR,
  "damage_diagonal_cracking_severe" VARCHAR,
  "damage_diagonal_cracking_moderate" VARCHAR,
  "damage_diagonal_cracking_insignificant" VARCHAR,
  "damage_in_plane_failure_severe" VARCHAR,
  "damage_in_plane_failure_moderate" VARCHAR,
  "damage_in_plane_failure_insignificant" VARCHAR,
  "damage_out_of_plane_failure_severe" VARCHAR,
  "damage_out_of_plane_failure_moderate" VARCHAR,
  "damage_out_of_plane_failure_insignificant" VARCHAR,
  "damage_out_of_plane_failure_walls_ncfr_severe" VARCHAR,
  "damage_out_of_plane_failure_walls_ncfr_moderate" VARCHAR,
  "damage_out_of_plane_failure_walls_ncfr_insignificant" VARCHAR,
  "damage_gable_failure_severe" VARCHAR,
  "damage_gable_failure_moderate" VARCHAR,
  "damage_gable_failure_insignificant" VARCHAR,
  "damage_delamination_failure_severe" VARCHAR,
  "damage_delamination_failure_moderate" VARCHAR,
  "damage_delamination_failure_insignificant" VARCHAR,
  "damage_column_failure_severe" VARCHAR,
  "damage_column_failure_moderate" VARCHAR,
  "damage_column_failure_insignificant" VARCHAR,
  "damage_beam_failure_severe" VARCHAR,
  "damage_beam_failure_moderate" VARCHAR,
  "damage_beam_failure_insignificant" VARCHAR,
  "damage_infill_partition_failure_severe" VARCHAR,
  "damage_infill_partition_failure_moderate" VARCHAR,
  "damage_infill_partition_failure_insignificant" VARCHAR,
  "damage_staircase_severe" VARCHAR,
  "damage_staircase_moderate" VARCHAR,
  "damage_staircase_insignificant" VARCHAR,
  "damage_parapet_severe" VARCHAR,
  "damage_parapet_moderate" VARCHAR,
  "damage_parapet_insignificant" VARCHAR,
  "damage_cladding_glazing_severe" VARCHAR,
  "damage_cladding_glazing_moderate" VARCHAR,
  "damage_cladding_glazing_insignificant" VARCHAR,
  "area_assesed" VARCHAR,
  "damage_grade" VARCHAR,
  "technical_solution_proposed" VARCHAR,
  "has_repair_started" DOUBLE,
  "has_damage_foundation" DOUBLE,
  "has_damage_roof" DOUBLE,
  "has_damage_corner_separation" DOUBLE,
  "has_damage_diagonal_cracking" DOUBLE,
  "has_damage_in_plane_failure" DOUBLE,
  "has_damage_out_of_plane_failure" DOUBLE,
  "has_damage_out_of_plane_walls_ncfr_failure" DOUBLE,
  "has_damage_gable_failure" DOUBLE,
  "has_damage_delamination_failure" DOUBLE,
  "has_damage_column_failure" DOUBLE,
  "has_damage_beam_failure" DOUBLE,
  "has_damage_infill_partition_failure" DOUBLE,
  "has_damage_staircase" DOUBLE,
  "has_damage_parapet" DOUBLE,
  "has_damage_cladding_glazing" DOUBLE,
  "has_geotechnical_risk" DOUBLE,
  "has_geotechnical_risk_land_settlement" BIGINT,
  "has_geotechnical_risk_fault_crack" BIGINT,
  "has_geotechnical_risk_liquefaction" BIGINT,
  "has_geotechnical_risk_landslide" BIGINT,
  "has_geotechnical_risk_rock_fall" BIGINT,
  "has_geotechnical_risk_flood" BIGINT,
  "has_geotechnical_risk_other" BIGINT
);

N, Building Ownership And Use

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.n__building_ownership_and_use

3.73 MB
762106 rows
17 columns


CREATE TABLE n__building_ownership_and_use (
  "building_id" BIGINT,
  "district_id" BIGINT,
  "vdcmun_id" BIGINT,
  "ward_id" BIGINT,
  "legal_ownership_status" VARCHAR,
  "count_families" DOUBLE,
  "has_secondary_use" DOUBLE,
  "has_secondary_use_agriculture" BIGINT,
  "has_secondary_use_hotel" BIGINT,
  "has_secondary_use_rental" BIGINT,
  "has_secondary_use_institution" BIGINT,
  "has_secondary_use_school" BIGINT,
  "has_secondary_use_industry" BIGINT,
  "has_secondary_use_health_post" BIGINT,
  "has_secondary_use_gov_office" BIGINT,
  "has_secondary_use_use_police" BIGINT,
  "has_secondary_use_other" BIGINT
);

N, Building Structure

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.n__building_structure

9.03 MB
762106 rows
31 columns


CREATE TABLE n__building_structure (
  "building_id" BIGINT,
  "district_id" BIGINT,
  "vdcmun_id" BIGINT,
  "ward_id" BIGINT,
  "count_floors_pre_eq" BIGINT,
  "count_floors_post_eq" BIGINT,
  "age_building" BIGINT,
  "plinth_area_sq_ft" BIGINT,
  "height_ft_pre_eq" BIGINT,
  "height_ft_post_eq" BIGINT,
  "land_surface_condition" VARCHAR,
  "foundation_type" VARCHAR,
  "roof_type" VARCHAR,
  "ground_floor_type" VARCHAR,
  "other_floor_type" VARCHAR,
  "position" VARCHAR,
  "plan_configuration" VARCHAR,
  "has_superstructure_adobe_mud" BIGINT,
  "has_superstructure_mud_mortar_stone" BIGINT,
  "has_superstructure_stone_flag" BIGINT,
  "has_superstructure_cement_mortar_stone" BIGINT,
  "has_superstructure_mud_mortar_brick" BIGINT,
  "has_superstructure_cement_mortar_brick" BIGINT,
  "has_superstructure_timber" BIGINT,
  "has_superstructure_bamboo" BIGINT,
  "has_superstructure_rc_non_engineered" BIGINT,
  "has_superstructure_rc_engineered" BIGINT,
  "has_superstructure_other" BIGINT,
  "condition_post_eq" VARCHAR,
  "damage_grade" VARCHAR,
  "technical_solution_proposed" VARCHAR
);

N, Household Demographics

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.n__household_demographics

6.39 MB
747365 rows
11 columns


CREATE TABLE n__household_demographics (
  "household_id" BIGINT,
  "district_id" BIGINT,
  "vdcmun_id" BIGINT,
  "ward_id" BIGINT,
  "gender_household_head" VARCHAR,
  "age_household_head" DOUBLE,
  "caste_household" VARCHAR,
  "education_level_household_head" VARCHAR,
  "income_level_household" VARCHAR,
  "size_household" DOUBLE,
  "is_bank_account_present_in_household" DOUBLE
);

N, Household Earthquake Impact

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.n__household_earthquake_impact

5.8 MB
747365 rows
28 columns


CREATE TABLE n__household_earthquake_impact (
  "household_id" BIGINT,
  "district_id" BIGINT,
  "vdcmun_id" BIGINT,
  "ward_id" BIGINT,
  "shelter_condition_household_post_eq" VARCHAR,
  "residence_household_pre_eq" VARCHAR,
  "residence_household_post_eq" VARCHAR,
  "household_eq_id_type" VARCHAR,
  "has_death_occurred_last_12_months" DOUBLE,
  "count_death_last_12_months" DOUBLE,
  "has_injury_loss_occurred_last_12_months" DOUBLE,
  "count_injury_loss_last_12_months" DOUBLE,
  "has_education_drop_occurred_last_12_months" DOUBLE,
  "count_education_drop_last_12_months" DOUBLE,
  "has_pregnancy_treatment_drop_occurred_last_12_months" DOUBLE,
  "count_pregnancy_treatment_drop_last_12_months" DOUBLE,
  "has_vaccination_drop_occurred_last_12_months" DOUBLE,
  "count_vaccination_drop_last_12_months" DOUBLE,
  "has_occupation_change_occurred_last_12_months" DOUBLE,
  "count_occupation_change_last_12_months" DOUBLE,
  "residence_district_household_head_pre_eq" VARCHAR,
  "residence_district_household_head_post_eq" VARCHAR,
  "is_recipient_rahat_15k" BIGINT,
  "is_recipient_rahat_10k" BIGINT,
  "is_recipient_rahat_200k" BIGINT,
  "is_recipient_rahat_social_security_3k" BIGINT,
  "is_recipient_rahat_none" BIGINT,
  "is_ineligible_rahat" BIGINT
);

N, Household Resources

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.n__household_resources

6.26 MB
747365 rows
36 columns


CREATE TABLE n__household_resources (
  "household_id" BIGINT,
  "district_id" BIGINT,
  "vdcmun_id" BIGINT,
  "ward_id" BIGINT,
  "source_water_pre_eq" VARCHAR,
  "source_water_post_eq" VARCHAR,
  "source_cooking_fuel_pre_eq" VARCHAR,
  "source_cooking_fuel_post_eq" VARCHAR,
  "source_light_pre_eq" VARCHAR,
  "source_light_post_eq" VARCHAR,
  "type_toilet_pre_eq" VARCHAR,
  "type_toilet_post_eq" VARCHAR,
  "has_asset_land_pre_eq" BIGINT,
  "has_asset_tv_pre_eq" BIGINT,
  "has_asset_cable_pre_eq" BIGINT,
  "has_asset_computer_pre_eq" BIGINT,
  "has_asset_internet_pre_eq" BIGINT,
  "has_asset_telephone_pre_eq" BIGINT,
  "has_asset_mobile_phone_pre_eq" BIGINT,
  "has_asset_fridge_pre_eq" BIGINT,
  "has_asset_motorcycle_pre_eq" BIGINT,
  "has_asset_four_wheeler_family_use_pre_eq" BIGINT,
  "has_asset_four_wheeler_commercial_use_pre_eq" BIGINT,
  "has_asset_none_pre_eq" BIGINT,
  "has_asset_land_post_eq" BIGINT,
  "has_asset_tv_post_eq" BIGINT,
  "has_asset_cable_post_eq" BIGINT,
  "has_asset_computer_post_eq" BIGINT,
  "has_asset_internet_post_eq" BIGINT,
  "has_asset_telephone_post_eq" BIGINT,
  "has_asset_mobile_phone_post_eq" BIGINT,
  "has_asset_fridge_post_eq" BIGINT,
  "has_asset_motorcycle_post_eq" BIGINT,
  "has_asset_four_wheeler_family_use_post_eq" BIGINT,
  "has_asset_four_wheeler_commercial_post_eq" BIGINT,
  "has_asset_none_post_eq" BIGINT
);

N, Individual Demographics

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.n__individual_demographics

26.65 MB
3677133 rows
11 columns


CREATE TABLE n__individual_demographics (
  "individual_id" BIGINT,
  "district_id" BIGINT,
  "vdcmun_id" BIGINT,
  "ward_id" BIGINT,
  "gender_individual" VARCHAR,
  "age_individual" BIGINT,
  "presence_in_household" VARCHAR,
  "disability_individual" VARCHAR,
  "has_registered_birth" DOUBLE,
  "education_level_individual" VARCHAR,
  "marital_status_individual" VARCHAR
);

Mapping

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.mapping

28.55 MB
3677133 rows
3 columns


CREATE TABLE mapping (
  "individual_id" BIGINT,
  "household_id" BIGINT,
  "building_id" BIGINT
);

Ward Vdcmun District Name Mapping

@kaggle.arashnic_earthquake_magnitude_damage_and_impact.ward_vdcmun_district_name_mapping

12.25 KB
949 rows
5 columns


CREATE TABLE ward_vdcmun_district_name_mapping (
  "ward_id" BIGINT,
  "vdcmun_id" BIGINT,
  "vdcmun_name" VARCHAR,
  "district_id" BIGINT,
  "district_name" VARCHAR
);