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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 350 new columns ({'sdg10_2_nssp', 'map_pf_incidence_rate.2016.mean', 'sdg8_6_mlm', 'esaLandCover2016_snow_ice', 'ln_NTLpc2013', 'modis_urban_2014', 'esaLandCover2012_sparse_vegetation', 'cru_ts_405_pre_yearly_mean.2020.mean', 'pop2005', 'slope500m2017min', 'modis_urban_2015', 'modis_landcover_51.2012.categorical_deciduous_needleleaf_forest', 'ln_t400NTLpc2013', 'esaLandCover2015_snow_ice', 'ln_NTLpc2017', 'sdg5_1_gpyp', 'map_pf_incidence_rate.2015.mean', 'pop2009', 'cru_ts_405_pre_yearly_mean.2017.max', 'gisa2018', 'esaLandCover2012_water_bodies', 'airTemp2016.mean', 'cru_ts_405_pre_yearly_mean.2012.max', 'index_sdg17', 'index_sdg2', 'sdg3_3_cdir', 'map_pf_incidence_rate.2013.mean', 'esaLandCover2016_forest', 'tr400_co2015', 'map_pf_incidence_rate.2014.max', 'distanceRoad2017min', 'airTemp2012.min', 'distanceRoad2017mean', 'esaLandCover2016_bare_areas', 'pop2004', 'sdg4_4_phe', 'dist_water2017max', 'esaLandCover2015_irrigated_cropland', 'sdg7_1_rec', 'cru_ts_405_pre_yearly_mean.2014.max', 'gisa2019', 'map_pf_incidence_rate.2017.min', 'slope500m2017max', 'esaLandCover2016_rainfed_cropland', 'ln_t400NTLpc2020', 'sdg2_4_td', 'pop2012', 'map_pf_incidence_rate.2019.max', 'co2017', 'airTemp2017.min', 'esaLandCover2013_urban', 'map_pf_incidence_rate.2018.min', 'photov2019max', 'pop2018', 'map_pf_incidence_rate.2015.max', 'modis_total_area_2013', 'esaLandCover2015_forest', 'esaLandCover2016_count', 'airTemp2012.mean', 'pop2007', 'esaLandCover2015_count', 'esaLandCover2016_water_bodies', 'sdg1_2_mpi',
...
_or_sparsely_vegetated', 'drugCult_none2017', 'ln_NTLpc2018', 'esaLandCover2016_mosaic_cropland', 'modis_urban_2012', 'airTemp2015.mean', 'esaLandCover2016_shrubland', 'pop2010', 'travelTimeCity2016min', 'sdg16_6_pbec', 'distanceDia2017max', 'sdg16_1_rhr', 'cru_ts_405_pre_yearly_mean.2016.max', 'esaLandCover2012_bare_areas', 'sdg1_1_ubn', 'sdg3_2_mrc', 'ghsl2015', 'cru_ts_405_pre_yearly_mean.2018.mean', 'esaLandCover2013_mosaic_cropland', 'map_pf_incidence_rate.2018.mean', 'modis_landcover_51.2012.categorical_savannas', 'asdf_id', 'esaLandCover2013_rainfed_cropland', 'esaLandCover2012_mosaic_cropland', 'esaLandCover2012_urban', 'esaLandCover2012_grassland', 'airTemp2014.mean', 'elev2017max', 'modis_total_area_2016', 'cru_ts_405_pre_yearly_mean.2018.min', 'sdg9_5_eutf', 'sdg16_9_cr', 'distanceDrug2017mean', 'pop2016', 'gisa2012', 'esaLandCover2015_shrubland', 'gisa2014', 'modis_total_area_2015', 'tr400_co2016', 'mun', 'modis_landcover_51.2012.categorical_closed_shrublands', 'map_pf_incidence_rate.2012.max', 'index_sdg15', 'esaLandCover2013_sparse_vegetation', 'sdg10_2_iec', 'co2018', 'cru_ts_405_pre_yearly_mean.2013.min', 'cru_ts_405_pre_yearly_mean.2015.mean', 'cru_ts_405_pre_yearly_mean.2020.min', 'tr400_co2020', 'modis_landcover_51.2012.categorical_evergreen_needleleaf_forest', 'modis_urban_2013', 'dep_mun', 'esaLandCover2014_mosaic_cropland', 'ln_t400NTLpc2012', 'map_pf_incidence_rate.2016.max', 'cru_ts_405_pre_yearly_mean.2019.mean', 'cru_ts_405_pre_yearly_mean.2020.max'}) and 2 missing columns ({'varname', 'varlabel'}).
This happened while the csv dataset builder was generating data using
hf://datasets/cmg777/project2026e/ds4bolivia_v20250523.csv (at revision e2c84514c46502612d37b84646345b2835b8c5d4), ['hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/definitions_ds4bolivia_v20250523.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/ds4bolivia_v20250523.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2018.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2019.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2020.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2018.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2019.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2020.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/predictions/sdg1_combined_forward_2017_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/predictions/sdg1_oos_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/regionNames/regionNames.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_embeddings_unweighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2018.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2019.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2020.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2022.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2023.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2024.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2025.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/sdg/sdg.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/sdgVariables/sdgVariables.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
Unnamed: 0: int64
poly_id: int64
asdf_id: int64
mun: string
mun_id: int64
dep: string
dep_id: int64
dep_mun: string
shapeID: string
imds: double
rank_imds: int64
population_2020: int64
urbano_2012: double
sdg1_1_eepr: double
sdg1_1_ubn: double
sdg1_2_mpi: double
sdg1_4_abs: double
sdg2_2_cmc: double
sdg2_2_oww: double
sdg2_4_pual: double
sdg2_4_td: double
sdg3_1_idca: double
sdg3_2_imr: double
sdg3_2_mrc: double
sdg3_3_cdir: double
sdg3_3_di: double
sdg3_3_imr: double
sdg3_3_ti: double
sdg3_3_hivi: double
sdg3_7_afr: double
sdg4_1_ssdrm: double
sdg4_1_ssdrf: double
sdg4_4_phe: double
sdg4_6_lr: double
sdg4_c_qti: double
sdg4_c_qts: double
sdg5_1_gpsd: double
sdg5_1_gpyp: double
sdg5_1_gpmpi: double
sdg5_5_gpop: double
sdg6_1_dwc: double
sdg6_2_sc: double
sdg6_3_wwt: double
sdg7_1_ec: double
sdg7_1_rec: int64
sdg7_1_cce: double
sdg7_3_co2epc: double
sdg8_4_rem: double
sdg8_5_oprm: double
sdg8_5_ofrm: double
sdg8_6_mlm: double
sdg8_6_wlm: double
sdg8_10_dbb: double
sdg8_11_idi: double
sdg9_1_routes: int64
sdg9_5_cd: double
sdg9_5_eutf: double
sdg9_c_mnc: double
sdg9_c_drb: double
sdg10_2_gcye: double
sdg10_2_iec: double
sdg10_2_nssp: double
sdg11_1_hocr: double
sdg11_1_hno: double
sdg11_2_samt: double
sdg13_1_ccvi: double
sdg13_2_tco2e: double
sdg13_2_dra: double
sdg15_1_pa: double
sdg15_5_blr: double
sdg16_1_rhr: double
sdg16_6_pbec: double
sdg16_9_cr: double
sdg17_1_pmtax: double
sdg17_5_pipc: int64
index_sdg1: double
index_sdg2: double
index_sdg3: double
index_sdg4: double
i
...
modis_landcover_51.2012.categorical_count: int64
modis_landcover_51.2012.categorical_grasslands: int64
modis_landcover_51.2012.categorical_open_shrublands: int64
modis_landcover_51.2012.categorical_water: int64
modis_landcover_51.2012.categorical_evergreen_needleleaf_forest: int64
modis_landcover_51.2012.categorical_evergreen_broadleaf_forest: int64
modis_landcover_51.2012.categorical_deciduous_needleleaf_forest: int64
modis_landcover_51.2012.categorical_deciduous_broadleaf_forest: int64
modis_landcover_51.2012.categorical_mixed_forest: int64
modis_landcover_51.2012.categorical_closed_shrublands: int64
modis_landcover_51.2012.categorical_woody_savannas: int64
modis_landcover_51.2012.categorical_savannas: int64
modis_landcover_51.2012.categorical_permanent_wetlands: int64
modis_landcover_51.2012.categorical_croplands: int64
modis_landcover_51.2012.categorical_urban_and_builtup: int64
modis_landcover_51.2012.categorical_cropland_natural_vegetation_mosaic: int64
modis_landcover_51.2012.categorical_snow_and_ice: int64
photov2019mean: double
photov2019max: double
photov2019min: double
elev2017mean: double
elev2017max: int64
elev2017min: int64
distanceRoad2017mean: double
distanceRoad2017max: double
distanceRoad2017min: double
slope500m2017mean: double
slope500m2017max: double
slope500m2017min: double
dist_water2017mean: double
dist_water2017max: double
dist_water2017min: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 49620
to
{'Unnamed: 0': Value('int64'), 'varname': Value('string'), 'varlabel': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 350 new columns ({'sdg10_2_nssp', 'map_pf_incidence_rate.2016.mean', 'sdg8_6_mlm', 'esaLandCover2016_snow_ice', 'ln_NTLpc2013', 'modis_urban_2014', 'esaLandCover2012_sparse_vegetation', 'cru_ts_405_pre_yearly_mean.2020.mean', 'pop2005', 'slope500m2017min', 'modis_urban_2015', 'modis_landcover_51.2012.categorical_deciduous_needleleaf_forest', 'ln_t400NTLpc2013', 'esaLandCover2015_snow_ice', 'ln_NTLpc2017', 'sdg5_1_gpyp', 'map_pf_incidence_rate.2015.mean', 'pop2009', 'cru_ts_405_pre_yearly_mean.2017.max', 'gisa2018', 'esaLandCover2012_water_bodies', 'airTemp2016.mean', 'cru_ts_405_pre_yearly_mean.2012.max', 'index_sdg17', 'index_sdg2', 'sdg3_3_cdir', 'map_pf_incidence_rate.2013.mean', 'esaLandCover2016_forest', 'tr400_co2015', 'map_pf_incidence_rate.2014.max', 'distanceRoad2017min', 'airTemp2012.min', 'distanceRoad2017mean', 'esaLandCover2016_bare_areas', 'pop2004', 'sdg4_4_phe', 'dist_water2017max', 'esaLandCover2015_irrigated_cropland', 'sdg7_1_rec', 'cru_ts_405_pre_yearly_mean.2014.max', 'gisa2019', 'map_pf_incidence_rate.2017.min', 'slope500m2017max', 'esaLandCover2016_rainfed_cropland', 'ln_t400NTLpc2020', 'sdg2_4_td', 'pop2012', 'map_pf_incidence_rate.2019.max', 'co2017', 'airTemp2017.min', 'esaLandCover2013_urban', 'map_pf_incidence_rate.2018.min', 'photov2019max', 'pop2018', 'map_pf_incidence_rate.2015.max', 'modis_total_area_2013', 'esaLandCover2015_forest', 'esaLandCover2016_count', 'airTemp2012.mean', 'pop2007', 'esaLandCover2015_count', 'esaLandCover2016_water_bodies', 'sdg1_2_mpi',
...
_or_sparsely_vegetated', 'drugCult_none2017', 'ln_NTLpc2018', 'esaLandCover2016_mosaic_cropland', 'modis_urban_2012', 'airTemp2015.mean', 'esaLandCover2016_shrubland', 'pop2010', 'travelTimeCity2016min', 'sdg16_6_pbec', 'distanceDia2017max', 'sdg16_1_rhr', 'cru_ts_405_pre_yearly_mean.2016.max', 'esaLandCover2012_bare_areas', 'sdg1_1_ubn', 'sdg3_2_mrc', 'ghsl2015', 'cru_ts_405_pre_yearly_mean.2018.mean', 'esaLandCover2013_mosaic_cropland', 'map_pf_incidence_rate.2018.mean', 'modis_landcover_51.2012.categorical_savannas', 'asdf_id', 'esaLandCover2013_rainfed_cropland', 'esaLandCover2012_mosaic_cropland', 'esaLandCover2012_urban', 'esaLandCover2012_grassland', 'airTemp2014.mean', 'elev2017max', 'modis_total_area_2016', 'cru_ts_405_pre_yearly_mean.2018.min', 'sdg9_5_eutf', 'sdg16_9_cr', 'distanceDrug2017mean', 'pop2016', 'gisa2012', 'esaLandCover2015_shrubland', 'gisa2014', 'modis_total_area_2015', 'tr400_co2016', 'mun', 'modis_landcover_51.2012.categorical_closed_shrublands', 'map_pf_incidence_rate.2012.max', 'index_sdg15', 'esaLandCover2013_sparse_vegetation', 'sdg10_2_iec', 'co2018', 'cru_ts_405_pre_yearly_mean.2013.min', 'cru_ts_405_pre_yearly_mean.2015.mean', 'cru_ts_405_pre_yearly_mean.2020.min', 'tr400_co2020', 'modis_landcover_51.2012.categorical_evergreen_needleleaf_forest', 'modis_urban_2013', 'dep_mun', 'esaLandCover2014_mosaic_cropland', 'ln_t400NTLpc2012', 'map_pf_incidence_rate.2016.max', 'cru_ts_405_pre_yearly_mean.2019.mean', 'cru_ts_405_pre_yearly_mean.2020.max'}) and 2 missing columns ({'varname', 'varlabel'}).
This happened while the csv dataset builder was generating data using
hf://datasets/cmg777/project2026e/ds4bolivia_v20250523.csv (at revision e2c84514c46502612d37b84646345b2835b8c5d4), ['hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/definitions_ds4bolivia_v20250523.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/ds4bolivia_v20250523.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2018.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2019.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2020.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_pop_weighted_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2018.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2019.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2020.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/nighttimeLights/bolivia_ntl_unweighted_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/predictions/sdg1_combined_forward_2017_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/predictions/sdg1_oos_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/regionNames/regionNames.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_embeddings_unweighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2017.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2018.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2019.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2020.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2021.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2022.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2023.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2024.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/satelliteEmbeddings/bolivia_pop_weighted_2025.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/sdg/sdg.csv', 'hf://datasets/cmg777/project2026e@e2c84514c46502612d37b84646345b2835b8c5d4/sdgVariables/sdgVariables.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Unnamed: 0 int64 | varname string | varlabel string |
|---|---|---|
0 | poly_id | Polygon ID |
1 | asdf_id | ASDF ID |
2 | mun | Municipality |
3 | mun_id | Municipality ID |
4 | dep | Department |
5 | dep_id | Department ID |
6 | dep_mun | Department-municipality |
7 | shapeID | Municipality Geoquery Polygon ID |
8 | imds | Municipal Sustainable Development Index |
9 | rank_imds | Bolivia Index Ranking |
10 | population_2020 | Population 2020 |
11 | urbano_2012 | Urbanization rate, 2012 (% of population) |
12 | sdg1_1_eepr | Extreme energy poverty rate, 2016 (% of houses) |
13 | sdg1_1_ubn | Unsatisfied basic needs, 2012 (% of population) |
14 | sdg1_2_mpi | Multidimensional poverty index, 2012 |
15 | sdg1_4_abs | Access to the 3 basic services, 2012 (% of households) |
16 | sdg2_2_cmc | Chronic malnutrition in children (< 5 years), 2016 (%) |
17 | sdg2_2_oww | Obesity in women (15-49 years), 2016 (%) |
18 | sdg2_4_pual | Average area per Production Unit Agriculture and Livestock, 2013 (ha) |
19 | sdg2_4_td | Tractor density, 2013 (per 1,000 UPAs) |
20 | sdg3_1_idca | Institutional childbirth coverage, average 2008-2012 (%) |
21 | sdg3_2_imr | Infant mortality rate (< 1 year), 2016 (per. 1,000 live births) |
22 | sdg3_2_mrc | Children mortality rate in (< 5 years), 2016 (per. 1,000 live births) |
23 | sdg3_3_cdir | Chagas disease infestation rate, 2017 (% of households) |
24 | sdg3_3_di | Dengue incidence, 2018 (per 10,000 population) |
25 | sdg3_3_imr | Malaria incidence, average 2014-17 (per 1,000 population) |
26 | sdg3_3_ti | Tuberculosis incidence, 2017 (per 100,000 population) |
27 | sdg3_3_hivi | HIV incidence, average 2014-17 (per 1,000,00 population) |
28 | sdg3_7_afr | Adolescent fertility rate (15-19 years), 2012 (births per 1,000 women) |
29 | sdg4_1_ssdrm | Secondary school dropout rate, male, 2017 (% of enrolled) |
30 | sdg4_1_ssdrf | Secondary school dropout rate, females, 2017 (% of enrolled) |
31 | sdg4_4_phe | Population with higher education (>= 19 years), 2012 (%) |
32 | sdg4_6_lr | Literacy rate for (>= 15 years), 2012 (%) |
33 | sdg4_c_qti | Qualified teachers at the initial level, 2016 (%) |
34 | sdg4_c_qts | Qualified teachers at the secondary level, 2016 |
35 | sdg5_1_gpsd | Gender parity in school dropouts in secondary school, 2017 |
36 | sdg5_1_gpyp | Gender parity in years of education of young people (25-35 years old), 2012. (25 |
37 | sdg5_1_gpmpi | Gender Parity in the Multidimensional Poverty Index, 2012 |
38 | sdg5_5_gpop | Gender parity in the overall participation rate (>=10 years), 2012 |
39 | sdg6_1_dwc | Drinking water coverage, 2017 (% of population) |
40 | sdg6_2_sc | Sanitation coverage, 2017 (% of population) |
41 | sdg6_3_wwt | Wastewater treatment, 2017 (% of wastewater) wastewater) |
42 | sdg7_1_ec | Electricity coverage, 2012 (% of population) population) |
43 | sdg7_1_rec | Residential electricity consumption per capita, 2016 (kWh/person/year) |
44 | sdg7_1_cce | Clean cooking energy, 2012 (% of households) |
45 | sdg7_3_co2epc | CO2 emissions per capita by energy, 2016. (tCO2/person/year) |
46 | sdg8_4_rem | Residential electric meters with zero consumption, 2016 (%) |
47 | sdg8_5_oprm | Overall participation rate males (>= 10 years), 2012 (%) |
48 | sdg8_5_ofrm | Overall female participation rate (>= 10 years), 2012 (%) |
49 | sdg8_6_mlm | Men who do not study or participate in the labor market (15-24 years), 2012 (%) |
50 | sdg8_6_wlm | Women who do not study or participate in the labor market (15-24 years),2012 (%) |
51 | sdg8_10_dbb | Density of bank branches, 2018 (per 100,000 inhabitants) |
52 | sdg8_11_idi | Index of the degree of intermediation in migration, 2012 |
53 | sdg9_1_routes | Number of railways/primary roads entering/leaving the municipality, 2019 |
54 | sdg9_5_cd | Kuaa computers delivered, 2016 (per 100 school-age population, 6-19 years) |
55 | sdg9_5_eutf | Educational units with technological floors, 2016 (%) |
56 | sdg9_c_mnc | Fixed and mobile network coverage, 2012 (% of households) |
57 | sdg9_c_drb | Density of radio bases, 2016 (number of radio bases per 1000 inhabitants) |
58 | sdg10_2_gcye | GINI coefficient of years of education, 2012 |
59 | sdg10_2_iec | Inequality in electricity consumption, 2016 |
60 | sdg10_2_nssp | Non-Spanish speaking population (>= 3 years), 2012 (%) |
61 | sdg11_1_hocr | Overcrowding rate, 2012 (% of households) |
62 | sdg11_1_hno | Households that do not have a toilet, bathroom or latrine, 2012 (%) latrine, 201 |
63 | sdg11_2_samt | Seats available for mass transit, 2017 (per 1,000 inhabitants) |
64 | sdg13_1_ccvi | Climate change vulnerability Index, 2015 |
65 | sdg13_2_tco2e | Total CO2 emissions per capita, 2016 (tCO2/person/year) |
66 | sdg13_2_dra | Deforestation rate, average 2016-2018 (% of forest area 2015) |
67 | sdg15_1_pa | Protected areas, 2019 (% of the municipality's land area) municipality) |
68 | sdg15_5_blr | Biodiversity loss rate due to deforestation deforestation, average 2016-2018 |
69 | sdg16_1_rhr | Registered homicide rate, average 2015-2017. (per 100,000 inhabitants) |
70 | sdg16_6_pbec | Programmed budget execution capacity, 2017 (%) |
71 | sdg16_9_cr | Children registered in the civil registry (< 5 years), 2012 (%) |
72 | sdg17_1_pmtax | Proportion of municipal revenues that come from local taxes, 2017 (%) |
73 | sdg17_5_pipc | Public investment per capita, 2017 (Bs./person) |
74 | index_sdg1 | SDG1 Index |
75 | index_sdg2 | SDG2 Index |
76 | index_sdg3 | SDG3 Index |
77 | index_sdg4 | SDG4 Index |
78 | index_sdg5 | SDG5 Index |
79 | index_sdg6 | SDG6 Index |
80 | index_sdg7 | SDG7 Index |
81 | index_sdg8 | SDG8 Index |
82 | index_sdg9 | SDG9 Index |
83 | index_sdg10 | SDG10 Index |
84 | index_sdg11 | SDG11 Index |
85 | index_sdg13 | SDG13 Index |
86 | index_sdg15 | SDG15 Index |
87 | index_sdg16 | SDG16 Index |
88 | index_sdg17 | SDG17 Index |
89 | pop2001 | Estimated population in 2001 |
90 | pop2002 | Estimated population in 2002 |
91 | pop2003 | Estimated population in 2003 |
92 | pop2004 | Estimated population in 2004 |
93 | pop2005 | Estimated population in 2005 |
94 | pop2006 | Estimated population in 2006 |
95 | pop2007 | Estimated population in 2007 |
96 | pop2008 | Estimated population in 2008 |
97 | pop2009 | Estimated population in 2009 |
98 | pop2010 | Estimated population in 2010 |
99 | pop2011 | Estimated population in 2011 |
End of preview.
Bolivian Municipalities — SDGs, Satellite Embeddings & Nighttime Lights
Socioeconomic, satellite, and geographic measures for Bolivia's 339 municipalities across its 9 departments — the analysis data for the project "Predicting the Sustainable Development of Bolivian Municipalities with Satellite Embeddings and Machine Learning" (Carlos Mendez, Nagoya University).
This dataset is a public mirror of the data/ folder in
cmg777/project2026e. Every file is keyed on
asdf_id (integer, 0–338), the universal join key across all datasets. To attach
municipality/department labels, merge any file to regionNames/regionNames.csv on asdf_id.
Contents
| Path | Description |
|---|---|
ds4bolivia_v20250523.csv |
Analysis-ready merged master (339 × 351) — all subfolders joined on asdf_id. |
definitions_ds4bolivia_v20250523.csv |
Data dictionary for the master file (varname → varlabel). |
regionNames/ |
Administrative IDs & names — the join foundation. |
sdg/ |
IMDS + 14 composite SDG indices (0–100 scale). |
sdgVariables/ |
64 individual SDG indicators across all 17 goals. |
satelliteEmbeddings/ |
64-dim Google Satellite Embeddings — simple-mean (2017) and population-weighted (2017–2025 panel). |
nighttimeLights/ |
VIIRS nighttime lights (simple-average & population-weighted, 2017–2021), plus rasters/ GeoTIFF. |
predictions/ |
Out-of-sample SDG 1 predictions and space-time forward projections. |
maps/ |
Municipality boundary polygons (GeoJSON), keyed by asdf_id. |
sdg/, regionNames/, … |
Each subfolder ships its own README.md with a full variable dictionary. |
Provenance & coverage
- Spatial unit: 339 Bolivian municipalities (9 departments); version
v20250523. - Source:
quarcs-lab/ds4bolivia; the 64-dim satellite embeddings are Google Satellite Embeddings (Google Earth Engine); SDG indices and the IMDS index follow Andersen et al. (2020). - Time coverage: population 2001–2020; nighttime lights 2012–2021; most SDG variables 2012–2019; satellite embeddings 2017 (simple-mean) and 2017–2025 (population-weighted panel).
Load from Python
Load any file straight from the Hub (no full clone needed):
import pandas as pd
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="cmg777/project2026e",
repo_type="dataset",
filename="satelliteEmbeddings/bolivia_pop_weighted_2017.csv",
)
df = pd.read_csv(path)
The companion repo ships a code/hf_data.py helper with a data_path() function that
prefers a local copy and otherwise streams the file from this dataset.
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