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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 1 new columns ({'Upazila_ID'})
This happened while the csv dataset builder was generating data using
hf://datasets/fairhealth/bangladesh-flood-pdna-2022/pdna_upload/bangladesh_floods_2022_upazila_level.csv (at revision f5ac589987fba3be11c34d8e67396cd89d91f36e), [/tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_district_level.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_district_level.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_upazila_level.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_upazila_level.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/district_performance_comparison.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/district_performance_comparison.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/fairness_metrics_summary.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/fairness_metrics_summary.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/model_predictions_comparison.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/model_predictions_comparison.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/modeling_dataset_upazila_level.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/modeling_dataset_upazila_level.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_district_summary.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_district_summary.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_human_impact.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_human_impact.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_sector_summary.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_sector_summary.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/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
District: string
Damage_Tk_Lac: int64
Loss_Tk_Lac: int64
Total_Damage_Loss_Tk_Lac: int64
Damage_USD_Million: double
Loss_USD_Million: double
Total_Damage_Loss_USD_Million: double
Recovery_Needs_USD_Million: double
Region: string
Poverty_Rate: double
Population_1000: int64
Population_Density: int64
Agricultural_Dependency: double
Housing_Quality_Index: double
Urban_Population_Pct: double
Flood_Inundation_Pct: int64
Roads_Damaged_km: double
Tubewells_Damaged: int64
Latrines_Damaged: int64
Embankment_Damaged_km: double
Cropland_Damaged_ha: int64
Avg_Flood_Depth_m: double
Flood_Duration_days: int64
Distance_to_River_km: double
Elevation_m: int64
Health_Facilities_Affected: int64
People_Affected: int64
Damage_Per_Capita_USD: double
Vulnerability_Score: double
Infrastructure_Damage_Index: double
Upazila_ID: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 4444
to
{'District': Value('string'), 'Damage_Tk_Lac': Value('int64'), 'Loss_Tk_Lac': Value('int64'), 'Total_Damage_Loss_Tk_Lac': Value('int64'), 'Damage_USD_Million': Value('float64'), 'Loss_USD_Million': Value('float64'), 'Total_Damage_Loss_USD_Million': Value('float64'), 'Recovery_Needs_USD_Million': Value('float64'), 'Region': Value('string'), 'Poverty_Rate': Value('float64'), 'Population_1000': Value('int64'), 'Population_Density': Value('int64'), 'Agricultural_Dependency': Value('float64'), 'Housing_Quality_Index': Value('float64'), 'Urban_Population_Pct': Value('float64'), 'Flood_Inundation_Pct': Value('int64'), 'Roads_Damaged_km': Value('int64'), 'Tubewells_Damaged': Value('int64'), 'Latrines_Damaged': Value('int64'), 'Embankment_Damaged_km': Value('float64'), 'Cropland_Damaged_ha': Value('int64'), 'Avg_Flood_Depth_m': Value('float64'), 'Flood_Duration_days': Value('int64'), 'Distance_to_River_km': Value('float64'), 'Elevation_m': Value('int64'), 'Health_Facilities_Affected': Value('int64'), 'People_Affected': Value('int64'), 'Damage_Per_Capita_USD': Value('float64'), 'Vulnerability_Score': Value('float64'), 'Infrastructure_Damage_Index': Value('float64')}
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 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
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 1 new columns ({'Upazila_ID'})
This happened while the csv dataset builder was generating data using
hf://datasets/fairhealth/bangladesh-flood-pdna-2022/pdna_upload/bangladesh_floods_2022_upazila_level.csv (at revision f5ac589987fba3be11c34d8e67396cd89d91f36e), [/tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_district_level.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_district_level.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_upazila_level.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/bangladesh_floods_2022_upazila_level.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/district_performance_comparison.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/district_performance_comparison.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/fairness_metrics_summary.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/fairness_metrics_summary.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/model_predictions_comparison.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/model_predictions_comparison.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/modeling_dataset_upazila_level.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/modeling_dataset_upazila_level.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_district_summary.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_district_summary.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_human_impact.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_human_impact.csv), /tmp/hf-datasets-cache/medium/datasets/37021350416694-config-parquet-and-info-fairhealth-bangladesh-flo-07a44906/hub/datasets--fairhealth--bangladesh-flood-pdna-2022/snapshots/f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_sector_summary.csv (origin=hf://datasets/fairhealth/bangladesh-flood-pdna-2022@f5ac589987fba3be11c34d8e67396cd89d91f36e/pdna_upload/pdna_sector_summary.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.
District string | Damage_Tk_Lac int64 | Loss_Tk_Lac int64 | Total_Damage_Loss_Tk_Lac int64 | Damage_USD_Million float64 | Loss_USD_Million float64 | Total_Damage_Loss_USD_Million float64 | Recovery_Needs_USD_Million float64 | Region string | Poverty_Rate float64 | Population_1000 int64 | Population_Density int64 | Agricultural_Dependency float64 | Housing_Quality_Index float64 | Urban_Population_Pct float64 | Flood_Inundation_Pct int64 | Roads_Damaged_km int64 | Tubewells_Damaged int64 | Latrines_Damaged int64 | Embankment_Damaged_km float64 | Cropland_Damaged_ha int64 | Avg_Flood_Depth_m float64 | Flood_Duration_days int64 | Distance_to_River_km float64 | Elevation_m int64 | Health_Facilities_Affected int64 | People_Affected int64 | Damage_Per_Capita_USD float64 | Vulnerability_Score float64 | Infrastructure_Damage_Index float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Sylhet | 95,083 | 55,873 | 150,956 | 91.6 | 53.8 | 145.4 | 133.2 | Haor | 24.8 | 3,847 | 1,108 | 52.3 | 3.2 | 30.2 | 84 | 653 | 7,764 | 147,699 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 3,109,853 | 23.810762 | 0.46315 | 0.58089 |
Sunamganj | 165,640 | 56,107 | 221,747 | 159.6 | 54 | 213.6 | 231.4 | Haor | 41.2 | 2,602 | 721 | 68.7 | 2.1 | 14.8 | 94 | 415 | 29,616 | 81,575 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 2,776,597 | 61.337433 | 0.62835 | 0.854211 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 22.3 | 9 | 31.4 | 34.1 | Haor | 28.5 | 2,011 | 761 | 54.2 | 2.8 | 16.2 | 63 | 220 | 1,467 | 18,855 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 1,355,106 | 11.08901 | 0.457 | 0.155875 |
Habiganj | 31,923 | 57,445 | 89,369 | 30.8 | 55.3 | 86.1 | 52 | Haor | 32.1 | 2,390 | 913 | 58.9 | 2.7 | 18.5 | 71 | 178 | 522 | 11,093 | 2.9 | 30,460 | 3.5 | 19 | 3.2 | 11 | 28 | 1,747,124 | 12.887029 | 0.50055 | 0.118245 |
Mymensingh | 9,188 | 3,849 | 13,037 | 8.9 | 3.7 | 12.6 | 11.3 | Non-Haor | 31.2 | 5,539 | 1,256 | 59.3 | 2.4 | 28.1 | 45 | 88 | 0 | 1,000 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 2,235,332 | 1.606788 | 0.46185 | 0.060302 |
Netrokona | 26,316 | 9,174 | 35,490 | 25.3 | 8.8 | 34.2 | 32.3 | Haor | 35.7 | 2,303 | 882 | 61.4 | 2.5 | 19.3 | 67 | 167 | 3,160 | 6,110 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 1,383,768 | 10.985671 | 0.5196 | 0.170787 |
Sherpur | 15,148 | 3,248 | 18,396 | 14.6 | 3.1 | 17.7 | 18.6 | Non-Haor | 33.5 | 1,463 | 1,068 | 64.1 | 2.3 | 17.4 | 48 | 137 | 0 | 260 | 10.5 | 250 | 2.5 | 13 | 5.5 | 16 | 6 | 609,140 | 9.979494 | 0.49175 | 0.094931 |
Kishoreganj | 15,187 | 7,305 | 22,492 | 14.6 | 7 | 21.7 | 16.6 | Non-Haor | 29.3 | 3,126 | 1,033 | 63.8 | 2.6 | 21.7 | 58 | 14 | 730 | 3,250 | 10.4 | 3,827 | 2.6 | 14 | 5.8 | 18 | 18 | 2,012,251 | 4.670505 | 0.4834 | 0.028109 |
Brahmanbaria | 13,646 | 23,501 | 37,148 | 13.1 | 22.6 | 35.8 | 18.8 | Non-Haor | 26.8 | 2,984 | 1,576 | 57.6 | 2.9 | 24.8 | 52 | 47 | 0 | 100 | 0 | 13,685 | 2.8 | 15 | 4.9 | 17 | 12 | 1,598,749 | 4.39008 | 0.4334 | 0.02879 |
Kurigram | 8,727 | 1,859 | 10,586 | 8.4 | 1.8 | 10.2 | 10.9 | Non-Haor | 39.4 | 2,286 | 981 | 72.5 | 2 | 12.3 | 55 | 104 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 1,335,782 | 3.674541 | 0.55945 | 0.063706 |
Jamalpur | 16,924 | 4,282 | 21,206 | 16.3 | 4.1 | 20.4 | 21.2 | Non-Haor | 36.8 | 2,479 | 1,146 | 66.3 | 2.4 | 15.7 | 62 | 186 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 1,315,924 | 6.575232 | 0.53015 | 0.113936 |
Sylhet | 95,083 | 55,873 | 150,956 | 8.370577 | 4.916343 | 145.4 | 133.2 | Haor | 26.448915 | 3,847 | 1,108 | 50.795534 | 2.996368 | 30.2 | 84 | 59.672343 | 709 | 13,497 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 284,184 | 23.810762 | 0.474517 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 6.153842 | 3.614374 | 145.4 | 133.2 | Haor | 23.829042 | 3,847 | 1,108 | 52.429476 | 3.156445 | 30.2 | 84 | 43.869635 | 521 | 9,922 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 208,925 | 23.810762 | 0.462739 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 6.457741 | 3.792865 | 145.4 | 133.2 | Haor | 25.35479 | 3,847 | 1,108 | 50.414553 | 3.066973 | 30.2 | 84 | 46.036081 | 547 | 10,412 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 219,242 | 23.810762 | 0.466752 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 6.6695 | 3.917239 | 145.4 | 133.2 | Haor | 24.582107 | 3,847 | 1,108 | 53.79147 | 3.007791 | 30.2 | 84 | 47.54567 | 565 | 10,754 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 226,431 | 23.810762 | 0.475835 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 7.086273 | 4.162025 | 145.4 | 133.2 | Haor | 25.258376 | 3,847 | 1,108 | 49.927936 | 3.268829 | 30.2 | 84 | 50.516772 | 600 | 11,426 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 240,581 | 23.810762 | 0.455154 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 6.117539 | 3.593052 | 145.4 | 133.2 | Haor | 22.642656 | 3,847 | 1,108 | 54.647671 | 3.498005 | 30.2 | 84 | 43.610839 | 518 | 9,864 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 207,692 | 23.810762 | 0.447647 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 7.91536 | 4.648978 | 145.4 | 133.2 | Haor | 23.830884 | 3,847 | 1,108 | 50.195825 | 3.317909 | 30.2 | 84 | 56.427184 | 670 | 12,762 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 268,729 | 23.810762 | 0.449087 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 6.877476 | 4.039391 | 145.4 | 133.2 | Haor | 22.92531 | 3,847 | 1,108 | 52.274775 | 2.902009 | 30.2 | 84 | 49.028295 | 582 | 11,089 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 233,492 | 23.810762 | 0.472362 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 8.199808 | 4.816044 | 145.4 | 133.2 | Haor | 23.603549 | 3,847 | 1,108 | 53.149992 | 3.079495 | 30.2 | 84 | 58.454961 | 695 | 13,221 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 278,386 | 23.810762 | 0.467711 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 7.102715 | 4.171682 | 145.4 | 133.2 | Haor | 25.031683 | 3,847 | 1,108 | 50.651789 | 3.500534 | 30.2 | 84 | 50.633982 | 602 | 11,452 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 241,139 | 23.810762 | 0.444698 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 7.821605 | 4.593912 | 145.4 | 133.2 | Haor | 26.979915 | 3,847 | 1,108 | 54.364947 | 3.262656 | 30.2 | 84 | 55.758823 | 662 | 12,611 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 265,546 | 23.810762 | 0.471719 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 8.23519 | 4.836826 | 145.4 | 133.2 | Haor | 22.758923 | 3,847 | 1,108 | 50.70999 | 2.908945 | 30.2 | 84 | 58.707196 | 698 | 13,278 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 279,587 | 23.810762 | 0.467604 | 0.58089 |
Sylhet | 95,083 | 55,873 | 150,956 | 6.553854 | 3.849316 | 145.4 | 133.2 | Haor | 24.247839 | 3,847 | 1,108 | 51.104155 | 3.410392 | 30.2 | 84 | 46.721252 | 555 | 10,567 | 85 | 27,317 | 3.8 | 21 | 2.3 | 12 | 175 | 222,505 | 23.810762 | 0.447984 | 0.58089 |
Sunamganj | 165,640 | 56,107 | 221,747 | 13.677739 | 4.627807 | 213.6 | 231.4 | Haor | 39.3949 | 2,602 | 721 | 68.993322 | 1.949188 | 14.8 | 94 | 35.56555 | 2,538 | 6,990 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 237,954 | 61.337433 | 0.631209 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 16.262932 | 5.502496 | 213.6 | 231.4 | Haor | 37.694297 | 2,602 | 721 | 72.044913 | 2.214343 | 14.8 | 94 | 42.2877 | 3,017 | 8,312 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 282,929 | 61.337433 | 0.620478 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 12.760546 | 4.317478 | 213.6 | 231.4 | Haor | 37.125502 | 2,602 | 721 | 70.86722 | 2.18688 | 14.8 | 94 | 33.180618 | 2,367 | 6,522 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 221,998 | 61.337433 | 0.617201 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 15.838165 | 5.358778 | 213.6 | 231.4 | Haor | 43.435268 | 2,602 | 721 | 65.773687 | 2.040556 | 14.8 | 94 | 41.183199 | 2,938 | 8,095 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 275,540 | 61.337433 | 0.630712 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 12.279735 | 4.154797 | 213.6 | 231.4 | Haor | 44.191972 | 2,602 | 721 | 69.547058 | 2.028977 | 14.8 | 94 | 31.930388 | 2,278 | 6,276 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 213,633 | 61.337433 | 0.642995 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 11.976142 | 4.052078 | 213.6 | 231.4 | Haor | 39.642494 | 2,602 | 721 | 67.499009 | 2.196435 | 14.8 | 94 | 31.140971 | 2,222 | 6,121 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 208,351 | 61.337433 | 0.615853 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 15.307424 | 5.179204 | 213.6 | 231.4 | Haor | 44.390633 | 2,602 | 721 | 68.509117 | 1.94023 | 14.8 | 94 | 39.80314 | 2,840 | 7,823 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 266,306 | 61.337433 | 0.645433 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 15.746686 | 5.327826 | 213.6 | 231.4 | Haor | 43.348869 | 2,602 | 721 | 69.120974 | 2.213806 | 14.8 | 94 | 40.94533 | 2,922 | 8,048 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 273,948 | 61.337433 | 0.630159 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 14.473083 | 4.896908 | 213.6 | 231.4 | Haor | 41.387319 | 2,602 | 721 | 68.202207 | 1.900676 | 14.8 | 94 | 37.633643 | 2,685 | 7,397 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 251,791 | 61.337433 | 0.637634 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 12.233435 | 4.139132 | 213.6 | 231.4 | Haor | 37.338976 | 2,602 | 721 | 69.63714 | 2.02203 | 14.8 | 94 | 31.809998 | 2,270 | 6,252 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 212,827 | 61.337433 | 0.623008 | 0.854211 |
Sunamganj | 165,640 | 56,107 | 221,747 | 14.558832 | 4.925921 | 213.6 | 231.4 | Haor | 44.558348 | 2,602 | 721 | 66.977638 | 2.062361 | 14.8 | 94 | 37.856612 | 2,701 | 7,441 | 238.4 | 19,136 | 4.2 | 24 | 1.8 | 9 | 174 | 253,283 | 61.337433 | 0.636001 | 0.854211 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 3.511359 | 1.417141 | 31.4 | 34.1 | Haor | 26.95415 | 2,011 | 761 | 51.907231 | 2.682261 | 16.2 | 63 | 34.641214 | 230 | 2,968 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 213,375 | 11.08901 | 0.452517 | 0.155875 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 2.754013 | 1.111485 | 31.4 | 34.1 | Haor | 30.949277 | 2,011 | 761 | 55.870012 | 2.874706 | 16.2 | 63 | 27.169639 | 181 | 2,328 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 167,353 | 11.08901 | 0.464788 | 0.155875 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 3.659061 | 1.476751 | 31.4 | 34.1 | Haor | 30.230931 | 2,011 | 761 | 52.50121 | 3.019833 | 16.2 | 63 | 36.098362 | 240 | 3,093 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 222,350 | 11.08901 | 0.446954 | 0.155875 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 3.235848 | 1.305947 | 31.4 | 34.1 | Haor | 30.252409 | 2,011 | 761 | 56.346815 | 2.698082 | 16.2 | 63 | 31.92316 | 212 | 2,735 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 196,633 | 11.08901 | 0.47272 | 0.155875 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 2.688809 | 1.08517 | 31.4 | 34.1 | Haor | 26.94923 | 2,011 | 761 | 53.804924 | 2.978088 | 16.2 | 63 | 26.526367 | 176 | 2,273 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 163,391 | 11.08901 | 0.442456 | 0.155875 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 3.645388 | 1.471233 | 31.4 | 34.1 | Haor | 25.689627 | 2,011 | 761 | 54.25825 | 2.75375 | 16.2 | 63 | 35.96347 | 239 | 3,082 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 221,519 | 11.08901 | 0.451027 | 0.155875 |
Moulvibazar | 23,162 | 9,383 | 32,545 | 2.8316 | 1.142798 | 31.4 | 34.1 | Haor | 26.333233 | 2,011 | 761 | 53.319874 | 3.048029 | 16.2 | 63 | 27.93507 | 186 | 2,394 | 3.6 | 7,388 | 2.9 | 16 | 5.1 | 16 | 22 | 172,068 | 11.08901 | 0.435898 | 0.155875 |
Habiganj | 31,923 | 57,445 | 89,369 | 4.77031 | 8.564875 | 86.1 | 52 | Haor | 32.220636 | 2,390 | 913 | 60.095782 | 2.62636 | 18.5 | 71 | 27.568675 | 80 | 1,718 | 2.9 | 30,460 | 3.5 | 19 | 3.2 | 11 | 28 | 270,594 | 12.887029 | 0.507583 | 0.118245 |
Habiganj | 31,923 | 57,445 | 89,369 | 6.102059 | 10.95597 | 86.1 | 52 | Haor | 35.068912 | 2,390 | 913 | 57.437998 | 2.698514 | 18.5 | 71 | 35.265147 | 103 | 2,197 | 2.9 | 30,460 | 3.5 | 19 | 3.2 | 11 | 28 | 346,138 | 12.887029 | 0.505876 | 0.118245 |
Habiganj | 31,923 | 57,445 | 89,369 | 4.72447 | 8.482571 | 86.1 | 52 | Haor | 30.718676 | 2,390 | 913 | 56.172264 | 2.759165 | 18.5 | 71 | 27.303756 | 80 | 1,701 | 2.9 | 30,460 | 3.5 | 19 | 3.2 | 11 | 28 | 267,994 | 12.887029 | 0.486628 | 0.118245 |
Habiganj | 31,923 | 57,445 | 89,369 | 5.138834 | 9.226543 | 86.1 | 52 | Haor | 29.220494 | 2,390 | 913 | 57.596228 | 2.920464 | 18.5 | 71 | 29.698458 | 87 | 1,850 | 2.9 | 30,460 | 3.5 | 19 | 3.2 | 11 | 28 | 291,499 | 12.887029 | 0.477629 | 0.118245 |
Habiganj | 31,923 | 57,445 | 89,369 | 4.598567 | 8.256518 | 86.1 | 52 | Haor | 29.820225 | 2,390 | 913 | 58.837877 | 2.962251 | 18.5 | 71 | 26.576134 | 77 | 1,656 | 2.9 | 30,460 | 3.5 | 19 | 3.2 | 11 | 28 | 260,852 | 12.887029 | 0.480443 | 0.118245 |
Habiganj | 31,923 | 57,445 | 89,369 | 4.603687 | 8.26571 | 86.1 | 52 | Haor | 33.20511 | 2,390 | 913 | 60.44094 | 2.558324 | 18.5 | 71 | 26.605723 | 78 | 1,658 | 2.9 | 30,460 | 3.5 | 19 | 3.2 | 11 | 28 | 261,143 | 12.887029 | 0.514801 | 0.118245 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.88295 | 0.367069 | 12.6 | 11.3 | Non-Haor | 30.374967 | 5,539 | 1,256 | 60.084574 | 2.464094 | 28.1 | 45 | 8.730292 | 0 | 99 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 221,762 | 1.606788 | 0.458132 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.820669 | 0.341177 | 12.6 | 11.3 | Non-Haor | 28.643408 | 5,539 | 1,256 | 61.288344 | 2.313974 | 28.1 | 45 | 8.114479 | 0 | 92 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 206,119 | 1.606788 | 0.463452 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.707637 | 0.294186 | 12.6 | 11.3 | Non-Haor | 28.334437 | 5,539 | 1,256 | 59.838995 | 2.485231 | 28.1 | 45 | 6.996859 | 0 | 79 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 177,730 | 1.606788 | 0.450339 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.652641 | 0.271323 | 12.6 | 11.3 | Non-Haor | 31.275461 | 5,539 | 1,256 | 57.67812 | 2.469683 | 28.1 | 45 | 6.453081 | 0 | 73 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 163,917 | 1.606788 | 0.454538 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.703704 | 0.292551 | 12.6 | 11.3 | Non-Haor | 32.391451 | 5,539 | 1,256 | 58.628341 | 2.60963 | 28.1 | 45 | 6.957973 | 0 | 79 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 176,742 | 1.606788 | 0.453264 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.69178 | 0.287594 | 12.6 | 11.3 | Non-Haor | 30.208254 | 5,539 | 1,256 | 57.007898 | 2.603853 | 28.1 | 45 | 6.840067 | 0 | 77 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 173,747 | 1.606788 | 0.442952 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.931212 | 0.387133 | 12.6 | 11.3 | Non-Haor | 29.689556 | 5,539 | 1,256 | 60.248705 | 2.552267 | 28.1 | 45 | 9.207486 | 0 | 104 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 233,883 | 1.606788 | 0.452077 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.826956 | 0.343791 | 12.6 | 11.3 | Non-Haor | 31.38502 | 5,539 | 1,256 | 57.769184 | 2.204689 | 28.1 | 45 | 8.176643 | 0 | 92 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 207,698 | 1.606788 | 0.468344 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.937644 | 0.389807 | 12.6 | 11.3 | Non-Haor | 33.698609 | 5,539 | 1,256 | 60.089292 | 2.322734 | 28.1 | 45 | 9.27109 | 0 | 105 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 235,499 | 1.606788 | 0.475182 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.76029 | 0.316075 | 12.6 | 11.3 | Non-Haor | 32.609963 | 5,539 | 1,256 | 61.654864 | 2.585801 | 28.1 | 45 | 7.517471 | 0 | 85 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 190,955 | 1.606788 | 0.462677 | 0.060302 |
Mymensingh | 9,188 | 3,849 | 13,037 | 0.899669 | 0.37402 | 12.6 | 11.3 | Non-Haor | 32.086277 | 5,539 | 1,256 | 56.83395 | 2.237582 | 28.1 | 45 | 8.895602 | 0 | 101 | 6.1 | 1,727 | 2.4 | 12 | 6.2 | 15 | 8 | 225,961 | 1.606788 | 0.466465 | 0.060302 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.933337 | 1.020291 | 34.2 | 32.3 | Haor | 36.459903 | 2,303 | 882 | 58.38647 | 2.300736 | 19.3 | 67 | 19.362342 | 366 | 708 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 160,437 | 10.985671 | 0.524309 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.695464 | 0.937553 | 34.2 | 32.3 | Haor | 32.16614 | 2,303 | 882 | 59.317361 | 2.524367 | 19.3 | 67 | 17.792192 | 336 | 650 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 147,426 | 10.985671 | 0.502573 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.724198 | 0.947547 | 34.2 | 32.3 | Haor | 36.785003 | 2,303 | 882 | 59.707014 | 2.60609 | 19.3 | 67 | 17.98186 | 340 | 657 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 148,998 | 10.985671 | 0.513318 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.264096 | 0.787512 | 34.2 | 32.3 | Haor | 34.453354 | 2,303 | 882 | 62.913457 | 2.574816 | 19.3 | 67 | 14.944824 | 282 | 546 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 123,833 | 10.985671 | 0.515903 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.883414 | 1.002927 | 34.2 | 32.3 | Haor | 36.825356 | 2,303 | 882 | 61.819415 | 2.296837 | 19.3 | 67 | 19.032812 | 360 | 696 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 157,706 | 10.985671 | 0.534183 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.396128 | 0.833436 | 34.2 | 32.3 | Haor | 34.023545 | 2,303 | 882 | 59.828096 | 2.736505 | 19.3 | 67 | 15.816342 | 299 | 578 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 131,054 | 10.985671 | 0.498816 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.421815 | 0.84237 | 34.2 | 32.3 | Haor | 38.499212 | 2,303 | 882 | 62.205191 | 2.647406 | 19.3 | 67 | 15.985893 | 302 | 584 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 132,459 | 10.985671 | 0.52264 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.532669 | 0.880928 | 34.2 | 32.3 | Haor | 36.249094 | 2,303 | 882 | 61.354059 | 2.347621 | 19.3 | 67 | 16.717616 | 316 | 611 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 138,522 | 10.985671 | 0.528751 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.755122 | 0.958303 | 34.2 | 32.3 | Haor | 34.134715 | 2,303 | 882 | 58.4793 | 2.572736 | 19.3 | 67 | 18.18598 | 344 | 665 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 150,689 | 10.985671 | 0.503966 | 0.170787 |
Netrokona | 26,316 | 9,174 | 35,490 | 2.203236 | 0.766343 | 34.2 | 32.3 | Haor | 38.844874 | 2,303 | 882 | 64.187121 | 2.707432 | 19.3 | 67 | 14.543099 | 275 | 532 | 29.7 | 3,809 | 3.1 | 18 | 4.5 | 14 | 35 | 120,504 | 10.985671 | 0.525631 | 0.170787 |
Sherpur | 15,148 | 3,248 | 18,396 | 2.768345 | 0.587799 | 17.7 | 18.6 | Non-Haor | 30.253559 | 1,463 | 1,068 | 66.845522 | 2.266965 | 17.4 | 48 | 25.976939 | 0 | 49 | 10.5 | 250 | 2.5 | 13 | 5.5 | 16 | 6 | 115,500 | 9.979494 | 0.490526 | 0.094931 |
Sherpur | 15,148 | 3,248 | 18,396 | 3.465053 | 0.73573 | 17.7 | 18.6 | Non-Haor | 36.606254 | 1,463 | 1,068 | 66.362791 | 2.205446 | 17.4 | 48 | 32.514537 | 0 | 61 | 10.5 | 250 | 2.5 | 13 | 5.5 | 16 | 6 | 144,568 | 9.979494 | 0.511453 | 0.094931 |
Sherpur | 15,148 | 3,248 | 18,396 | 2.785794 | 0.591504 | 17.7 | 18.6 | Non-Haor | 35.852616 | 1,463 | 1,068 | 62.92647 | 2.147967 | 17.4 | 48 | 26.140671 | 0 | 49 | 10.5 | 250 | 2.5 | 13 | 5.5 | 16 | 6 | 116,228 | 9.979494 | 0.503476 | 0.094931 |
Sherpur | 15,148 | 3,248 | 18,396 | 2.986344 | 0.634087 | 17.7 | 18.6 | Non-Haor | 36.422237 | 1,463 | 1,068 | 65.356551 | 2.332228 | 17.4 | 48 | 28.022542 | 0 | 53 | 10.5 | 250 | 2.5 | 13 | 5.5 | 16 | 6 | 124,595 | 9.979494 | 0.502047 | 0.094931 |
Sherpur | 15,148 | 3,248 | 18,396 | 2.449502 | 0.5201 | 17.7 | 18.6 | Non-Haor | 34.270548 | 1,463 | 1,068 | 67.241245 | 2.134439 | 17.4 | 48 | 22.985054 | 0 | 43 | 10.5 | 250 | 2.5 | 13 | 5.5 | 16 | 6 | 102,197 | 9.979494 | 0.510193 | 0.094931 |
Kishoreganj | 15,187 | 7,305 | 22,492 | 2.941409 | 1.410265 | 21.7 | 16.6 | Non-Haor | 31.511406 | 3,126 | 1,033 | 65.336104 | 2.702448 | 21.7 | 58 | 2.820529 | 147 | 654 | 10.4 | 3,827 | 2.6 | 14 | 5.8 | 18 | 18 | 405,400 | 4.670505 | 0.488752 | 0.028109 |
Kishoreganj | 15,187 | 7,305 | 22,492 | 3.156501 | 1.513391 | 21.7 | 16.6 | Non-Haor | 28.476618 | 3,126 | 1,033 | 62.483116 | 2.760868 | 21.7 | 58 | 3.026782 | 157 | 702 | 10.4 | 3,827 | 2.6 | 14 | 5.8 | 18 | 18 | 435,046 | 4.670505 | 0.469594 | 0.028109 |
Kishoreganj | 15,187 | 7,305 | 22,492 | 3.282212 | 1.573664 | 21.7 | 16.6 | Non-Haor | 31.451044 | 3,126 | 1,033 | 66.436475 | 2.605898 | 21.7 | 58 | 3.147327 | 164 | 730 | 10.4 | 3,827 | 2.6 | 14 | 5.8 | 18 | 18 | 452,372 | 4.670505 | 0.496149 | 0.028109 |
Kishoreganj | 15,187 | 7,305 | 22,492 | 2.921771 | 1.400849 | 21.7 | 16.6 | Non-Haor | 31.04801 | 3,126 | 1,033 | 64.75677 | 2.705023 | 21.7 | 58 | 2.801698 | 146 | 650 | 10.4 | 3,827 | 2.6 | 14 | 5.8 | 18 | 18 | 402,694 | 4.670505 | 0.485785 | 0.028109 |
Kishoreganj | 15,187 | 7,305 | 22,492 | 3.265486 | 1.565644 | 21.7 | 16.6 | Non-Haor | 31.585431 | 3,126 | 1,033 | 62.766409 | 2.535303 | 21.7 | 58 | 3.131288 | 163 | 726 | 10.4 | 3,827 | 2.6 | 14 | 5.8 | 18 | 18 | 450,066 | 4.670505 | 0.490907 | 0.028109 |
Brahmanbaria | 13,646 | 23,501 | 37,148 | 2.743116 | 4.732399 | 35.8 | 18.8 | Non-Haor | 27.219582 | 2,984 | 1,576 | 54.927027 | 2.880047 | 24.8 | 52 | 9.841715 | 0 | 20 | 0 | 13,685 | 2.8 | 15 | 4.9 | 17 | 12 | 334,775 | 4.39008 | 0.428974 | 0.02879 |
Brahmanbaria | 13,646 | 23,501 | 37,148 | 3.330864 | 5.746377 | 35.8 | 18.8 | Non-Haor | 25.655861 | 2,984 | 1,576 | 58.1232 | 2.62769 | 24.8 | 52 | 11.95043 | 0 | 25 | 0 | 13,685 | 2.8 | 15 | 4.9 | 17 | 12 | 406,505 | 4.39008 | 0.444891 | 0.02879 |
Brahmanbaria | 13,646 | 23,501 | 37,148 | 2.668926 | 4.604407 | 35.8 | 18.8 | Non-Haor | 28.529139 | 2,984 | 1,576 | 56.794698 | 2.683695 | 24.8 | 52 | 9.575536 | 0 | 20 | 0 | 13,685 | 2.8 | 15 | 4.9 | 17 | 12 | 325,720 | 4.39008 | 0.447389 | 0.02879 |
Brahmanbaria | 13,646 | 23,501 | 37,148 | 3.304139 | 5.70027 | 35.8 | 18.8 | Non-Haor | 28.247165 | 2,984 | 1,576 | 55.963129 | 2.971276 | 24.8 | 52 | 11.854543 | 0 | 25 | 0 | 13,685 | 2.8 | 15 | 4.9 | 17 | 12 | 403,243 | 4.39008 | 0.430085 | 0.02879 |
Kurigram | 8,727 | 1,859 | 10,586 | 0.875846 | 0.187681 | 10.2 | 10.9 | Non-Haor | 35.867252 | 2,286 | 981 | 72.727321 | 2.016254 | 12.3 | 55 | 10.843807 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 139,278 | 3.674541 | 0.548607 | 0.063706 |
Kurigram | 8,727 | 1,859 | 10,586 | 1.107721 | 0.237369 | 10.2 | 10.9 | Non-Haor | 41.1816 | 2,286 | 981 | 75.949928 | 2.00652 | 12.3 | 55 | 13.714635 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 176,151 | 3.674541 | 0.573094 | 0.063706 |
Kurigram | 8,727 | 1,859 | 10,586 | 0.975642 | 0.209066 | 10.2 | 10.9 | Non-Haor | 41.726067 | 2,286 | 981 | 70.838534 | 1.975589 | 12.3 | 55 | 12.079374 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 155,148 | 3.674541 | 0.563495 | 0.063706 |
Kurigram | 8,727 | 1,859 | 10,586 | 0.872952 | 0.187061 | 10.2 | 10.9 | Non-Haor | 35.659764 | 2,286 | 981 | 75.854201 | 2.134392 | 12.3 | 55 | 10.807973 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 138,818 | 3.674541 | 0.549895 | 0.063706 |
Kurigram | 8,727 | 1,859 | 10,586 | 1.132309 | 0.242638 | 10.2 | 10.9 | Non-Haor | 38.682549 | 2,286 | 981 | 70.131384 | 1.862575 | 12.3 | 55 | 14.019066 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 180,061 | 3.674541 | 0.558247 | 0.063706 |
Kurigram | 8,727 | 1,859 | 10,586 | 0.945102 | 0.202522 | 10.2 | 10.9 | Non-Haor | 39.787906 | 2,286 | 981 | 74.05582 | 2.064079 | 12.3 | 55 | 11.701263 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 150,291 | 3.674541 | 0.561299 | 0.063706 |
Kurigram | 8,727 | 1,859 | 10,586 | 0.957572 | 0.205194 | 10.2 | 10.9 | Non-Haor | 42.984338 | 2,286 | 981 | 74.224753 | 2.021742 | 12.3 | 55 | 11.855656 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 152,274 | 3.674541 | 0.573428 | 0.063706 |
Kurigram | 8,727 | 1,859 | 10,586 | 1.096923 | 0.235055 | 10.2 | 10.9 | Non-Haor | 38.766448 | 2,286 | 981 | 70.67105 | 1.942389 | 12.3 | 55 | 13.580948 | 0 | 0 | 0 | 0 | 2.7 | 15 | 2.5 | 13 | 0 | 174,434 | 3.674541 | 0.555858 | 0.063706 |
Jamalpur | 16,924 | 4,282 | 21,206 | 2.568737 | 0.646124 | 20.4 | 21.2 | Non-Haor | 33.225936 | 2,479 | 1,146 | 63.754562 | 2.182081 | 15.7 | 62 | 29.311964 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 207,378 | 6.575232 | 0.52396 | 0.113936 |
Jamalpur | 16,924 | 4,282 | 21,206 | 1.900793 | 0.478114 | 20.4 | 21.2 | Non-Haor | 39.41619 | 2,479 | 1,146 | 67.650252 | 2.387603 | 15.7 | 62 | 21.690032 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 153,453 | 6.575232 | 0.541994 | 0.113936 |
Jamalpur | 16,924 | 4,282 | 21,206 | 1.953983 | 0.491493 | 20.4 | 21.2 | Non-Haor | 36.738293 | 2,479 | 1,146 | 66.124118 | 2.243137 | 15.7 | 62 | 22.29698 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 157,748 | 6.575232 | 0.537368 | 0.113936 |
Jamalpur | 16,924 | 4,282 | 21,206 | 2.266959 | 0.570217 | 20.4 | 21.2 | Non-Haor | 36.052995 | 2,479 | 1,146 | 67.068086 | 2.464845 | 15.7 | 62 | 25.868366 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 183,015 | 6.575232 | 0.526587 | 0.113936 |
Jamalpur | 16,924 | 4,282 | 21,206 | 1.905055 | 0.479186 | 20.4 | 21.2 | Non-Haor | 35.877149 | 2,479 | 1,146 | 67.134451 | 2.401505 | 15.7 | 62 | 21.73866 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 153,797 | 6.575232 | 0.529392 | 0.113936 |
Jamalpur | 16,924 | 4,282 | 21,206 | 2.660616 | 0.669235 | 20.4 | 21.2 | Non-Haor | 37.967985 | 2,479 | 1,146 | 64.065255 | 2.193873 | 15.7 | 62 | 30.360406 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 214,795 | 6.575232 | 0.538373 | 0.113936 |
Jamalpur | 16,924 | 4,282 | 21,206 | 2.461225 | 0.619081 | 20.4 | 21.2 | Non-Haor | 33.315123 | 2,479 | 1,146 | 66.868692 | 2.611311 | 15.7 | 62 | 28.085142 | 0 | 0 | 0 | 0 | 3 | 17 | 3.8 | 14 | 0 | 198,698 | 6.575232 | 0.510552 | 0.113936 |
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Bangladesh Flood PDNA 2022
Research datasets from the 2022 Bangladesh floods, curated by Farjana Yesmin for the paper:
Yesmin, F. & Akter, R. (2026). Toward Equitable Recovery: A Fairness-Aware AI Framework for Prioritizing Post-Flood Aid in Bangladesh. CCAI 2026 (IEEE). arXiv:2512.22210
Part of the FairHealth
library — pip install fairhealth
What Is Included
| File | Description | Rows |
|---|---|---|
bangladesh_floods_2022_district_level.csv |
District-level damage and loss | 11 |
modeling_dataset_upazila_level.csv |
ML-ready upazila features | 87 |
pdna_district_summary.csv |
PDNA official summary by district | 11 |
pdna_human_impact.csv |
Human impact indicators | — |
pdna_sector_summary.csv |
Sector-level damage breakdown | — |
fairness_metrics_summary.csv |
Fair vs baseline model comparison | — |
district_performance_comparison.csv |
MAE by district, fair vs baseline | 11 |
model_predictions_comparison.csv |
Model prediction outputs | 87 |
dataset_metadata.json |
Data provenance and field descriptions | — |
data_source_citations.txt |
Full citations for all sources | — |
Key Findings
The adversarial debiasing model reduces statistical parity difference by 41.6% and regional fairness gap by 43.2% compared to baseline, with only 2.7 percentage point R² cost (0.784 vs 0.811).
Sunamganj (42.7% poverty, $159.6M damage, 94% inundation) moves from rank 14 → rank 6 under the fair model.
Data Sources
- Ministry of Disaster Management and Relief, Government of Bangladesh. Post Disaster Needs Assessment: Bangladesh Floods 2022 (2023)
- Bangladesh Bureau of Statistics (BBS) — poverty and population data
- World Bank Bangladesh Country Data
- NASA SEDAC — gridded population data
- EM-DAT International Disaster Database
Usage
from fairhealth.equity.flood_aid import generate_priority_ranking
rankings = generate_priority_ranking(verbose=True)
Citation
@dataset{fairhealth_pdna_2026,
author = {Yesmin, Farjana and Akter, Romana},
title = {Bangladesh Flood PDNA 2022 Research Dataset},
year = {2026},
publisher = {Hugging Face},
doi = {10.57967/hf/8799},
url = {https://huggingface.co/datasets/fairhealth/bangladesh-flood-pdna-2022}
}
Also cite:
@inproceedings{yesmin2026ccai,
author = {Yesmin, Farjana and Akter, Romana},
title = {Toward Equitable Recovery: A Fairness-Aware AI Framework},
note = {CCAI 2026, IEEE, Nanjing. Oral. arXiv:2512.22210},
year = {2026}
}
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