Dataset Preview
Duplicate
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 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
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
End of preview.

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}
}
Downloads last month
138

Paper for fairhealth/bangladesh-flood-pdna-2022