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Datasets:
electricsheepafrica
/
africa-demographics-madagascar

Tasks:
Tabular Classification
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Modalities:
Tabular
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Formats:
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Languages:
English
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Tags:
africa
humanitarian
hdx
electric-sheep-africa
affected-population
cyclones-hurricanes-typhoons
Libraries:
Datasets
pandas
Polars
License:
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0
0
0
0
7
100
HDX
2026-04-27
MG52518
MG52518
73,680
22,455
0
26,177
7,987
0
13,261
4,033
0
5,985
1,815
0
65,308
19,888
0
33,231
10,104
0
2
100
0
0
0
0
1
100
0
0
0
0
4
80
1
20
0
0
HDX
2026-04-27
MG51506
MG51506
0
105,840
0
0
40,107
0
0
17,991
0
0
7,377
0
0
92,143
0
0
44,644
0
0
0
1
100
0
0
0
0
1
100
0
0
0
0
1
100
0
0
HDX
2026-04-27
MG31309
MG31309
0
0
72,393
0
0
16,916
0
0
8,620
0
0
4,768
0
0
49,316
0
0
24,519
0
0
0
0
0
0
0
0
0
0
1
100
0
0
0
0
0
0
HDX
2026-04-27
MG31301
MG31301
0
0
15,660
0
0
3,652
0
0
1,862
0
0
1,033
0
0
10,652
0
0
5,297
0
0
0
0
37
100
0
0
0
0
4
100
0
0
0
0
12
100
HDX
2026-04-27
MG54511
MG54511
0
93,305
0
0
28,001
0
0
13,373
0
0
6,613
0
0
78,319
0
0
37,664
0
0
0
4
100
0
0
0
0
0
0
0
0
0
0
1
100
0
0
HDX
2026-04-27
MG25214
MG25214
44,600
198,868
0
16,656
74,274
0
8,122
36,215
0
2,041
9,098
0
39,326
175,353
0
19,626
87,515
0
0
0
5
100
0
0
0
0
2
100
0
0
12
26
34
74
0
0
HDX
2026-04-27
MG42423
MG42423
0
0
96,071
0
0
28,042
0
0
13,978
0
0
5,383
0
0
80,964
0
0
39,337
0
0
0
0
21
100
0
0
0
0
0
0
0
0
0
0
0
0
HDX
2026-04-27
MG32315
MG32315
0
0
77,005
0
0
20,934
0
0
10,662
0
0
4,917
0
0
61,901
0
0
30,083
0
0
0
0
0
0
0
0
0
0
1
100
0
0
0
0
1
100
HDX
2026-04-27
MG72710
MG72710
0
0
116,385
0
0
34,498
0
0
16,248
0
0
8,688
0
0
95,943
0
0
44,728
0
0
0
0
113
99
0
0
0
0
0
0
0
0
0
0
36
100
HDX
2026-04-27
MG42414
MG42414
0
0
91,971
0
0
26,850
0
0
13,384
0
0
5,158
0
0
77,608
0
0
37,691
0
0
0
0
40
100
0
0
0
0
0
0
0
0
0
0
9
100
HDX
2026-04-27
MG72711
MG72711
0
0
142,796
0
0
42,326
0
0
19,935
0
0
10,660
0
0
117,715
0
0
54,878
0
0
0
0
202
100
0
0
0
0
2
100
0
0
0
0
47
100
HDX
2026-04-27
MG23212
MG23212
0
99,792
0
0
35,053
0
0
17,426
0
0
4,363
0
0
85,918
0
0
41,748
0
0
0
28
100
0
0
0
0
2
100
0
0
0
0
28
100
0
0
HDX
2026-04-27
MG32303
MG32303
0
0
126,650
0
0
34,479
0
0
17,561
0
0
8,085
0
0
101,966
0
0
49,545
0
0
0
0
74
100
0
0
0
0
1
100
0
0
0
0
17
100
HDX
2026-04-27
MG25217
MG25217
0
84,166
0
0
31,399
0
0
15,315
0
0
3,847
0
0
74,180
0
0
37,003
0
0
0
1
100
0
0
0
0
0
0
0
0
0
0
3
100
0
0
HDX
2026-04-27
MG52516
MG52516
282,943
58,395
0
100,329
20,746
0
50,814
10,510
0
22,919
4,743
0
250,560
51,759
0
127,369
26,337
0
1
50
1
50
0
0
1
100
0
0
0
0
0
0
0
0
0
0
HDX
2026-04-27
MG32302
MG32302
0
0
20,110
0
0
5,475
0
0
2,788
0
0
1,284
0
0
16,190
0
0
7,867
0
0
0
0
15
100
0
0
0
0
6
100
0
0
0
0
7
100
HDX
2026-04-27
MG42410
MG42410
0
0
135,020
0
0
39,413
0
0
19,647
0
0
7,575
0
0
113,925
0
0
55,329
0
0
0
0
6
100
0
0
0
0
2
100
0
0
0
0
0
0
HDX
2026-04-27
MG22202
MG22202
0
18,935
88,017
0
5,839
27,129
0
2,808
13,044
0
1,324
6,153
0
16,071
74,700
0
7,831
36,396
0
0
3
17
15
83
0
0
0
0
2
100
0
0
1
25
3
75
HDX
2026-04-27
MG71715
MG71715
0
0
18,070
0
0
4,424
0
0
2,223
0
0
1,091
0
0
13,828
0
0
6,849
0
0
0
0
34
100
0
0
0
0
8
100
0
0
0
0
0
0
HDX
2026-04-27
MG32304
MG32304
0
0
133,629
0
0
36,460
0
0
18,557
0
0
8,499
0
0
107,744
0
0
52,352
0
0
0
0
2
100
0
0
0
0
0
0
0
0
0
0
0
0
HDX
2026-04-27
MG33312
MG33312
0
0
154,772
0
0
44,808
0
0
21,373
0
0
7,498
0
0
124,509
0
0
59,373
0
0
0
0
24
100
0
0
0
0
0
0
0
0
0
0
5
100
HDX
2026-04-27
MG52513
MG52513
1,057
86,268
0
375
30,649
0
190
15,526
0
86
7,007
0
937
76,465
0
477
38,908
0
0
0
3
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HDX
2026-04-27
MG23207
MG23207
0
0
146,905
0
0
51,496
0
0
25,606
0
0
6,436
0
0
126,331
0
0
61,379
0
0
0
0
73
100
0
0
0
0
1
100
0
0
0
0
26
100
HDX
2026-04-27
MG21220
MG21220
0
0
60,989
0
0
20,752
0
0
10,112
0
0
4,012
0
0
51,615
0
0
25,261
0
0
0
0
21
100
0
0
0
0
0
0
0
0
0
0
9
100
HDX
2026-04-27
MG54502
MG54502
0
36,208
0
0
10,908
0
0
5,204
0
0
2,572
0
0
30,415
0
0
14,627
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HDX
2026-04-27
MG11101002A
MG11101002A
0
0
63,830
0
0
14,251
0
0
7,500
0
0
4,493
0
0
40,155
0
0
20,506
0
0
0
0
56
100
0
0
0
0
10
100
0
0
0
0
14
100
HDX
2026-04-27
MG31307
MG31307
0
0
58,565
0
0
13,658
0
0
6,964
0
0
3,863
0
0
39,839
0
0
19,810
0
0
0
0
79
100
0
0
0
0
5
100
0
0
0
0
10
100
HDX
2026-04-27
MG13112
MG13112
0
0
130,762
0
0
40,740
0
0
19,807
0
0
7,492
0
0
111,762
0
0
55,119
0
0
0
0
50
100
0
0
0
0
1
100
0
0
0
0
4
100
HDX
2026-04-27
MG11101003A
MG11101003A
0
0
40,971
0
0
9,147
0
0
4,814
0
0
2,884
0
0
25,775
0
0
13,163
0
0
0
0
51
100
0
0
0
0
18
100
0
0
0
0
20
100
HDX
2026-04-27
MG14111
MG14111
0
62,855
160,177
0
16,521
42,039
0
9,157
23,344
0
3,611
9,176
0
51,902
132,243
0
26,852
68,474
0
0
1
2
60
98
0
0
0
0
1
100
0
0
0
0
15
100
HDX
2026-04-27
MG53517
MG53517
55,907
63,056
0
18,184
20,518
0
9,086
10,245
0
3,978
4,482
0
47,507
53,577
0
23,065
26,006
0
1
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HDX
2026-04-27
MG52514
MG52514
123
97,024
0
44
34,469
0
22
17,463
0
10
7,881
0
109
86,000
0
55
43,760
0
0
0
4
100
0
0
0
0
2
100
0
0
0
0
2
100
0
0
HDX
2026-04-27
MG44421
MG44421
0
105,712
60
0
29,757
17
0
13,949
8
0
5,324
3
0
88,125
50
0
43,553
25
0
0
8
100
0
0
0
0
1
100
0
0
0
0
4
100
0
0
HDX
2026-04-27
MG54509
MG54509
0
86,324
0
0
25,911
0
0
12,374
0
0
6,133
0
0
72,475
0
0
34,850
0
0
0
3
100
0
0
0
0
0
0
0
0
0
0
2
100
0
0
HDX
2026-04-27
MG42409
MG42409
0
0
181,263
0
0
52,919
0
0
26,379
0
0
10,165
0
0
152,944
0
0
74,281
0
0
0
0
26
100
0
0
0
0
1
100
0
0
0
0
3
100
HDX
2026-04-27
MG44420
MG44420
0
51,134
0
0
14,395
0
0
6,748
0
0
2,576
0
0
42,628
0
0
21,067
0
0
0
10
100
0
0
0
0
1
100
0
0
0
0
2
100
0
0
HDX
2026-04-27
MG43408
MG43408
0
0
63,023
0
0
19,132
0
0
9,231
0
0
3,754
0
0
52,487
0
0
25,388
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
100
HDX
2026-04-27
MG72716
MG72716
0
0
171,702
0
0
50,880
0
0
23,965
0
0
12,814
0
0
141,528
0
0
65,986
0
0
0
0
133
100
0
0
0
0
2
100
0
0
0
0
41
100
HDX
2026-04-27
MG13105
MG13105
0
0
146,396
0
0
45,651
0
0
22,169
0
0
8,390
0
0
125,077
0
0
61,655
0
0
0
0
18
100
0
0
0
0
4
100
0
0
0
0
8
100
HDX
2026-04-27
MG32305
MG32305
0
0
168,978
0
0
45,961
0
0
23,409
0
0
10,790
0
0
135,913
0
0
66,047
0
0
0
0
8
100
0
0
0
0
2
100
0
0
0
0
2
100
HDX
2026-04-27
MG41406
MG41406
0
0
124,149
0
0
35,492
0
0
17,647
0
0
6,364
0
0
92,007
0
0
46,659
0
0
0
0
5
100
0
0
0
0
1
100
0
0
0
0
3
100
HDX
2026-04-27
MG54508
MG54508
0
100,023
0
0
30,023
0
0
14,338
0
0
7,107
0
0
83,976
0
0
40,380
0
0
0
7
100
0
0
0
0
1
100
0
0
0
0
2
100
0
0
HDX
2026-04-27
MG12109
MG12109
0
0
128,254
0
0
37,795
0
0
18,145
0
0
6,696
0
0
104,989
0
0
51,144
0
0
0
0
5
100
0
0
0
0
1
100
0
0
0
0
0
0
HDX
2026-04-27
MG51505
MG51505
0
38,443
0
0
14,556
0
0
6,539
0
0
2,669
0
0
33,466
0
0
16,222
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HDX
2026-04-27
MG31306
MG31306
0
0
82,905
0
0
19,337
0
0
9,859
0
0
5,467
0
0
56,403
0
0
28,046
0
0
0
0
25
100
0
0
0
0
3
100
0
0
0
0
6
100
HDX
2026-04-27
MG53515
MG53515
139,276
0
0
45,279
0
0
22,622
0
0
9,902
0
0
118,324
0
0
57,433
0
0
86
100
0
0
0
0
3
100
0
0
0
0
18
100
0
0
0
0
HDX
2026-04-27

Madagascar - Risk Assessment Indicators

Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology) · Source: HDX · License: cc-by-sa · Updated: 2026-04-13

Abstract

This dataset provides comprehensive Risk Assessment Indicators for Madagascar, aggregated at admin level 2 and can in particular be used to perform a structured risk assessment for flood and cyclone hazards. It includes demographic, environmental, infrastructure, accessibility, and hazard-related data to support disaster risk and resilience analysis.

All layers are derived from HeiGIT’s GAIA Pipeline, integrating open data sources such as WorldPop, OpenStreetMap, and Google Earth Engine based on HDX COD-AB boundaries.

Data Overview

  • Access to Services (MDG_ADM2_access)
  • Facilities (MDG_ADM2_facilities)
  • Coping Capacity (MDG_ADM2_coping)
  • Demographics (MDG_ADM2_demographics)
  • Rural Population (MDG_ADM2_rural_population)
  • Vulnerability (MDG_ADM2_vulnerability)
  • Flood Exposure (MDG_ADM2_flood_exposure)
  • Cyclone Exposure (MDG_ADM2_cyclone_exposure)

 

 

Indicator Descriptions

Access to Services (MDG_ADM2_access)

Represents the share of the population with access to key facilities within defined distances or travel times.

  • ADM2_PCODE – Administrative division code (ADM2)
  • access_pop_education_5km / 10km / 20km – Population within 5, 10, and 20 km of educational facilities
  • access_pop_hospitals_30min / 1h / 2h – Population within 30 minutes, 1 hour, and 2 hours of a hospital
  • access_pop_primary_healthcare_30min / 1h / 2h – Population within 30 minutes, 1 hour, and 2 hours of a primary health care facility

Data Source: openrouteservice (ORS)

Facilities (MDG_ADM2_facilities)

Counts of essential service facilities within each district.

  • ADM2_PCODE – Administrative division code (ADM2)
  • education_count – Number of educational facilities
  • hospitals_count – Number of hospitals
  • primary_healthcare_count – Number of primary health care facilities

Data Source: OpenStreetMap (OSM)

Coping Capacity (MDG_ADM2_coping)

Combines Access to Services and Facilities data to represent a district’s coping capacity.

Demographics (MDG_ADM2_demographics)

Shows the population composition by age and gender.

  • ADM2_PCODE – Administrative division code (ADM2)
  • female_pop – Total female population
  • children_u5 – Population under 5 years old
  • female_u5 – Female population under 5 years old
  • elderly – Population aged 65 and older
  • pop_u15 – Population under 15 years old
  • female_u15 – Female population under 15 years old

Data Source: Worldpop

Rural Population (MDG_ADM2_rural_population)

Same demographic breakdown as above, but limited to rural populations. Rural areas are those outside urban extents, typically characterized by lower population density, agricultural or natural land use, and limited infrastructure compared to urban centers.

  • ADM2_PCODE – Administrative division code (ADM2)
  • female_pop_rural, children_u5_rural, female_u5_rural, elderly_rural, pop_u15_rural, female_u15_rural – Rural demographic counts
  • rural_pop_perc – Percentage of total population living in rural areas

Data Source: Global Human Settlement Layer (GHSL)

Vulnerability (MDG_ADM2_vulnerability)

Combines Demographics and Rural Population indicators.

Flood Exposure (MDG_ADM2_flood_exposure)

Shows population and facility exposure to flooding at 30 cm depth for multiple return periods.

  • ADM2_PCODE – Administrative division code (ADM2)
  • female_pop_30cm, children_u5_30cm, female_u5_30cm, elderly_30cm, pop_u15_30cm, female_u15_30cm – Exposed population by group
  • education_30cm_pct / count, hospitals_30cm_pct / count, primary_healthcare_30cm_pct / count – Facility exposure (percentage and count)

Data Source: The Joint Research Centre (JRC)

Cyclone Exposure (MDG_ADM2_cyclone_exposure)

Represents the exposure of populations and facilities to cyclones, based on historical cyclone tracks and intensity categories (1–3). Vulnerable populations and facilities are quantified per admin unit.

  • ADM2_PCODE – Administrative division code (ADM2)
  • kt34_female_pop_cat1 / cat2 / cat3, kt34_children_u5_cat1 / cat2 / cat3, etc. – Population exposed to cyclone categories 1–3
  • kt34_education_perc / count_cat1 / cat2 / cat3, kt34_hospitals_perc / count_cat1 / cat2 / cat3, kt34_primary_healthcare_perc / count_cat1 / cat2 / cat3 – Facilities exposed to cyclone categories

Data Source: IBTrACS – NOAA International Best Track Archive for Climate Stewardship

 

 

QGIS Plugin Risk Assessment Inputs

  • Coping Capacity = Access + Facilities
  • Vulnerability = Demographics + Rural Population
  • Exposure = Vulnerable Population + Facilities exposed to Floods and Cyclones

This dataset is part of HeiGIT’s Risk Assessment Indicator Collection on HDX. See more at HeiGIT on HDX and learn about HeiGIT’s research at HeiGIT.

We are happy to hear about your use-cases — contact us at communications@heigit.org!

Each row in this dataset represents tabular records. Data was last updated on HDX on 2026-04-13. Geographic scope: MDG.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics

Domain Public health
Unit of observation Tabular records
Rows (total) 119
Columns 40 (36 numeric, 4 categorical, 0 datetime)
Train split 95 rows
Test split 23 rows
Geographic scope MDG
Publisher HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated 2026-04-13

Variables

Geographic — kt34_elderly_cat1 (range 0.0–22919.0), kt34_elderly_cat2 (range 0.0–19283.0), kt34_elderly_cat3 (range 0.0–47349.0), kt34_primary_healthcare_count_cat1, kt34_primary_healthcare_perc_cat1 and 4 others.

Demographic — kt34_female_pop_cat1 (range 0.0–282943.0), kt34_female_pop_cat2 (range 0.0–282370.0), kt34_female_pop_cat3 (range 0.0–672711.0), kt34_female_u5_cat1 (range 0.0–50814.0), kt34_female_u5_cat2 (range 0.0–51368.0) and 7 others.

Outcome / Measurement — kt34_education_count_cat1 (range 0.0–86.0), kt34_education_count_cat2, kt34_education_count_cat3, kt34_hospitals_count_cat1, kt34_hospitals_count_cat2 and 1 others.

Identifier / Metadata — adm2_pcode (MG11101001A, MG42410, MG44421), adm_pcode (MG11101001A, MG42410, MG44421), esa_source (HDX), esa_processed (2026-04-27).

Other — kt34_children_u5_cat1 (range 0.0–100329.0), kt34_children_u5_cat2 (range 0.0–105302.0), kt34_children_u5_cat3 (range 0.0–150190.0), kt34_education_perc_cat1 (range 0.0–100.0), kt34_education_perc_cat2 and 4 others.

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-demographics-madagascar")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
adm2_pcode object 0.0% MG11101001A, MG42410, MG44421
adm_pcode object 0.0% MG11101001A, MG42410, MG44421
kt34_female_pop_cat1 int64 0.0% 0.0 – 282943.0 (mean 6684.4454)
kt34_female_pop_cat2 int64 0.0% 0.0 – 282370.0 (mean 28319.3361)
kt34_female_pop_cat3 int64 0.0% 0.0 – 672711.0 (mean 83456.5546)
kt34_children_u5_cat1 int64 0.0% 0.0 – 100329.0 (mean 2309.8992)
kt34_children_u5_cat2 int64 0.0% 0.0 – 105302.0 (mean 9831.4958)
kt34_children_u5_cat3 int64 0.0% 0.0 – 150190.0 (mean 22881.8403)
kt34_female_u5_cat1 int64 0.0% 0.0 – 50814.0 (mean 1157.0756)
kt34_female_u5_cat2 int64 0.0% 0.0 – 51368.0 (mean 4724.5966)
kt34_female_u5_cat3 int64 0.0% 0.0 – 79043.0 (mean 11434.4034)
kt34_elderly_cat1 int64 0.0% 0.0 – 22919.0 (mean 480.3361)
kt34_elderly_cat2 int64 0.0% 0.0 – 19283.0 (mean 1715.9328)
kt34_elderly_cat3 int64 0.0% 0.0 – 47349.0 (mean 5077.8824)
kt34_pop_u15_cat1 int64 0.0% 0.0 – 250560.0 (mean 5819.2857)
kt34_pop_u15_cat2 int64 0.0% 0.0 – 248833.0 (mean 24424.1849)
kt34_pop_u15_cat3 int64 0.0% 0.0 – 423197.0 (mean 63666.4034)
kt34_female_u15_cat1 int64 0.0% 0.0 – 127369.0 (mean 2898.1765)
kt34_female_u15_cat2 int64 0.0% 0.0 – 124107.0 (mean 12021.2437)
kt34_female_u15_cat3 int64 0.0% 0.0 – 216116.0 (mean 31397.4118)
kt34_education_count_cat1 int64 0.0% 0.0 – 86.0 (mean 0.7731)
kt34_education_perc_cat1 int64 0.0% 0.0 – 100.0 (mean 4.6218)
kt34_education_count_cat2 int64 0.0%
kt34_education_perc_cat2 int64 0.0%
kt34_education_count_cat3 int64 0.0%
kt34_education_perc_cat3 int64 0.0%
kt34_hospitals_count_cat1 int64 0.0%
kt34_hospitals_perc_cat1 int64 0.0%
kt34_hospitals_count_cat2 int64 0.0%
kt34_hospitals_perc_cat2 int64 0.0%
kt34_hospitals_count_cat3 int64 0.0%
kt34_hospitals_perc_cat3 int64 0.0%
kt34_primary_healthcare_count_cat1 int64 0.0%
kt34_primary_healthcare_perc_cat1 int64 0.0%
kt34_primary_healthcare_count_cat2 int64 0.0%
kt34_primary_healthcare_perc_cat2 int64 0.0%
kt34_primary_healthcare_count_cat3 int64 0.0%
kt34_primary_healthcare_perc_cat3 int64 0.0%
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-27

Numeric Summary

Column Min Max Mean Median
kt34_female_pop_cat1 0.0 282943.0 6684.4454 0.0
kt34_female_pop_cat2 0.0 282370.0 28319.3361 0.0
kt34_female_pop_cat3 0.0 672711.0 83456.5546 72942.0
kt34_children_u5_cat1 0.0 100329.0 2309.8992 0.0
kt34_children_u5_cat2 0.0 105302.0 9831.4958 0.0
kt34_children_u5_cat3 0.0 150190.0 22881.8403 18441.0
kt34_female_u5_cat1 0.0 50814.0 1157.0756 0.0
kt34_female_u5_cat2 0.0 51368.0 4724.5966 0.0
kt34_female_u5_cat3 0.0 79043.0 11434.4034 9231.0
kt34_elderly_cat1 0.0 22919.0 480.3361 0.0
kt34_elderly_cat2 0.0 19283.0 1715.9328 0.0
kt34_elderly_cat3 0.0 47349.0 5077.8824 4225.0
kt34_pop_u15_cat1 0.0 250560.0 5819.2857 0.0
kt34_pop_u15_cat2 0.0 248833.0 24424.1849 0.0
kt34_pop_u15_cat3 0.0 423197.0 63666.4034 51956.0

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

  • Data originates from HeiGIT (Heidelberg Institute for Geoinformation Technology) and has not been independently validated by ESA.
  • Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
  • Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation 

@dataset{hdx_africa_demographics_madagascar,
  title     = {Madagascar - Risk Assessment Indicators},
  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/madagascar---risk-assessment-indicators},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.
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