electricsheepafrica/africa-commuting-zones

Facebook Commuting Zones

Publisher: AI for Good at Meta · Source: HDX · License: cc-by · Updated: 2026-03-26

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Abstract

Commuting zones are geographic areas where people live and work and are useful for understanding local economies, as well as how they differ from traditional boundaries. Learn more here: https://ai.meta.com/ai-for-good/datasets/commuting-zones/

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the cz_gen_ds column(s). Geographic scope: AFG, ALB, DZA, ASM, AND, AGO, AIA, ATG, and 208 others.

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	First-level administrative unit observations
Rows (total)	6,541
Columns	12 (4 numeric, 7 categorical, 1 datetime)
Train split	5,232 rows
Test split	1,308 rows
Geographic scope	AFG, ALB, DZA, ASM, AND, AGO, AIA, ATG, and 208 others
Publisher	AI for Good at Meta
HDX last updated	2026-03-26

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Variables

Geographic — region (Europe, North, Asia), win_population (range 1924.466–4442659.362), country (United States, Australia, France), geography (POLYGON ((-73.171737 48.196908, -73.836874 48.251112, -74.101395 48.434243, -73.454396 49.503114, -73.294749 49.628049, -72.35835 50.498351, -70.762395 52.413453, -70.482238 52.039537, -70.622205 51.648719, -71.472274 49.553803, -71.500548 49.498663, -71.574685 49.381618, -72.025571 48.846201, -72.073891 48.707361, -72.238414 48.71652, -72.336656 48.753356, -72.346522 48.763222, -72.4168 48.765866, -72.650907 48.680679, -73.171737 48.196908)), POLYGON ((-69.680454 45.151859, -69.731672 45.19535, -69.785801 45.245749, -69.826246 45.359338, -69.83213 45.366738, -69.870738 45.518099, -69.998347 45.65225, -70.28576 45.375852, -70.527884 45.509149, -70.520473 45.5353, -70.481191 45.703096, -70.411773 45.835644, -70.328549 45.887897, -70.01939 45.95313, -69.895301 46.123478, -69.711148 46.228322, -69.317316 46.328557, -69.286926 46.329558, -69.280901 46.326627, -69.213239 46.212382, -69.219943 45.861754, -69.205386 45.795863, -69.21385 45.764633, -69.262376 45.664322, -69.47938 45.414352, -69.515416 45.393873, -69.673058 45.158904, -69.680334 45.151881, -69.680454 45.151859)), MULTIPOLYGON (((-123.220089 49.47107, -123.24931373453539 49.488083639724586, -123.249442 49.501754, -123.261289 49.508269, -123.261165 49.526269, -123.238278 49.550035, -123.22181 49.588994, -123.22507 49.593916, -123.210235 49.615501, -123.207777 49.629049, -123.224913 49.64711, -123.219146 49.655275, -123.181645 49.659124, -123.17921082528545 49.661029312082306, -123.158802 49.677004, -123.183761 49.684799, -123.20940570245989 49.679209467251724, -123.218515 49.677224, -123.25705 49.664083, -123.259265 49.637214, -123.25254 49.630291, -123.255475 49.609911, -123.270061 49.585311, -123.309097 49.583662, -123.344199 49.569358, -123.357735 49.554037, -123.36809580995293 49.55723449666206, -123.395381 49.573119, -123.525542 49.753351, -123.546391 49.80277, -123.55304 49.827557, -123.678378 50.086184, -123.759908 50.336321, -123.8216 50.43486, -123.918906 50.973352, -124.121268 51.689487, -122.622706 51.096108, -122.204561 50.115342, -122.220455 49.893439, -122.296483 49.843913, -122.349162 49.834274, -122.512879 49.771166, -122.683251 49.738348, -122.732661 49.70182, -122.824177 49.621934, -122.912518 49.552721, -122.976722 49.539112, -123.098239 49.488244, -123.220089 49.47107)), ((-123.29607199956644 49.51530470051546, -123.3276295166913 49.533676406383805, -123.327861 49.534009, -123.308961 49.549645, -123.295091 49.547005, -123.292206 49.525969, -123.283769 49.52351, -123.29607199956644 49.51530470051546)))).

Identifier / Metadata — fbcz_id (North1271, North743, North722), name (springfield, greenville, columbus), fbcz_id_num (range 100242.0–600967.0), esa_source (HDX), esa_processed (2026-04-16).

Other — cz_gen_ds, win_roads_km (range 377.3358–60370392.52), area (range 1.3241–254948932.0).

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Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
region	object	0.0%	Europe, North, Asia
fbcz_id	object	0.0%	North1271, North743, North722
name	object	0.0%	springfield, greenville, columbus
fbcz_id_num	int64	0.0%	100242.0 – 600967.0 (mean 317469.9882)
cz_gen_ds	datetime64[ns]	0.0%
win_population	float64	0.0%	1924.466 – 4442659.362 (mean 672256.6153)
win_roads_km	float64	0.0%	377.3358 – 60370392.52 (mean 159459.6279)
area	float64	0.0%	1.3241 – 254948932.0 (mean 54115.3705)
country	object	0.0%	United States, Australia, France
geography	object	0.0%	POLYGON ((-73.171737 48.196908, -73.836874 48.251112, -74.101395 48.434243, -73.454396 49.503114, -73.294749 49.628049, -72.35835 50.498351, -70.762395 52.413453, -70.482238 52.039537, -70.622205 51.648719, -71.472274 49.553803, -71.500548 49.498663, -71.574685 49.381618, -72.025571 48.846201, -72.073891 48.707361, -72.238414 48.71652, -72.336656 48.753356, -72.346522 48.763222, -72.4168 48.765866, -72.650907 48.680679, -73.171737 48.196908)), POLYGON ((-69.680454 45.151859, -69.731672 45.19535, -69.785801 45.245749, -69.826246 45.359338, -69.83213 45.366738, -69.870738 45.518099, -69.998347 45.65225, -70.28576 45.375852, -70.527884 45.509149, -70.520473 45.5353, -70.481191 45.703096, -70.411773 45.835644, -70.328549 45.887897, -70.01939 45.95313, -69.895301 46.123478, -69.711148 46.228322, -69.317316 46.328557, -69.286926 46.329558, -69.280901 46.326627, -69.213239 46.212382, -69.219943 45.861754, -69.205386 45.795863, -69.21385 45.764633, -69.262376 45.664322, -69.47938 45.414352, -69.515416 45.393873, -69.673058 45.158904, -69.680334 45.151881, -69.680454 45.151859)), MULTIPOLYGON (((-123.220089 49.47107, -123.24931373453539 49.488083639724586, -123.249442 49.501754, -123.261289 49.508269, -123.261165 49.526269, -123.238278 49.550035, -123.22181 49.588994, -123.22507 49.593916, -123.210235 49.615501, -123.207777 49.629049, -123.224913 49.64711, -123.219146 49.655275, -123.181645 49.659124, -123.17921082528545 49.661029312082306, -123.158802 49.677004, -123.183761 49.684799, -123.20940570245989 49.679209467251724, -123.218515 49.677224, -123.25705 49.664083, -123.259265 49.637214, -123.25254 49.630291, -123.255475 49.609911, -123.270061 49.585311, -123.309097 49.583662, -123.344199 49.569358, -123.357735 49.554037, -123.36809580995293 49.55723449666206, -123.395381 49.573119, -123.525542 49.753351, -123.546391 49.80277, -123.55304 49.827557, -123.678378 50.086184, -123.759908 50.336321, -123.8216 50.43486, -123.918906 50.973352, -124.121268 51.689487, -122.622706 51.096108, -122.204561 50.115342, -122.220455 49.893439, -122.296483 49.843913, -122.349162 49.834274, -122.512879 49.771166, -122.683251 49.738348, -122.732661 49.70182, -122.824177 49.621934, -122.912518 49.552721, -122.976722 49.539112, -123.098239 49.488244, -123.220089 49.47107)), ((-123.29607199956644 49.51530470051546, -123.3276295166913 49.533676406383805, -123.327861 49.534009, -123.308961 49.549645, -123.295091 49.547005, -123.292206 49.525969, -123.283769 49.52351, -123.29607199956644 49.51530470051546)))
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-16

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Numeric Summary

Column	Min	Max	Mean	Median
fbcz_id_num	100242.0	600967.0	317469.9882	301482.0
win_population	1924.466	4442659.362	672256.6153	169167.2207
win_roads_km	377.3358	60370392.52	159459.6279	17895.094
area	1.3241	254948932.0	54115.3705	4312.8595

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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. 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from AI for Good at Meta and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 216 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_commuting_zones,
  title     = {Facebook Commuting Zones},
  author    = {AI for Good at Meta},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/commuting-zones},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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