Malaysia

MYS Malaysia - Economic, Social, Environmental, Health MYS-HDX-Malaysia_-_Economic,_Social,_Enviro Indicators

83,035 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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MYS Malaysia - Economic, Social, Environmental, Health — 83,035 rows, 3,033 columns

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Field details — 3034 fields (type, nulls, unique, samples)

Extension Fields (3034) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Fertilizer_consumption_pct_of_fertilizer_production`	Fertilizer_consumption_pct_of_fertilizer_production	float	0%	1	256.389410187668
`Fertilizer_consumption_kilograms_per_hectare_of_arable_land`	Fertilizer_consumption_kilograms_per_hectare_of_arable_land	float	0%	1	2926.42866250159
`Agricultural_land_sq._km`	Agricultural_land_sq._km	float	0%	1	85710.0
`Agricultural_land_pct_of_land_area`	Agricultural_land_pct_of_land_area	float	0%	1	26.0873535230558
`Arable_land_hectares`	Arable_land_hectares	float	0%	1	784300.0
`Arable_land_hectares_per_person`	Arable_land_hectares_per_person	float	0%	1	0.0223280005197246
`Arable_land_pct_of_land_area`	Arable_land_pct_of_land_area	float	0%	1	2.38715568406635
`Land_under_cereal_production_hectares`	Land_under_cereal_production_hectares	float	0%	1	615072.0
`Permanent_cropland_pct_of_land_area`	Permanent_cropland_pct_of_land_area	float	0%	1	22.7058286409983
`Rural_land_area_where_elevation_is_below_5_meters_sq._km`	Rural_land_area_where_elevation_is_below_5_meters_sq._km	float	0%	1	20980.5969639
`Rural_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area`	Rural_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area	float	0%	1	6.36517881131312
`Forest_area_sq._km`	Forest_area_sq._km	float	0%	1	189635.9
`Forest_area_pct_of_land_area`	Forest_area_pct_of_land_area	float	0%	1	57.7190381981434
`Agricultural_irrigated_land_pct_of_total_agricultural_land`	Agricultural_irrigated_land_pct_of_total_agricultural_land	float	0%	1	5.15692451289231
`Average_precipitation_in_depth_mm_per_year`	Average_precipitation_in_depth_mm_per_year	float	0%	1	2875.0
`Land_area_sq._km`	Land_area_sq._km	float	0%	1	328550.0
`Rural_land_area_sq._km`	Rural_land_area_sq._km	float	0%	1	318197.5660893
`Cereal_production_metric_tons`	Cereal_production_metric_tons	float	0%	1	2167306.44
`Crop_production_index_2014-2016_=_100`	Crop_production_index_2014-2016_=_100	float	0%	1	97.93
`Food_production_index_2014-2016_=_100`	Food_production_index_2014-2016_=_100	float	0%	1	102.8
`Livestock_production_index_2014-2016_=_100`	Livestock_production_index_2014-2016_=_100	float	0%	1	99.69
`Surface_area_sq._km`	Surface_area_sq._km	float	0%	1	330411.0
`Cereal_yield_kg_per_hectare`	Cereal_yield_kg_per_hectare	float	0%	1	3523.7
`Access_to_electricity_rural_pct_of_rural_population`	Access_to_electricity_rural_pct_of_rural_population	float	0%	1	100.0
`Rural_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_`	Rural_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_	float	0%	1	3.60734089468673
`Annual_freshwater_withdrawals_agriculture_pct_of_total_freshwater_withdrawal`	Annual_freshwater_withdrawals_agriculture_pct_of_total_freshwater_withdrawal	float	0%	1	45.64504373
`Agriculture_forestry_and_fishing_value_added_current_USusd`	Agriculture_forestry_and_fishing_value_added_current_USusd	float	0%	1	34320586273.8363
`Agriculture_forestry_and_fishing_value_added_pct_of_GDP`	Agriculture_forestry_and_fishing_value_added_pct_of_GDP	float	0%	1	8.12846782241948
`Employment_in_agriculture_female_pct_of_female_employment_modeled_ILO_estimate`	Employment_in_agriculture_female_pct_of_female_employment_modeled_ILO_estimate	float	0%	1	4.69720831895064
+3004 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Health Indicators MYS-HDX-Malaysia_-_Health_Indicators Indicators

21,755 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Health Indicators — 21,755 rows, 17 columns

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Field details — 17 fields (type, nulls, unique, samples)

Extension Fields (17) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`GHO (CODE)`	GHO (CODE)	string	0%	63	NCD_BMI_MINUS2C, MORT_200, NUTRITION_WA_2,...
`GHO (DISPLAY)`	GHO (DISPLAY)	string	0%	63	Prevalence of thinness among children and adolescents,...
`GHO (URL)`	GHO (URL)	string	0%	62	https://www.who.int/data/gho/data/indicators/indicator-de...
`YEAR (DISPLAY)`	YEAR (DISPLAY)	string	0%	32	2009, 2011, 2016, 2012, 2009
`STARTYEAR`	STARTYEAR	string	0%	32	2009, 2011, 2016, 2012, 2009
`ENDYEAR`	ENDYEAR	string	0%	32	2009, 2011, 2016, 2012, 2009
`REGION (CODE)`	REGION (CODE)	string	0%	1	WPR, WPR, WPR, WPR, WPR
`REGION (DISPLAY)`	REGION (DISPLAY)	string	0%	1	Western Pacific, Western Pacific, Western Pacific,...
`COUNTRY (CODE)`	COUNTRY (CODE)	string	0%	1	MYS, MYS, MYS, MYS, MYS
`COUNTRY (DISPLAY)`	COUNTRY (DISPLAY)	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`DIMENSION (TYPE)`	DIMENSION (TYPE)	string	12%	10	SEX, AGEGROUP, SEX, RESIDENCEAREATYPE, SEX
`DIMENSION (CODE)`	DIMENSION (CODE)	string	12%	27	SEX_FMLE, AGEGROUP_DAYS0-27, SEX_FMLE,...
`DIMENSION (NAME)`	DIMENSION (NAME)	string	12%	27	Female, 0-27 days, Female, Total, Both sexes
`Numeric`	Numeric	string	15%	80	6.3741907, 0.0, 15.0, 97.214996553, 86.45316
`Value`	Value	string	1%	81	6.4 [5.4-7.4], 0, 15.0 [9.9-21.9], 97, 86.5 [83.6-88.9]
`Low`	Low	float	40%	54	5.4197509, 9.9, 83.57765, 4.605184802, 22.0
`High`	High	float	40%	60	7.3907971, 21.9, 88.89712, 8.352160523, 47.0

MYS Malaysia - Education MYS-HDX-Malaysia_-_Education Indicators

16,666 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Education — 16,666 rows, 851 columns

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Field details — 852 fields (type, nulls, unique, samples)

Extension Fields (852) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Barro-Lee:_Percentage_of_female_population_age_15-19_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_15-19_with_no_education	float	0%	1	1.63
`Barro-Lee:_Percentage_of_population_age_15-19_with_no_education`	Barro-Lee:_Percentage_of_population_age_15-19_with_no_education	float	0%	1	1.68
`Barro-Lee:_Percentage_of_female_population_age_15+_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_15+_with_no_education	float	0%	1	9.25
`Barro-Lee:_Percentage_of_population_age_15+_with_no_education`	Barro-Lee:_Percentage_of_population_age_15+_with_no_education	float	0%	1	6.88
`Barro-Lee:_Percentage_of_female_population_age_20-24_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_20-24_with_no_education	float	0%	1	1.45
`Barro-Lee:_Percentage_of_population_age_20-24_with_no_education`	Barro-Lee:_Percentage_of_population_age_20-24_with_no_education	float	0%	1	1.48
`Barro-Lee:_Percentage_of_female_population_age_25-29_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_25-29_with_no_education	float	0%	1	1.99
`Barro-Lee:_Percentage_of_population_age_25-29_with_no_education`	Barro-Lee:_Percentage_of_population_age_25-29_with_no_education	float	0%	1	1.88
`Barro-Lee:_Percentage_of_female_population_age_25+_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_25+_with_no_education	float	0%	1	11.97
`Barro-Lee:_Percentage_of_population_age_25+_with_no_education`	Barro-Lee:_Percentage_of_population_age_25+_with_no_education	float	0%	1	8.78
`Barro-Lee:_Percentage_of_female_population_age_30-34_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_30-34_with_no_education	float	0%	1	2.27
`Barro-Lee:_Percentage_of_population_age_30-34_with_no_education`	Barro-Lee:_Percentage_of_population_age_30-34_with_no_education	float	0%	1	2.17
`Barro-Lee:_Percentage_of_female_population_age_35-39_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_35-39_with_no_education	float	0%	1	4.32
`Barro-Lee:_Percentage_of_population_age_35-39_with_no_education`	Barro-Lee:_Percentage_of_population_age_35-39_with_no_education	float	0%	1	3.71
`Barro-Lee:_Percentage_of_female_population_age_40-44_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_40-44_with_no_education	float	0%	1	4.32
`Barro-Lee:_Percentage_of_population_age_40-44_with_no_education`	Barro-Lee:_Percentage_of_population_age_40-44_with_no_education	float	0%	1	3.71
`Barro-Lee:_Percentage_of_female_population_age_45-49_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_45-49_with_no_education	float	0%	1	10.08
`Barro-Lee:_Percentage_of_population_age_45-49_with_no_education`	Barro-Lee:_Percentage_of_population_age_45-49_with_no_education	float	0%	1	7.33
`Barro-Lee:_Percentage_of_female_population_age_50-54_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_50-54_with_no_education	float	0%	1	10.08
`Barro-Lee:_Percentage_of_population_age_50-54_with_no_education`	Barro-Lee:_Percentage_of_population_age_50-54_with_no_education	float	0%	1	7.33
`Barro-Lee:_Percentage_of_female_population_age_55-59_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_55-59_with_no_education	float	0%	1	20.13
`Barro-Lee:_Percentage_of_population_age_55-59_with_no_education`	Barro-Lee:_Percentage_of_population_age_55-59_with_no_education	float	0%	1	14.1
`Barro-Lee:_Percentage_of_female_population_age_60-64_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_60-64_with_no_education	float	0%	1	20.13
`Barro-Lee:_Percentage_of_population_age_60-64_with_no_education`	Barro-Lee:_Percentage_of_population_age_60-64_with_no_education	float	0%	1	14.1
`Barro-Lee:_Population_in_thousands_age_15-19_total`	Barro-Lee:_Population_in_thousands_age_15-19_total	float	0%	1	2741.0
`Barro-Lee:_Population_in_thousands_age_15-19_female`	Barro-Lee:_Population_in_thousands_age_15-19_female	float	0%	1	1336.0
`Barro-Lee:_Population_in_thousands_age_15+_total`	Barro-Lee:_Population_in_thousands_age_15+_total	float	0%	1	19391.0
`Barro-Lee:_Population_in_thousands_age_15+_female`	Barro-Lee:_Population_in_thousands_age_15+_female	float	0%	1	9611.0
`Barro-Lee:_Population_in_thousands_age_20-24_total`	Barro-Lee:_Population_in_thousands_age_20-24_total	float	0%	1	2295.0
+822 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Social Protection and Labor MYS-HDX-Malaysia_-_Social_Protection_and_La Indicators

6,525 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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MYS Malaysia - Social Protection And Labor — 6,525 rows, 1,067 columns

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Field details — 1068 fields (type, nulls, unique, samples)

Extension Fields (1068) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Adequacy_of_benefits_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_a`	Adequacy_of_benefits_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_a	float	0%	1	21.2632124809488
`Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	8.7520618680703
`Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	7.44653043515593
`Adequacy_of_benefits_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labo`	Adequacy_of_benefits_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labo	float	0%	1	8.331593503447
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	9.20537742098638
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	9.54086986406926
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor	float	0%	1	7.53928868755877
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	7.30162834797261
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_preT`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	8.05441021753944
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	8.44562228693007
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor	float	0%	1	7.30784703602366
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	6.93708839887744
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_preT`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	7.05883862165199
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	7.75819239412685
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor	float	0%	1	7.20331798669103
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	7.11528511484203
`Adequacy_of_benefits_in_5th_quintile_richest_pct_-All_Social_Protection_and_Labo`	Adequacy_of_benefits_in_5th_quintile_richest_pct_-All_Social_Protection_and_Labo	float	0%	1	7.80732554780427
`Average_per_capita_transfer_held_by_extreme_poor_<usd2.15_a_day_-All_Social_Prot`	Average_per_capita_transfer_held_by_extreme_poor_<usd2.15_a_day_-All_Social_Prot	float	0%	1	0.39936765783767
`Average_per_capita_transfer_-All_Social_Protection_and_Labor_-rural`	Average_per_capita_transfer_-All_Social_Protection_and_Labor_-rural	float	0%	1	1.44434795974237
`Average_per_capita_transfer_-All_Social_Protection_and_Labor`	Average_per_capita_transfer_-All_Social_Protection_and_Labor	float	0%	1	1.78734730408898
`Average_per_capita_transfer_-All_Social_Protection_and_Labor_-urban`	Average_per_capita_transfer_-All_Social_Protection_and_Labor_-urban	float	0%	1	1.91276179565424
`Average_per_capita_transfer_held_by_1st_quintile_poorest_-All_Social_Protection_`	Average_per_capita_transfer_held_by_1st_quintile_poorest_-All_Social_Protection_	float	0%	1	0.82716636949981
`Average_per_capita_transfer_held_by_2nd_quintile_-All_Social_Protection_and_Labo`	Average_per_capita_transfer_held_by_2nd_quintile_-All_Social_Protection_and_Labo	float	0%	1	1.23408758853938
`Average_per_capita_transfer_held_by_3rd_quintile_-All_Social_Protection_and_Labo`	Average_per_capita_transfer_held_by_3rd_quintile_-All_Social_Protection_and_Labo	float	0%	1	1.68766441481281
`Average_per_capita_transfer_held_by_4th_quintile_-All_Social_Protection_and_Labo`	Average_per_capita_transfer_held_by_4th_quintile_-All_Social_Protection_and_Labo	float	0%	1	2.52000824420938
`Average_per_capita_transfer_held_by_5th_quintile_richest_-All_Social_Protection_`	Average_per_capita_transfer_held_by_5th_quintile_richest_-All_Social_Protection_	float	0%	1	5.23483011029156
`Benefits_incidence_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_and`	Benefits_incidence_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_and	float	0%	1	0.0044936182922566
`Benefits_incidence_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labor_`	Benefits_incidence_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labor_	float	0%	1	11.4343737716546
`Benefits_incidence_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT`	Benefits_incidence_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	18.5781818852899
+1038 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Gender MYS-HDX-Malaysia_-_Gender Indicators

4,793 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Gender — 4,793 rows, 170 columns

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Field details — 171 fields (type, nulls, unique, samples)

Extension Fields (171) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Firms_with_female_top_manager_pct_of_firms`	Firms_with_female_top_manager_pct_of_firms	float	0%	1	25.54145432
`Firms_with_female_participation_in_ownership_pct_of_firms`	Firms_with_female_participation_in_ownership_pct_of_firms	float	0%	1	38.55695343
`Literacy_rate_youth_female_pct_of_females_ages_15-24`	Literacy_rate_youth_female_pct_of_females_ages_15-24	float	0%	1	98.2799987792969
`Literacy_rate_youth_ages_15-24_gender_parity_index_GPI`	Literacy_rate_youth_ages_15-24_gender_parity_index_GPI	float	0%	1	0.990000009536743
`Literacy_rate_youth_male_pct_of_males_ages_15-24`	Literacy_rate_youth_male_pct_of_males_ages_15-24	float	0%	1	99.2600021362305
`Literacy_rate_adult_female_pct_of_females_ages_15_and_above`	Literacy_rate_adult_female_pct_of_females_ages_15_and_above	float	0%	1	94.6699981689453
`Literacy_rate_adult_male_pct_of_males_ages_15_and_above`	Literacy_rate_adult_male_pct_of_males_ages_15_and_above	float	0%	1	96.8199996948242
`School_enrollment_primary_gross_gender_parity_index_GPI`	School_enrollment_primary_gross_gender_parity_index_GPI	float	0%	1	1.0134334564209
`School_enrollment_primary_and_secondary_gross_gender_parity_index_GPI`	School_enrollment_primary_and_secondary_gross_gender_parity_index_GPI	float	0%	1	1.03951001167297
`School_enrollment_secondary_gross_gender_parity_index_GPI`	School_enrollment_secondary_gross_gender_parity_index_GPI	float	0%	1	1.0560075044632
`School_enrollment_tertiary_gross_gender_parity_index_GPI`	School_enrollment_tertiary_gross_gender_parity_index_GPI	float	0%	1	1.30503585927416
`School_enrollment_preprimary_female_pct_gross`	School_enrollment_preprimary_female_pct_gross	float	0%	1	88.5871887207031
`School_enrollment_preprimary_male_pct_gross`	School_enrollment_preprimary_male_pct_gross	float	0%	1	86.4733657836914
`Primary_completion_rate_female_pct_of_relevant_age_group`	Primary_completion_rate_female_pct_of_relevant_age_group	float	0%	1	89.8850736172053
`Primary_completion_rate_male_pct_of_relevant_age_group`	Primary_completion_rate_male_pct_of_relevant_age_group	float	0%	1	85.7763433002456
`Educational_attainment_at_least_completed_primary_population_25+_years_female_pc`	Educational_attainment_at_least_completed_primary_population_25+_years_female_pc	float	0%	1	93.5199966430664
`Educational_attainment_at_least_completed_primary_population_25+_years_male_pct_`	Educational_attainment_at_least_completed_primary_population_25+_years_male_pct_	float	0%	1	96.0100021362305
`Educational_attainment_at_least_completed_primary_population_25+_years_total_pct`	Educational_attainment_at_least_completed_primary_population_25+_years_total_pct	float	0%	1	94.7900009155273
`Primary_education_pupils_pct_female`	Primary_education_pupils_pct_female	float	0%	1	48.90068
`School_enrollment_primary_female_pct_gross`	School_enrollment_primary_female_pct_gross	float	0%	1	90.3936077644114
`School_enrollment_primary_male_pct_gross`	School_enrollment_primary_male_pct_gross	float	0%	1	90.3276653777399
`Gross_intake_ratio_in_first_grade_of_primary_education_female_pct_of_relevant_ag`	Gross_intake_ratio_in_first_grade_of_primary_education_female_pct_of_relevant_ag	float	0%	1	106.20437
`Gross_intake_ratio_in_first_grade_of_primary_education_male_pct_of_relevant_age_`	Gross_intake_ratio_in_first_grade_of_primary_education_male_pct_of_relevant_age_	float	0%	1	104.35813
`School_enrollment_primary_female_pct_net`	School_enrollment_primary_female_pct_net	float	0%	1	99.8099
`School_enrollment_primary_male_pct_net`	School_enrollment_primary_male_pct_net	float	0%	1	99.97004
`Net_intake_rate_in_grade_1_female_pct_of_official_school-age_population`	Net_intake_rate_in_grade_1_female_pct_of_official_school-age_population	float	0%	1	99.502
`Net_intake_rate_in_grade_1_male_pct_of_official_school-age_population`	Net_intake_rate_in_grade_1_male_pct_of_official_school-age_population	float	0%	1	100.0
`Persistence_to_grade_5_female_pct_of_cohort`	Persistence_to_grade_5_female_pct_of_cohort	float	0%	1	88.2010726928711
`Persistence_to_grade_5_male_pct_of_cohort`	Persistence_to_grade_5_male_pct_of_cohort	float	0%	1	88.7375183105469
+141 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Human Development Indicators MYS-HDX-Malaysia_-_Human_Development_Indica Indicators

926 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Human Development Indicators — 926 rows, 36 columns

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Field details — 37 fields (type, nulls, unique, samples)

Extension Fields (37) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`abr`	abr	float	0%	1	5.962
`co2_prod`	co2_prod	float	0%	1	8.418
`coef_ineq`	coef_ineq	float	0%	1	13.292
`diff_hdi_phdi`	diff_hdi_phdi	float	0%	1	17.338
`eys`	eys	float	0%	1	12.676
`eys_f`	eys_f	float	0%	1	13.056
`eys_m`	eys_m	float	0%	1	12.18
`gdi_group`	gdi_group	float	0%	1	2.0
`gii_rank`	gii_rank	float	0%	1	47.0
`gni_pc_f`	gni_pc_f	float	0%	1	22511.871
`gni_pc_m`	gni_pc_m	float	0%	1	41670.457
`gnipc`	gnipc	float	0%	1	32553.091
`hdi_f`	hdi_f	float	0%	1	0.805
`hdi_m`	hdi_m	float	0%	1	0.828
`hdi_rank`	hdi_rank	float	0%	1	67.0
`ineq_edu`	ineq_edu	float	0%	1	9.02
`ineq_inc`	ineq_inc	float	0%	1	24.759
`ineq_le`	ineq_le	float	0%	1	6.096
`le`	le	float	0%	1	76.657
`le_f`	le_f	float	0%	1	79.37
`le_m`	le_m	float	0%	1	74.272
`lfpr_f`	lfpr_f	float	0%	1	55.79
`lfpr_m`	lfpr_m	float	0%	1	81.88
`loss`	loss	float	0%	1	13.675
`mf`	mf	float	0%	1	21.394
`mmr`	mmr	float	0%	1	21.132
`mys`	mys	float	0%	1	11.09
`mys_f`	mys_f	float	0%	1	10.98
`mys_m`	mys_m	float	0%	1	11.21
+7 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Urban Development MYS-HDX-Malaysia_-_Urban_Development Indicators

621 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Urban Development — 621 rows, 18 columns

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Field details — 19 fields (type, nulls, unique, samples)

Extension Fields (19) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Urban_land_area_where_elevation_is_below_5_meters_sq._km`	Urban_land_area_where_elevation_is_below_5_meters_sq._km	float	0%	1	1730.753545104
`Urban_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area`	Urban_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area	float	0%	1	0.52508304753466
`Urban_land_area_sq._km`	Urban_land_area_sq._km	float	0%	1	11417.635397424
`Access_to_electricity_urban_pct_of_urban_population`	Access_to_electricity_urban_pct_of_urban_population	float	0%	1	100.0
`PM2.5_air_pollution_mean_annual_exposure_micrograms_per_cubic_meter`	PM2.5_air_pollution_mean_annual_exposure_micrograms_per_cubic_meter	float	0%	1	16.1938532237922
`PM2.5_air_pollution_population_exposed_to_levels_exceeding_WHO_guideline_value_p`	PM2.5_air_pollution_population_exposed_to_levels_exceeding_WHO_guideline_value_p	float	0%	1	90.537343806725
`Population_density_people_per_sq._km_of_land_area`	Population_density_people_per_sq._km_of_land_area	float	0%	1	106.913096941105
`Urban_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_`	Urban_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_	float	0%	1	6.51856811135229
`Population_living_in_slums_pct_of_urban_population`	Population_living_in_slums_pct_of_urban_population	float	0%	1	0.2
`Population_in_largest_city`	Population_in_largest_city	float	0%	1	9000280.0
`Population_in_the_largest_city_pct_of_urban_population`	Population_in_the_largest_city_pct_of_urban_population	float	0%	1	32.3227810517542
`Population_in_urban_agglomerations_of_more_than_1_million`	Population_in_urban_agglomerations_of_more_than_1_million	float	0%	1	9000280.0
`Population_in_urban_agglomerations_of_more_than_1_million_pct_of_total_populatio`	Population_in_urban_agglomerations_of_more_than_1_million_pct_of_total_populatio	float	0%	1	25.0161780149213
`Mortality_caused_by_road_traffic_injury_per_100000_population`	Mortality_caused_by_road_traffic_injury_per_100000_population	float	0%	1	22.5
`Urban_population_growth_annual_pct`	Urban_population_growth_annual_pct	float	0%	1	1.83816120540281
`Urban_population`	Urban_population	float	0%	1	27349906.0
`Urban_population_pct_of_total_population`	Urban_population_pct_of_total_population	float	0%	1	76.9170296648992
`year`	Reference year	integer	0%	-	-

MYS Malaysia - Demographic, Health, Education and Tran MYS-HDX-Malaysia_-_Demographic,_Health,_Edu Indicators

319 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Demographic, Health, Education And Tran — 319 rows, 11 columns

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Field details — 11 fields (type, nulls, unique, samples)

Extension Fields (11) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`category`	category	string	0%	6	Streets, Crime, Crime, Transport, Population
`indicator`	indicator	string	0%	20	road_density_national_roads, recorded_theft_rate,...
`indicator_friendly`	indicator_friendly	string	0%	20	Road density (km/100km²), Recorded theft rate per 100,...
`type_data`	type_data	string	0%	4	n, 000 population, 000 population, n, n
`latitude`	latitude	string	0%	5	2.3, n, n, 2.3, 2.3
`longitude`	longitude	float	0%	5	112.3, 2.3, 2.3, 112.3, 112.3
`region_id`	region_id	string	0%	2	789, 112.3, 112.3, 789, 789
`country_id`	country_id	string	0%	2	MY, 789, 789, MY, MY
`name`	name	string	0%	5	Malaysia, MY, MY, Malaysia, Malaysia
`year`	year	string	0%	28	2009, Malaysia, Malaysia, 2000, 2020
`value`	value	string	0%	90	30, 2000, 1999, 1931, 6

MYS World Bank Open Data MYS-WORLD_BANK Development Indicators

198 rows · 3 tables · 2024 World Bank Open Data 0 1 2 3 19 SEL 4 ext ▶

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0 Admin 0 — 3 table(s)

MYS World Bank - Development Indicators — 66 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MY, MY, MY, MY, MY
`population_count`	Population count	float	SEL	2%	65	7956197.0, 8164443.0, 8380172.0, 8602160.0, 8828406.0
`life_expectancy`	Life expectancy	float	SEL	3%	64	57.168, 58.015, 58.855, 59.667, 60.448
`gdp_per_capita_usd`	Gdp per capita usd	float	SEL	2%	65	240.847414554537, 232.943768818315, 238.836339180717,...
`literacy_rate_pct`	Literacy rate percent	float	SEL	86%	9	69.5199966430664, 82.9199981689453, 88.6900024414062,...
`under5_mortality_per_1000`	Under5 mortality per 1000	float	SEL	3%	49	94.6, 88.3, 82.5, 77.4, 72.8
`poverty_headcount_pct`	Poverty headcount percent	float	SEL	92%	5	7.6, 5.6, 8.4, 6.2, 5.8

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`year`	year	integer	0%	66	1960, 1961, 1962, 1963, 1964

MYS World Bank - Health Indicators — 66 rows, 14 columns

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Field details — 14 fields (type, nulls, unique, samples)

Standards-Aligned Fields (12)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MY, MY, MY, MY, MY
`life_expectancy`	Life expectancy	float	SEL	3%	64	57.168, 58.015, 58.855, 59.667, 60.448
`infant_mortality_per_1000`	Infant mortality per 1000	float	SEL	3%	46	21.0, 20.1, 19.4, 18.7, 18.1
`under5_mortality_per_1000`	Under5 mortality per 1000	float	SEL	3%	49	94.6, 88.3, 82.5, 77.4, 72.8
`maternal_mortality_per_100k`	Maternal mortality per 100k	float	SEL	41%	24	63.0, 59.0, 56.0, 55.0, 52.0
`fertility_rate`	Fertility rate	float	SEL	3%	63	6.412, 6.367, 6.296, 6.191, 6.03
`birth_rate_per_1000`	Birth rate per 1000	float	SEL	3%	64	42.885, 42.187, 41.34, 40.389, 39.133
`death_rate_per_1000`	Death rate per 1000	float	SEL	3%	64	11.326, 10.676, 10.059, 9.488, 8.957
`physicians_per_1000`	Physicians per 1000	float	SEL	32%	43	0.143, 0.161, 0.232, 0.234, 0.255
`hospital_beds_per_1000`	Hospital beds per 1000	float	SEL	42%	27	3.72813272476196, 3.46970009803772, 3.34669995307922,...
`vaccination_rate_pct`	Vaccination rate percent	float	SEL	32%	20	67.0, 66.0, 73.0, 58.0, 54.0
`health_expenditure_pct_gdp`	Health expenditure percent gdp	float	SEL	64%	24	2.51463366, 2.6753974, 2.66876554, 2.92417073, 2.86016941

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`year`	year	integer	0%	66	1960, 1961, 1962, 1963, 1964

MYS World Bank - Migration Indicators — 66 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Pending (9) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS, MYS, MYS, MYS, MYS
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`year`	year	integer	0%	66	1960, 1961, 1962, 1963, 1964
`intl_migrant_stock`	intl_migrant_stock	float	88%	8	1027572.0, 1312828.0, 1613819.0, 2174308.0, 3086876.0
`intl_migrant_stock_pct`	intl_migrant_stock_pct	float	88%	8	5.8, 6.5, 7.0, 8.4, 10.8
`net_migration`	net_migration	float	0%	66	-46948.0, -44916.0, -43033.0, -40952.0, -38785.0
`remittances_received_usd`	remittances_received_usd	float	35%	43	4177487.373, 3934568.167, 4062921.047, 4317711.83, 4569461.346
`remittances_received_pct_gdp`	remittances_received_pct_gdp	float	35%	43	0.0449250119783127, 0.0356061957950615,...
`remittances_paid_usd`	remittances_paid_usd	float	23%	51	4569877.625, 4595236.301, 5901852.131, 10563594.82, 19429702.76

MYS Malaysia - Risk Assessment Indicators MYS-HDX-Malaysia_-_Risk_Assessment_Indicato Indicators

144 rows · 1 table · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Risk Assessment Indicators — 144 rows, 50 columns

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Field details — 50 fields (type, nulls, unique, samples)

Extension Fields (50) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`ADM2_PCODE`	ADM2_PCODE	string	0%	100	MY0101, MY0102, MY0103, MY0104, MY0105
`ADM_PCODE`	ADM_PCODE	string	0%	100	MY0101, MY0102, MY0103, MY0104, MY0105
`RP10_female_pop_30cm`	RP10_female_pop_30cm	string	0%	97	25839, 2506, 606, 7439, 131
`RP10_children_u5_30cm`	RP10_children_u5_30cm	string	0%	94	4101, 398, 96, 1181, 21
`RP10_female_u5_30cm`	RP10_female_u5_30cm	string	0%	93	1977, 192, 46, 569, 10
`RP10_elderly_30cm`	RP10_elderly_30cm	string	0%	94	3726, 361, 87, 1073, 19
`RP10_pop_u15_30cm`	RP10_pop_u15_30cm	string	0%	96	11763, 1141, 276, 3386, 60
`RP10_female_u15_30cm`	RP10_female_u15_30cm	string	0%	94	5729, 556, 134, 1649, 29
`RP10_education_30cm_pct`	RP10_education_30cm_pct	string	0%	28	8, 0, 0, 5, 0
`RP10_education_30cm_count`	RP10_education_30cm_count	string	0%	18	13, 1, 0, 3, 0
`RP10_hospitals_30cm_pct`	RP10_hospitals_30cm_pct	string	0%	4	14, 0, 0, 0, 0
`RP10_hospitals_30cm_count`	RP10_hospitals_30cm_count	string	0%	3	1, 0, 0, 0, 0
`RP10_primary_healthcare_30cm_pct`	RP10_primary_healthcare_30cm_pct	string	0%	15	4, 0, 0, 0, 0
`RP10_primary_healthcare_30cm_count`	RP10_primary_healthcare_30cm_count	string	0%	9	2, 0, 0, 0, 0
`RP50_female_pop_30cm`	RP50_female_pop_30cm	string	0%	97	39581, 2976, 686, 10883, 131
`RP50_children_u5_30cm`	RP50_children_u5_30cm	string	0%	94	6282, 472, 109, 1727, 21
`RP50_female_u5_30cm`	RP50_female_u5_30cm	string	0%	93	3028, 228, 53, 833, 10
`RP50_elderly_30cm`	RP50_elderly_30cm	string	0%	96	5707, 429, 99, 1569, 19
`RP50_pop_u15_30cm`	RP50_pop_u15_30cm	string	0%	97	18018, 1355, 312, 4954, 60
`RP50_female_u15_30cm`	RP50_female_u15_30cm	string	0%	94	8775, 660, 152, 2413, 29
`RP50_education_30cm_pct`	RP50_education_30cm_pct	string	0%	30	12, 0, 0, 8, 0
`RP50_education_30cm_count`	RP50_education_30cm_count	string	0%	25	19, 1, 0, 5, 0
`RP50_hospitals_30cm_pct`	RP50_hospitals_30cm_pct	string	0%	9	43, 0, 0, 0, 0
`RP50_hospitals_30cm_count`	RP50_hospitals_30cm_count	string	0%	4	3, 0, 0, 0, 0
`RP50_primary_healthcare_30cm_pct`	RP50_primary_healthcare_30cm_pct	string	0%	23	11, 0, 0, 7, 0
`RP50_primary_healthcare_30cm_count`	RP50_primary_healthcare_30cm_count	string	0%	11	5, 0, 0, 1, 0
`RP100_female_pop_30cm`	RP100_female_pop_30cm	string	0%	97	46534, 3275, 715, 12731, 131
`RP100_children_u5_30cm`	RP100_children_u5_30cm	string	0%	94	7386, 520, 113, 2021, 21
`RP100_female_u5_30cm`	RP100_female_u5_30cm	string	0%	95	3560, 251, 55, 974, 10
`RP100_elderly_30cm`	RP100_elderly_30cm	string	0%	94	6710, 472, 103, 1836, 19
+20 more extension fields — download the CSV/Parquet to see them all.

MYS Glottolog Language Catalog MYS-GLOTTOLOG Language

131 rows · 1 table · 2024 Glottolog Language Catalog 0 1 2 3 12 CCL 4 ext ▶

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0 Admin 0 — 1 table(s)

MYS Glottolog - Languages — 131 rows, 16 columns

Loading data…

Field details — 16 fields (type, nulls, unique, samples)

Standards-Aligned Fields (12)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`glottocode`	Glottocode	string	CCL	0%	100	abai1240, baba1267, bala1306, bala1311, banj1239
`name`	Name	string	CCL	0%	100	Abai Sungai, Baba Malay, Balau, Balangingi, Banjar
`iso639_3`	Iso639 3	string	CCL	1%	99	abf, mbf, blg, sse, bjn
`level`	Level	string	CCL	0%	1	language, language, language, language, language
`family_name`	Family name	string	CCL	100%	-	-
`family_glottocode`	Family glottocode	string	CCL	1%	5	aust1307, aust1307, book1242, aust1307, aust1307
`parent_name`	Parent name	string	CCL	100%	-	-
`parent_glottocode`	Parent glottocode	string	CCL	1%	68	pait1248, vehi1234, book1242, inne1244, banj1241
`endangerment_status`	Endangerment status	string	CCL	100%	-	-
`child_family_count`	Child family count	integer	CCL	0%	1	0, 0, 0, 0, 0
`child_language_count`	Child language count	integer	CCL	0%	1	0, 0, 0, 0, 0
`child_dialect_count`	Child dialect count	integer	CCL	0%	16	0, 0, 0, 6, 2

Extension Fields (4) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`latitude`	latitude	float	0%	100	5.55394, 1.75414, 1.33476, 6.011614, 0.747105
`longitude`	longitude	float	0%	99	118.306, 103.076, 110.913, 121.686646, 115.79
`country_codes`	Country codes	string	0%	10	['MY'], ['MY', 'SG'], ['MY'], ['MY', 'PH'], ['ID', 'MY']
`classification`	classification	string	0%	1	[], [], [], [], []

MYS UNHCR Refugee Data Finder MYS-UNHCR Migration

84 rows · 2 tables · 2025 UNHCR Refugee Data Finder 0 1 2 3 12 CCL 36 ext ▶

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0 Admin 0 — 2 table(s)

MYS UNHCR - Hosted Populations — 49 rows, 24 columns

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Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_of_origin_iso`	Country of origin iso	string	CCL	0%	1	-, -, -, -, -
`refugees`	Refugees	integer	CCL	0%	75	2116011, 1952928, 1847304, 1749628, 1717966
`idps`	Idps	integer	CCL	0%	34	0, 0, 0, 0, 0
`stateless`	Stateless	integer	CCL	0%	23	0, 0, 0, 0, 0
`others_of_concern`	Others of concern	integer	CCL	0%	30	0, 0, 0, 0, 0
`total_population`	Total population	string	CCL	100%	-	-

Extension Fields (18) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	75	1951, 1952, 1953, 1954, 1955
`country_of_origin`	country_of_origin	string	100%	-	-
`country_of_asylum`	country_of_asylum	string	100%	-	-
`country_of_asylum_iso`	country_of_asylum_iso	string	0%	1	-, -, -, -, -
`population_type`	population_type	string	100%	-	-
`asylum_seekers`	asylum_seekers	integer	0%	34	0, 0, 0, 0, 0
`female_total`	female_total	string	100%	-	-
`male_total`	male_total	string	100%	-	-
`female_0_4`	female_0_4	string	100%	-	-
`female_5_11`	female_5_11	string	100%	-	-
`female_12_17`	female_12_17	string	100%	-	-
`female_18_59`	female_18_59	string	100%	-	-
`female_60_plus`	female_60_plus	string	100%	-	-
`male_0_4`	male_0_4	string	100%	-	-
`male_5_11`	male_5_11	string	100%	-	-
`male_12_17`	male_12_17	string	100%	-	-
`male_18_59`	male_18_59	string	100%	-	-
`male_60_plus`	male_60_plus	string	100%	-	-

MYS UNHCR - Origin Populations — 35 rows, 24 columns

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Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_of_origin_iso`	Country of origin iso	string	CCL	0%	1	-, -, -, -, -
`refugees`	Refugees	integer	CCL	0%	75	2116011, 1952928, 1847304, 1749628, 1717966
`idps`	Idps	integer	CCL	0%	34	0, 0, 0, 0, 0
`stateless`	Stateless	integer	CCL	0%	23	0, 0, 0, 0, 0
`others_of_concern`	Others of concern	integer	CCL	0%	30	0, 0, 0, 0, 0
`total_population`	Total population	string	CCL	100%	-	-

Extension Fields (18) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	75	1951, 1952, 1953, 1954, 1955
`country_of_origin`	country_of_origin	string	100%	-	-
`country_of_asylum`	country_of_asylum	string	100%	-	-
`country_of_asylum_iso`	country_of_asylum_iso	string	0%	1	-, -, -, -, -
`population_type`	population_type	string	100%	-	-
`asylum_seekers`	asylum_seekers	integer	0%	34	0, 0, 0, 0, 0
`female_total`	female_total	string	100%	-	-
`male_total`	male_total	string	100%	-	-
`female_0_4`	female_0_4	string	100%	-	-
`female_5_11`	female_5_11	string	100%	-	-
`female_12_17`	female_12_17	string	100%	-	-
`female_18_59`	female_18_59	string	100%	-	-
`female_60_plus`	female_60_plus	string	100%	-	-
`male_0_4`	male_0_4	string	100%	-	-
`male_5_11`	male_5_11	string	100%	-	-
`male_12_17`	male_12_17	string	100%	-	-
`male_18_59`	male_18_59	string	100%	-	-
`male_60_plus`	male_60_plus	string	100%	-	-

MYS LandScan Global (ORNL) MYS-LANDSCAN Modeled Population

60 rows · 3 tables · 2024 LandScan Global (ORNL) 0 1 2 3 Locality 21 SEL 2 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

MYS LandScan - Population Admin0 (admin_0) — 1 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`admin_level`	Admin level	string	SEL	0%	1	admin_0
`admin_name`	Admin name	string	SEL	0%	1	Malaysia
`admin_code`	Admin code	string	SEL	0%	1	87038898B63284295545115
`area_sqkm`	Area sqkm	float	SEL	0%	1	329656.5154
`pop_2024`	Population count	integer	SEL	0%	1	34257393
`pop_density_2024`	Population density	float	SEL	0%	1	103.92

1 Admin 1 — 1 table(s)

MYS LandScan - Population Admin1 (admin_1) — 16 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`admin_level`	Admin level	string	SEL	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`admin_name`	Admin name	string	SEL	0%	16	Selangor, Johor, Sabah, Perak, Sarawak
`admin_code`	Admin code	string	SEL	0%	16	15666254B89722251658211, 15666254B42356713390762,...
`area_sqkm`	Area sqkm	float	SEL	0%	16	7936.9045, 19172.0193, 74043.8426, 20817.4217, 123770.4102
`pop_2024`	Population count	integer	SEL	0%	16	7424553, 4237897, 3542432, 2632292, 2595638
`pop_density_2024`	Population density	float	SEL	0%	16	935.45, 221.05, 47.84, 126.45, 20.97

Locality Locality — 1 table(s)

MYS LandScan - Cities (locality) — 43 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`admin_level`	Admin level	string	SEL	0%	1	locality, locality, locality, locality, locality
`admin_name`	Admin name	string	SEL	0%	43	Kuala Lumpur, Johor Bahru, George Town, Kota Kinabalu,...
`admin_code`	Admin code	integer	SEL	0%	43	1648, 2979, 176, 3380, 2239
`area_sqkm`	Area sqkm	float	SEL	0%	43	1331.0814, 415.2033, 133.1234, 169.8752, 202.6392
`pop_2024`	Population count	integer	SEL	0%	43	7659699, 1598115, 747181, 653346, 548073
`pop_density_2024`	Population density	float	SEL	0%	43	5754.49, 3848.99, 5612.69, 3846.04, 2704.67

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`ghs_pop_2025`	ghs_pop_2025	integer	0%	43	8413206, 1703324, 756641, 810843, 544035
`landscan_vs_ghs_ratio`	landscan_vs_ghs_ratio	float	0%	42	0.91, 0.938, 0.987, 0.806, 1.007

MYS GHS Urban Centre Database (EU JRC) MYS-GHSL_UCDB Modeled Population

43 rows · 1 table · 2025 GHS Urban Centre Database (EU JRC) 0 1 2 3 Locality 3 SEL 7 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

Locality Locality — 1 table(s)

MYS GHS-UCDB - Cities (locality) — 43 rows, 10 columns

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Field details — 10 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`city_name`	Admin name	string	SEL	0%	43	Kuala Lumpur, Johor Bahru, Ipoh, Kota Kinabalu, George Town
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`population`	Population count	integer	SEL	0%	43	8413206, 1703324, 861933, 810843, 756641

Extension Fields (7) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`city_id`	city_id	integer	0%	43	1648, 2979, 1351, 3380, 176
`name_alternates`	name_alternates	string	100%	-	-
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`population_year`	population_year	integer	0%	1	2025, 2025, 2025, 2025, 2025
`latitude`	latitude	string	100%	-	-
`longitude`	longitude	string	100%	-	-
`region`	region	string	100%	-	-

MYS Department of Statistics Malaysia (DOSM) MYS-DOSM_MALAYSIA Population

17 rows · 2 tables · 2023 Department of Statistics Malaysia (DOSM) 0 1 2 3 10 SEL 4 ext ▶

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0 Admin 0 — 1 table(s)

MYS Dosm Malaysia - MYS Population (admin_0) — 1 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`admin_code`	Admin code	string	SEL	0%	1	MYS
`admin_name`	Admin name	string	SEL	0%	1	Malaysia
`population_count`	Population count	integer	SEL	0%	1	33379500
`population_male`	Population male	integer	SEL	0%	1	17460000
`population_female`	Population female	integer	SEL	0%	1	15919500

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	admin_level	string	0%	1	admin_0
`year`	year	integer	0%	1	2023

1 Admin 1 — 1 table(s)

MYS Dosm Malaysia - MYS Population (admin_1) — 16 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`admin_code`	Admin code	string	SEL	0%	16	johor, kedah, kelantan, melaka, negeri_sembilan
`admin_name`	Admin name	string	SEL	0%	16	Johor, Kedah, Kelantan, Melaka, Negeri Sembilan
`population_count`	Population count	integer	SEL	0%	16	4100900, 2187500, 1857600, 1027500, 1224300
`population_male`	Population male	integer	SEL	0%	16	2188400, 1115200, 932800, 541500, 635200
`population_female`	Population female	integer	SEL	0%	15	1912500, 1072300, 924800, 486000, 589100

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	admin_level	string	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`year`	year	integer	0%	1	2023, 2023, 2023, 2023, 2023

MYS HDX COD — Population Statistics (OCHA/UNFPA) MYS-COD_PS Census Derived Population

16 rows · 1 table · 2020 HDX COD — Population Statistics (OCHA/UNFPA) 0 1 2 3 3 SEL ▶

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1 Admin 1 — 1 table(s)

MYS COD PS - MYS Codps — 16 rows, 61 columns

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Field details — 61 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`pcode`	Admin code	string	SEL	0%	16	MY01, MY02, MY03, MY06, MY07
`region_name`	Region name	string	SEL	0%	16	Johor, Kedeh, Kelantan, Melaka, Negeri
`T_TL`	Total population	integer	SEL	0%	16	1978300, 1105600, 963900, 467200, 580000

Pending (58) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	Admin level	string	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`year`	Reference year	integer	0%	1	2020, 2020, 2020, 2020, 2020
`ADM0_EN`	ADM0_EN	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`T_00_04`	Total population age 0-4	integer	0%	16	301600, 179400, 192000, 71500, 88400
`T_05_09`	Total population age 5-9	integer	0%	16	293900, 182200, 195700, 70200, 84800
`T_10_14`	Total population age 10-14	integer	0%	16	286400, 172500, 176100, 69300, 81900
`T_15_19`	Total population age 15-19	integer	0%	16	322200, 192800, 184700, 75600, 101900
`T_20_24`	Total population age 20-24	integer	0%	16	362500, 226700, 192800, 88500, 111600
`T_25_29`	Total population age 25-29	integer	0%	16	335400, 203300, 200300, 102400, 110400
`T_30_34`	Total population age 30-34	integer	0%	16	314900, 159900, 127300, 79500, 97500
`T_35_39`	Total population age 35-39	integer	0%	16	297500, 137200, 106500, 65300, 75100
`T_40_44`	Total population age 40-44	integer	0%	16	243300, 122300, 88000, 49900, 62500
`T_45_49`	Total population age 45-49	integer	0%	16	219300, 117000, 84500, 46500, 58900
`T_50_54`	Total population age 50-54	integer	0%	15	198600, 113000, 84200, 49100, 56100
`T_55_59`	Total population age 55-59	integer	0%	16	176700, 106300, 80300, 46300, 54900
`T_60_64`	Total population age 60-64	integer	0%	16	146800, 91700, 67400, 38300, 49400
`T_65_69`	Total population age 65-69	integer	0%	16	109000, 71100, 49800, 29400, 39000
`T_70_74`	Total population age 70-74	integer	0%	16	77600, 49700, 36800, 23100, 25600
`T_75_79`	Total population age 75-79	integer	0%	16	45300, 28200, 19700, 13300, 15200
`T_80_84`	Total population age 80-84	integer	0%	15	28500, 19200, 11500, 8100, 9400
`T_85Plus`	T_85Plus	integer	0%	15	21600, 12600, 9100, 6300, 6300
`M_00_04`	Male population age 0-4	integer	0%	16	155800, 93000, 99300, 36700, 45300
`M_05_09`	Male population age 5-9	integer	0%	16	152300, 93600, 100000, 35700, 43600
`M_10_14`	Male population age 10-14	integer	0%	16	147700, 88500, 92900, 34900, 41700
`M_15_19`	Male population age 15-19	integer	0%	16	166300, 100700, 95500, 39800, 54900
`M_20_24`	Male population age 20-24	integer	0%	16	196900, 121100, 99700, 45900, 59700
`M_25_29`	Male population age 25-29	integer	0%	16	184200, 108000, 106000, 52400, 60300
`M_30_34`	Male population age 30-34	integer	0%	16	170400, 81400, 68300, 40700, 54100
`M_35_39`	Male population age 35-39	integer	0%	16	157100, 69000, 56600, 32100, 38900
`M_40_44`	Male population age 40-44	integer	0%	16	129500, 61400, 43400, 25100, 31700
+28 more pending fields — download the CSV/Parquet to see them all.

MYS Google Community Mobility (2020-2022) MYS-GOOGLE-MOBILITY Mobility

16 rows · 1 table · 2022 Google 0 1 2 3 3 SEL 8 ext ▶

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1 Admin 1 — 1 table(s)

MYS Mobility by Region — 16 rows, 11 columns

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Field details — 11 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MY, MY, MY, MY, MY
`admin_level`	Admin level	integer	SEL	0%	1	1, 1, 1, 1, 1
`iso3`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS

Extension Fields (8) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`retail`	retail	float	0%	16	-35.89, -31.78, -19.52, 1.32, -19.0
`grocery`	grocery	float	0%	16	-8.4, 1.82, 10.77, 6.41, -4.59
`parks`	parks	float	0%	16	-30.95, -19.22, 0.55, 18.43, 4.72
`transit`	transit	float	0%	16	-54.65, -29.25, -1.75, -24.71, -41.34
`workplaces`	workplaces	float	0%	16	-30.08, -23.6, -16.24, -11.97, -18.75
`residential`	residential	float	0%	16	15.99, 15.68, 14.29, 14.95, 14.04
`region`	region	string	0%	16	Federal Territory of Kuala Lumpur, Johor, Kedah,...
`observation_count`	observation_count	integer	0%	3	974, 974, 974, 974, 971

MYS OpenFactBook MYS-OPENFACTBOOK Country Profile

16 rows · 16 tables · 2024 OpenFactBook 0 1 2 3 97 SEL 4 CCL 625 ext ▶

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0 Admin 0 — 16 table(s)

MYS OpenFactBook - Communications — 1 rows, 21 columns

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Field details — 22 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`telephones_fixed_lines_subscriptions_per_100_inhabitants_numeric`	Fixed line subscriptions per 100	float	SEL	0%	1	24.0
`telephones_mobile_cellular_subscriptions_per_100_inhabitants_numeric`	Mobile subscriptions per 100	float	SEL	0%	1	140.0
`internet_users_percent_of_population_numeric`	Internet penetration percent	float	SEL	0%	1	98.0
`broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_numeric`	Broadband subscriptions per 100	float	SEL	0%	1	13.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (15) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`telephones_fixed_lines_total_subscriptions_text`	telephones_fixed_lines_total_subscriptions_text	string	0%	1	8.402 million (2023 est.)
`telephones_fixed_lines_total_subscriptions_numeric`	telephones_fixed_lines_total_subscriptions_numeric	float	0%	1	8.402
`telephones_fixed_lines_subscriptions_per_100_inhabitants_text`	telephones_fixed_lines_subscriptions_per_100_inhabitants_text	string	0%	1	24 (2023 est.)
`telephones_mobile_cellular_total_subscriptions_text`	telephones_mobile_cellular_total_subscriptions_text	string	0%	1	49.7 million (2024 est.)
`telephones_mobile_cellular_total_subscriptions_numeric`	telephones_mobile_cellular_total_subscriptions_numeric	float	0%	1	49.7
`telephones_mobile_cellular_subscriptions_per_100_inhabitants_text`	telephones_mobile_cellular_subscriptions_per_100_inhabitants_text	string	0%	1	140 (2024 est.)
`broadcast_media_text`	broadcast_media_text	string	0%	1	state-owned TV broadcaster operates 2 TV networks with...
`broadcast_media_numeric`	broadcast_media_numeric	float	0%	1	2.0
`internet_country_code_text`	Internet country code text	string	0%	1	.my
`internet_users_percent_of_population_text`	internet_users_percent_of_population_text	string	0%	1	98% (2023 est.)
`broadband_fixed_subscriptions_total_text`	broadband_fixed_subscriptions_total_text	string	0%	1	4.58 million (2023 est.)
`broadband_fixed_subscriptions_total_numeric`	broadband_fixed_subscriptions_total_numeric	float	0%	1	4.58
`broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text`	broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text	string	0%	1	13 (2023 est.)
`source_section`	source_section	string	0%	1	Communications
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Economy — 1 rows, 150 columns

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Field details — 156 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`real_gdp_per_capita_real_gdp_per_capita_2024_numeric`	Real gdp per capita 2024 (numeric)	float	SEL	0%	1	34100.0
`gdp_official_exchange_rate_numeric`	Gdp total usd	float	SEL	0%	1	421.972
`population_below_poverty_line_numeric`	Poverty headcount percent	float	SEL	0%	1	6.2
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (150) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`economic_overview_text`	economic_overview_text	string	0%	1	upper middle-income Southeast Asian economy;...
`economic_overview_numeric`	economic_overview_numeric	float	0%	1	40.0
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_text`	Real gdp purchasing power parity 2024 (text)	string	0%	1	$1.212 trillion (2024 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_numeric`	Real gdp purchasing power parity 2024 (numeric)	float	0%	1	1.212
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_text`	Real gdp purchasing power parity 2023 (text)	string	0%	1	$1.153 trillion (2023 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_numeric`	Real gdp purchasing power parity 2023 (numeric)	float	0%	1	1.153
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_text`	Real gdp purchasing power parity 2022 (text)	string	0%	1	$1.113 trillion (2022 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_numeric`	Real gdp purchasing power parity 2022 (numeric)	float	0%	1	1.113
`real_gdp_purchasing_power_parity_note`	real_gdp_purchasing_power_parity_note	string	0%	1	note: data in 2021 dollars
`real_gdp_growth_rate_real_gdp_growth_rate_2024_text`	Real gdp growth rate 2024 (text)	string	0%	1	5.1% (2024 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2024_numeric`	Real gdp growth rate 2024 (numeric)	float	0%	1	5.1
`real_gdp_growth_rate_real_gdp_growth_rate_2023_text`	Real gdp growth rate 2023 (text)	string	0%	1	3.6% (2023 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2023_numeric`	Real gdp growth rate 2023 (numeric)	float	0%	1	3.6
`real_gdp_growth_rate_real_gdp_growth_rate_2022_text`	Real gdp growth rate 2022 (text)	string	0%	1	8.9% (2022 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2022_numeric`	Real gdp growth rate 2022 (numeric)	float	0%	1	8.9
`real_gdp_growth_rate_note`	real_gdp_growth_rate_note	string	0%	1	note: annual GDP % growth based on constant local currency
`real_gdp_per_capita_real_gdp_per_capita_2024_text`	Real gdp per capita 2024 (text)	string	0%	1	$34,100 (2024 est.)
`real_gdp_per_capita_real_gdp_per_capita_2023_text`	Real gdp per capita 2023 (text)	string	0%	1	$32,800 (2023 est.)
`real_gdp_per_capita_real_gdp_per_capita_2023_numeric`	Real gdp per capita 2023 (numeric)	float	0%	1	32800.0
`real_gdp_per_capita_real_gdp_per_capita_2022_text`	Real gdp per capita 2022 (text)	string	0%	1	$32,100 (2022 est.)
`real_gdp_per_capita_real_gdp_per_capita_2022_numeric`	Real gdp per capita 2022 (numeric)	float	0%	1	32100.0
`real_gdp_per_capita_note`	real_gdp_per_capita_note	string	0%	1	note: data in 2021 dollars
`gdp_official_exchange_rate_text`	gdp_official_exchange_rate_text	string	0%	1	$421.972 billion (2024 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_text`	Inflation rate consumer prices 2024 (text)	string	0%	1	1.8% (2024 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_numeric`	Inflation rate consumer prices 2024 (numeric)	float	0%	1	1.8
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_text`	Inflation rate consumer prices 2023 (text)	string	0%	1	2.5% (2023 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_numeric`	Inflation rate consumer prices 2023 (numeric)	float	0%	1	2.5
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_text`	Inflation rate consumer prices 2022 (text)	string	0%	1	3.4% (2022 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_numeric`	Inflation rate consumer prices 2022 (numeric)	float	0%	1	3.4
`inflation_rate_consumer_prices_note`	inflation_rate_consumer_prices_note	string	0%	1	note: annual % change based on consumer prices
+120 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Energy — 1 rows, 53 columns

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Field details — 53 fields (type, nulls, unique, samples)

Standards-Aligned Fields (4)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`electricity_access_electrification_total_population_numeric`	Electricity access percent	float	SEL	0%	1	100.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (49) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`electricity_access_electrification_total_population_text`	electricity_access_electrification_total_population_text	string	0%	1	100% (2022 est.)
`electricity_installed_generating_capacity_text`	electricity_installed_generating_capacity_text	string	0%	1	37.22 million kW (2023 est.)
`electricity_installed_generating_capacity_numeric`	electricity_installed_generating_capacity_numeric	float	0%	1	37.22
`electricity_consumption_text`	electricity_consumption_text	string	0%	1	178.653 billion kWh (2023 est.)
`electricity_consumption_numeric`	electricity_consumption_numeric	float	0%	1	178.653
`electricity_exports_text`	electricity_exports_text	string	0%	1	1.2 billion kWh (2023 est.)
`electricity_exports_numeric`	electricity_exports_numeric	float	0%	1	1.2
`electricity_imports_text`	electricity_imports_text	string	0%	1	61.678 million kWh (2023 est.)
`electricity_imports_numeric`	electricity_imports_numeric	float	0%	1	61.678
`electricity_transmission_distribution_losses_text`	electricity_transmission_distribution_losses_text	string	0%	1	13.188 billion kWh (2023 est.)
`electricity_transmission_distribution_losses_numeric`	electricity_transmission_distribution_losses_numeric	float	0%	1	13.188
`electricity_generation_sources_fossil_fuels_text`	electricity_generation_sources_fossil_fuels_text	string	0%	1	81.9% of total installed capacity (2023 est.)
`electricity_generation_sources_fossil_fuels_numeric`	electricity_generation_sources_fossil_fuels_numeric	float	0%	1	81.9
`electricity_generation_sources_solar_text`	electricity_generation_sources_solar_text	string	0%	1	1.1% of total installed capacity (2023 est.)
`electricity_generation_sources_solar_numeric`	electricity_generation_sources_solar_numeric	float	0%	1	1.1
`electricity_generation_sources_hydroelectricity_text`	electricity_generation_sources_hydroelectricity_text	string	0%	1	16.3% of total installed capacity (2023 est.)
`electricity_generation_sources_hydroelectricity_numeric`	electricity_generation_sources_hydroelectricity_numeric	float	0%	1	16.3
`electricity_generation_sources_biomass_and_waste_text`	electricity_generation_sources_biomass_and_waste_text	string	0%	1	0.6% of total installed capacity (2023 est.)
`electricity_generation_sources_biomass_and_waste_numeric`	electricity_generation_sources_biomass_and_waste_numeric	float	0%	1	0.6
`coal_production_text`	coal_production_text	string	0%	1	4.476 million metric tons (2023 est.)
`coal_production_numeric`	coal_production_numeric	float	0%	1	4.476
`coal_consumption_text`	coal_consumption_text	string	0%	1	35.741 million metric tons (2023 est.)
`coal_consumption_numeric`	coal_consumption_numeric	float	0%	1	35.741
`coal_exports_text`	coal_exports_text	string	0%	1	462,000 metric tons (2023 est.)
`coal_exports_numeric`	coal_exports_numeric	float	0%	1	462000.0
`coal_imports_text`	coal_imports_text	string	0%	1	31.706 million metric tons (2023 est.)
`coal_imports_numeric`	coal_imports_numeric	float	0%	1	31.706
`coal_proven_reserves_text`	coal_proven_reserves_text	string	0%	1	226 million metric tons (2023 est.)
`coal_proven_reserves_numeric`	coal_proven_reserves_numeric	float	0%	1	226.0
`petroleum_total_petroleum_production_text`	petroleum_total_petroleum_production_text	string	0%	1	582,000 bbl/day (2023 est.)
+19 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Environment — 1 rows, 54 columns

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Field details — 60 fields (type, nulls, unique, samples)

Standards-Aligned Fields (8)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`land_use_agricultural_land_numeric`	Agricultural land percent	float	SEL	0%	1	26.1
`land_use_forest_numeric`	Forest area percent	float	SEL	0%	1	57.8
`urbanization_urban_population_numeric`	Urban population percent	float	SEL	0%	1	78.7
`urbanization_rate_of_urbanization_numeric`	Urban growth rate percent	float	SEL	0%	1	1.87
`waste_and_recycling_municipal_solid_waste_generated_annually_numeric`	Municipal waste kg per capita	float	SEL	0%	1	12.983
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (52) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`environmental_issues_text`	environmental_issues_text	string	0%	1	air pollution from industrial and vehicular emissions;...
`international_environmental_agreements_party_to_text`	international_environmental_agreements_party_to_text	string	0%	1	Antarctic-Environmental Protection, Antarctic Treaty,...
`international_environmental_agreements_party_to_numeric`	international_environmental_agreements_party_to_numeric	float	0%	1	2006.0
`international_environmental_agreements_signed_but_not_ratified_text`	international_environmental_agreements_signed_but_not_ratified_text	string	0%	1	none of the selected agreements
`climate_text`	climate_text	string	0%	1	tropical; annual southwest (April to October) and...
`land_use_agricultural_land_text`	land_use_agricultural_land_text	string	0%	1	26.1% (2023 est.)
`land_use_agricultural_land_arable_land_text`	land_use_agricultural_land_arable_land_text	string	0%	1	arable land: 2.4% (2023 est.)
`land_use_agricultural_land_arable_land_numeric`	land_use_agricultural_land_arable_land_numeric	float	0%	1	2.4
`land_use_agricultural_land_permanent_crops_text`	land_use_agricultural_land_permanent_crops_text	string	0%	1	permanent crops: 22.7% (2023 est.)
`land_use_agricultural_land_permanent_crops_numeric`	land_use_agricultural_land_permanent_crops_numeric	float	0%	1	22.7
`land_use_agricultural_land_permanent_pasture_text`	land_use_agricultural_land_permanent_pasture_text	string	0%	1	permanent pasture: 0.9% (2023 est.)
`land_use_agricultural_land_permanent_pasture_numeric`	land_use_agricultural_land_permanent_pasture_numeric	float	0%	1	0.9
`land_use_forest_text`	land_use_forest_text	string	0%	1	57.8% (2023 est.)
`land_use_other_text`	land_use_other_text	string	0%	1	16% (2023 est.)
`land_use_other_numeric`	land_use_other_numeric	float	0%	1	16.0
`urbanization_urban_population_text`	urbanization_urban_population_text	string	0%	1	78.7% of total population (2023)
`urbanization_rate_of_urbanization_text`	urbanization_rate_of_urbanization_text	string	0%	1	1.87% annual rate of change (2020-25 est.)
`carbon_dioxide_emissions_total_emissions_text`	carbon_dioxide_emissions_total_emissions_text	string	0%	1	260.005 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_total_emissions_numeric`	carbon_dioxide_emissions_total_emissions_numeric	float	0%	1	260.005
`carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text`	carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text	string	0%	1	76.78 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric`	carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric	float	0%	1	76.78
`carbon_dioxide_emissions_from_petroleum_and_other_liquids_text`	carbon_dioxide_emissions_from_petroleum_and_other_liquids_text	string	0%	1	90.273 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric`	carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric	float	0%	1	90.273
`carbon_dioxide_emissions_from_consumed_natural_gas_text`	carbon_dioxide_emissions_from_consumed_natural_gas_text	string	0%	1	92.951 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_consumed_natural_gas_numeric`	carbon_dioxide_emissions_from_consumed_natural_gas_numeric	float	0%	1	92.951
`particulate_matter_emissions_text`	particulate_matter_emissions_text	string	0%	1	23.7 micrograms per cubic meter (2019 est.)
`particulate_matter_emissions_numeric`	particulate_matter_emissions_numeric	float	0%	1	23.7
`methane_emissions_energy_text`	methane_emissions_energy_text	string	0%	1	818.9 kt (2022-2024 est.)
`methane_emissions_energy_numeric`	methane_emissions_energy_numeric	float	0%	1	818.9
`methane_emissions_agriculture_text`	methane_emissions_agriculture_text	string	0%	1	182.2 kt (2019-2021 est.)
+22 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Ethnic Groups — 1 rows, 8 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (6) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`nationality_noun_text`	nationality_noun_text	string	0%	1	Malaysian(s)
`nationality_adjective_text`	nationality_adjective_text	string	0%	1	Malaysian
`ethnic_groups_text`	ethnic_groups_text	string	0%	1	Bumiputera 63.8% (Malay 52.8% and indigenous peoples,...
`ethnic_groups_numeric`	ethnic_groups_numeric	float	0%	1	63.8
`source_section`	source_section	string	0%	1	People and Society:ethnic_groups
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Geography — 1 rows, 50 columns

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Field details — 55 fields (type, nulls, unique, samples)

Standards-Aligned Fields (13)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`area_total_numeric`	Area sqkm	float	SEL	0%	1	329847.0
`area_land_numeric`	Area land sqkm	float	SEL	0%	1	328657.0
`area_water_numeric`	Area water sqkm	float	SEL	0%	1	1190.0
`land_boundaries_total_numeric`	Land boundary km	float	SEL	0%	1	2742.0
`coastline_numeric`	Coastline km	float	SEL	0%	1	4675.0
`elevation_highest_point_numeric`	Elevation max m	float	SEL	0%	1	4095.0
`elevation_lowest_point_numeric`	Elevation min m	float	SEL	0%	1	0.0
`land_use_agricultural_land_numeric`	Agricultural land percent	float	SEL	0%	1	26.1
`land_use_forest_numeric`	Forest area percent	float	SEL	0%	1	57.8
`irrigated_land_numeric`	Irrigated land sqkm	float	SEL	0%	1	4420.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (42) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`location_text`	location_text	string	0%	1	Southeastern Asia, peninsula bordering Thailand and...
`geographic_coordinates_text`	geographic_coordinates_text	string	0%	1	2 30 N, 112 30 E
`geographic_coordinates_numeric`	geographic_coordinates_numeric	float	0%	1	2.0
`map_references_text`	map_references_text	string	0%	1	Southeast Asia
`area_total_text`	area_total_text	string	0%	1	329,847 sq km
`area_land_text`	area_land_text	string	0%	1	328,657 sq km
`area_water_text`	area_water_text	string	0%	1	1,190 sq km
`area_comparative_text`	area_comparative_text	string	0%	1	slightly larger than New Mexico
`land_boundaries_total_text`	land_boundaries_total_text	string	0%	1	2,742 km
`land_boundaries_border_countries_text`	land_boundaries_border_countries_text	string	0%	1	Brunei 266 km; Indonesia 1,881 km; Thailand 595 km
`land_boundaries_border_countries_numeric`	land_boundaries_border_countries_numeric	float	0%	1	266.0
`coastline_text`	coastline_text	string	0%	1	4,675 km (Peninsular Malaysia 2,068 km; East Malaysia 2,607 km)
`maritime_claims_territorial_sea_text`	maritime_claims_territorial_sea_text	string	0%	1	12 nm
`maritime_claims_territorial_sea_numeric`	maritime_claims_territorial_sea_numeric	float	0%	1	12.0
`maritime_claims_exclusive_economic_zone_text`	maritime_claims_exclusive_economic_zone_text	string	0%	1	200 nm
`maritime_claims_exclusive_economic_zone_numeric`	maritime_claims_exclusive_economic_zone_numeric	float	0%	1	200.0
`maritime_claims_continental_shelf_text`	maritime_claims_continental_shelf_text	string	0%	1	200-m depth or to the depth of exploitation; specified...
`maritime_claims_continental_shelf_numeric`	maritime_claims_continental_shelf_numeric	float	0%	1	200.0
`climate_text`	climate_text	string	0%	1	tropical; annual southwest (April to October) and...
`terrain_text`	terrain_text	string	0%	1	coastal plains rising to hills and mountains
`elevation_highest_point_text`	elevation_highest_point_text	string	0%	1	Gunung Kinabalu 4,095 m
`elevation_lowest_point_text`	elevation_lowest_point_text	string	0%	1	Indian Ocean 0 m
`elevation_mean_elevation_text`	elevation_mean_elevation_text	string	0%	1	419 m
`elevation_mean_elevation_numeric`	elevation_mean_elevation_numeric	float	0%	1	419.0
`natural_resources_text`	natural_resources_text	string	0%	1	tin, petroleum, timber, copper, iron ore, natural gas, bauxite
`land_use_agricultural_land_text`	land_use_agricultural_land_text	string	0%	1	26.1% (2023 est.)
`land_use_agricultural_land_arable_land_text`	land_use_agricultural_land_arable_land_text	string	0%	1	arable land: 2.4% (2023 est.)
`land_use_agricultural_land_arable_land_numeric`	land_use_agricultural_land_arable_land_numeric	float	0%	1	2.4
`land_use_agricultural_land_permanent_crops_text`	land_use_agricultural_land_permanent_crops_text	string	0%	1	permanent crops: 22.7% (2023 est.)
`land_use_agricultural_land_permanent_crops_numeric`	land_use_agricultural_land_permanent_crops_numeric	float	0%	1	22.7
+12 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Government — 1 rows, 82 columns

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Field details — 113 fields (type, nulls, unique, samples)

Standards-Aligned Fields (2)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (111) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name_conventional_long_form_text`	country_name_conventional_long_form_text	string	0%	1	none
`country_name_conventional_short_form_text`	country_name_conventional_short_form_text	string	0%	1	Malaysia
`country_name_local_long_form_text`	country_name_local_long_form_text	string	0%	1	none
`country_name_local_short_form_text`	country_name_local_short_form_text	string	0%	1	Malaysia
`country_name_former_text`	country_name_former_text	string	0%	1	British Malaya, Malayan Union, Federation of Malaya
`country_name_etymology_text`	country_name_etymology_text	string	0%	1	devised in the early 19th century by British...
`country_name_etymology_numeric`	country_name_etymology_numeric	float	0%	1	19.0
`government_type_text`	government_type_text	string	0%	1	federal parliamentary constitutional monarchy
`capital_name_text`	capital_name_text	string	0%	1	Kuala Lumpur
`capital_geographic_coordinates_text`	capital_geographic_coordinates_text	string	0%	1	3 10 N, 101 42 E
`capital_geographic_coordinates_numeric`	capital_geographic_coordinates_numeric	float	0%	1	3.0
`capital_time_difference_text`	capital_time_difference_text	string	0%	1	UTC+8 (13 hours ahead of Washington, DC, during Standard Time)
`capital_time_difference_numeric`	capital_time_difference_numeric	float	0%	1	8.0
`capital_etymology_text`	capital_etymology_text	string	0%	1	the name means "muddy river junction," referring to the...
`capital_note`	capital_note	string	0%	1	note: nearby Putrajaya is referred to as a federal...
`administrative_divisions_text`	administrative_divisions_text	string	0%	1	13 states (negeri-negeri, singular - negeri); Johor,...
`administrative_divisions_numeric`	administrative_divisions_numeric	float	0%	1	13.0
`legal_system_text`	legal_system_text	string	0%	1	mixed system of English common law, Islamic law...
`constitution_history_text`	constitution_history_text	string	0%	1	previous 1948; latest drafted 21 February 1957,...
`constitution_history_numeric`	constitution_history_numeric	float	0%	1	1948.0
`constitution_amendment_process_text`	constitution_amendment_process_text	string	0%	1	proposed as a bill by Parliament; passage requires at...
`international_law_organization_participation_text`	international_law_organization_participation_text	string	0%	1	has not submitted an ICJ jurisdiction declaration;...
`citizenship_citizenship_by_birth_text`	Citizenship by birth (text)	string	0%	1	no
`citizenship_citizenship_by_descent_only_text`	Citizenship by descent only (text)	string	0%	1	at least one parent must be a citizen of Malaysia
`citizenship_dual_citizenship_recognized_text`	citizenship_dual_citizenship_recognized_text	string	0%	1	no
`citizenship_residency_requirement_for_naturalization_text`	citizenship_residency_requirement_for_naturalization_text	string	0%	1	10 out 12 years preceding application
`citizenship_residency_requirement_for_naturalization_numeric`	citizenship_residency_requirement_for_naturalization_numeric	float	0%	1	10.0
`suffrage_text`	suffrage_text	string	0%	1	18 years of age; universal
`suffrage_numeric`	suffrage_numeric	float	0%	1	18.0
`executive_branch_chief_of_state_text`	executive_branch_chief_of_state_text	string	0%	1	King Sultan IBRAHIM ibni al-Marhum Sultan Iskandar...
+81 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Introduction — 1 rows, 6 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (4) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`background_text`	background_text	string	0%	1	Malaysia’s location has long made it an important...
`background_numeric`	background_numeric	float	0%	1	14.0
`source_section`	source_section	string	0%	1	Introduction
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Languages — 1 rows, 8 columns

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Field details — 8 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (5) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`languages_languages_text`	Languages (text)	string	0%	1	Bahasa Malaysia (official), English, Chinese (Cantonese,...
`languages_major_language_sample_s_text`	languages_major_language_sample_s_text	string	0%	1	Buku Fakta Dunia, sumber yang diperlukan untuk maklumat...
`languages_note`	languages_note	string	0%	1	note: Malaysia has 134 languages (112 indigenous and 22...
`source_section`	source_section	string	0%	1	People and Society:languages
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Migration — 1 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (6) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`refugees_and_internally_displaced_persons_refugees_text`	refugees_and_internally_displaced_persons_refugees_text	string	0%	1	191,343 (2024 est.)
`refugees_and_internally_displaced_persons_refugees_numeric`	refugees_and_internally_displaced_persons_refugees_numeric	float	0%	1	191343.0
`refugees_and_internally_displaced_persons_stateless_persons_text`	refugees_and_internally_displaced_persons_stateless_persons_text	string	0%	1	120,857 (2024 est.)
`refugees_and_internally_displaced_persons_stateless_persons_numeric`	refugees_and_internally_displaced_persons_stateless_persons_numeric	float	0%	1	120857.0
`source_section`	source_section	string	0%	1	Transnational Issues:migration
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Military And Security — 1 rows, 23 columns

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Field details — 27 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (24) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`military_and_security_forces_text`	military_and_security_forces_text	string	0%	1	Malaysian Armed Forces (Angkatan Tentera Malaysia, ATM):...
`military_and_security_forces_numeric`	military_and_security_forces_numeric	float	0%	1	2025.0
`military_expenditures_military_expenditures_2024_text`	Military expenditures 2024 (text)	string	0%	1	1% of GDP (2024 est.)
`military_expenditures_military_expenditures_2024_numeric`	Military expenditures 2024 (numeric)	float	0%	1	1.0
`military_expenditures_military_expenditures_2023_text`	Military expenditures 2023 (text)	string	0%	1	0.9% of GDP (2023 est.)
`military_expenditures_military_expenditures_2023_numeric`	Military expenditures 2023 (numeric)	float	0%	1	0.9
`military_expenditures_military_expenditures_2022_text`	Military expenditures 2022 (text)	string	0%	1	1.1% of GDP (2022 est.)
`military_expenditures_military_expenditures_2022_numeric`	Military expenditures 2022 (numeric)	float	0%	1	1.1
`military_expenditures_military_expenditures_2021_text`	Military expenditures 2021 (text)	string	0%	1	1% of GDP (2021 est.)
`military_expenditures_military_expenditures_2021_numeric`	Military expenditures 2021 (numeric)	float	0%	1	1.0
`military_expenditures_military_expenditures_2020_text`	Military expenditures 2020 (text)	string	0%	1	1.1% of GDP (2020 est.)
`military_expenditures_military_expenditures_2020_numeric`	Military expenditures 2020 (numeric)	float	0%	1	1.1
`military_and_security_service_personnel_strengths_text`	military_and_security_service_personnel_strengths_text	string	0%	1	approximately 110,000 active Malaysian Armed Forces (2025)
`military_and_security_service_personnel_strengths_numeric`	military_and_security_service_personnel_strengths_numeric	float	0%	1	110000.0
`military_equipment_inventories_and_acquisitions_text`	military_equipment_inventories_and_acquisitions_text	string	0%	1	the military fields a diverse array of mostly older but...
`military_equipment_inventories_and_acquisitions_numeric`	military_equipment_inventories_and_acquisitions_numeric	float	0%	1	2025.0
`military_service_age_and_obligation_text`	military_service_age_and_obligation_text	string	0%	1	17 years 6 months of age for voluntary military service...
`military_service_age_and_obligation_numeric`	military_service_age_and_obligation_numeric	float	0%	1	17.0
`military_deployments_text`	military_deployments_text	string	0%	1	825 Lebanon (UNIFIL) (2025)
`military_deployments_numeric`	military_deployments_numeric	float	0%	1	825.0
`military_note_text`	military_note_text	string	0%	1	the Malaysian military is responsible for defense of the...
`military_note_numeric`	military_note_numeric	float	0%	1	1971.0
`source_section`	source_section	string	0%	1	Military and Security
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - People And Society — 1 rows, 129 columns

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Field details — 143 fields (type, nulls, unique, samples)

Standards-Aligned Fields (29)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`population_total_numeric`	Population count	float	SEL	0%	1	34905275.0
`population_male_numeric`	Population male	float	SEL	0%	1	17833074.0
`population_female_numeric`	Population female	float	SEL	0%	1	17072201.0
`age_structure_0_14_years_numeric`	Population 0 14 percent	float	SEL	0%	1	22.2
`age_structure_15_64_years_numeric`	Population 15 64 percent	float	SEL	0%	1	69.4
`age_structure_65_years_and_over_numeric`	Population 65 plus percent	float	SEL	0%	1	8.4
`dependency_ratios_total_dependency_ratio_numeric`	Total dependency ratio	float	SEL	0%	1	44.3
`dependency_ratios_youth_dependency_ratio_numeric`	Youth dependency ratio	float	SEL	0%	1	31.7
`dependency_ratios_elderly_dependency_ratio_numeric`	Elderly dependency ratio	float	SEL	0%	1	12.6
`median_age_total_numeric`	Median age	float	SEL	0%	1	32.2
`population_growth_rate_numeric`	Population growth rate percent	float	SEL	0%	1	0.97
`birth_rate_numeric`	Birth rate per 1000	float	SEL	0%	1	14.05
`death_rate_numeric`	Death rate per 1000	float	SEL	0%	1	5.8
`net_migration_rate_numeric`	Net migration per 1000	float	SEL	0%	1	1.43
`urbanization_urban_population_numeric`	Urban population percent	float	SEL	0%	1	78.7
`urbanization_rate_of_urbanization_numeric`	Urban growth rate percent	float	SEL	0%	1	1.87
`sex_ratio_at_birth_numeric`	Sex ratio at birth	float	SEL	0%	1	1.07
`sex_ratio_total_population_numeric`	Sex ratio overall	float	SEL	0%	1	1.05
`maternal_mortality_ratio_numeric`	Maternal mortality per 100k	float	SEL	0%	1	26.0
`infant_mortality_rate_total_numeric`	Infant mortality per 1000	float	SEL	0%	1	6.3
`life_expectancy_at_birth_total_population_numeric`	Life expectancy	float	SEL	0%	1	76.6
`total_fertility_rate_numeric`	Fertility rate	float	SEL	0%	1	1.73
`gross_reproduction_rate_numeric`	Gross reproduction rate	float	SEL	0%	1	0.83
`physician_density_numeric`	Physicians per 1000	float	SEL	0%	1	2.34
`hospital_bed_density_numeric`	Hospital beds per 1000	float	SEL	0%	1	2.0
`literacy_total_population_numeric`	Literacy rate percent	float	SEL	0%	1	95.8
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (114) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`population_total_text`	population_total_text	string	0%	1	34,905,275 (2025 est.)
`population_male_text`	population_male_text	string	0%	1	17,833,074
`population_female_text`	population_female_text	string	0%	1	17,072,201
`age_structure_0_14_years_text`	age_structure_0_14_years_text	string	0%	1	22.2% (male 3,947,914/female 3,730,319)
`age_structure_15_64_years_text`	age_structure_15_64_years_text	string	0%	1	69.4% (male 12,308,938/female 11,666,947)
`age_structure_65_years_and_over_text`	age_structure_65_years_and_over_text	string	0%	1	8.4% (2024 est.) (male 1,409,360/female 1,501,332)
`dependency_ratios_total_dependency_ratio_text`	dependency_ratios_total_dependency_ratio_text	string	0%	1	44.3 (2025 est.)
`dependency_ratios_youth_dependency_ratio_text`	dependency_ratios_youth_dependency_ratio_text	string	0%	1	31.7 (2025 est.)
`dependency_ratios_elderly_dependency_ratio_text`	dependency_ratios_elderly_dependency_ratio_text	string	0%	1	12.6 (2025 est.)
`dependency_ratios_potential_support_ratio_text`	dependency_ratios_potential_support_ratio_text	string	0%	1	7.9 (2025 est.)
`dependency_ratios_potential_support_ratio_numeric`	dependency_ratios_potential_support_ratio_numeric	float	0%	1	7.9
`median_age_total_text`	median_age_total_text	string	0%	1	32.2 years (2025 est.)
`median_age_male_text`	median_age_male_text	string	0%	1	31.7 years
`median_age_male_numeric`	median_age_male_numeric	float	0%	1	31.7
`median_age_female_text`	median_age_female_text	string	0%	1	31.9 years
`median_age_female_numeric`	median_age_female_numeric	float	0%	1	31.9
`population_growth_rate_text`	population_growth_rate_text	string	0%	1	0.97% (2025 est.)
`birth_rate_text`	birth_rate_text	string	0%	1	14.05 births/1,000 population (2025 est.)
`death_rate_text`	death_rate_text	string	0%	1	5.8 deaths/1,000 population (2025 est.)
`net_migration_rate_text`	net_migration_rate_text	string	0%	1	1.43 migrant(s)/1,000 population (2025 est.)
`population_distribution_text`	population_distribution_text	string	0%	1	a highly uneven distribution, with over 80% of the...
`population_distribution_numeric`	population_distribution_numeric	float	0%	1	80.0
`urbanization_urban_population_text`	urbanization_urban_population_text	string	0%	1	78.7% of total population (2023)
`urbanization_rate_of_urbanization_text`	urbanization_rate_of_urbanization_text	string	0%	1	1.87% annual rate of change (2020-25 est.)
`major_urban_areas_population_text`	major_urban_areas_population_text	string	0%	1	8.622 million KUALA LUMPUR (capital), 1.086 million...
`major_urban_areas_population_numeric`	major_urban_areas_population_numeric	float	0%	1	8.622
`sex_ratio_at_birth_text`	sex_ratio_at_birth_text	string	0%	1	1.07 male(s)/female
`sex_ratio_0_14_years_text`	sex_ratio_0_14_years_text	string	0%	1	1.06 male(s)/female
`sex_ratio_0_14_years_numeric`	sex_ratio_0_14_years_numeric	float	0%	1	1.06
`sex_ratio_15_64_years_text`	sex_ratio_15_64_years_text	string	0%	1	1.06 male(s)/female
+84 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Religions — 1 rows, 6 columns

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Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national
`composition_religion_buddhist_pct_synth`	Buddhist	numeric	CCL	0%	-	18.7
`composition_religion_christian_pct_synth`	Christian	numeric	CCL	0%	-	9.1
`composition_religion_hindu_pct_synth`	Hindu	numeric	CCL	0%	-	6.1
`composition_ethnicity_primary_label_synth`	Bumiputera	string	CCL	0%	-	Bumiputera

Extension Fields (4) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`religions_text`	religions_text	string	0%	1	Muslim (official) 63.5%, Buddhist 18.7%, Christian 9.1%,...
`religions_numeric`	religions_numeric	float	0%	1	63.5
`source_section`	source_section	string	0%	1	People and Society:religions
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

Pending (13) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`composition_religion_muslim_official_pct_synth`	Muslim (official)	numeric	0%	-	63.5
`composition_religion_other_confucianism_taoism_other_traditional_chinese_religions_pct_synth`	other (Confucianism, Taoism, other traditional Chinese religions)	numeric	0%	-	0.9
`composition_religion_none_unspecified_pct_synth`	none/unspecified	numeric	0%	-	1.8
`composition_ethnicity_bumiputera_pct_synth`	Bumiputera	numeric	0%	-	63.8
`composition_ethnicity_bumiputera__malay_and_indigenous_peoples_pct_synth`	Malay and indigenous peoples	numeric	0%	-	52.8
`composition_ethnicity_bumiputera__including_orang_asli_pct_synth`	including Orang Asli	numeric	0%	-	-
`composition_ethnicity_bumiputera__dayak_pct_synth`	Dayak	numeric	0%	-	-
`composition_ethnicity_bumiputera__anak_negeri_pct_synth`	Anak Negeri	numeric	0%	-	-
`composition_ethnicity_chinese_pct_synth`	Chinese	numeric	0%	-	20.6
`composition_ethnicity_indian_pct_synth`	Indian	numeric	0%	-	6.0
`composition_ethnicity_other_pct_synth`	other	numeric	0%	-	0.6
`composition_ethnicity_non_citizens_pct_synth`	non-citizens	numeric	0%	-	9.0
`composition_ethnicity_primary_share_pct_synth`	Bumiputera	numeric	0%	-	63.8

MYS OpenFactBook - Space — 1 rows, 9 columns

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Field details — 13 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (10) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`space_agency_agencies_text`	space_agency_agencies_text	string	0%	1	Malaysian Space Agency (MYSA; established 2019) (2025)
`space_agency_agencies_numeric`	space_agency_agencies_numeric	float	0%	1	2019.0
`space_launch_site_s_text`	space_launch_site_s_text	string	0%	1	has launched feasibility studies for potential space...
`space_launch_site_s_numeric`	space_launch_site_s_numeric	float	0%	1	2025.0
`space_program_overview_text`	space_program_overview_text	string	0%	1	has a national space policy and program focused on the...
`space_program_overview_numeric`	space_program_overview_numeric	float	0%	1	2025.0
`key_space_program_milestones_text`	key_space_program_milestones_text	string	0%	1	1996 - first of a series of domestically produced...
`key_space_program_milestones_numeric`	key_space_program_milestones_numeric	float	0%	1	1996.0
`source_section`	source_section	string	0%	1	Space
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Terrorism — 1 rows, 6 columns

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Field details — 6 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (3) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`terrorist_group_s_text`	terrorist_group_s_text	string	0%	1	Abu Sayyaf Group, al-Qa'ida, Islamic State of Iraq and...
`source_section`	source_section	string	0%	1	Terrorism
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Transportation — 1 rows, 31 columns

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Field details — 34 fields (type, nulls, unique, samples)

Standards-Aligned Fields (4)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`airports_numeric`	Airports count	float	SEL	0%	1	100.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (30) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`civil_aircraft_registration_country_code_prefix_text`	Civil aircraft registration country code prefix text	string	0%	1	9M
`civil_aircraft_registration_country_code_prefix_numeric`	civil_aircraft_registration_country_code_prefix_numeric	float	0%	1	9.0
`airports_text`	airports_text	string	0%	1	100 (2025)
`heliports_text`	heliports_text	string	0%	1	24 (2025)
`heliports_numeric`	heliports_numeric	float	0%	1	24.0
`railways_total_text`	railways_total_text	string	0%	1	1,851 km (2014)
`railways_total_numeric`	railways_total_numeric	float	0%	1	1851.0
`railways_standard_gauge_text`	railways_standard_gauge_text	string	0%	1	59 km (2014) 1.435-m gauge (59 km electrified)
`railways_standard_gauge_numeric`	railways_standard_gauge_numeric	float	0%	1	59.0
`railways_narrow_gauge_text`	railways_narrow_gauge_text	string	0%	1	1,792 km (2014) 1.000-m gauge (339 km electrified)
`railways_narrow_gauge_numeric`	railways_narrow_gauge_numeric	float	0%	1	1792.0
`merchant_marine_total_text`	merchant_marine_total_text	string	0%	1	1,750 (2023)
`merchant_marine_total_numeric`	merchant_marine_total_numeric	float	0%	1	1750.0
`merchant_marine_by_type_text`	merchant_marine_by_type_text	string	0%	1	bulk carrier 14, container ship 35, general cargo 169,...
`merchant_marine_by_type_numeric`	merchant_marine_by_type_numeric	float	0%	1	14.0
`ports_total_ports_text`	ports_total_ports_text	string	0%	1	35 (2024)
`ports_total_ports_numeric`	ports_total_ports_numeric	float	0%	1	35.0
`ports_large_text`	ports_large_text	float	0%	1	3
`ports_large_numeric`	ports_large_numeric	float	0%	1	3.0
`ports_medium_text`	ports_medium_text	float	0%	1	4
`ports_medium_numeric`	ports_medium_numeric	float	0%	1	4.0
`ports_small_text`	ports_small_text	float	0%	1	10
`ports_small_numeric`	ports_small_numeric	float	0%	1	10.0
`ports_very_small_text`	ports_very_small_text	float	0%	1	18
`ports_very_small_numeric`	ports_very_small_numeric	float	0%	1	18.0
`ports_ports_with_oil_terminals_text`	Ports with oil terminals (text)	float	0%	1	24
`ports_ports_with_oil_terminals_numeric`	Ports with oil terminals (numeric)	float	0%	1	24.0
`ports_key_ports_text`	ports_key_ports_text	string	0%	1	Johor, Kota Kinabalu, Port Dickson, Port Klang, Pulau...
`source_section`	source_section	string	0%	1	Transportation
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS D-PLACE MYS-DPLACE Ethnographic Societies

14 rows · 2 tables · 2024 D-PLACE 0 1 2 3 2 SEL 15 CCL 55 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 2 table(s)

MYS D-PLACE - Dplace Ea Societies — 7 rows, 23 columns

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Field details — 23 fields (type, nulls, unique, samples)

Standards-Aligned Fields (12)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`society_id`	Society id	string	CCL	0%	7	Ej14, Ej16, Ej3, Ej8, Ib1
`society_name`	Society name	string	CCL	0%	7	Senoi, Negri Sembilan, Semang, Malays, Iban
`language_glottocode`	Language glottocode	string	CCL	0%	7	sema1266, nege1240, kens1248, mala1479, iban1264
`language_name`	Language name	string	CCL	0%	1	, , , ,
`kinship_system`	Kinship system	string	CCL	0%	5	EA017:1; EA018:1; EA019:1; EA020:1; EA021:2; EA022:9;...
`marriage_pattern`	Marriage pattern	string	CCL	0%	6	EA006:6; EA007:8; EA008:8; EA009:2; EA023:8; EA025:15,...
`subsistence_pattern`	Subsistence pattern	string	CCL	0%	7	EA001:1; EA002:1; EA003:2; EA004:0; EA005:6; EA028:3;...
`political_complexity`	Political complexity	string	CCL	0%	5	EA032:3; EA033:1, EA032:3; EA033:4, EA032:2; EA033:1,...
`religion_importance`	Religion importance	string	CCL	0%	7	EA034:1; EA112:4, EA034:4; EA112:NA, EA034:3; EA112:4,...
`residence_pattern`	Residence pattern	string	CCL	0%	6	EA010:2; EA011:2; EA012:2; EA013:9; EA014:11, EA010:5;...
`settlement_pattern`	settlement_pattern	string	CCL	0%	6	EA030:4; EA031:2, EA030:5; EA031:7, EA030:1; EA031:1,...

Extension Fields (11) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`dataset`	dataset	string	0%	1	EA, EA, EA, EA, EA
`region`	region	string	0%	1	, , , ,
`latitude`	latitude	float	0%	6	4.0, 2.58, 5.0, 5.0, 2.0
`longitude`	longitude	float	0%	7	102.0, 102.25, 101.0, 103.0, 112.0
`assignment_method`	assignment_method	string	0%	1	point_in_polygon, point_in_polygon, point_in_polygon,...
`assignment_confidence`	assignment_confidence	string	0%	1	approximate, approximate, approximate, approximate, approximate
`source`	source	string	0%	1	D-PLACE: Database of Places, Language, Culture, and...
`source_url`	source_url	string	0%	1	https://d-place.org, https://d-place.org,...
`license`	license	string	0%	1	CC BY 4.0, CC BY 4.0, CC BY 4.0, CC BY 4.0, CC BY 4.0
`data_vintage`	data_vintage	integer	0%	1	2024, 2024, 2024, 2024, 2024

MYS D-PLACE - Dplace Ea Societies Decoded — 7 rows, 49 columns

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Field details — 49 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`society_id`	society_id	string	CCL	0%	7	Ej14, Ej16, Ej3, Ej8, Ib1
`society_name`	society_name	string	CCL	0%	7	Senoi, Negri Sembilan, Semang, Malays, Iban
`language_glottocode`	language_glottocode	string	CCL	0%	7	sema1266, nege1240, kens1248, mala1479, iban1264
`language_name`	language_name	string	CCL	0%	1	, , , ,

Extension Fields (44) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`dataset`	dataset	string	0%	1	EA, EA, EA, EA, EA
`region`	region	string	0%	1	, , , ,
`latitude`	latitude	float	0%	6	4.0, 2.58, 5.0, 5.0, 2.0
`longitude`	longitude	float	0%	7	102.0, 102.25, 101.0, 103.0, 112.0
`assignment_method`	assignment_method	string	0%	1	point_in_polygon, point_in_polygon, point_in_polygon,...
`assignment_confidence`	assignment_confidence	string	0%	1	approximate, approximate, approximate, approximate, approximate
`source`	source	string	0%	1	D-PLACE: Database of Places, Language, Culture, and...
`source_url`	source_url	string	0%	1	https://d-place.org, https://d-place.org,...
`license`	license	string	0%	1	CC BY 4.0, CC BY 4.0, CC BY 4.0, CC BY 4.0, CC BY 4.0
`data_vintage`	data_vintage	integer	0%	1	2024, 2024, 2024, 2024, 2024
`jurisdictional_hierarchy_of_local_community`	jurisdictional_hierarchy_of_local_community	integer	14%	2	3, 3, 2, 3, 3
`jurisdictional_hierarchy_beyond_local_community`	jurisdictional_hierarchy_beyond_local_community	integer	14%	3	1, 4, 1, 3, 1
`religion_high_gods`	religion_high_gods	integer	14%	4	1, 4, 3, 4, 1
`trance_states`	trance_states	integer	29%	2	4, 4, 4, 5, 4
`settlement_patterns`	settlement_patterns	integer	14%	4	4, 5, 1, 7, 7
`mean_size_of_local_communities`	mean_size_of_local_communities	integer	43%	4	2, 7, 1, 4
`marital_residence_first_years`	marital_residence_first_years	integer	14%	4	2, 5, 9, 9, 2
`residence_transfer_prevailing_pattern`	residence_transfer_prevailing_pattern	integer	14%	3	2, 3, 1, 1, 2
`marital_residence_prevailing_pattern`	marital_residence_prevailing_pattern	integer	14%	4	2, 5, 10, 10, 2
`residence_transfer_alternate`	residence_transfer_alternate	integer	14%	3	9, 9, 9, 2, 9
`marital_residence_alternate`	marital_residence_alternate	integer	14%	3	11, 11, 11, 6, 11
`largest_patrilineal_kin_group`	largest_patrilineal_kin_group	integer	0%	1	1, 1, 1, 1, 1
`largest_patrilineal_exogamous_group`	largest_patrilineal_exogamous_group	integer	0%	1	1, 1, 1, 1, 1
`largest_matrilineal_kin_group`	largest_matrilineal_kin_group	integer	0%	2	1, 4, 1, 1, 1
`largest_matrilineal_exogamous_group`	largest_matrilineal_exogamous_group	integer	0%	2	1, 4, 1, 1, 1
`cognatic_kin_groups`	cognatic_kin_groups	integer	0%	3	2, 9, 1, 2, 2
`secondary_cognatic_kin_group`	secondary_cognatic_kin_group	integer	0%	2	9, 9, 9, 9, 9
`kin_terms_for_cousins`	kin_terms_for_cousins	integer	0%	3	4, 7, 4, 4, 3
`descent_major_type`	descent_major_type	integer	0%	2	6, 3, 6, 6, 6
+14 more extension fields — download the CSV/Parquet to see them all.

MYS NGA GEOnet Names Server (GNS) MYS-NGA_GNS Language

10 rows · 2 tables · 2026 NGA GEOnet Names Server (GNS) 0 1 2 3 4 SEL 12 CCL 24 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 2 table(s)

MYS NGA GNS - Language — 9 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (9)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`gns_language_code`	gns_language_code	string	CCL	0%	9	msa, mly, ind, eng, tha
`gns_language_name`	gns_language_name	string	CCL	0%	9	Malay (generic), Malay (specific), Indonesian, English, Thai
`gns_toponym_count`	gns_toponym_count	integer	CCL	0%	7	36588, 627, 66, 26, 20
`gns_toponym_share_pct`	gns_toponym_share_pct	float	CCL	0%	7	98.0044, 1.6795, 0.1768, 0.0696, 0.0536
`gns_non_roman_toponym_count`	gns_non_roman_toponym_count	integer	CCL	0%	2	0, 0, 0, 0, 6
`gns_dominant_script_code`	gns_dominant_script_code	string	CCL	0%	2	, , , , Thai
`gns_dominant_script_name`	gns_dominant_script_name	string	CCL	0%	2	, , , , Thai
`gns_script_count`	gns_script_count	integer	CCL	0%	2	0, 0, 0, 0, 1

MYS NGA GNS - Summary — 1 rows, 31 columns

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Field details — 31 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	MYS
`admin_level`	admin_level	integer	SEL	0%	1	0
`gns_country_name`	gns_country_name	string	SEL	0%	1	Malaysia
`gns_language_count`	gns_language_count	integer	CCL	0%	1	9
`gns_script_count`	gns_script_count	integer	CCL	0%	1	1
`gns_endonym_share_pct`	gns_endonym_share_pct	float	CCL	0%	1	99.99
`gns_non_roman_name_count`	gns_non_roman_name_count	integer	CCL	0%	1	13

Extension Fields (24) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`gns_name_count`	gns_name_count	integer	0%	1	90178
`gns_feature_count`	gns_feature_count	integer	0%	1	67798
`gns_endonym_count`	gns_endonym_count	integer	0%	1	90169
`gns_exonym_count`	gns_exonym_count	integer	0%	1	9
`gns_source_build_date`	gns_source_build_date	string	0%	1	Wed, 05 Aug 2026
`gns_source_change_date`	gns_source_change_date	string	0%	1	2026-08-05
`gns_name_count_hypsographic`	gns_name_count_hypsographic	integer	0%	1	11181
`gns_feature_count_hypsographic`	gns_feature_count_hypsographic	integer	0%	1	8000
`gns_name_count_populated_places`	gns_name_count_populated_places	integer	0%	1	29860
`gns_feature_count_populated_places`	gns_feature_count_populated_places	integer	0%	1	20252
`gns_name_count_hydrographic`	gns_name_count_hydrographic	integer	0%	1	37949
`gns_feature_count_hydrographic`	gns_feature_count_hydrographic	integer	0%	1	30779
`gns_name_count_administrative_regions`	gns_name_count_administrative_regions	integer	0%	1	588
`gns_feature_count_administrative_regions`	gns_feature_count_administrative_regions	integer	0%	1	389
`gns_name_count_spot_features`	gns_name_count_spot_features	integer	0%	1	6631
`gns_feature_count_spot_features`	gns_feature_count_spot_features	integer	0%	1	4975
`gns_name_count_areas_localities`	gns_name_count_areas_localities	integer	0%	1	3337
`gns_feature_count_areas_localities`	gns_feature_count_areas_localities	integer	0%	1	2835
`gns_name_count_vegetation`	gns_name_count_vegetation	integer	0%	1	591
`gns_feature_count_vegetation`	gns_feature_count_vegetation	integer	0%	1	538
`gns_name_count_undersea`	gns_name_count_undersea	integer	0%	1	14
`gns_feature_count_undersea`	gns_feature_count_undersea	integer	0%	1	6
`gns_name_count_transportation_networks`	gns_name_count_transportation_networks	integer	0%	1	27
`gns_feature_count_transportation_networks`	gns_feature_count_transportation_networks	integer	0%	1	24

MYS INFORM Risk Index (EC-JRC) MYS-INFORM Risk Index

1 row · 1 table · 2026 INFORM Risk Index (EC-JRC) 0 1 2 3 ▶

1 rows × 267 columns ingested. Field-level inventory in progress — contact us for the full export.

0 Admin 0 — 1 table(s)

MYS Inform - Risk Index — 1 rows, 267 columns

Loading data…

Field details — 267 fields (type, nulls, unique, samples)

Pending (267) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	Country code (ISO 3166-1 alpha-3)	string	0%	1	MYS
`inform_aff_dr`	People affected by drought (absolute) - raw	float	0%	1	63000.0
`inform_aff_dr_freq`	Frequency of Droughts events	float	0%	1	0.0571428571428571
`inform_aff_dr_rel`	People affected by droughts (relative) - raw	float	0%	1	0.179855961068893
`inform_ag_lnd_totl_k2`	Land area (sq. km)	float	0%	1	328550.0
`inform_asi`	Agriculture Stress Index Probability	float	0%	1	0.05
`inform_bx_trf_pwkr_dt_gd_zs_inst`	Remittences Instability	float	0%	1	0.00656947504045076
`inform_bx_trf_pwkr_dt_gd_zs_inst_norm`	ODA % GNI Normalized [BX.TRF.PWKR.DT.GD.ZS.INST.NORM]	float	0%	1	1.1
`inform_bx_trf_pwkr`	Personal remittances, received (% of GDP)	float	0%	1	0.380193531513214
`inform_lack_of_coping_capacity`	Lack of Coping Capacity Index	float	0%	1	3.3
`inform_infrastructure_capacity`	Infrastructure	float	0%	1	2.2
`inform_cc_inf_ahc`	Access to Health Care	float	0%	1	2.7
`inform_cc_inf_ahc_health_exp`	Health expenditure per capita [CC.INF.AHC.HEALTH-EXP]	float	0%	1	5.3
`inform_cc_inf_ahc_imm`	Immunization coverage	float	0%	1	1.2
`inform_cc_inf_ahc_imm_dtp3`	Diphtheria-Tetanus-Pertussis	float	0%	1	1.0
`inform_cc_inf_ahc_imm_mcv2`	Measles	float	0%	1	1.0
`inform_cc_inf_ahc_imm_pcv3`	Pneumococcal	float	0%	1	1.5
`inform_cc_inf_ahc_mmr`	Maternal Mortality Ratio [CC.INF.AHC.MMR]	float	0%	1	0.3
`inform_cc_inf_ahc_phys`	Physicians density [CC.INF.AHC.PHYS]	float	0%	1	4.1
`inform_cc_inf_com`	Communication	float	0%	1	1.1
`inform_cc_inf_com_cel`	Mobile cellular subscriptions [CC.INF.COM.CEL]	float	0%	1	3.1
`inform_cc_inf_com_elaccs`	Access to electricity [CC.INF.COM.ELACCS]	float	0%	1	0.0
`inform_cc_inf_com_litr`	Adult literacy rate	float	0%	1	0.9
`inform_cc_inf_com_netus`	Internet users [CC.INF.COM.NETUS]	float	0%	1	0.2
`inform_cc_inf_phy`	Physical Infrastructure	float	0%	1	2.9
`inform_cc_inf_phy_h2o`	Access to improved water source	float	0%	1	0.4
`inform_cc_inf_phy_rod`	Road density [CC.INF.PHY.ROD]	float	0%	1	8.0
`inform_cc_inf_phy_sta`	Access to improved sanitation facilities	float	0%	1	0.4
`inform_institutional_capacity`	Institutional	float	0%	1	4.2
`inform_cc_ins_drr`	Disaster Risk Reduction	float	0%	1	4.2
+237 more pending fields — download the CSV/Parquet to see them all.

HERA · Human Entity Reference Atlas by Masego Inc

Build a download

Language	Place names	Share*	Script
Malay (generic) (msa)	36,588	98.0%	—
Malay (specific) (mly)	627	1.7%	—
Indonesian (ind)	66	0.2%	—
English (eng)	26	0.1%	—
Thai (tha)	20	0.1%	Thai
+ 4 further languages (6 names, each under 0.05%)

Source	Type	Access
Department of Statistics Malaysia (DOSM)	official_api	api
HDX COD — Population Statistics (OCHA/UNFPA)	international_organization	bulk_download
HDX (Humanitarian Data Exchange)	international_organization	bulk_download
D-PLACE (Database of Places, Language, Culture & Environment)	academic	bulk_download
World Bank Open Data	international_organization	api
UNHCR Refugee Data Finder (UN High Commissioner for Refugees)	international_organization	api
Google	commercial	bulk_download
INFORM Risk Index (EC-JRC)	international_organization	api
Glottolog Language Catalog	academic	bulk_download
NGA GEOnet Names Server (GNS)	official_government	bulk_download
GHS Urban Centre Database (Global Human Settlement · EU Joint Research Centre)	international_organization	bulk_download
LandScan Global (ORNL — Oak Ridge National Laboratory)	research_institution	earth_engine
OpenFactBook	community_compilation	bulk_download