Datasets:

electricsheepafrica
/

africa-demographics-namibia

adm2_pcode stringlengths 6 6	adm_pcode stringlengths 6 6	rp10_female_pop_30cm int64 0 13.9k	rp10_children_u5_30cm int64 0 3.05k	rp10_female_u5_30cm int64 0 1.56k	rp10_elderly_30cm int64 0 967	rp10_pop_u15_30cm int64 0 9.34k	rp10_female_u15_30cm int64 0 4.79k	rp10_education_30cm_pct int64 0 100	rp10_education_30cm_count int64 0 11	rp10_hospitals_30cm_pct int64 0 100	rp10_hospitals_30cm_count int64 0 2	rp10_primary_healthcare_30cm_pct int64 0 100	rp10_primary_healthcare_30cm_count int64 0 4	rp50_female_pop_30cm int64 0 15.2k	rp50_children_u5_30cm int64 0 3.34k	rp50_female_u5_30cm int64 0 1.7k	rp50_elderly_30cm int64 0 1.06k	rp50_pop_u15_30cm int64 0 10.2k	rp50_female_u15_30cm int64 0 5.24k	rp50_education_30cm_pct int64 0 100	rp50_education_30cm_count int64 0 16	rp50_hospitals_30cm_pct int64 0 100	rp50_hospitals_30cm_count int64 0 2	rp50_primary_healthcare_30cm_pct int64 0 100	rp50_primary_healthcare_30cm_count int64 0 4	rp100_female_pop_30cm int64 0 15.7k	rp100_children_u5_30cm int64 0 3.45k	rp100_female_u5_30cm int64 0 1.76k	rp100_elderly_30cm int64 0 1.09k	rp100_pop_u15_30cm int64 0 10.6k	rp100_female_u15_30cm int64 0 5.41k	rp100_education_30cm_pct int64 0 100	rp100_education_30cm_count int64 0 17	rp100_hospitals_30cm_pct int64 0 100	rp100_hospitals_30cm_count int64 0 2	rp100_primary_healthcare_30cm_pct int64 0 100	rp100_primary_healthcare_30cm_count int64 0 4	rp500_female_pop_30cm int64 0 16.8k	rp500_children_u5_30cm int64 0 3.69k	rp500_female_u5_30cm int64 0 1.88k	rp500_elderly_30cm int64 0 1.17k	rp500_pop_u15_30cm int64 0 11.3k	rp500_female_u15_30cm int64 0 5.79k	rp500_education_30cm_pct int64 0 100	rp500_education_30cm_count int64 0 17	rp500_hospitals_30cm_pct int64 0 100	rp500_hospitals_30cm_count int64 0 2	rp500_primary_healthcare_30cm_pct int64 0 100	rp500_primary_healthcare_30cm_count int64 0 4	esa_source stringclasses 1 value	esa_processed stringdate 2026-04-27 00:00:00 2026-04-27 00:00:00
NA1007	NA1007	118	30	14	12	93	45	0	0	0	0	0	0	198	50	24	20	156	76	0	0	0	0	0	0	242	60	29	24	191	93	0	0	0	0	0	0	292	73	35	29	230	112	0	0	0	0	0	0	HDX	2026-04-27
NA0102	NA0102	325	87	43	22	232	116	0	0	0	0	0	0	562	150	74	38	401	200	6	1	0	0	0	0	910	243	120	62	650	324	11	2	0	0	0	0	1,744	465	230	119	1,246	621	17	3	0	0	17	1	HDX	2026-04-27
NA0702	NA0702	86	29	13	8	74	33	0	0	50	1	0	0	104	35	16	10	90	41	0	0	50	1	0	0	106	36	16	10	91	41	0	0	50	1	0	0	111	37	17	10	95	43	0	0	50	1	0	0	HDX	2026-04-27
NA0404	NA0404	4	1	1	0	2	1	0	0	0	0	0	0	7	1	1	0	4	2	0	0	0	0	0	0	7	1	1	0	4	2	0	0	0	0	0	0	8	2	1	0	4	2	0	0	0	0	0	0	HDX	2026-04-27
NA1204	NA1204	1,587	421	217	128	1,213	623	33	4	0	0	0	0	2,160	573	296	174	1,652	847	33	4	0	0	0	0	2,340	621	320	189	1,789	918	42	5	0	0	0	0	2,749	730	376	222	2,102	1,078	67	8	0	0	0	0	HDX	2026-04-27
NA0601	NA0601	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA0902	NA0902	21	6	3	1	16	8	0	0	0	0	0	0	29	9	5	2	23	12	0	0	0	0	0	0	34	10	5	2	26	14	0	0	0	0	0	0	35	10	5	2	27	14	0	0	0	0	0	0	HDX	2026-04-27
NA1004	NA1004	634	154	77	62	484	243	0	0	0	0	0	0	1,021	247	124	100	779	391	0	0	0	0	0	0	1,165	282	141	115	889	446	0	0	0	0	0	0	1,511	366	183	149	1,153	579	0	0	0	0	0	0	HDX	2026-04-27
NA0509	NA0509	358	105	52	22	284	142	0	0	0	0	0	0	676	198	99	42	537	269	0	0	0	0	0	0	793	232	116	50	630	315	5	1	0	0	0	0	970	283	142	61	771	386	5	1	0	0	0	0	HDX	2026-04-27
NA1010	NA1010	22	6	3	2	18	8	0	0	0	0	0	0	25	7	3	3	21	10	0	0	0	0	0	0	25	7	3	3	21	10	0	0	0	0	0	0	28	8	4	3	24	11	0	0	0	0	0	0	HDX	2026-04-27
NA0803	NA0803	6,355	1,849	932	628	5,426	2,726	55	11	0	0	0	0	8,586	2,498	1,260	849	7,331	3,683	80	16	0	0	100	2	9,134	2,657	1,340	903	7,799	3,918	85	17	0	0	100	2	10,134	2,948	1,487	1,002	8,653	4,347	85	17	100	1	100	2	HDX	2026-04-27
NA0703	NA0703	126	40	19	11	100	49	0	0	0	0	0	0	150	47	23	13	120	58	0	0	0	0	0	0	154	49	24	13	123	59	0	0	0	0	0	0	174	55	27	15	139	67	0	0	0	0	0	0	HDX	2026-04-27
NA0809	NA0809	2,480	714	364	244	2,095	1,064	21	4	0	0	0	0	3,076	886	451	302	2,598	1,319	32	6	0	0	0	0	3,271	942	480	321	2,763	1,403	32	6	0	0	0	0	3,660	1,054	537	359	3,092	1,570	37	7	0	0	0	0	HDX	2026-04-27
NA1110	NA1110	329	80	37	25	244	114	33	1	0	0	0	0	477	116	53	36	354	165	33	1	0	0	0	0	518	126	58	39	384	179	33	1	0	0	0	0	598	145	67	45	444	206	33	1	0	0	0	0	HDX	2026-04-27
NA0602	NA0602	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA1404	NA1404	205	63	30	13	170	81	0	0	0	0	33	2	240	73	35	16	200	96	0	0	0	0	33	2	250	76	37	16	208	99	0	0	0	0	33	2	282	86	41	18	234	112	0	0	0	0	33	2	HDX	2026-04-27
NA1201	NA1201	956	299	130	89	861	375	0	0	0	0	0	0	1,174	367	160	109	1,058	460	0	0	0	0	0	0	1,261	394	172	118	1,136	494	0	0	0	0	0	0	1,439	450	196	134	1,296	564	0	0	0	0	0	0	HDX	2026-04-27
NA1102	NA1102	2,345	525	263	166	1,607	810	8	1	0	0	100	1	3,131	701	352	221	2,146	1,082	23	3	0	0	100	1	3,340	748	375	236	2,289	1,154	23	3	0	0	100	1	3,772	845	424	266	2,584	1,303	46	6	0	0	100	1	HDX	2026-04-27
NA1306	NA1306	19	5	3	1	13	7	0	0	0	0	0	0	19	5	3	1	13	7	0	0	0	0	0	0	18	5	3	1	12	6	0	0	0	0	0	0	21	6	3	1	14	7	0	0	0	0	0	0	HDX	2026-04-27
NA0610	NA0610	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA0105	NA0105	167	44	22	11	117	59	4	1	0	0	0	0	360	95	47	24	253	128	8	2	0	0	0	0	458	120	60	31	322	163	17	4	0	0	0	0	610	160	81	41	429	217	17	4	0	0	0	0	HDX	2026-04-27
NA1301	NA1301	204	60	30	13	149	74	0	0	0	0	0	0	280	82	41	17	204	102	0	0	0	0	0	0	291	86	43	18	213	106	0	0	0	0	0	0	314	92	46	19	229	114	0	0	0	0	0	0	HDX	2026-04-27
NA0904	NA0904	31	10	5	2	25	12	0	0	0	0	0	0	49	16	8	4	40	19	0	0	0	0	0	0	54	17	8	4	44	21	0	0	0	0	0	0	70	22	11	5	58	28	0	0	0	0	0	0	HDX	2026-04-27
NA0104	NA0104	145	37	19	10	100	52	0	0	0	0	0	0	164	42	22	11	113	59	0	0	0	0	0	0	162	41	21	11	111	58	0	0	0	0	0	0	190	49	25	13	130	67	0	0	0	0	0	0	HDX	2026-04-27
NA1002	NA1002	325	77	39	31	243	125	0	0	0	0	0	0	619	147	75	60	464	237	12	1	0	0	0	0	742	177	90	72	556	284	12	1	0	0	0	0	990	235	120	96	741	379	12	1	0	0	0	0	HDX	2026-04-27
NA0506	NA0506	113	33	16	7	89	45	0	0	0	0	0	0	185	54	27	12	146	73	0	0	0	0	0	0	265	77	39	17	210	105	0	0	0	0	0	0	446	130	65	28	353	177	0	0	0	0	25	1	HDX	2026-04-27
NA1207	NA1207	1,559	407	213	124	1,171	611	0	0	0	0	0	0	2,355	615	322	188	1,769	923	12	1	0	0	0	0	2,731	713	373	218	2,051	1,071	25	2	0	0	0	0	3,570	932	488	284	2,681	1,399	38	3	0	0	0	0	HDX	2026-04-27
NA0609	NA0609	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA0103	NA0103	42	11	6	3	29	15	0	0	0	0	0	0	102	26	13	7	70	36	0	0	0	0	0	0	267	69	35	18	185	95	0	0	0	0	0	0	3,847	1,003	509	258	2,680	1,371	0	0	0	0	50	1	HDX	2026-04-27
NA0101	NA0101	2,357	676	312	171	1,809	840	16	3	0	0	0	0	2,730	783	361	198	2,095	972	32	6	0	0	0	0	2,890	829	382	209	2,218	1,030	37	7	0	0	0	0	3,403	976	450	246	2,611	1,212	42	8	0	0	33	2	HDX	2026-04-27
NA1210	NA1210	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA0508	NA0508	38	11	6	2	30	15	0	0	0	0	0	0	53	16	8	3	43	21	0	0	0	0	0	0	53	16	8	3	43	21	0	0	0	0	0	0	71	21	10	5	57	28	0	0	0	0	0	0	HDX	2026-04-27
NA0203	NA0203	85	19	10	6	47	24	0	0	0	0	0	0	111	25	12	7	61	31	0	0	0	0	0	0	124	28	14	8	69	34	0	0	0	0	0	0	145	33	16	10	80	40	0	0	0	0	0	0	HDX	2026-04-27
NA1205	NA1205	3,779	1,006	516	306	2,895	1,481	38	6	0	0	0	0	4,929	1,312	673	399	3,776	1,932	56	9	0	0	33	1	5,314	1,414	726	431	4,072	2,083	62	10	0	0	33	1	6,050	1,610	826	490	4,636	2,371	62	10	0	0	33	1	HDX	2026-04-27
NA1101	NA1101	4,478	998	503	316	3,054	1,547	22	2	0	0	0	0	5,602	1,249	629	395	3,822	1,935	22	2	0	0	50	1	6,075	1,355	682	429	4,145	2,099	22	2	0	0	50	1	6,806	1,518	764	481	4,645	2,351	33	3	0	0	50	1	HDX	2026-04-27
NA1003	NA1003	217	52	26	21	164	83	0	0	0	0	0	0	394	95	48	39	298	151	0	0	0	0	0	0	430	103	52	42	325	164	0	0	0	0	0	0	552	133	67	54	418	211	0	0	0	0	0	0	HDX	2026-04-27
NA0402	NA0402	383	90	44	24	231	114	0	0	0	0	0	0	622	146	72	40	375	185	0	0	0	0	0	0	684	160	79	44	412	204	0	0	0	0	0	0	789	185	91	50	475	235	0	0	0	0	0	0	HDX	2026-04-27
NA0401	NA0401	32	8	4	2	20	10	0	0	0	0	0	0	45	11	5	3	28	13	0	0	0	0	0	0	46	11	5	3	29	14	0	0	0	0	0	0	58	14	7	4	36	17	0	0	0	0	0	0	HDX	2026-04-27
NA1202	NA1202	350	109	48	33	315	137	0	0	0	0	0	0	504	158	69	47	454	198	0	0	0	0	0	0	553	173	76	52	498	217	0	0	0	0	0	0	680	213	93	63	613	267	0	0	0	0	0	0	HDX	2026-04-27
NA0503	NA0503	223	67	33	14	182	89	0	0	0	0	0	0	299	90	44	19	244	119	0	0	0	0	0	0	328	98	48	21	267	130	0	0	0	0	0	0	422	127	62	27	345	168	0	0	0	0	0	0	HDX	2026-04-27
NA0802	NA0802	8,911	2,593	1,306	881	7,605	3,821	67	10	0	0	50	1	10,209	2,970	1,496	1,010	8,713	4,377	87	13	0	0	50	1	10,517	3,059	1,541	1,040	8,975	4,509	87	13	0	0	50	1	11,112	3,233	1,629	1,099	9,483	4,764	93	14	0	0	50	1	HDX	2026-04-27
NA1009	NA1009	154	38	19	16	121	59	0	0	0	0	0	0	201	50	24	20	158	77	0	0	0	0	0	0	222	55	27	22	175	85	0	0	0	0	0	0	299	74	36	30	235	114	0	0	0	0	0	0	HDX	2026-04-27
NA0801	NA0801	1,424	415	209	141	1,218	611	7	1	0	0	50	1	1,807	527	265	179	1,546	775	7	1	0	0	50	1	2,000	583	293	198	1,710	858	7	1	0	0	50	1	2,562	747	376	254	2,191	1,099	14	2	0	0	50	1	HDX	2026-04-27
NA1106	NA1106	13,870	3,053	1,557	967	9,340	4,794	46	11	0	0	100	1	15,158	3,337	1,702	1,057	10,208	5,239	58	14	0	0	100	1	15,665	3,449	1,759	1,092	10,550	5,414	58	14	0	0	100	1	16,750	3,687	1,881	1,168	11,280	5,789	67	16	50	1	100	1	HDX	2026-04-27
NA0706	NA0706	137	44	21	12	111	53	0	0	0	0	0	0	159	51	25	14	129	62	0	0	0	0	0	0	168	54	26	14	136	65	0	0	0	0	0	0	187	60	29	16	151	72	0	0	0	0	0	0	HDX	2026-04-27
NA0304	NA0304	33	9	4	3	23	11	5	1	0	0	0	0	38	10	5	3	27	12	5	1	0	0	0	0	46	12	6	4	33	15	5	1	0	0	0	0	52	14	6	5	37	17	5	1	0	0	0	0	HDX	2026-04-27
NA1103	NA1103	243	55	27	17	168	84	0	0	0	0	0	0	400	90	45	28	276	138	0	0	0	0	0	0	470	105	52	33	324	162	0	0	0	0	0	0	597	134	66	42	411	206	0	0	0	0	0	0	HDX	2026-04-27
NA1006	NA1006	415	101	50	41	318	159	3	1	0	0	0	0	598	146	72	59	459	229	3	1	0	0	0	0	686	167	83	68	527	263	6	2	0	0	0	0	911	222	110	90	699	349	6	2	0	0	0	0	HDX	2026-04-27
NA0704	NA0704	70	22	11	6	55	27	0	0	0	0	0	0	102	31	16	8	79	39	0	0	0	0	0	0	105	32	16	9	82	41	0	0	0	0	0	0	117	36	18	10	91	45	0	0	0	0	0	0	HDX	2026-04-27
NA1302	NA1302	42	13	6	3	31	15	0	0	0	0	0	0	63	19	9	4	47	23	0	0	0	0	0	0	71	21	10	4	52	26	0	0	0	0	0	0	76	23	11	5	57	28	0	0	0	0	0	0	HDX	2026-04-27
NA0406	NA0406	14	3	2	1	9	4	0	0	0	0	0	0	22	5	3	1	14	6	0	0	0	0	0	0	24	6	3	2	15	7	0	0	0	0	0	0	41	10	5	3	26	12	0	0	0	0	0	0	HDX	2026-04-27
NA0807	NA0807	1,051	308	154	104	901	450	18	2	0	0	33	1	1,355	397	199	134	1,162	581	27	3	0	0	33	1	1,441	422	211	142	1,236	618	27	3	0	0	33	1	1,632	478	239	161	1,400	699	36	4	0	0	33	1	HDX	2026-04-27
NA0207	NA0207	2,357	560	263	163	1,379	654	30	3	100	2	25	1	3,041	722	340	210	1,780	844	40	4	100	2	50	2	3,847	913	430	265	2,251	1,068	40	4	100	2	50	2	7,866	1,867	879	543	4,602	2,184	60	6	100	2	75	3	HDX	2026-04-27
NA0705	NA0705	13	4	2	1	11	5	0	0	0	0	0	0	18	6	3	2	15	7	0	0	0	0	0	0	25	8	4	2	20	10	0	0	0	0	0	0	26	8	4	2	21	10	0	0	0	0	0	0	HDX	2026-04-27
NA0505	NA0505	625	184	92	39	499	248	3	1	100	1	0	0	988	290	145	62	790	393	5	2	100	1	11	1	1,131	332	166	71	903	449	5	2	100	1	11	1	1,423	418	208	89	1,136	565	5	2	100	1	33	3	HDX	2026-04-27
NA1304	NA1304	42	14	6	3	34	15	0	0	0	0	0	0	56	18	8	4	45	20	0	0	0	0	0	0	60	19	9	4	48	22	0	0	0	0	0	0	67	22	10	4	54	24	0	0	0	0	0	0	HDX	2026-04-27
NA1303	NA1303	198	59	29	12	147	72	0	0	0	0	0	0	292	88	43	18	218	106	0	0	0	0	0	0	328	98	48	21	244	119	0	0	0	0	0	0	396	119	58	25	296	144	0	0	0	0	0	0	HDX	2026-04-27
NA0607	NA0607	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA0204	NA0204	72	16	8	5	39	20	0	0	0	0	0	0	90	20	10	6	50	25	0	0	0	0	0	0	98	22	11	6	54	27	0	0	0	0	0	0	106	24	12	7	58	29	0	0	0	0	0	0	HDX	2026-04-27
NA1108	NA1108	6,147	1,387	691	439	4,242	2,124	25	2	0	0	0	0	7,711	1,739	866	550	5,321	2,665	25	2	0	0	0	0	8,442	1,904	949	602	5,825	2,917	38	3	0	0	0	0	9,484	2,139	1,066	677	6,544	3,277	50	4	0	0	0	0	HDX	2026-04-27
NA0604	NA0604	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA1109	NA1109	1,504	340	169	107	1,041	520	17	1	0	0	0	0	2,529	572	284	181	1,751	874	33	2	0	0	0	0	2,771	626	311	198	1,919	958	33	2	0	0	0	0	3,248	734	364	232	2,249	1,122	50	3	0	0	0	0	HDX	2026-04-27
NA1104	NA1104	1,786	396	201	127	1,214	617	100	1	0	0	0	0	2,090	464	235	149	1,422	722	100	1	0	0	0	0	2,191	486	246	156	1,490	757	100	1	0	0	0	0	2,401	533	270	171	1,633	829	100	1	0	0	0	0	HDX	2026-04-27
NA1206	NA1206	1,134	319	155	96	918	445	0	0	0	0	0	0	1,351	380	185	115	1,094	530	0	0	0	0	0	0	1,443	406	197	123	1,169	566	0	0	0	0	0	0	1,592	448	218	135	1,289	624	0	0	0	0	0	0	HDX	2026-04-27
NA0507	NA0507	203	59	30	13	162	81	0	0	0	0	20	1	266	78	39	17	211	106	4	1	0	0	20	1	283	83	42	18	225	113	4	1	0	0	20	1	338	99	50	21	269	134	7	2	0	0	20	1	HDX	2026-04-27
NA0303	NA0303	409	103	51	34	273	135	0	0	0	0	0	0	489	123	61	40	326	162	0	0	0	0	0	0	514	130	64	42	343	170	0	0	0	0	0	0	538	136	67	44	359	178	0	0	0	0	0	0	HDX	2026-04-27
NA0603	NA0603	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA0205	NA0205	430	98	48	29	241	119	6	1	0	0	0	0	1,097	249	122	73	614	304	6	1	0	0	0	0	1,308	297	146	87	732	363	6	1	0	0	0	0	1,743	396	194	116	976	483	6	1	0	0	0	0	HDX	2026-04-27
NA1001	NA1001	537	131	65	53	414	206	0	0	0	0	0	0	813	199	99	81	627	311	7	1	0	0	50	1	922	226	112	91	711	353	14	2	0	0	50	1	1,133	277	137	112	873	434	14	2	0	0	50	1	HDX	2026-04-27
NA0906	NA0906	73	23	11	5	60	28	0	0	0	0	0	0	84	27	13	6	69	33	0	0	0	0	0	0	90	29	14	7	74	35	0	0	0	0	0	0	99	32	15	7	82	39	0	0	0	0	0	0	HDX	2026-04-27
NA0903	NA0903	43	14	7	3	35	17	0	0	0	0	0	0	49	16	8	4	40	19	0	0	0	0	0	0	51	16	8	4	42	20	0	0	0	0	0	0	60	19	9	4	49	24	0	0	0	0	0	0	HDX	2026-04-27
NA0201	NA0201	100	22	11	6	54	28	0	0	0	0	0	0	124	27	14	8	67	35	0	0	0	0	0	0	134	29	15	8	72	37	0	0	0	0	0	0	140	30	16	9	75	39	0	0	0	0	0	0	HDX	2026-04-27
NA1402	NA1402	136	40	20	9	110	54	9	2	0	0	0	0	190	56	28	12	153	76	9	2	0	0	0	0	266	79	39	17	214	106	9	2	0	0	0	0	578	171	85	37	465	230	9	2	0	0	0	0	HDX	2026-04-27
NA0306	NA0306	7	2	1	1	4	2	0	0	0	0	0	0	9	2	1	1	6	3	0	0	0	0	0	0	9	2	1	1	6	3	0	0	0	0	0	0	10	2	1	1	6	3	0	0	0	0	0	0	HDX	2026-04-27
NA1011	NA1011	1,430	347	173	141	1,093	548	12	3	0	0	0	0	1,990	483	241	196	1,521	762	17	4	0	0	0	0	2,163	525	262	213	1,653	828	17	4	0	0	0	0	2,616	635	317	258	1,999	1,002	25	6	0	0	0	0	HDX	2026-04-27
NA1107	NA1107	11,337	2,586	1,272	816	7,914	3,916	60	6	0	0	57	4	12,485	2,847	1,401	898	8,715	4,313	60	6	0	0	57	4	12,740	2,906	1,430	917	8,893	4,401	70	7	0	0	57	4	13,207	3,012	1,482	950	9,218	4,562	80	8	0	0	57	4	HDX	2026-04-27
NA0305	NA0305	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA0905	NA0905	235	80	37	18	207	93	0	0	0	0	0	0	297	101	47	23	261	117	0	0	0	0	0	0	318	108	50	25	279	126	0	0	0	0	0	0	350	119	55	27	307	138	0	0	0	0	0	0	HDX	2026-04-27
NA1401	NA1401	163	49	24	10	133	65	0	0	0	0	0	0	197	59	29	13	161	78	0	0	0	0	0	0	203	61	30	13	166	81	0	0	0	0	0	0	230	69	34	15	188	91	0	0	0	0	0	0	HDX	2026-04-27
NA0811	NA0811	5,185	1,503	760	511	4,409	2,223	38	3	0	0	33	1	6,961	2,019	1,021	687	5,922	2,985	38	3	0	0	33	1	7,568	2,195	1,109	747	6,440	3,245	38	3	0	0	33	1	8,903	2,583	1,305	879	7,577	3,818	38	3	0	0	33	1	HDX	2026-04-27
NA1209	NA1209	3,599	943	491	287	2,715	1,410	40	4	0	0	0	0	5,177	1,355	707	413	3,901	2,029	50	5	0	0	0	0	5,763	1,509	787	459	4,342	2,258	50	5	0	0	0	0	6,764	1,771	924	539	5,096	2,651	50	5	0	0	0	0	HDX	2026-04-27
NA0301	NA0301	73	20	9	6	51	24	0	0	0	0	0	0	97	26	12	8	69	32	0	0	0	0	0	0	108	29	13	9	77	36	0	0	0	0	0	0	122	33	15	11	86	40	0	0	0	0	0	0	HDX	2026-04-27
NA0606	NA0606	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	HDX	2026-04-27
NA1203	NA1203	1,211	339	165	103	976	475	43	3	0	0	50	1	1,490	417	204	126	1,202	584	43	3	0	0	50	1	1,653	463	226	140	1,333	648	43	3	0	0	50	1	1,939	543	265	164	1,564	760	43	3	0	0	50	1	HDX	2026-04-27
NA0206	NA0206	488	117	54	34	289	135	0	0	0	0	0	0	1,255	300	140	87	738	348	0	0	0	0	0	0	1,284	306	143	89	755	356	0	0	0	0	0	0	1,386	331	154	96	815	385	0	0	0	0	0	0	HDX	2026-04-27

Namibia - Risk Assessment Indicators

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

Abstract

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

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

Data Overview

Access to Services (NAM_ADM2_access)
Facilities (NAM_ADM2_facilities)
Coping Capacity (NAM_ADM2_coping)
Demographics (NAM_ADM2_demographics)
Rural Population (NAM_ADM2_rural_population)
Vulnerability (NAM_ADM2_vulnerability)
Flood Exposure (NAM_ADM2_flood_exposure)

Indicator Descriptions

Access to Services (`NAM_ADM2_access`)

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

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

Data Source: openrouteservice (ORS)

Facilities (`NAM_ADM2_facilities`)

Counts of essential service facilities within each district.

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

Data Source: OpenStreetMap (OSM)

Coping Capacity (`NAM_ADM2_coping`)

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

Demographics (`NAM_ADM2_demographics`)

Shows the population composition by age and gender.

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

Data Source: Worldpop

Rural Population (`NAM_ADM2_rural_population`)

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

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

Data Source: Global Human Settlement Layer (GHSL)

Vulnerability (`NAM_ADM2_vulnerability`)

Combines Demographics and Rural Population indicators.

Flood Exposure (`NAM_ADM2_flood_exposure`)

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

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

Data Source: The Joint Research Centre (JRC)

QGIS Plugin Risk Assessment Inputs

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

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

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

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

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

Dataset Characteristics


Domain	Public health
Unit of observation	Tabular records
Rows (total)	107
Columns	52 (48 numeric, 4 categorical, 0 datetime)
Train split	85 rows
Test split	21 rows
Geographic scope	NAM
Publisher	HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated	2026-04-13

Variables

Geographic — rp10_elderly_30cm (range 0.0–967.0), rp10_primary_healthcare_30cm_pct (range 0.0–100.0), rp10_primary_healthcare_30cm_count (range 0.0–4.0), rp50_elderly_30cm (range 0.0–1057.0), rp50_primary_healthcare_30cm_pct and 7 others.

Demographic — rp10_female_pop_30cm (range 0.0–13870.0), rp10_female_u5_30cm (range 0.0–1557.0), rp10_pop_u15_30cm (range 0.0–9340.0), rp10_female_u15_30cm (range 0.0–4794.0), rp50_female_pop_30cm (range 0.0–15158.0) and 11 others.

Outcome / Measurement — rp10_education_30cm_pct (range 0.0–100.0), rp10_education_30cm_count (range 0.0–11.0), rp10_hospitals_30cm_pct (range 0.0–100.0), rp10_hospitals_30cm_count (range 0.0–2.0), rp50_education_30cm_pct (range 0.0–100.0) and 11 others.

Identifier / Metadata — adm2_pcode (NA0901, NA0904, NA1012), adm_pcode (NA0901, NA0904, NA1012), esa_source (HDX), esa_processed (2026-04-27).

Other — rp10_children_u5_30cm (range 0.0–3053.0), rp50_children_u5_30cm (range 0.0–3337.0), rp100_children_u5_30cm, rp500_children_u5_30cm.

Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`adm2_pcode`	object	0.0%	NA0901, NA0904, NA1012
`adm_pcode`	object	0.0%	NA0901, NA0904, NA1012
`rp10_female_pop_30cm`	int64	0.0%	0.0 – 13870.0 (mean 1129.8879)
`rp10_children_u5_30cm`	int64	0.0%	0.0 – 3053.0 (mean 291.8318)
`rp10_female_u5_30cm`	int64	0.0%	0.0 – 1557.0 (mean 145.7944)
`rp10_elderly_30cm`	int64	0.0%	0.0 – 967.0 (mean 93.1869)
`rp10_pop_u15_30cm`	int64	0.0%	0.0 – 9340.0 (mean 857.5514)
`rp10_female_u15_30cm`	int64	0.0%	0.0 – 4794.0 (mean 429.0)
`rp10_education_30cm_pct`	int64	0.0%	0.0 – 100.0 (mean 9.1495)
`rp10_education_30cm_count`	int64	0.0%	0.0 – 11.0 (mean 1.1402)
`rp10_hospitals_30cm_pct`	int64	0.0%	0.0 – 100.0 (mean 2.3364)
`rp10_hospitals_30cm_count`	int64	0.0%	0.0 – 2.0 (mean 0.0374)
`rp10_primary_healthcare_30cm_pct`	int64	0.0%	0.0 – 100.0 (mean 7.1869)
`rp10_primary_healthcare_30cm_count`	int64	0.0%	0.0 – 4.0 (mean 0.215)
`rp50_female_pop_30cm`	int64	0.0%	0.0 – 15158.0 (mean 1462.9626)
`rp50_children_u5_30cm`	int64	0.0%	0.0 – 3337.0 (mean 379.1215)
`rp50_female_u5_30cm`	int64	0.0%	0.0 – 1702.0 (mean 189.514)
`rp50_elderly_30cm`	int64	0.0%	0.0 – 1057.0 (mean 121.1682)
`rp50_pop_u15_30cm`	int64	0.0%	0.0 – 10208.0 (mean 1112.1308)
`rp50_female_u15_30cm`	int64	0.0%	0.0 – 5239.0 (mean 556.3645)
`rp50_education_30cm_pct`	int64	0.0%	0.0 – 100.0 (mean 12.3551)
`rp50_education_30cm_count`	int64	0.0%	0.0 – 16.0 (mean 1.6449)
`rp50_hospitals_30cm_pct`	int64	0.0%
`rp50_hospitals_30cm_count`	int64	0.0%
`rp50_primary_healthcare_30cm_pct`	int64	0.0%
`rp50_primary_healthcare_30cm_count`	int64	0.0%
`rp100_female_pop_30cm`	int64	0.0%
`rp100_children_u5_30cm`	int64	0.0%
`rp100_female_u5_30cm`	int64	0.0%
`rp100_elderly_30cm`	int64	0.0%
`rp100_pop_u15_30cm`	int64	0.0%
`rp100_female_u15_30cm`	int64	0.0%
`rp100_education_30cm_pct`	int64	0.0%
`rp100_education_30cm_count`	int64	0.0%
`rp100_hospitals_30cm_pct`	int64	0.0%
`rp100_hospitals_30cm_count`	int64	0.0%
`rp100_primary_healthcare_30cm_pct`	int64	0.0%
`rp100_primary_healthcare_30cm_count`	int64	0.0%
`rp500_female_pop_30cm`	int64	0.0%
`rp500_children_u5_30cm`	int64	0.0%
`rp500_female_u5_30cm`	int64	0.0%
`rp500_elderly_30cm`	int64	0.0%
`rp500_pop_u15_30cm`	int64	0.0%
`rp500_female_u15_30cm`	int64	0.0%
`rp500_education_30cm_pct`	int64	0.0%
`rp500_education_30cm_count`	int64	0.0%
`rp500_hospitals_30cm_pct`	int64	0.0%
`rp500_hospitals_30cm_count`	int64	0.0%
`rp500_primary_healthcare_30cm_pct`	int64	0.0%
`rp500_primary_healthcare_30cm_count`	int64	0.0%
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-27

Numeric Summary

Column	Max	Mean	Median
`rp10_female_pop_30cm`	13870.0	1129.8879	203.0
`rp10_children_u5_30cm`	3053.0	291.8318	55.0
`rp10_female_u5_30cm`	1557.0	145.7944	27.0
`rp10_elderly_30cm`	967.0	93.1869	13.0
`rp10_pop_u15_30cm`	9340.0	857.5514	149.0
`rp10_female_u15_30cm`	4794.0	429.0	74.0
`rp10_education_30cm_pct`	100.0	9.1495	0.0
`rp10_education_30cm_count`	11.0	1.1402	0.0
`rp10_hospitals_30cm_pct`	100.0	2.3364	0.0
`rp10_hospitals_30cm_count`	2.0	0.0374	0.0
`rp10_primary_healthcare_30cm_pct`	100.0	7.1869	0.0
`rp10_primary_healthcare_30cm_count`	4.0	0.215	0.0
`rp50_female_pop_30cm`	15158.0	1462.9626	280.0
`rp50_children_u5_30cm`	3337.0	379.1215	82.0
`rp50_female_u5_30cm`	1702.0	189.514	41.0

Curation

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

Limitations

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

Citation

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

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

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