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GEE：世界人口网格化第4版行政单位中心点与人口估计数据集

此星光明发表于 2023/01/27 18:10:31 2023/01/27

【摘要】世界人口网格化第4版行政单位中心点与人口估计世界人口网格化第4版（GPWv4）。带有人口估计的行政单位中心点，修订版11包括2000年、2005年、2010年、2015年和2020年的联合国世界人口方案调整后的人口估计和密度，以及2010年的基本人口特征（年龄和性别）。该数据集还包括行政名称、土地和水域面积，以及按行政单位中心点（中心点）位置划分的数据背景。中心点是基于GPWv4中使用的大...

世界人口网格化第4版行政单位中心点与人口估计
世界人口网格化第4版（GPWv4）。带有人口估计的行政单位中心点，修订版11包括2000年、2005年、2010年、2015年和2020年的联合国世界人口方案调整后的人口估计和密度，以及2010年的基本人口特征（年龄和性别）。该数据集还包括行政名称、土地和水域面积，以及按行政单位中心点（中心点）位置划分的数据背景。中心点是基于GPWv4中使用的大约1350万个输入行政单位，因此，这些文件需要能够将大量数据读入内存的硬件和软件。

目的：提供GPWv4中使用的输入行政单位的矢量（点）版本，包括人口估计、密度、2010年基本人口特征，以及行政名称、面积和数据背景，以便在数据整合中使用。

代码：

var gpw = ee.FeatureCollection("projects/sat-io/open-datasets/sedac/gpw-v4-admin-unit-center-points-population-estimates-rev11");

Map.addLayer(ee.FeatureCollection(gpw),{},'gpw-v4-admin-center-points-rev11')

矢量数据属性表：

Feature Index	A00_04B (Float)	A00_04F (Float)	A05_09B (Float)	A05_09M (Float)	A10_14B (Float)	A10_14F (Float)	A10_14M (Float)	A15_19B (Float)	A15_19F (Float)	A15_19M (Float)	A20_24B (Float)	A20_24F (Float)	A20_24M (Float)	A25_29B (Float)	A25_29M (Float)	A30_34B (Float)	A30_34F (Float)	A30_34M (Float)	A35_39B (Float)	A35_39F (Float)	A35_39M (Float)	A40_44B (Float)	A40_44F (Float)	A40_44M (Float)	A45_49B (Float)	A45_49F (Float)	A45_49M (Float)	A50_54B (Float)	A50_54F (Float)	A50_54M (Float)	A55_59B (Float)	A55_59F (Float)	A55_59M (Float)	A60_64B (Float)	A60_64F (Float)	A60_64M (Float)	A65PLUSB (Float)	A65PLUSF (Float)	A65PLUSM (Float)	A65_69B (Float)	A65_69F (Float)	A65_69M (Float)	A70PLUSB (Float)	A70PLUSF (Float)	A70PLUSM (Float)	A70_74B (Float)	A70_74F (Float)	A70_74M (Float)	A75PLUSB (Float)	A75PLUSF (Float)	A75PLUSM (Float)	A75_79B (Float)	A75_79F (Float)	A80PLUSB (Float)	A80PLUSF (Float)	A80PLUSM (Float)	A80_84B (Float)	A80_84F (Float)	A80_84M (Float)	B_2010_E (Float)	CENTROID_X (Float)	CENTROID_Y (Float)	CONTEXT_NM (String)	COUNTRYNM (String)	F_2010_E (Float)	GUBID (String)	INSIDE_X (Float)	INSIDE_Y (Float)	ISOALPHA (String)	LAND_A_KM (Float)	M_2010_E (Float)	NAME1 (String)	NAME2 (String)	NAME3 (String)	NAME4 (String)	NAME5 (String)	NAME6 (String)	TOTAL_A_KM (Float)	UN_2000_DS (Float)	UN_2000_E (Long)	UN_2005_DS (Float)	UN_2005_E (Long)	UN_2010_DS (Float)	UN_2010_E (Long)	UN_2015_DS (Float)	UN_2015_E (Long)	UN_2020_DS (Float)	UN_2020_E (Long)	WATER_A_KM (Float)	WATER_CODE (String)
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	2	1	0	1	1	0	1	1	1	0	2	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	7	-43.5595403393	-19.3252575766	Not applicable	Brazil	2	{42464A48-60A0-4ED1-B57C-8B48CD18754A}	-43.5595403393	-19.3252575766	BRA	26.7675033417	5	Minas Gerais	SANTANA DO RIACHO	SERRA DO CIPO	SERRA DO CIPO	315900110000004	NA	26.7675033417	0.251654348805	7	0.264475109663	7	0.272297089116	7	0.277452147384	7	0.27967126833	7	0	L
1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	2	1	1	3	1	2	1	1	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	8	-43.7991016004	-19.9832664835	Not applicable	Brazil	3	{EBA9FA5C-69EF-48E3-9588-23408CCC4003}	-43.7991016004	-19.9832664835	BRA	1.63870372643	5	Minas Gerais	RAPOSOS	RAPOSOS	RAPOSOS	315390505000015	NA	1.63870372643	4.70730567322	8	4.94217901005	8	5.08326054154	8	5.17431860928	8	5.2104908035	9	0	L
2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	2	0	2	0	0	0	1	0	1	0	0	0	0	0	0	5	3	2	0	0	0	5	3	2	2	1	1	3	2	1	1	1	2	1	1	2	1	1	9	-43.6810336371	-17.892245422	Not applicable	Brazil	3	{1EE63B36-32A8-4C5A-9395-6E30704C56AA}	-43.6810336371	-17.892245422	BRA	196.774706684	6	Minas Gerais	DIAMANTINA	INHAI	INHAI	312160530000004	NA	196.774706684	0.0456726696786	9	0.0471196781765	9	0.0476240208853	9	0.0476361553958	9	0.0471370084339	9	0	L
3	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	1	1	1	0	1	1	0	0	0	0	3	1	2	2	1	1	1	1	0	1	0	1	0	0	0	1	0	1	0	0	1	0	1	1	0	1	9	-44.067915103	-19.603577114	Not applicable	Brazil	4	{75F71A7A-DD3F-422A-9450-5A482CE3E63A}	-44.067915103	-19.603577114	BRA	4.20270140151	5	Minas Gerais	PEDRO LEOPOLDO	PEDRO LEOPOLDO	PEDRO LEOPOLDO	314930905000033	NA	4.20270140151	2.03821973673	9	2.15387248044	9	2.22980455796	9	2.28454916732	10	2.31552188142	10	0	L
4	0	0	0	0	0	0	0	2	0	2	0	0	0	0	0	0	0	0	0	0	0	3	1	2	1	0	1	2	1	1	1	0	1	0	0	0	2	1	1	2	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	11	-44.7811337617	-23.1200534578	Not applicable	Brazil	3	{0751F980-4B42-4B73-BC6C-C4D3DA07668A}	-44.7811337617	-23.1200534578	BRA	54.4731117239	8	Rio de Janeiro	PARATY	PARATY	PARATY	330380705000029	NA	54.4731117239	0.164436258772	9	0.187863125141	10	0.210263226687	11	0.232901349968	13	0.255208630303	14	0	L
5	0	0	0	0	0	0	0	1	1	0	0	0	0	2	2	0	0	0	0	0	0	1	1	0	4	1	3	2	0	2	1	0	1	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	-43.9500190254	-22.7931133552	Not applicable	Brazil	3	{850652A7-51D7-46E8-BF7D-882C5C57FB9C}	-43.9500190254	-22.7931133552	BRA	97.0767253612	9	Rio de Janeiro	RIO CLARO	SAO JOAO MARCOS	SAO JOAO MARCOS	330440925000004	NA	109.003096119	0.119311766706	12	0.125202269435	12	0.128711974382	12	0.13095214071	13	0.131801672792	13	11.9263707577	L
6	0	0	0	0	0	0	0	2	0	2	2	1	1	0	0	0	0	0	0	0	0	2	1	1	2	2	0	1	0	1	1	0	1	1	1	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	-43.7011305173	-22.4946956777	Not applicable	Brazil	5	{42D31B18-8E1B-4E10-910E-D0972660162B}	-43.7011305173	-22.4946956777	BRA	1.39505886545	7	Rio de Janeiro	MENDES	MENDES	MENDES	330280905000033	NA	1.39505886545	8.58099364087	12	8.85728553084	12	8.95656613297	12	8.96332879608	13	8.87384396898	12	0	L
7	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	0	4	2	2	0	0	0	0	0	0	4	0	4	1	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	-44.6567868318	-23.0897034345	Not applicable	Brazil	4	{C1280B3D-D3B5-4E73-888F-61618BAA008C}	-44.640792633	-23.071472843	BRA	1.24001939392	8	Rio de Janeiro	PARATY	PARATY	PARATY	330380705000035	NA	1.24001939392	7.8802474508	10	9.00292869743	11	10.0764044893	12	11.1612869514	14	12.2303144902	15	0	L
8	0	0	1	1	2	1	1	0	0	0	0	0	0	0	0	0	0	0	2	1	1	1	1	0	1	0	1	0	0	0	2	0	2	2	0	2	1	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	-40.4485026469	-20.0247911487	Not applicable	Brazil	4	{EB27AB9D-06E9-4671-893E-AFB9B62FD44D}	-40.4485026469	-20.0247911487	BRA	0.101316903409	8	Espirito Santo	SANTA LEOPOLDINA	DJALMA COUTINHO	DJALMA COUTINHO	320450010000001	NA	0.101316903409	124.709663619	13	125.296004376	13	123.325294865	12	120.130698461	12	115.763196865	12	0	L
9	2	2	0	0	0	0	0	1	1	0	1	1	0	1	1	0	0	0	0	0	0	1	1	0	1	1	0	1	0	1	2	1	1	1	0	1	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	-44.2527626164	-22.4057957975	Not applicable	Brazil	7	{E54E00BF-51D9-4181-AFDB-BEBFA9ED0DFD}	-44.2527626164	-22.4057957975	BRA	0.0500853236047	5	Rio de Janeiro	QUATIS	QUATIS	QUATIS	330412805000033	NA	0.0500853236047	207.37185642	10	229.798816409	12	249.473021007	12	268.031775839	13	284.881108896	14	0	L

数据集引用：

Doxsey-Whitfield, Erin, Kytt MacManus, Susana B. Adamo, Linda Pistolesi, John Squires, Olena Borkovska, and Sandra R. Baptista. "Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4." Papers in Applied Geography 1, no. 3 (2015): 226-234.

有关数据文档介绍：