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) A00_04M (Float) A05_09B (Float) A05_09F (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_29F (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) A75_79M (Float) A80PLUSB (Float) A80PLUSF (Float) A80PLUSM (Float) A80_84B (Float) A80_84F (Float) A80_84M (Float) A85PLUSB (Float) A85PLUSF (Float) A85PLUSM (Float) B_2010_E (Float) CENTROID_X (Float) CENTROID_Y (Float) CONTEXT (Integer) 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) system:index (String)
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 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 0 0 0 0 7 -43.5595403393 -19.3252575766 0 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 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 0 0 0 0 8 -43.7991016004 -19.9832664835 0 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 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 0 2 1 1 2 1 1 0 0 0 9 -43.6810336371 -17.892245422 0 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 0 0 1 0 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 0 1 0 1 1 0 1 0 0 0 9 -44.067915103 -19.603577114 0 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 0 0 2 0 2 0 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 0 0 0 0 11 -44.7811337617 -23.1200534578 0 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 0 0 1 1 0 0 0 0 2 0 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 0 0 0 0 12 -43.9500190254 -22.7931133552 0 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 0 0 2 0 2 2 1 1 0 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 0 0 0 0 12 -43.7011305173 -22.4946956777 0 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 0 0 1 0 1 0 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 0 0 0 0 12 -44.6567868318 -23.0897034345 0 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 0 1 0 1 2 1 1 0 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 0 0 0 0 12 -40.4485026469 -20.0247911487 0 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 0 0 1 1 0 1 1 0 1 0 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 0 0 0 0 12 -44.2527626164 -22.4057957975 0 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：
Data Collection Documentation:
- GPWv4 Revision 11 documentation (PDF)
- Country-level Information and Sources Revision 11 (Microsoft Excel .xlsx file)
- Log of changes to the data set by version
Additional Documentation:
- Detailed descriptions of the methods and improvements made in the GPWv4 data collection are described in the following paper by Doxsey-Whitfield et al. (2015): Taking Advantage of the Improved Availability of Census Data: A First Look at the Gridded Population of the World, Version 4 (GPWv4)
- NASA EarthData Webinar: Discover NASA’s Updated Gridded Population of the World Data, January 2015 (1 hour long)
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策划者：Samapriya Roy
关键词：人口普查地理学、GPWv4、网格化人口、均匀分布
最后更新。2021-04-07