Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7338
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorLu, AM-
dc.creatorLi, CM-
dc.creatorLin, ZI-
dc.creatorShi, W-
dc.date.accessioned2015-11-10T08:32:51Z-
dc.date.available2015-11-10T08:32:51Z-
dc.identifier.issn0375-5444-
dc.identifier.urihttp://hdl.handle.net/10397/7338-
dc.language.isozhen_US
dc.publisher科學出版社en_US
dc.rights© 2002 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。en_US
dc.rights© 2002 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use.en_US
dc.subjectPopulation growth rateen_US
dc.subjectSpatial statisticsen_US
dc.subjectSpatial associationen_US
dc.subjectGISen_US
dc.subjectChinaen_US
dc.title中国省级人口增长率及其空间关联分析en_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: 林宗坚en_US
dc.description.otherinformationAuthor name used in this publication: SHI Wen-zhongen_US
dc.identifier.spage143-
dc.identifier.epage150-
dc.identifier.volume57-
dc.identifier.issue2-
dcterms.abstract分析了1982-1990年和1990-1998年2个时期的人日增长率,并用空间绞计分析方法研究了2个时期人口增长率的空间关联关系。1982~1990年中国可分为北部人口低增长、中西部高人口增长率、中东部低人口增长率和南部高人口增长率等 4个区域,1990-1998年中国可分为北部低人曰增长率和南部高人口增长率2个区域,2个时期的空间聚类虽然不完全相同,但它们有共同的特点,南部和西部的人口增长率都比较高,北部地区的人口增长率都比较低。最后对实证研究的结果进行了分析。-
dcterms.abstractSpatial autocorrelation is concerned with the degree to which objects or activities are similar to other objects or activities located nearby. In contrast to other types of spatial statistical analysis, such as point pattern analysis for example, spatial autocorrelation deals simultaneously with both locational and attribute information. Spatial association statistics measure the concentration of an attribute over space. While they are constructed in a very similar way to spatial autocorrelation measures, they offer the twin advantages of being able to differentiate spatial patterns caused by clusters of low values as opposed to clusters of high values, and they can be disaggregated by polygon or point to provide much more detailed information. Both spatial autocorrelation and spatial association statistics examine the relationship of an attribute value in one polygon or for one point with the values for proximal polygons or points. The spatial data of this paper are polygons. This paper aims to examine the relationship of an attribute value in one polygon with the values for proximal polygons. So spatial autocorrelation and spatial association statistics are fitted to analyze the spatial association of population growth rate by providence in China. Population growth rate indicates population growth degree in a period. This paper analyzes population growth rate in the periods of 1982-1990 and 1990-1998 in China. The spatial association of population growth rates in the two periods is studied with spatial statistics methods as well. In 1982-1990, China is divided into four regions: lower population growth rate in northern sub-region, higher population growth rate in central and western sub-region, lower population growth rate in central and eastern sub-region and higher population growth rate in southern sub-region. In 1990-1998, China is divided into two regions: lower population growth rate in northern sub-region and higher population growth rate in southern sub-region. Although the spatial clusters of the two periods are different, there are the same features with them. The population growth rate in the southern and eastern parts of China is higher, the population growth rate in the northern part of China is lower. The reasons are analyzed in detail.-
dcterms.accessRightsopen accessen_US
dcterms.alternativePopulation growth rate and its spatial association by providence in China-
dcterms.bibliographicCitation地理學報 (Acta geographica sinica), Mar. 2002, v. 57, no. 2, p. 143-150-
dcterms.isPartOf地理學報 (Acta geographica sinica)-
dcterms.issued2002-
dc.identifier.rosgroupidr07056-
dc.description.ros2001-2002 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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