空间自相关--Moransx27I

1、重庆各区县乡村人口所占比例的空间自相关分析选题:在ArcGIS中分别计算全局Moran'I指数和局部Moran'I指数，分析重庆各区县乡村人口所占比例的空间关联程度。实验目的：根据重庆市各区县之间的邻接关系，采用二进制邻近权重矩阵，选取各区县2008年的重庆各区县的总人口及乡村人口，计算出重庆各区县乡村人口所占的比例，在ArcGIS里面分别计算全局Moran'I指数和局部Moran'I指数，分析空间关联程度。实验数据：1 .重庆统计年鉴中2008年重庆市各区县的总人口及乡村人口数量（excel表格）2 .重庆市各区县的矢量图（shp.文件）软件：ArcGIS10

2、.2操作过程与结果分析：第一步：导入Excel数据文件和重庆市各区县的矢量图，并建立关联1.CatalogFolderConnection在对应的文件夹下打开重庆市各区县城镇化率的EXCEL表格及重庆市各区县shp文件Add口白gsJArcToabaa*AreTffdbwc电扑身礴rtook电Zg左Todf“上&CirlDgrap-yTbdti上01T田S0SUXHPpffFbm知T<wk田所bMwagemertTod*mEditingT口由第GAacodingTgfaL±jUBailMnAcdlJkrwivfT-oah出QiliwrR冯rEEgTxh田电M1fific

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4、s-JjMyHmladSvrvkri，t5Rwdy-T<-yMSwwkw二耳Tr耐G的Ctfihvfliarth2右键单击重庆区县界shp.文件后，JoinsandRelates选择“地区”为关联字段，将两个文件关联起来JoinDataJoinletsyouappendadditionaldatatothislayer'sattributetablesoyoucanfforexamplejsymbolizethelayer'sfeaturesusingthisdata.Whatdoyouwanttojointothislayer?Joinattributesfromata

5、ble1.Choosethefieldinthislayerthatthejoinwillbebasedon:FULLNAME2.Choosethetabletojointothislayerjorloadthetablefromdisk:pSheetl$0Showtheattributetablesoflayersinthislist3.Choosethefieldinthetabletobasethejoinon:地区JoinOptions,4'KeepallreoardsAllrecordsinthetargettableareshownintheresultingtable.U

6、nmatchedrecordswillcontainnullvaluesforallfieldsbeingappendedintothetargettablefromthejointable.KeeponlymatchingrecordsIfarecordinthetargettabledoesn'thaveamatchinthejointablefthatreoordisremovedfromtheresultingtargettable.ValidateJoin,boutioininci曲taKCancel3右键单击关联后的重庆区县界shp.文件，导出为Export_Output文

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13、#187;=H4口M曰OO-fiuE41'34BcevdlEiparL_CXrtpuE第二步：计算全局MoransI1.打开ArcToolbox,选择SpatialStatisticsToolsAnalyingPatternsSpatialAutocorrelation(MoransI)选择二进制邻接矩阵方法来确定空间权重矩阵(即当区域i和具有公共边或公共点时，两区域的距离矩阵设为1,若不相邻接，其距离矩阵设为0),选择欧式距离作为计算距离的方法，对数据进行标准化处理后计算全局Moran'I指数度量空间自相关田£ServerTools2 Spati-alAnalyst

14、Took3 SpatialStatiEtncsToolsAnalyzingPatternsjj*AverageNearsstNeighborHigh/LowClustering(Getis-OrdGeneralG)IriCreiiTientalSpat!己IAutocorrelation詈DistanceSpatialClusterAnalysis(RipieSpahalAdtocorrehtioifi!(MoransI)EgMappingClusterst±&MeasuringGeographicDistriibutionstfi&ModelingSpatialRe

15、lationships»rI2.输出结果工SpatialAutacoirdaticni(MoransJ)SpatialAutocorrelationReportMoranFsIndex:z-score:p-value:CriticalValue<-2.58ase-i.se-1.96-1.65-1.65-16516昌-1.961.96-2.58>2.5SDispersedFlandomClusteredGiventhez-scareaf4,66rttiereisalessthan10A)likelihoodthatthisclusteredpatterncouldbethe

16、resultofrandomchance.GlobalMoran'sISummaryMoran'sIndex:0*414678ExpectedIndex:'0.026316Variance:0,008943z-score:4.663389rvalue:0.000003DatasetInformationInputFeatureClass:Export_t>utputInputField:乡村人口所Conceptualization:FIXED_DISTANCEDistanceMethod:EUCLIDEANRowStandardization!：TrueDista

17、nceThreshold:0.7062DegreesWeightsMatrixFile:NoneSelectionSet:False3.结果分析：Z得分值在-1.65,1.65之间，区县乡村人口所占比例的观测值在空间上表现为独立随机分布；Z值大于1.65且显著时相似观察值在空间上表现为集聚分布（高值或低值），小于-1.65且显著时相似观测值在空间上趋于分散分布。2008年，重庆市各区县城镇化率的全局Moran'sI指数为正值1=0.414678,对应的标准化统计量Z=4.663389,在正态分布的假设下显著性水平P值为0.000003,对Moran指数检验的结果高度显著，在随机分布假设

18、下，Moran指数I的期望值与方差值分别为-0.026316和0.008943。说明从整体来看，2008年重庆市各区县乡村人口所占比例存在正的空间自相关，表现为低低集聚，各区县乡村人口所占比例的空间分布并非完全是随机性分布的，而是表现出相似值之间的空间集聚性。第三步：计算局部MoransI1.打开ArcToolbox,选择SpatialStatisticsToolsMappingClustersClusterandOutlierAnalysis,选择二进制邻接矩阵方法来确定空间权重矩阵，最后得到重庆市各区县城镇化率的局部Moran'I指数以及相对应的Z统计值和P值.SpatialAna

19、lystTool?.SpatialStatisticsToolsH名,AnalyzingPatternsA*AverageNearestNeighberHigh/LoxvClusteringI'Getis-OrdGeneralG.；IncrementalSpatidlAutocorrelation,:fMulti-CistanceSpatialClusterAnalysis(RipleysKFun<SpatialAutocorrelation(MorarsI)i-RMappingClustersClusterandOutlierAnalysis(AnselinLocalMoran

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