多尺度土地资源配置研究(6)

发布时间:2021-06-07

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WiththerapideconomicdevelopmentinChina,Urbanizationhasbecome

useallirreversibletrendofsocialdevelopment.Inthisprocess,people’Srequirementofland

growing,theincreasinglyintensifiedcontradiction

economicandenvironmentalspaceandlandproductsisalsobetweenpeopleandorlandledtomanysocial,issues,and

asmayresultinthedisappearancefragmentationofhabitat,thereductioninbiodiversity

theseecologicalproblems

awellastheweakeningofecosystemservices.Howtorespondtointegratethemintoandpolicy,planningandprojectdecision-makingprocesshasbecome

allocationofhotspotofresearch.Therefore,thispaperdealtwithmulti-scaleoptimalunderconsiderationofinfluencingfactorswithvariousscalelandresourceslandusepattern,takingrepresentativeblocksofYubeidistrict,LongxingtownandHeyanvillageetc.forexample.Thisstudywouldenrichandimprovelandqualityassessmentsystembyanatomizingsimilaritiesanddifferencesofmulti—scalelandqualityinhilly-landregions.Theresearchwill

development.improvethesystemofland

1.Landuseoptimizationinthecontextofruralandurbanresourcesdispositionatcountyscale

ThisresearchmakesthetownshipofYubeidistrictinChongqmgasbasiczoningunitstoestablishCO-ordinatedevelopinglandusemainfunctionzoning.Firstofall,weconstructtheindexsystemaccordingtocertainprinciples,andtheindexdataderivefrom“ChongqingStatisticalYearbook”,“YubeiStatistical

wehandleYearbook'’and“ChongqingStatisticalYearbookofLandandResources”.Then,useandselecttheindicatorbyprincipalcomponentanalysis.FinallyweclusteranalysisforlandusemainfunctionzoninginYubeidistrict.

(1)TheprincipalcomponentanalysisCanscientificallyandreasonablyrefinezoningindex.Thisstudyselected15primaryindicatorsfromeconomicdevelopment,socialprogressandenvironmental

analysis,So

aProtectionwhichistodealwithdimension reductiontreatmentbyprincipalcomponentWeCangetlesscomprehensiveindextoreplacetheoriginalvariableindicatorwhichisfewmore.

Therefore,wechose9indicatorssuchas‘'theFinancialrevenue”.‘'theperunitareayieldofgrain’’and“forest

countyarea'’toconstitutethecomprehensiveevaluationsystemofmainfunctionzoningatscale.Theseindicatorsnotonlyreflecttherepresentativenessandaccuracyofthelanduseoriginalinformation,butalsoeffectivelyreducetheworkloadandimprovethezoningprecision.

wellasthe(2)ThecomparisonofresultsfromK—meansclusterandhierarchicalclusteras

verificationofSinglefactorANOVAanalysis

moreanddiscriminantanalysismakethezoningresultsuseaccurate.Weconstitute19x19fuzzysimilarmatrixbased9selectedindicatorstoK.meansclusterandhierarchicalclusterbydifferentcriterion.WefoundthezoningresultsfromeachmethodweresimilarexcepttheMuertown.Intheone-tailedANOVAanalysistableonlytheindex“ruralperV

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