城市规划定量分析方法

第二讲城市规划定量分析方法：空间分析和建模主讲人沈青博士美国马里兰大学教授城市研究与规划第一部分.空间分析PARTI.SPATIALANALYSIS•Usingasetofmathematical,statistical,andGIS(geographicinformationsystems)toolsforstudyingspatialdistributions,relationships,andinteractions•Usefulnessforurbanplanning–Understandingthecity–Supportinglocationchoiceforsocialservices–Evaluatingeffectsoflanduseplans,andinfrastructureandtransportationinvestments常用方法FREQUENTLYAPPLIEDMETHODS•Accessibilitymeasures•Serviceareadelineations•Spatialassociation/segregationmeasures例子EXAMPLEOFAPPLICATION•Astudyoflow-incomepeople’sgeo-spatialpositionintheurbanlabormarket(Shen1998,2001)•Background–Alternativepolicystrategiesforhelpinglow-incomeaccessemploymentopportunities–Debateon“spatialmismatch”–Welfarereform问题QUESTIONS•WhereareemploymentopportunitieslocatedinU.S.metropolitanareas?•Howdoesemploymentaccessibilityvary?–Amongresidentiallocations–Amongtravelmodes方法METHODOLOGY•Analyticaltools–Accessibilitymeasures–3-DGISvisualization•Casestudyanddata–Bostonmetropolitanarea–Dataonlaborforce,employment,transportation,etc.BOSTONMETROPOLITANAREA波士顿都市区分析结果RESULTSTheCompositionofJobOpeningsPositionsfromGrowthVacanciesfromTurnoverTotalJobOpeningsTotal1,64031,28032,910Low-Skilled29010,40010,690•Employmentgrowthaccountsforonly5%oftotaljobopenings,andonly3%ofjobopeningsforthelowskilled.•Turnoveristhedominantsourceofjobopenings.分析结果(续)RESULTS(Continued)SpatialDistributionsofJobOpeningsandSeekersWholeMetroWithinBostonOutsideBostonPositionsCreatedbyGrowth29020(6.9%)270(93.1%)VacanciesCreatedbyTurnover10,4002,120(20.4%)8,280(79.6%)JobOpeningsforLow-Skilled10,6902,140(20.0%)8,550(80.0%)Low-SkilledSeekers50,48010,650(21.1%)39,830(78.9%)就业量变化导致的低收入工作机会分布图劳动力流动导致的低收入工作机会分布图低收入工作机会总量分布图低收入失业者总量分布图低收入工作机会与失业者比率图NMap6.AutomobileCommuters'AccessbilitytoJobOpeningsAccessibility0.00-0.050.05-0.100.10-0.150.15-0.200.20-0.250.25andoverBOSTON私人汽车拥有者的就业可达度NMap7.TransitCommuters'AccessbilitytoJobOpeningsAccessibility0.00-0.050.05-0.100.10-0.150.15-0.200.20-0.250.25andoverBOSTON公共交通依赖者的就业可达度分析结果(续)RESULTS(Continued)SpatialVariationinJobAccessibility•Foragivenlocation,accessibilityforautodriversisusuallymuchhigherthanfortransitriders•Forseekerswhocancommutebycar,mostlocationsgivethemgoodaccesstojobopenings•Forseekerswhodependonpublictransportation,onlyveryfewlocationsgivethemgoodaccesstojobopenings•Foragiventravelmode,livinginthecentralcity—includingitslow-incomeneighborhoods—stillhassomeadvantageinaccesstojobopenings第二部分城市建摸PARTII.URBANMODELING•Developingmathematicalequationsandanalyticalproceduresforpredictingfuturepatternsofurbangrowthunderdifferentassumptions•Usefulnessforurbanplanning–Understandingquantityandlocationoffuturelandconsumption–Assessingtransportationandenvironmentalimpactsofurbangrowth–Simulatingeffectsoflanduseregulations,andinfrastructureandtransportationinvestments–Planningforefficientandequitableurbangrowth通用方法COMMONAPPROACHES•Spatialinteractionmodels,withtransportationanalysiszones-basedgrowthallocationmechanisms•Urbangrowthmodels,withrichGISdataandrulesorprobabilityfunctionsforgrowthallocation.•Spatialeconomicmodels,withutilitymaximizationproceduresforgrowthallocation例子EXAMPLEOFAPPLICATION•CaliforniaUrbanFuturesModels(Landis1994,1995,LandisandZhang1998,Landis2000)•Background–RapidpopulationgrowthandurbanizationinCalifornia–Concernsoverenvironmentalandecologicalimpactsofgrowth–Infrastructureandserviceinvestments–Inter-jurisdictionalissuesandfiscalimpacts–Alternativepolicyinitiativesforregulatingfuturegrowth目标OBJECTIVES•Developaspatially-explicitmodelforprojectingurbangrowth•Framealternativedevelopmentscenarios•Usemodeltoexplorealternativescenarios模型输入MODELINPUTS•MapLayers(unit-of-analysis:1hectaregrid-cell)–Initialemploymentandpopulationdistributions–Currentlanduse–Environmentalcharacteristics(primeagriculturallands,wetlands,publiclands,floodzones,slope)–Policycharacteristics(administrativeboundaries,zonings,developmentfees,etc.)–Infrastructure(freeways,roads,transitservices,etc.)•ActivityProjections–Regionalpopulationandemploymentprojections•Scenarios–Baseline,CompactGrowth,Sprawl模型参数测定MODELCALIBRATIONProb[gridcellchanginglandusebetweentimet-1andt]=ƒ{Proximitytohighways(+)Proximitytoinitialurbandevelopment(+)Proximitytocitycenter(+)Siteslope(-)Infloodzone(-)Inwetlands(-)Primeoruniquefarmland(-)Percentofurbanizedneighboringcells(+)…}Testedusingbi-nomial/multi-nomiallogisticregressionmodel{changed=1,nochangeinundevelopedstatus=0}2020SouthernCaliforniaUrbanFootprintScenarios:GreenfieldSharesandAllocationDensities1.新开发区人口增长所占比例,2.人口密度2295%2775%2290%1,153,000SanDiego1980%2460%1960%254,000Ventura798%979%795%1,130,000SanBernardino1598%1979%1595%1,350,000Riverside2575%3150%2550%727,000Orange30p/ha67%41p/ha33%30p/ha33%2,051,000LosAngelesSprawlAllocationDensitySprawlGreenfieldShareCompactGrowthAllocationDensityCompactGrowthGreenfieldShareBaselineAllocationDensityBaselineGreenfieldShareProjectedPopulationGrowth,1997-2020County三种方案下的人口增长分布SouthernCalifornia2020Footprint:BaselineScenario:ProjectedPrimeFarmlandConversion优质农业