Basic Quantitative Research Methods for Urban Planners


OverviewQuantitative Methods in PlanningWhat’s Different?Definitions and Concepts You Should KnowData and MeasurementsConceptual FrameworkStatisticsStructure of the BookStructureDatasetsUZA DatasetHousehold DatasetComputer Software Used in This BookWorks CitedTechnical WritingOverviewPurposeTechnical WritingPublic ScholarshipTechnical Versus Non-Technical WritingPlanning and JAPAResearch You Can UsePreliminariesAudienceScopeObjectivesLearn Everything PracticalExampleMechanicsWordsSentencesParagraphsCohesion Within and Between ParagraphsTone and VoiceOrganizationRewriting, Editing, and PolishingLiterature ReviewsStyleScopeAccuracyAnalysisConnection to PlanningPlanning ExamplesJPER WinnerJAPA WinnerConclusionWorks CitedTypes of ResearchOverviewHistoryGeneral ConceptsData TypeDeductive Versus Inductive LogicTimeframeResearch DesignTriangulationQualitative MethodsQuantitative MethodsMixed MethodsWhich Method to Use?ConclusionWorks CitedPlanning Data and AnalysisOverviewPlanning Data Types and Structures/FormatsSampling and BiasTabular DataQualitative DataStructured and Unstructured DataSpatial DataData Processing and ManagementPlanning Data SourcesDemographic DataEconomic DataHousing DataTransportation DataPublic Health DataEnvironmental DataThe Emergence of Big DataChallenges in Using Big DataOpen DataMachine Learning and Data MiningUse of Machine Learning in Urban PlanningTypes of Machine LearningPlanning ExampleSummaryNoteWorks CitedConceptual FrameworksOverviewPurposeMechanicsStep by StepConclusionWorks CitedValidity and ReliabilityOverviewReliabilityInter-Rater ReliabilityEquivalency ReliabilityInternal ConsistencyValidityFace ValidityConstruct ValidityInternal ValidityExternal ValidityPlanning ExamplesSecurity of Public SpacesUrban Sprawl and Air QualityConclusionWorks CitedDescriptive Statistics and Visualizing DataOverviewPurposeHistoryMechanicsFrequency DistributionCentral TendencyDispersionData VisualizationData MatrixFrequency TableCross TabulationInformative GraphsStep by StepFrequency TableCentral Tendency and DispersionCross TabulationInformative GraphsIn RPlanning ExamplesResidential Water UseUrban ParksConclusionWorks CitedChi-SquareOverviewPurposeHistoryMechanicsTypes of DataAssumptionsHypothesis in Chi-Square TestCalculate the Test StatisticDetermine the Degrees of Freedom and a Critical ValueStrength Test for the Chi-SquareStep by StepIn RPlanning ExamplesSustainable Development PoliciesAttitudes Toward Growth ManagementConclusionWorks CitedCorrelationOverviewPurposeHistoryMechanicsPearson Correlation CoefficientSpearman Correlation CoefficientIntraclass Correlation CoefficientStep by StepPearson Correlation CoefficientPartial Correlation CoefficientSpearman Correlation CoefficientIntraclass Correlation CoefficientIn RPlanning ExamplesEnvironment EquityUrban Design QualitiesConclusionWorks CitedDifference of Means Tests (T-Tests)OverviewPurposeHistoryMechanicsIndependent Samples T-TestStandard ErrorDetermining SignificanceDependent Samples T-TestStep by StepCheck AssumptionsNull HypothesisIndependent Samples T-TestResultsIn RPlanning ExamplesTransit and VMTShrinking CitiesConclusionsWorks CitedAnalysis of Variance (ANOVA)OverviewPurposeHistoryMechanicsAssumptionsInterpreting ResultsStep by StepCheck AssumptionsNull HypothesisOne-Way ANOVA TestResultsPost-Hoc TestIn RPlanning ExamplesUrban Heat IslandsUrban Form and Travel BehaviorConclusionWorks CitedLinear RegressionOverviewPurposeExplanationPredictionHypothesis TestingWise Use of RegressionHistoryMechanicsSimple Linear RegressionInterpreting Simple Linear Regression ResultsR-squared: Goodness of FitF-statistict-Statistic(s)Multiple Linear RegressionStep by StepSimple RegressionMultiple RegressionAssumption Behind the ModelData-Related ProblemsNonlinearity and OutliersLog-transformationMulticollinearityResidual Error-Related ProblemsIn RPlanning ExamplesRole of the Arts in Neighborhood ChangeResidential Yard Management and CrimeConclusionCausalityConceptual FrameworksWhen Other Models Are More AppropriateWorks CitedLogistic RegressionOverviewPurposeHistoryMechanicsTerminology and TransformationsGoodness of FitStep by StepMultinomial Logistic RegressionIn RPlanning ExamplesSmart Growth Policies and Automobile DependenceMobility Disability and the Urban Built EnvironmentConclusionWorks CitedQuasi-Experimental ResearchOverviewPurposeHistoryMechanicsTwo-Group Pretest-Posttest DesignRegression to the MeanPropensity Score MatchingRegression Discontinuity (RD)Planning ExamplesLight Rail Transit ExpansionAssessing BidsConclusionWorks CitedContributors
 
Next >