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A casebook for spatial statistical data analysis / Daniel A. Griffith (1999)

Public Titre : A casebook for spatial statistical data analysis : A compilation of analyses of different thematic data sets Type de document : Monographie Auteurs : Daniel A. Griffith, Auteur ; Larry J. Layne, Auteur Editeur : Oxford, Londres, ... : Oxford University Press Année de publication : 1999 Collection : Spatial informtion systems Importance : 506 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-0-19-510958-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agriculture [Termes IGN] analyse de données [Termes IGN] analyse spatiale [Termes IGN] blé (céréale) [Termes IGN] données localisées numériques [Termes IGN] épidémie [Termes IGN] jeu de données localisées [Termes IGN] krigeage [Termes IGN] pollution [Termes IGN] ressources naturelles [Termes IGN] socio économie [Termes IGN] statistique mathématique Note de contenu : PART 1 : THEORETICAL BACKGROUND 1. Introduction 1. 1.Parallels between Spatial Autoregression and Geostatistics 1.2. The Many Faces of Spatial Autocorrelation 1.3. The Moran Coefficient Scatterplot Tool 1.4. Multivariate Spatial Association 1.5. Heterogeneity and Locational Information 1.6. The Semivariogram Plot Tool 1.7. Computer Code for Implementing Spatial Statistical Analyses Appendix 1-A : SAS Code for Computing MC Using Standard Regression Techniques. Appendix 1-B : Directions for Using ArcInfo to Construct a Thiessen Polygon Surface Partitioning for a Set of Georeferenced Points. Appendix 1-C : SAS Macros Used for Converting among Degrees, Decimal Degrees and Radians 2. Important Modeling Assumptions 2.1. The Relative Importance of the Principal Assumptions 2.2. Variable Transformations 2.3. Transforming in Search of Normality 2.4. Transforming in Search of Constant Variance 2.5. Linearity: Exploitation of Linear Relationships by Linear Statistical Models 2.6. An Absence of Independence: The Presence of Spatial Autocorrelation in Georeferenced Data 2.7. Exploring Residuals in Spatial Analysis 2.8. Other Statistical Frequency Distribution Assumptions 3. Popular Spatial Autoregressive and Geostatistical Models 3.1. Spatial Autoregressive Models 3.2. Geostatistical Models 3.3. Articulating Relationships Between Spatial Autoregressive and Geostatistical Models 3.4. Computer Code for Spatial Autoregressive and Semivariogram Modeling Appendix 3-A : SAS Code for Estimating Equations (3.7ac) - the CAR Model Appendix 3-B : SPSS Code for Estimating Equations (3.7ac) - the CAR Model Appendix 3-C : SAS Code for Estimating Equations (3.8) & (3.9) - the SAR & AR Models Appendix 3-D : SPSS Code for Estimating Equations (3.8) & (3.9) - the SAR & AR Models Appendix 3-E : SAS Code for Selected Semivariogram Models Appendix 3-F : SPSS Code for Selected Semivariogram Models PART 2 : GEOREFERENCED DATA SET CASE STUDIES 4. Analysis of Georeferenced Socioeconomic Attribute Variables 4.1. The Cliff-Ord Eire Population Data 4.2. Urban Population Density 4.3. Residential Insurance Coverage in Chicago 4.4. Urban Crime in Columbus, Ohio 4.5. Geographic Distribution of Minorities across Syracuse, New York 4.6. Concluding Comments: Spatial Autocorrelation and Socioeconomic Attribute Variables Appendix 4-A : Centroids Derived from a Digitized Version of Cliff and Ord's Map of Eire (1981, 207) Using ArcInfo 5. Analysis of Georeferenced Natural Resources Attribute Variables 5.1. Kansas Oil Wells Data 5.2. Natural Resources Inventory Data 5.3. Island Biogeography: Plant Species Data 5.4. Weather Station Rainfall Data 5.5. Drainage Basin Runoff Data 5.6. Digital Elevation Data 5.7. Preclassified Remotely Sensed Image Reflectance Data 5.8. Concluding Comments: Spatial Autocorrelation and Natural Resources Attribute Variables 6. Analysis of Georeferenced Agricultural Yield Variables 6.1. The Mercer-Hall Straw Yield Data 6.2. The Wiebe Wheat Yield Data 6.3. The Broadbalk Wheat and Straw Yield Data 6.4. Sugar Cane Production in Puerto Rico 6.5. Milk Production in Puerto Rico 6.6. Concluding Comments : Spatial Autocorrelation and Agricultural Yield Variables 7. Analysis of Georeferenced Pollution Variables 7.1. Southwestern Pennsylvania Coal Ash 7.2. EMAP Indicators of Ecological Condition 7.3. Great Smoky Mountains Water pH 7.4. Chemical Elements in Northwest Texas Groundwater 7.5. Hazardous Waste Contamination of Soil: Dioxin 7.6. Concluding Comments: Spatial Autocorrelation and Pollution Variables 8. Analysis of Georeferenced Epidemiological Variables 8.1. Glasgow Standardized Mortality Rates 8.2 Pediatric Lead Poisoning in Syracuse, New York 8.3. Fox Rabies in Germany 8.4. Concluding Comments : Spatial Autocorrelation and Epidemiological Variables PART 3 : VISUALIZING WHAT IS NOT OBSERVED 9. Exploding Georeferenced Data When Maps Have Holes or Gaps : Estimating Missing Data Values and Kriging 9.1. An Introduction to EM Estimation 9.2. Estimating Missing Values: Two Simplified Georeferenced Data Illustrations 9.3. Estimating a Conspicuous Missing Data Value for the Coal-Ash Data Set 9.4. Estimating Conspicuous Missing Data Values for an Agricultural Experiment 9.5. Estimating Missing Median Family Income Data for Ottawa-Hull 9.6. Generalizing a Map Surface with Kriging 9.7. A Cross-Validation Example 9.8. Concluding Comments : Exploding Georeferenced Data 10. Concluding Comments 10.1. More about the Nature of Georeferenced Data 10.2. Reflections on Spatial Data Model Specifications 10.3. Implications regarding Relations between Spatial Autoregressive and Geostatistical Models 10.4. Reflections on Kriging 10.5. Spatial Statistics and GIS 10.6. Now Is the Time for All Good Spatial Scientists to ... 10.7. Some Questions Yet Unanswered : Future Research Numéro de notice : 13033 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Monographie Accessibilité hors numérique : Non accessible via le SUDOC Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=54855