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Auteur Daniel A. Griffith |
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Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
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Titre : Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets Type de document : Article/Communication Auteurs : Daniel A. Griffith, Auteur ; Yongwan Chun, Auteur Année de publication : 2019 Article en page(s) : pp 1703 - 1717 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approximation
[Termes IGN] autocorrélation spatiale
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-TM
[Termes IGN] régression linéaire
[Termes IGN] segmentation d'image
[Termes IGN] tessellation
[Termes IGN] vecteur propreMots-clés libres : Moran eigenvector spatial filtering Résumé : (auteur) Moran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are widely used. The utility of this MESF methodology is even more impressive for the non-Gaussian models because its flexible structure enables it to be easily applied to generalized linear models, which include Poisson, binomial, and negative binomial regression. However, the implementation of MESF can be computationally challenging, especially when the number of geographic units, n, is large, or massive, such as with a remotely sensed image. This intensive computation aspect has been a drawback to the use of MESF, particularly for analyzing a remotely sensed image, which can easily contain millions of pixels. Motivated by Curry, this paper proposes an approximation approach to constructing eigenvector spatial filters (ESFs) for a large spatial tessellation. This approximation is based on a divide-and-conquer approach. That is, it constructs ESFs separately for each sub-region, and then combines the resulting ESFs across an entire remotely sensed image. This paper, employing selected specimen remotely sensed images, demonstrates that the proposed technique provides a computationally efficient and successful approach to implement MESF for large or massive spatial tessellations. Numéro de notice : A2019-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1080/13658816.2019.1593421 Date de publication en ligne : 02/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1593421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93479
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1703 - 1717[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation : a simulation study / Sang-Il Lee in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)
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Titre : Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation : a simulation study Type de document : Article/Communication Auteurs : Sang-Il Lee, Auteur ; Monghyeon Lee, Auteur ; Yongwan Chun, Auteur ; Daniel A. Griffith, Auteur Année de publication : 2019 Article en page(s) : pp 1135 - 1154 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] autocorrélation spatiale
[Termes IGN] incertitude géométrique
[Termes IGN] modèle de simulation
[Termes IGN] problème d'unité zonale modifiableRésumé : (Auteur) The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects. Numéro de notice : A2019-444 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542699 Date de publication en ligne : 13/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542699 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92779
in International journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1135 - 1154[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019051 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019052 RAB Revue Centre de documentation En réserve L003 Disponible Detecting negative spatial autocorrelation in georeferenced random variables / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 24 n°3-4 (march 2010)
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Titre : Detecting negative spatial autocorrelation in georeferenced random variables Type de document : Article/Communication Auteurs : Daniel A. Griffith, Auteur ; Guiseppe Arbia, Auteur Année de publication : 2010 Article en page(s) : pp 417 - 437 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] agrégation spatiale
[Termes IGN] autocorrélation spatiale
[Termes IGN] distribution spatiale
[Termes IGN] inférence statistique
[Termes IGN] régression
[Termes IGN] variable aléatoire
[Termes IGN] variable régionalisée
[Termes IGN] vecteur propreRésumé : (Auteur) Negative spatial autocorrelation refers to a geographic distribution of values, or a map pattern, in which the neighbors of locations with large values have small values, the neighbors of locations with intermediate values have intermediate values, and the neighbors of locations with small values have large values. Little is known about negative spatial autocorrelation and its consequences in statistical inference in general, and regression-based inference in particular, with spatial researchers to date concentrating mostly on understanding the much more frequently encountered case of positive spatial autocorrelation. What are the spatial contexts within which negative spatial autocorrelation should be readily found? What are its inferential consequences for regression models? This paper presents selected empirical examples of negative spatial autocorrelation, adding to the slowly growing literature about this phenomenon. Copyright Taylor & Francis Numéro de notice : A2010-143 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810902832591 En ligne : http://dx.doi.org/10.1080/13658810902832591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30338
in International journal of geographical information science IJGIS > vol 24 n°3-4 (march 2010) . - pp 417 - 437[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2010022 RAB Revue Centre de documentation En réserve L003 Disponible 079-2010021 RAB Revue Centre de documentation En réserve L003 Disponible Error propagation modeling in raster GIS: adding and ratioing operations / Guiseppe Arbia in Cartography and Geographic Information Science, vol 26 n° 4 (October 1999)
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Titre : Error propagation modeling in raster GIS: adding and ratioing operations Type de document : Article/Communication Auteurs : Guiseppe Arbia, Auteur ; Daniel A. Griffith, Auteur ; Robert Haining, Auteur Année de publication : 1999 Article en page(s) : pp 297 - 315 Note générale : Bibliographie 1 page Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] corrélation
[Termes IGN] données localisées
[Termes IGN] données maillées
[Termes IGN] erreur d'attribut
[Termes IGN] méthode des moindres carrés
[Termes IGN] modélisation
[Termes IGN] probabilités
[Termes IGN] propagation d'erreur
[Termes IGN] système d'information géographiqueNuméro de notice : A1999-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1559/152304099782294159 En ligne : https://doi.org/10.1559/152304099782294159 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26312
in Cartography and Geographic Information Science > vol 26 n° 4 (October 1999) . - pp 297 - 315[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-99041 RAB Revue Centre de documentation En réserve L003 Disponible
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 mathématiqueNote 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 ResearchNuméro 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 Permalink