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50 resultados encontrados para: TEMA: Análisis espacial (Estadística)
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1.
Artículo - Ponencia
ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean
Arellano Verdejo, Javier (autor) ; Lazcano Hernández, Hugo Enrique (autor) ; Cabanillas Terán, Nancy (autora) ;
Disponible en línea
Contenido en: PeerJ Volumen 7, número e6842 (2019), p. 1-19 ISSN: 2167-8359
PDF
Resumen en: Inglés |
Resumen en inglés

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep neural network (named ERISNet) was designed specifically to detect these macroalgae along the coastline through remote sensing support. A new dataset which includes pixel values with and without Sargassum was built to train and test ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achievesa 90% of probability in its classification skills. ERISNet provides a novel insight to detect accurately algal blooms arrivals.


2.
Capítulo de libro
*Solicítelo con su bibliotecario/a
Herramientas de análisis espacial para estudios de biodiversidad
Zuria Jordan, Iriana Leticia (autora) ; Martínez Morales, Miguel Ángel (autor) (-2020) ;
Disponible en línea
Contenido en: La biodiversidad en un mundo cambiante: fundamentos teóricos y metodológicos para su estudio Cuidad de México, México : Universidad Autónoma del Estado de Hidalgo : Libermex, 2019 páginas 21-38
Nota: Solicítelo con su bibliotecario/a
Resumen en español

El medir y modelar la biodiversidad de una manera espacialmente explícita es una necesidad imperante debido a las altas tasas de extinción de especies que son consecuencia de la degradación y transformación de los ecosistemas. Hasta hace poco, la disponibilidad de mapas de alta resolución para mostrar los patrones de biodiversidad era limitada. Sin embargo, ahora existen los desarrollos conceptuales, herramientas, métodos y numerosas bases de datos accesibles para estudiar y entender de manera más eficiente los patrones de biodiversidad a diversas escalas espacio-temporales. En este capítulo se presentan conceptos básicos y se mencionan algunas herramientas disponibles para analizar la biodiversidad de manera espacialmente explícita mediante la creación de mapas y modelos; asimismo, se presentan algunos ejemplos de la manera en que se han utilizado estos mapas para analizar espacialmente la biodiversidad.


3.
- Artículo con arbitraje
Conflicto armado y pobreza en Antioquia Colombia
Maya Taborda, María (autora) ; Muñetón Santa, Guberney (autor) ; Horbath Corredor, Jorge Enrique (autor) ;
Disponible en línea
Contenido en: Apuntes del CENES Vol. 37, no. 65 (enero-junio 2018), p. 213-246 ISSN: 2256-5779
PDF PDF
Resumen en: Español | Inglés | Portugués |
Resumen en español

El artículo explora la relación entre el conflicto armado y la pobreza en el departamento de Antioquia, Colombia. Se usa análisis espacial con datos de diversas bases oficiales y otros provenientes de la prensa. Los resultados evidencian las relaciones espaciales entre el conflicto armado y la pobreza con diversas dinámicas territoriales. Se muestran resultados diferenciados para zonas de control armado y zonas de confrontación armada. Además, se destacan dos casos de la relación conflicto armado y pobreza en el departamento de Antioquia (Oriente y Urabá), con dinámicas y resultados diferentes en cuanto al conflicto armado y a la pobreza. En las consideraciones finales se entregan rutas de análisis para comprender mejor el fenómeno de la pobreza en lugares afectados por el conflicto armado.

Resumen en inglés

The aim of this paper is to explore the relationship between armed conflict and poverty in the department of Antioquia, Colombia. Drawing on various official and press databases, we present a spatial analysis made up of quantitative and qualitative data. We highlight the spatial relationships between armed conflict and poverty, showing how these relationships are expressed unevenly in the territory. The paper focuses on two spatial units of analysis, Urabá and Oriente Antioqueño, which have distinct dynamics and results in terms of armed conflict and poverty. To conclude, we offer some avenues of research to better understand the phenomenon of poverty in places affected by armed conflict.

Resumen en portugués

O artigo explora a relação entre o conflito armado e a pobreza no estado de Antioquia, Colômbia. A análise espacial é realizada através de mapas, jornais e outros materiais provenientes de diversas fontes oficiais. Os resultados apresentam as relações espaciais entre o conflito armado e a pobreza, evidenciando as diversas dinâmicas territoriais. É importante destacar que foram identificados resultados diferenciados para as zonas de controle e zonas de confronto armado. Além disso, nos casos da relação de conflito armado e pobreza de Antioquia, destacam-se as regiões do Oriente e Urabá. Essas regiões possuem dinâmicas diferenciadas no que diz respeito ao conflito armado e à pobreza. Nas considerações finais foi elaborado um roteiro analítico para compreender melhor o fenômeno da pobreza em lugares afetados pelo conflito armado.


4.
Artículo
*En hemeroteca, SIBE-San Cristóbal
Propuesta metodológica interdisciplinaria y multiescalar para el estudio de la vulnerabilidad del paisaje
Andablo Reyes, Araceli (coaut.) ; Castillo Santiago, Miguel Ángel (coaut.) ; Hernández Moreno, María del Carmen (coaut.) ; Francois Mas, Jean (coaut.) ; Pérez Vega, Azucena (coaut.) ; Flamenco Sandoval, Alejandro Fidel (coaut.) ;
Contenido en: Revista Internacional de Estadística y Geografía Vol. 9, no. 1 (enero-abril 2018), p. 82-99 ISSN: 2007-2961
Nota: En hemeroteca, SIBE-San Cristóbal
PDF

5.
Libro
Spatial point patterns: methodology and applications with R / Adrian Baddeley, Ege Rubak, Rolf Turner
Baddeley, Adrian ; Rubak, Ege (coaut.) ; Turner, Rolf (coaut.) ;
Boca Raton, FL : CRC Press :: Taylor & Francis Group , 2016
Clasificación: 519.5 / B33
Bibliotecas: San Cristóbal
Cerrar
SIBE San Cristóbal
ECO010018524 (Disponible)
Disponibles para prestamo: 1
Resumen en: Inglés |
Resumen en inglés

Spatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on their 25 years of software development experiences, methodological research, and broad scientific collaborations to deliver a book that clearly and succinctly explains concepts and addresses real scientific questions. Practical Advice on Data Analysis and Guidance on the Validity and Applicability of Methods. The first part of the book gives an introduction to R software, advice about collecting data, information about handling and manipulating data, and an accessible introduction to the basic concepts of point processes. The second part presents tools for exploratory data analysis, including non-parametric estimation of intensity, correlation, and spacing properties. The third part discusses model-fitting and statistical inference for point patterns. The final part describes point patterns with additional "structure," such as complicated marks, space-time observations, three- and higher-dimensional spaces, replicated observations, and point patterns constrained to a network of lines. Easily Analyze Your Own Data. Throughout the book, the authors use their spatstat package, which is free, open-source code written in the R language. This package provides a wide range of capabilities for spatial point pattern data, from basic data handling to advanced analytic tools. The book focuses on practical needs from the user’s perspective, offering answers to the most frequently asked questions in each chapter.


6.
Libro
Geografía aplicada en Iberoamérica: avances, retos y perspectivas / Carlos Garrocho Rangel, Gustavo D. Buzai, coordinadores
Garrocho Rangel, Carlos (coord.) ; Buzai, Gustavo D. (coord.) ;
Zinacantepec, Estado de México, México : El Colegio Mexiquense , 2015
Clasificación: 304.2098 / G4
Bibliotecas: San Cristóbal
Cerrar
SIBE San Cristóbal
ECO010018726 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Español |
Resumen en español

El libro incluye 23 autores de diversos países iberoamericanos, además de una introducción y la reflexión final. Se divide en tres grandes apartados: el primero: El radar geográfico: aproximaciones de amplio espectro examina la Geografía Social, Económica y Ambiental; el segundo: Temas transversales estudia los ejes que cruzan las ciencias sociales especialmente integradas: terminología, análisis espacial, tecnologías vinculadas a la información geográfica y el tercero de ellos: Investigación aplicada, donde se presentan ejemplos de investigación de punta en la región iberoamericana. Los autores destacan que desde algunos años, los trabajos en ciencias sociales que le otorgan un papel estratégico a la dimensión espacial están registrando un enorme interés en el mundo y que Iberoamérica no escapa a esta tendencia. Esto se puede observar en la abundante producción científica de la región, derivada de este enfoque, en sus contribuciones claves al diseño de políticas públicas y privadas o en la consolidación de numerosas revistas especializadas (como EURE: Chile Economía, Sociedad y Territorio: México). Aseguran que esta fascinación creciente por la investigación socioespacial (que sitúa los procesos sociales en territorio concreto y explora la relación mutua entre lo social y lo espacial a diversas escalas geográficas y temporales) ha generado un boom tanto en el ámbito académico como en las esferas pública, privada y social. Afirman que en el mundo del siglo XXI las aplicaciones geográficas, inclusive, en teléfonos celulares, son de uso cotidiano para gran parte de la sociedad (GPS o Google Maps, por ejemplo) con tendencia progresiva y aceleración a velocidades alucinantes.

Finalmente invitan a un público amplio: estudiantes, investigadores, funcionarios, empresarios, medios de comunicación y a la sociedad en general, interesados en conocer el enorme potencial del análisis socioespacial para resolver problemas prácticos y ampliar el conocimiento sobre nuestras realidades y a comprender que la Geografía Aplicada de la segunda década del siglo XXI vive uno de los momentos más prometedores de su historia y anticipa un desarrollo espectacular de la disciplina en las próximas décadas, como se muestra en este libro.

Índice

Los puntos cardinales de la Geografía aplicada en el siglo XXI
Parte I: El radar geográfico: aproximaciones de amplio espectro
Estudios urbano – regionales en América Latina: medio siglo de enfoques teóricos
Avances, retos y perspectivas de la Geografía económica en Cataluña
Geografía ambiental: disciplina híbrida fértil
Parte II: Temas transversales
Terminología en Geografía humana y aplicada
Geografía aplicada mediante el análisis espacial cuantitativo con Sistemas de Información Geográfica
Las tecnologías de la información geográfica: desarrollo, estado actual y perspectivas del futuro
Geografía, Ordenamiento Territorial y Sistemas de Información Geográfica: articulaciones conceptuales para una Geografía aplicada
Parte III: Investigación aplicada
Geografía social
Calidad de vida desde una perspectiva geográfica en Iberoamérica: el caso de Argentina
Recomposiciones socio-territoriales en los espacios perimetropolitanos: ¿qué significados para las regiones urbanas en América Latina?: el caso de Santiago de Chile
Brecha digital y marginación socioterritorial: el caso de México
Geografía económica
Las relaciones entre lo rural y lo urbano: principio de la cooperación y políticas públicas
Servicios y equipamiento para la población. Análisis aplicados a la planificación y la gestión territorial
Hacia una geografía de las actividades económicas en la Ciudad de México: métodos, conceptos, cultura y subjetividad
Estructura espacial de la población e infraestructuras de trasporte en Barcelona: el caso catalán
Geografía ambiental
El enfoque integrador del paisaje en los estudios territoriales: experiencias prácticas
Análisis de la interacción del sistema hídrico con el sistema territorial: el caso de Uruguay
A modo de cierre: línea de reflexión para el futuro de la Geografía aplicada en Iberoamérica


7.
Libro
An introduction to R for spatial analysis & mapping / Chris Brunsdon and Lex Comber
Brunsdon, Chris ; Comber, Lex (coaut.) ;
Los Angeles, California : SAGE Publications Ltd , c2015
Clasificación: 519.5 / B7
Bibliotecas: Chetumal
Cerrar
SIBE Chetumal
ECO030008672 (Prestado)
Disponibles para prestamo: 0
Índice | Resumen en: Inglés |
Resumen en inglés

In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses. R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring R. Scripts and coding to exemplify specific functionality. Advice for developing greater understanding - through functions such as locator, View, and alternative coding to achieve the same ends. Self-contained exercises for students to work through. Embedded code within the descriptive text. This is a definitive 'how to' that takes students - of any discipline -from coding to actual applications and uses of R.

Índice

About the Authors
Further Resources
Preface
1 Introduction
1.1 Objectives of this book
1.2 Spatial Data Analysis in R
1.3 Chapters and Learning Arcs
1.4 The R Project for Statistical Computing
1.5 Obtaining and Running the R software
1.6 The R interface
1.7 Other resources and accompanying website
2 Data and Plots
2.1 Introduction
2.2 The basic ingredients of R: variables and assignment
2.3 Data types and Data classes
2.4 Plots
2.5 Reading, writing, loading and saving data
3 Handling Spatial Data in R
3.1 Overview
3.2 Introduction: GISTools
3.3 Mapping spatial objects
3.4 Mapping spatial data attributes
3.5 Simple descriptive statistical analyses
3.6 Self-Test Questions
4 Programming in R
4.1 Overview
4.2 Introduction
4.3 Building blocks for Programs
4.4 Writing Functions
4.5. Writing Functions for Spatial Data
5 Using R as a GIS
5.1 Introduction
5.2 Spatial Intersection or Clip Operations
5.3 Buffers
5.4 Merging spatial features
5.5 Point-in-polygon and Area calculations
5.6 Creating distance attributes
5.7 Combining spatial datasets and their attributes
5.8 Converting between Raster and Vector
5.9 Introduction to Raster Analysis
6: Point Pattern Analysis using R
6.1 Introduction
6.2 What is Special about Spatial?
6.3 Techniques for Point Patterns Using R
6.4 Further Uses of Kernal Density Estimation
6.5 Second Order Analysis of Point Patterns
6.6 Looking at Marked Point Patterns 6.7 Interpolation of Point Patterns With Continuous Attributes
6.8 The Kringing approach
6.9 Concluding Remarks
7 Spatial Attribute Analysis With R
7.1 Introduction
7.2The Pennsylvania Lung Cancer Data
7.3 A Visual Exploration of Autocorrelation
7.4 Moran's I: An Index of Autocorrelation
7.5 Spatial Autoregression
7.6 Calibrating Spatial Regression Models in R

8 Localised Spatial Analysis
8.1 Introduction
8.2 Setting Up The Data Used in This Chapter
8.3 Local Indicators of Spatial Association
8.4 Further Issues with the Above Analysis
8.5 The Normality Assumption and Local Moran's I
8.6 Getis and Ord's G-statistic
8.7 Geographically Weighted Approaches
9: R and Internet Data
9.1 Introduction
9.2 Direct Access to Data
9.3 Using RCurl
9.4 Working with APIs
9.5 Using Specific Packages
9.6 Web Scraping
10 Epilogue
Index


8.
Libro
Spatial and spatio-temporal Bayesian models with R-INLA / by Marta Blangiardo and Michela Cameletti
Blangiardo, Marta ; Cameletti, Michela (coaut.) ;
Chichester, West Sussex, United Kingdom : John Wiley and Sons , 2015
Clasificación: 519.542 / B5
Bibliotecas: Chetumal
Cerrar
SIBE Chetumal
ECO030008380 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

The Bayesian approach is particularly effective at modeling large datasets including spatial and temporal information due to its flexibility and ease with which it can formally include correlation and hierarchical structures in the data. However, classical simulation methods such as Markov Chain Monte Carlo can become computationally unfeasible; this book presents the Integrated Nested Laplace Approximations (INLA) approach as a computationally effective and extremely powerful alternative. Spatial and Spatio-temporal Bayesian Models with R-INLA introduces the basic paradigms of the Bayesian approach and describes the associated computational issues. Detailing the theory behind the INLA approach and the R-INLA package, it focuses on spatial and spatio-temporal modeling for area and point-referenced data. The combination of detailed theory and practical data analysis is beneficial for readers at any level. The coding of all the examples in R-INLA and the availability of all the datasets used throughout the book on the INLA website (www.r-inla.org) make an appealing feature for applied researchers wanting to approach or increase their knowledge and practice of the INLA method.

Índice

Preface
1 Introduction
1.1 Why spatial and spatio-temporal statistics?
1.2 Why do we use Bayesian methods for modeling spatial and spatio-temporal structures?
1.3 Why INLA?
1.4 Datasets
1.4.1 National Morbidity, Mortality, and Air Pollution Study
1.4.2 Average income in Swedish municipalities
1.4.3 Stroke in Sheffield
1.4.4 Ship accidents
1.4.5 CD4 in HIV patients
1.4.6 Lip cancer in Scotland
1.4.7 Suicides in London
1.4.8 Brain cancer in Navarra, Spain
1.4.9 Respiratory hospital admission in Turin province
1.4.10 Malaria in the Gambia
1.4.11 Swiss rainfall data
1.4.12 Lung cancer mortality in Ohio
1.4.13 Low birth weight births in Georgia
1.4.14 Air pollution in Piemonte
2 Introduction to R
2.1 The R language
2.2 R objects
2.3 Data and session management
2.4 Packages
2.5 Programming in R
2.6 Basic statistical analysis with R
3 Introduction to Bayesian methods
3.1 Bayesian philosophy
3.1.1 Thomas Bayes and Simon Pierre Laplace
3.1.2 Bruno de Finetti and colleagues
3.1.3 After the Second World War
3.1.4 The 1990s and beyond
3.2 Basic probability elements
3.2.1 What is an event?
3.2.2 Probability of events
3.2.3 Conditional probability
3.3 Bayes theorem
3.4 Prior and posterior distributions
3.4.1 Bayesian inference
3.5 Working with the posterior distribution
3.6 Choosing the prior distribution
3.6.1 Type of distribution
3.6.2 Conjugacy
3.6.3 Noninformative or informative prior
4 Bayesian computing
4.1 Monte Carlo integration
4.2 Monte Carlo method for Bayesian inference
4.3 Probability distributions and random number generation in R
4.4 Examples of Monte Carlo simulation
4.5 Markov chain Monte Carlo methods
4.5.1 Gibbs sampler
4.5.2 Metropolis–Hastings algorithm
4.5.3 MCMC implementation: software and output analysis
4.6 The integrated nested Laplace approximations algorithm
4.7 Laplace approximation.

4.7.1 INLA setting: the class of latent Gaussian models
4.7.2 Approximate Bayesian inference with INLA
4.8 The R-INLA package
4.9 How INLA works: step-by-step example
5 Bayesian regression and hierarchical models
5.1 Linear regression
5.1.1 Comparing the Bayesian to the classical regression model
5.1.2 Example: studying the relationship between temperature and PM10
5.2 Nonlinear regression: random walk
5.2.1 Example: studying the relationship between average household age and income in Sweden
5.3 Generalized linear models
5.4 Hierarchical models
5.4.1 Exchangeability
5.4.2 INLA as a hierarchical model
5.4.3 Hierarchical regression
5.4.4 Example: a hierarchical model for studying CD4 counts in AIDS patients
5.4.5 Example: a hierarchical model for studying lip cancer in Scotland
5.4.6 Example: studying stroke mortality in Sheffield (UK)
5.5 Prediction
5.6 Model checking and selection
5.6.1 Methods based on the predictive distribution
5.6.2 Methods based on the deviance
6 Spatial modeling
6.1 Areal data – GMRF
6.1.1 Disease mapping
6.1.2 BYM model: suicides in London
6.2 Ecological regression
6.3 Zero-inflated models
6.3.1 Zero-inflated Poisson model: brain cancer in Navarra
6.3.2 Zero-inflated binomial model: air pollution and respiratory hospital admissions
6.4 Geostatistical data
6.5 The stochastic partial differential equation approach
6.5.1 Nonstationary Gaussian field
6.6 SPDE within R-INLA
6.7 SPDE toy example with simulated data
6.7.1 Mesh construction
6.7.2 The observation or projector matrix
6.7.3 Model fitting
6.8 More advanced operations through the inla.stack function
6.8.1 Spatial prediction
6.9 Prior specification for the stationary case
6.9.1 Example with simulated data
6.10 SPDE for Gaussian response: Swiss rainfall data
6.11 SPDE with nonnormal outcome: malaria in the Gambia

6.12 Prior specification for the nonstationary case
6.12.1 Example with simulated data
7 Spatio-temporal models
7.1 Spatio-temporal disease mapping
7.1.1 Nonparametric dynamic trend
7.1.2 Space–time interactions
7.2 Spatio-temporal modeling particulate matter concentration
7.2.1 Change of support
8 Advanced modeling
8.1 Bivariate model for spatially misaligned data
8.1.1 Joint model with Gaussian distributions
8.1.2 Joint model with non-Gaussian distributions
8.2 Semicontinuous model to daily rainfall
8.3 Spatio-temporal dynamic models
8.3.1 Dynamic model with Besag proper specification
8.3.2 Dynamic model with generic1 specification
8.4 Space–time model lowering the time resolution
8.4.1 Spatio-temporal model
Index


9.
Libro
Spatial statistics & geostatistics: theory and applications for geographic information science & technology / Yongwan Chun and Daniel A. Griffith
Chun, Yongwan ; Griffith, Daniel A. (coaut.) ;
Los Angeles, California : SAGE Publications , 2013
Clasificación: 551.0727 / C4
Bibliotecas: San Cristóbal
Cerrar
SIBE San Cristóbal
ECO010007632 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

"Spatial Statistics and Geostatistics is the definitive text on spatial statistics. Its focus is on spatial statistics as a distinct form of statistical analysis and it includes computer components for ArcGIS, R, SAS, and WinBUGS. The teaching and learning objective of the text is to illustrate the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all the relevant programs and software. The text is a systematic overview of the canonical spatial statistical and geostatistical methods. It explains and demonstrates methods and techniques in spatial sampling; spatial autocorrelation; spatial composition (heterogeneity, homogeneity) and configuration (contiguity), spatially adjusted regression and related spatial econometrics; local statistics: hot and cold spots; geostatistics and related techniques in measuring spatial variance and co-variance; and methods for spatial interpolation in two-dimensions. A concluding section discusses advanced topics in spatial statistics: these include Bayesian methods, the Monte Carlo simulation, and error and uncertainty. Fully explanatory, Spatial Statistics and Geostatistics uses boxed computer code, diagrams, illustrations; and includes further readings. Case study and exemplary materials and data sets are also included."

Índice

About the Authors
Preface
1 Introduction
1.1. Spatial Statistics and Geostatistics
1.2. R Basics
2 Spatial Autocorrelation
2.1. Indices Measuring Spatial Dependencyv 2.1.1. Important Properties of MC
2.1.2. Relationships Between MC And GR, and MC and Join Count Statistics
2.2. Graphic Portrayals: The Moran Scatterplot and the Semi-variogram Plot
2.3. Impacts of Spatial Autocorrelation
2.4. Testing for Spatial Autocorrelation in Regression Residuals
2.5. R Code for Concept Implementations
3 Spatial Sampling
3.1. Selected Spatial Sampling Designs
3.2. Puerto Rico DEM Data
3.3. Properties of the Selected Sampling Designs: Simulation Experiment Results
3.3.1. Sampling Simulation Experiments On A Unit Square Landscape
3.3.2. Sampling Simulation Experiments On A Hexagonal Landscape Structure
3.4. Resampling Techniques: Reusing Sampled Data
3.4.1. The Bootstrap
3.4.2. The Jackknife
3.5. Spatial Autocorrelation and Effective Sample Size
3.6. R Code for Concept Implementations
4 Spatial Composition and Configuration
4.1. Spatial Heterogeneity: Mean and Variance
4.1.1. ANOVA
4.1.2. Testing for Heterogeneity Over a Plane: Regional Supra-Partitionings
4.1.2.1. Establishing a Relationship to the Superpopulation
4.1.2.2. A Null Hypothesis Rejection Case With Heterogeneity
4.1.3. Testing for Heterogeneity Over a Plane: Directional Supra-Partitionings
4.1.4. Covariates Across a Geographic Landscape
4.2. Spatial Weights Matrices
4.2.1. Weights Matrices for Geographic Distributions
4.2.2. Weights Matrices for Geographic Flows
4.3. Spatial Heterogeneity: Spatial Autocorrelation
4.3.1. Regional Differences
4.3.2. Directional Differences: Anisotropy
4.4. R Code for Concept Implementations
5 Spatially Adjusted Regression And Related Spatial Econometrics
5.1. Linear Regression
5.2. Nonlinear Regression
5.2.1. Binomial/Logistic Regression
5.2.2. Poisson/Negative Binomial Regression

5.2.2.1. Geographic Distributions
5.2.2.2. Geographic Flows: A Journey-To-Work Example
5.3. R Code for Concept Implementations
6 Local Statistics: Hot And Cold Spots
6.1. Multiple Testing with Positively Correlated Data
6.2. Local Indices of Spatial Association
6.3. Getis-Ord Statistics
6.4. Spatially Varying Coefficients
6.5. R Code For Concept Implementations
7 Analyzing Spatial Variance And Covariance With Geostatistics And Related Techniques
7.1. Semi-variogram Models
7.2. Co-kriging
7.2.1.DEM Elevation as a Covariate
7.2.2. Landsat 7 ETM+ Data as a Covariate
7.3. Spatial Linear Operators
7.3.1. Multivariate Geographic Data
7.4. Eigenvector Spatial Filtering: Correlation Coefficient Decomposition
7.5. R Code for Concept Implementations
8 Methods For Spatial Interpolation In Two Dimensions
8.1. Kriging: An Algebraic Basis
8.2. The EM Algorithm
8.3. Spatial Autoregression: A Spatial EM Algorithm
8.4. Eigenvector Spatial Filtering: Another Spatial EM Algorithm
8.5. R Code for Concept Implementations
9 More Advanced Topics In Spatial Statistics
9.1. Bayesian Methods for Spatial Data
9.1.1 Markov Chain Monte Carlo Techniques
9.1.2. Selected Puerto Rico Examples
9.2. Designing Monte Carlo Simulation Experiments
9.2.1 A Monte Carlo Experiment Investigating Eigenvector Selection when Constructing a Spatial Filter
9.2.2. A Monte Carlo Experiment Investigating Eigenvector Selection from a Restricted Candidate Set of Vectors
9.3. Spatial Error: A Contributor to Uncertainty
9.4. R Code for Concept Implementations
References
Index


10.
Libro
Quantile regression for spatial data / Daniel P. McMillen
McMillen, Daniel P. ;
Berlin : Springer , c2013
Clasificación: F/519.536 / M3
Bibliotecas: San Cristóbal
Cerrar
SIBE San Cristóbal
ECO010007647 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

"Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an explanatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail."

Índice

1 Quantile Regression: An Overview
1.1 A Monte Carlo Study of Gentrification
1.2 Quantile Regression Estimates
1.3 Implied Distribution of Sales Prices
1.4 Nonlinear Quantile Regression
1.5 Conclusion
2 Linear and Nonparametric Quantile Regression
2.1 Linear Quantile Regression: Simulated Data
2.2 Simulating the Distribution of the Dependent Variable
2.3 The Effect of a Discrete Change in an Explanatory Variable
2.4 Nonparametric Quantile Regression
2.5 Conclusion
3 A Quantile Regression Analysis of Assessment Regressivity
3.1 A Monte Carlo Analysis of Assessment Ratios
3.2 Assessment Ratios in DuPage County, Illinois
3.3 Conclusion
4 Quantile Version of the Spatial AR Model
4.1 Quantile Regression with an Endogenous Explanatory Variable
4.2 An Application to Hedonic House Price Functions
4.3 Conclusion
5 Conditionally Parametric Quantile Regression
5.1 CPAR Quantile Regression for Spatial Data
5.2 An Empirical Example: House Prices in Tacoma, WA
5.3 Assessment Ratios in Cook County, IL
5.4 Conclusion
6 Guide to Further Reading