Spatial Regression Models for the Social Sciences 1st Edition by Guangqing Chi, Jun Zhu ISBN 154430207X 978-1544302072

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Product details:

ISBN 10: 154430207X

ISBN 13: 978-1544302072 

Author: Guangqing Chi, Jun Zhu 

Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.

Table of contents:

Chapter 1: Introduction

1. Learning Objectives

2. Spatial Thinking in the Social Sciences

3. Introduction to Spatial Effects

4. Introduction to the Data Example

5. Structure of the Book

6. Study Questions

Chapter 2: Exploratory Spatial Data Analysis

1. Learning Objectives

2. Exploratory Data Analysis

3. Neighborhood Structure and Spatial Weight Matrix

4. Spatial Autocorrelation, Dependence, and Heterogeneity

5. Exploratory Spatial Data Analysis

6. Study Questions

Chapter 3: Models Dealing With Spatial Dependence

1. Learning Objectives

2. Standard Linear Regression and Diagnostics for Spatial Dependence

3. Spatial Lag Models

4. Spatial Error Models

5. Study Questions

Chapter 4: Advanced Models Dealing With Spatial Dependence

1. Learning Objectives

2. Spatial Error Models With Spatially Lagged Responses

3. Spatial Cross-Regressive Models

4. Multilevel Linear Regression

5. Study Questions

Chapter 5: Models Dealing With Spatial Heterogeneity

1. Learning Objectives

2. Aspatial Regression Methods

3. Spatial Regime Models

4. Geographically Weighted Regression

5. Study Questions

Chapter 6: Models Dealing With Both Spatial Dependence and Spatial Heterogeneity

1. Learning Objectives

2. Spatial Regime Lag Models

3. Spatial Regime Error Models

4. Spatial Regime Error and Lag Models

5. Model Fitting

6. Data Example

7. Study Questions

Chapter 7: Advanced Spatial Regression Models

1. Learning Objectives

2. Spatio-temporal Regression Models

3. Spatial Regression Forecasting Models

4. Geographically Weighted Regression for Forecasting

5. Study Questions

Chapter 8: Practical Considerations for Spatial Data Analysis

1. Learning Objectives

2. Data Example of U.S. Poverty in R

3. General Procedure for Spatial Social Data Analysis

4. Study Questions

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Tags: Guangqing Chi, Jun Zhu, Spatial Regression Models, Social Sciences