Spatial regression models
Michael d. Ward
Spatial regression models by Michael d. Ward - 2nd Ed - New Delhi Sage 2019 - xv, 112 pages; illustrations: 21 cm.
Chapter 1: Why Space in the Social Sciences? Chapter 2: Maps as Displays of Information Chapter 3: Interdependency Among Observations Chapter 4: Spatially Lagged Dependent Variables Chapter 5: Spatial Error Model Chapter 6: Extensions
patial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures.
Using social science examples based on real data, the authors illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.
9781544328836
519.5
Spatial regression models by Michael d. Ward - 2nd Ed - New Delhi Sage 2019 - xv, 112 pages; illustrations: 21 cm.
Chapter 1: Why Space in the Social Sciences? Chapter 2: Maps as Displays of Information Chapter 3: Interdependency Among Observations Chapter 4: Spatially Lagged Dependent Variables Chapter 5: Spatial Error Model Chapter 6: Extensions
patial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures.
Using social science examples based on real data, the authors illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.
9781544328836
519.5