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Spatial regression models for the social sciences/ Guangqing Chi & Jun Zhu

By: Contributor(s): Series: Advanced Quantitative Techniques in the Social Sciences ; 14Publication details: Los Angeles: Sage Publication, 2020Description: xxii, 243 pages ill, 26 cmISBN:
  • 9781544302072
Subject(s): DDC classification:
  • 23 SA.052 C532
Contents:
Chapter 1: Introduction -- Chapter 2: Exploratory spatial data analysis -- Chapter 3: Models dealing with spatial dependence -- Chapter 4: Advance Models dealing with spatial dependence -- Chapter 5: Models dealing with spatial heterogeneity -- Chapter 6: Models dealing with both spatial dependence spatial heterogeneity -- Chapter 7: Advanced spatial regression models -- Chapter 8: Practical considerations for Spatial Data Analysis
Summary: 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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata SA.052 C532 (Browse shelf(Opens below)) Available 138650
Total holds: 0

Includes bibliography and index

Chapter 1: Introduction -- Chapter 2: Exploratory spatial data analysis -- Chapter 3: Models dealing with spatial dependence -- Chapter 4: Advance Models dealing with spatial dependence -- Chapter 5: Models dealing with spatial heterogeneity -- Chapter 6: Models dealing with both spatial dependence spatial heterogeneity -- Chapter 7: Advanced spatial regression models -- Chapter 8: Practical considerations for Spatial Data Analysis

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.

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