David Darmofal

Spatial Analysis for the Social Sciences (eBook, PDF)

• Format: PDF

Produktbeschreibung

Many theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result. The book is structured around the well-known Galton's problem and presents a step-by-step guide to the application of spatial analysis. The book examines a variety of spatial diagnostics and models through a series of applied examples drawn from the social sciences. These include spatial lag models that capture behavioral diffusion between actors, spatial error models that account for spatial dependence in errors, and models that incorporate spatial heterogeneity in the effects of covariates. Spatial Analysis for the Social Sciences also examines advanced spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 29. Oktober 2015
• Englisch
• ISBN-13: 9781316396896
• Artikelnr.: 44041712

Autorenporträt

David Darmofal is an Associate Professor of Political Science at the University of South Carolina. His research focuses on spatial analysis and political geography and has appeared in a variety of journals including the American Journal of Political Science, the Journal of Politics, and Political Geography. He has received best article awards from the Journal of Politics and Political Research Quarterly. He teaches regularly in the ICPSR Summer Program in Quantitative Methods of Social Research.

Inhaltsangabe

Part I. General Topics: 1. The social sciences and spatial analysis
2. Defining neighbors via a spatial weights matrix
3. Spatial autocorrelation and statistical inference
4. Diagnosing spatial dependence
5. Diagnosing spatial dependence in the presence of covariates
6. Spatial lag and spatial error models
7. Spatial heterogeneity
Part II. Advanced Topics: 8. Time-series-cross-section (TSCS) and panel data models
9. Advanced spatial models
10. Conclusion
Part III. Appendices on Implementing Spatial Analyses: 11. Getting data ready for a spatial analysis
12. Spatial software
13. Web resources for spatial analysis
14. Glossary.