Spatial Socio-econometric Modeling (SSEM) : A Low-Code Toolkit for Spatial Data Science and Interactive Visualizations Using R (Springer Texts in Social Sciences) / González Canché, Manuel S.

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空間的計量社会経済モデリング（SSEM)：Rを用いる空間データサイエンスのためのインタラクティブ可視化ロ－コード・ツールキット<br>Spatial Socio-econometric Modeling (SSEM) : A Low-Code Toolkit for Spatial Data Science and Interactive Visualizations Using R (Springer Texts in Social Sciences)

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空間的計量社会経済モデリング（SSEM)：Rを用いる空間データサイエンスのためのインタラクティブ可視化ロ－コード・ツールキット
Spatial Socio-econometric Modeling (SSEM) : A Low-Code Toolkit for Spatial Data Science and Interactive Visualizations Using R (Springer Texts in Social Sciences)

González Canché, Manuel S.

ウェブストア価格 ¥20,413（本体¥18,558）
Springer International Publishing AG（2023/07発売）
外貨定価 US$ 99.99
ポイント 185pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 503 p.
商品コード 9783031248566

Full Description

With the primary goal of expanding access to spatial data science tools, this book offers dozens of minimal or low-code functions and tutorials designed to ease the implementation of fully reproducible Spatial Socio-Econometric Modeling (SSEM) analyses. Designed as a University of Pennsylvania Ph.D. level course for sociologists, political scientists, urban planners, criminologists, and data scientists, this textbook equips social scientists with all concepts, explanations, and functions required to strengthen their data storytelling. It specifically provides social scientists with a comprehensive set of open-access minimal code tools to:
•Identify and access place-based longitudinal and cross-sectional data sources and formats•Conduct advanced data management, including crosswalks, joining, and matching
•Fully connect social network analyses with geospatial statistics•Formulate research questions designed to account for place-based factors in model specification and assess their relevance compared to individual- or unit-level indicators•Estimate distance measures across units that follow road network paths •Create sophisticated and interactive HTML data visualizations cross-sectionally or longitudinally, to strengthen research storytelling capabilities•Follow best practices for presenting spatial analyses, findings, and implications•Master theories on neighborhood effects, equality of opportunity, and geography of (dis)advantage that undergird SSEM applications and methods•Assess multicollinearity issues via machine learning that may affect coefficients' estimates and guide the identification of relevant predictors•Strategize how to address feedback loops by using SSEM as an identification framework that can be merged with standard quasi-experimental techniques like propensity score models, instrumental variables, and difference in differences•Expand the SSEM analyses to connections that emerge via social interactions, such as co-authorship and advice networks, or any form of relational data
The applied nature of the book along with the cost-free, multi-operative R software makes the usability and applicability of this textbook worldwide.

Part I Conceptual and Theoretical Underpinnings.- Chapter 1. SPlaces.- Chapter 2. Operationalizing Splaces.- Chapter 3. Data Formats, Coordinate Reference Systems, and Differential Privacy Frameworks.- Part II Data Science SSEM Identification Tools: Distances, Networks, and Neighbors.- Chapter 4. Access and Management of Spatial or Geocoded Data.- Chapter 5. Distances.- Chapter 6. Geographical Networks as Identification Tools.- Part III SSEM Hypothesis Testing of Cross-sectional and Spatio-temporal Data and Interactive Visualizations.- Chapter 7. SODA: Spatial Outcome Dependence or Autocorrelation.- Chapter 8. SSEM Regression Based analyses.- Chapter 9. Visualization, Mining, and Density Analyses of Spatial and Spatio-temporal Data.- Chapter 10. Final Words.- Glossary.- Index.