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Can lend chapters, not whole ebooks
Title
Spatial socio-econometric modeling (SSEM) : a low-code toolkit for spatial data science and interactive visualizations using R / Manuel S. González Canché.
ISBN
9783031248573 electronic book
3031248570 electronic book
3031248562
9783031248566
Published
Cham, Switzerland : Springer, 2023.
Language
English
Description
1 online resource (518 pages) : illustrations (black and white, and color).
Other Standard Identifiers
10.1007/978-3-031-24857-3 doi
Call Number
HA29 .G66 2023
Dewey Decimal Classification
300.15195
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Series
Springer texts in social sciences.
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.
