Spatial Socio-econometric Modeling (SSEM): A Low-Code Toolkit for Spatial Data Science and Interactive Visualizations Using R

This book provides a comprehensive set of open-access minimal code tools and tutorials for implementing Spatial Socio-Econometric Modeling (SSEM) analyses, equipping social scientists with the concepts and functions needed to strengthen their data storytelling. It is designed as a University of Pennsylvania Ph.D. level course for sociologists, political scientists, urban planners, criminologists, and data scientists.

Format: Paperback / softback
Length: 503 pages
Publication date: 02 July 2023
Publisher: Springer International Publishing AG

This comprehensive textbook is a valuable resource for social scientists seeking to enhance their data storytelling abilities and implement robust Spatial Socio-Econometric Modeling (SSEM) analyses. Designed as a University of Pennsylvania Ph.D. level course, it equips students with a deep understanding of the concepts, explanations, and functions necessary to strengthen their data analysis skills.

The book offers a multitude of minimal or low-code functions and tutorials, meticulously crafted to facilitate the implementation of fully reproducible SSEM analyses. Whether you are a sociologist, political scientist, urban planner, criminologist, or data scientist, this textbook provides you with the essential tools and knowledge to excel in your field.

One of the key strengths of this textbook is its focus on equipping social scientists with a comprehensive set of open-access minimal code tools. These tools enable researchers to identify and access place-based longitudinal and cross-sectional data sources and formats, conduct advanced data management tasks such as crosswalks, joining, and matching, seamlessly integrate social network analyses with geospatial statistics, formulate research questions that account for place-based factors in model specification, assess the relevance of these factors compared to individual- or unit-level indicators, estimate distance measures across units that follow road network paths, create sophisticated and interactive HTML data visualizations that enhance research storytelling capabilities, adhere to best practices for presenting spatial analyses, findings, and implications, and master theories on neighborhood effects, equality of opportunity, and geography of (dis)advantage that underpin SSEM applications and methods.

Furthermore, the textbook delves into the assessment of multicollinearity issues via machine learning, which can affect coefficient estimates and guide the identification of relevant predictors. By leveraging SSEM as an identification framework, researchers can merge it with standard quasi-experimental techniques like Prope to address feedback loops and enhance the robustness of their analyses.

In conclusion, this textbook is a must-have for social scientists who aspire to expand their access to spatial data science tools and enhance their data storytelling capabilities. With its comprehensive coverage of SSEM concepts, explanations, and functions, coupled with a wealth of open-access minimal code tools, it equips students with the skills and knowledge required to tackle complex social science problems and make meaningful contributions to the field.

Weight: 831g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031248566
Edition number: 1st ed. 2023