Spatial Socio-econometric Modeling (SSEM)

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 formatsConduct advanced data management, including crosswalks, joining, and matching Fully connect social network analyses with geospatial statisticsFormulate research questions designed to account for place-based factors in model specification and assess their relevance compared to individual- or unit-level indicatorsEstimate distance measures across units that follow road network paths Create sophisticated and interactive HTML data visualizations cross-sectionally or longitudinally, to strengthen research storytelling capabilitiesFollow best practices for presenting spatial analyses, findings, and implicationsMaster theories on neighborhood effects, equality of opportunity, and geography of (dis)advantage that undergird SSEM applications and methodsAssess multicollinearity issues via machine learning that may affect coefficients' estimates and guide the identification of relevant predictorsStrategize 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 differencesExpand 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.