Spatial Socio-econometric Modeling (SSEM): A Low-Code Toolkit for Spatial Data Science and Interactive Visualizations Using R by Manuel S. Gonzalez Canche
*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.