Spatial Econometrics and Network Analysis with Python: Modeling Spatial Dependence, Interconnected Markets, and Graph-Based Economic Systems

Reactive Publishing

Unlock the power of spatial and network modeling in economics with Python.

In today’s interconnected world, traditional econometric models often fall short when analyzing phenomena that exhibit spatial dependence or complex network structures. Spatial Econometrics and Network Analysis with Python bridges this gap by providing a practical, code-first guide to modeling spatial relationships, interconnected markets, and graph-based economic systems.

This book equips researchers, data scientists, economists, and quantitative analysts with the tools to:

• Model spatial dependence and heterogeneity using advanced spatial econometric techniques
• Analyze interconnected markets and economic networks through graph theory
• Implement robust Python workflows with libraries such as PySAL, GeoPandas, NetworkX, and statsmodels
• Simulate and visualize spatial and network effects in real-world economic data
• Address challenges like spatial autocorrelation, spillover effects, and systemic risk in financial and economic systems

From foundational concepts to advanced applications, you’ll explore how to integrate spatial weights matrices, construct economic networks, estimate spatial regression models, and leverage graph algorithms to uncover hidden patterns in data. Each chapter includes clear explanations, reproducible Python code examples, and practical case studies drawn from urban economics, regional development, trade networks, and financial contagion.

Whether you are working on policy analysis, market research, academic studies, or data-driven decision-making, this book delivers the technical foundation and hands-on implementation skills needed to tackle modern economic questions where location and connectivity matter.

Perfect for:

• Graduate students and academics in economics, geography, and regional science
• Quantitative analysts and data scientists in finance, consulting, and government
• Professionals seeking to move beyond standard regression models into spatially aware and network-aware analysis

Master the intersection of spatial econometrics, network science, and Python programming to build more accurate and insightful economic models.