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Urban Economics with Python: City Data, Housing, Transportation, and Land Use Analysis
Reactive PublishingUrban Economics with Python is a practical guide to analyzing how cities grow, function, and change through data-driven economic modeling. Designed for readers who want to connect urban theory with computational methods, this book shows how Python can be used to study housing markets, transportation systems, land use patterns, demographic change, and local development.
The book introduces core concepts in urban economics while demonstrating how to work with real-world city data. Readers will explore methods for cleaning and structuring urban datasets, modeling neighborhood-level variation, analyzing housing affordability, evaluating transportation access, and examining the relationship between land use and economic activity.
Rather than treating cities as abstract systems, this guide focuses on practical analytical workflows that help explain how people, firms, infrastructure, and policy interact across urban space. Topics include spatial data analysis, regression modeling, geographic visualization, accessibility metrics, demographic segmentation, local market analysis, and scenario-based urban modeling.
Inside, readers will learn how
Analyze housing prices, rents, affordability, and neighborhood change
Model transportation access, commuting patterns, and spatial connectivity
Use Python for urban datasets, geographic data, and local economic indicators
Study land use, zoning patterns, density, and development dynamics
Build reproducible workflows for city-level economic and planning analysis
Create maps, visualizations, and analytical models for urban decision-making
Written for economists, analysts, planners, data scientists, policy researchers, and Python users working with city-scale data, Urban Economics with Python provides a structured foundation for understanding modern urban systems through computation, evidence, and economic reasoning.
The book introduces core concepts in urban economics while demonstrating how to work with real-world city data. Readers will explore methods for cleaning and structuring urban datasets, modeling neighborhood-level variation, analyzing housing affordability, evaluating transportation access, and examining the relationship between land use and economic activity.
Rather than treating cities as abstract systems, this guide focuses on practical analytical workflows that help explain how people, firms, infrastructure, and policy interact across urban space. Topics include spatial data analysis, regression modeling, geographic visualization, accessibility metrics, demographic segmentation, local market analysis, and scenario-based urban modeling.
Inside, readers will learn how
Analyze housing prices, rents, affordability, and neighborhood change
Model transportation access, commuting patterns, and spatial connectivity
Use Python for urban datasets, geographic data, and local economic indicators
Study land use, zoning patterns, density, and development dynamics
Build reproducible workflows for city-level economic and planning analysis
Create maps, visualizations, and analytical models for urban decision-making
Written for economists, analysts, planners, data scientists, policy researchers, and Python users working with city-scale data, Urban Economics with Python provides a structured foundation for understanding modern urban systems through computation, evidence, and economic reasoning.
556 pages, Kindle Edition
Published May 2, 2026
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