Recently, my longtime colleague and friend, Tom Sanchez, PhD, AICP, reached out on LinkedIn to ask for advice on resources for AI. I obtained my Doctorate from the Edward J. Bloustein School of Planning and Public Policy in 2005, but since 2021, I have been pursuing a MS in Electrical Engineering and Computer Science with a Major in AI at Florida Atlantic University.
Tom and I have collaborated on several fronts over the past two decades, including serving together on the Editorial Board of Housing Policy Debate during his tenure as Editor-in-Chief. We also led a Federal Transit Administration grant on vulnerable populations and carless evacuation planning. Most recently, Tom and I have both been focusing urbanism and AI.
Now, as AI increasingly reshapes urban planning, Tom and I are exploring its transformative potential for creating smarter, more equitable, and resilient cities. Tom’s recent work, including the Planning With Artificial Intelligence PAS Report 604, has been instrumental in advancing this field. Published by the American Planning Association (APA), this report provides a clear primer on AI applications for planners, addressing both its opportunities and challenges.
Inspired by Tom’s inquiry, I’ve compiled this guide for anyone looking to learn more about AI. This is a list of resources that has been helpful to my own learning over the past few years.
1. Begin With the Basics of AI
Understanding the fundamentals of AI is crucial for urbanists aiming to leverage its power for planning and development.
Key Resources
- Listen to Jon Krohn’s SuperDataScience podcast to stay on top of what’s going on in AI.
- Planning With Artificial Intelligence (PAS Report 604) by Tom Sanchez, AICP
This must-read report explains AI concepts like machine learning, neural networks, and natural language processing, while addressing barriers such as resource gaps, ethical concerns, and skills shortages. It offers actionable insights and case studies that highlight real-world applications in urban planning. - Interpretable Machine Learning
A free online book that focuses on explainable AI, which is critical for fostering trust and transparency in urban planning initiatives. - How to Think Like a Computer Scientist
The book Think Python: How to Think Like a Computer Scientist is a beginner-friendly guide that introduces fundamental programming concepts and problem-solving techniques using Python, tailored for readers with no prior coding experience.
2. Understand Data Structures and Develop Data Analysis Skills
Python is the language of choice for AI applications, making it essential for urbanists interested in data analysis, modeling, and geospatial work.
Books and Courses
- Python for Data Structures, Algorithms, and Interviews
A comprehensive course designed to build the skills needed for tackling complex challenges. - Hands-On Data Analysis with Pandas by Stefanie Molin
Learn how to process, analyze, and visualize data using Python’s Pandas library. - Data Analysis with Pandas and Python
This course offers hands-on experience analyzing real-world datasets. - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
A comprehensive guide with practical examples for building and deploying AI models.
3. Explore Geospatial Data Science
Geospatial data is at the heart of many urban challenges, from optimizing evacuation routes to planning transit systems.
Courses
- Geospatial Data Science with Python: GeoPandas
Learn how to analyze and visualize geospatial data to address urban challenges.
4. Strengthen Mathematical Foundations
Understanding the math behind AI models can help urbanists design innovative solutions for complex urban systems.
Courses
- Mathematical Foundations of Machine Learning
This course introduces the core mathematical principles driving AI algorithms.
5. Dive Into Deep Learning
Deep learning, a subset of AI, is transforming urban analytics by enabling advanced capabilities like image recognition for urban mapping and predictive modeling for transportation systems.
Books
- Deep Learning Illustrated by Jon Krohn
A visually rich guide that makes complex deep learning concepts accessible and engaging, perfect for those new to the field. - Deep Learning with Python by François Chollet
Written by the creator of Keras, this book provides a comprehensive introduction to deep learning, blending theory with hands-on coding exercises.
Applications for Urbanists
Deep learning can revolutionize urban planning by enabling:
- Image recognition to analyze satellite imagery for urban growth patterns.
- Predictive modeling to anticipate traffic congestion or housing demand.
- Natural language processing to analyze public feedback on planning proposals.
Final Thoughts
AI is revolutionizing the way we address urban challenges, providing powerful tools to analyze data, predict trends, and design cities that are more resilient and sustainable. As urban planners, researchers, and technologists, we are entering an era where AI can help solve complex issues revolutionize cities for the better.
The resources shared above serve as a starting point for those interested in exploring AI’s vast potential in urbanism. By building a strong foundation, mastering essential tools like Python, and delving into advanced topics such as deep learning and geospatial data science, we can unlock new ways to shape the future of cities.
Through thoughtful innovation and collaboration, AI can empower us to create cities that are not only smarter but also more dynamic, sustainable, resilient, and prepared to adapt to the challenges of tomorrow.
Professor John L. Renne 
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