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Smart Cities Council
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Making Smart Cities for People
Rethinking Smart Cities Through the Lens of People, Place and Spatial Networks

Smart cities are often defined by technology, data and digital innovation. However, truly successful cities are designed around people, places and the way communities interact with the urban environment.

Making Cities Smart for People introduces participants to the principles of spatial network analysis and explores how the structure of streets, public spaces and urban networks influences movement, economic activity, sustainability, accessibility and quality of life. Drawing on the internationally recognised Space Syntax methodology, the course challenges traditional assumptions about smart cities and provides a practical framework for understanding how cities function in the real world.

Through global case studies and applied examples, participants will learn how spatial networks shape human behaviour, urban performance and investment outcomes. The course demonstrates how evidence-based analysis can support better planning, design and policy decisions while creating cities that are more connected, inclusive and resilient.

Designed for planners, urban designers, architects, policymakers, technology providers, consultants and investors, this course bridges the gap between data-driven city management and human-centred urban outcomes.

As cities worldwide face increasing pressure to improve sustainability, liveability and economic performance, understanding how people experience and move through urban environments has never been more important. This course provides practical insights that can be applied immediately to planning, development, investment and city-shaping decisions.


Powered by Space Syntax

Course Provider - Space Syntax 

Creative team - Tim Stonor, Ed Parham, Francesca Froy, Su Jin Kwon

Course Provider - SmartAI Connect

Qualification - Smart Cities Academy Micro-credential

Qualification - Smart Cities Academy Micro-credential

Duration - 2-3 hours

Duration - 2-3 hours

Language - English

Language - English

Format - Online On Demand

Format - Online On Demand

Target Audience - Designed for: Urban planners, architects, policymakers, smart city professionals, developers, investors and consultants interested in improving city performance through people-centred design and spatial network analysis.

Target Audience - Anyone seeking a structured understanding of Responsible AI and its strategic, ethical, and governance implications.

Cost - TBC

Cost - USD245

Prerequisites - There are no prerequisites for this course

Prerequisites - There are no prerequisites for this course

Outline

MODULE 1 -.What is a smart city? Challenging common assumptions
MODULE 2 - Why do we need to understand spatial networks to make cities smarter
MODULE 3 - How do we analyse spatial networks
MODULE 4 - What are the key insights from spatial network analysis? And how have they been applied to global cities
MODULE 5 - ow does this translate into professional practice (with separate sections for i. property investors; ii. consultancies and tech providers, iii, architects, planners and urban designers and; iv. local, regional and national policy makers)

Outline

MODULE 1 - Foundations of Artificial Intelligence
MODULE 2 - AI Ethics and Responsible AI
MODULE 3 - Introduction to AI Governance Frameworks
MODULE 4 - Developing an AI Strategy
MODULE 5 - Building a Risk-Aware AI Culture, and Assessment

Learning Outcomes

  • Critically evaluate dominant narratives of the smart city, distinguishing technology-led approaches from approaches that make cities work for people
  • Appreciate and articulate the importance of spatial networks for urban performance, connectivity, sustainability, and human behaviour at multiple scales.
  • Understand spatial network analysis methods for cities drawing on a tried and tested analytical framework to assess and address real-world urban contexts.

Learning Outcomes

  • Explain key AI concepts, including LLMs and computer vision systems.
  • Differentiate between types of AI and common misconceptions.
  • Describe core AI ethical principles and why they matter in real-world deployment.
  • Identify major AI governance frameworks at global, regional, and national levels.
  • Understand AI risk categories across the lifecycle.