Description
Cities are the epicenters of global energy consumption, economic activity, and innovation—yet they also face mounting pressures from population growth, infrastructural complexity, and more. This course explores how data, AI, and integrated urban systems can transform cities into more integrated, responsive, and citizen-centered environments.
What you’ll learn
By the end of the course, learners will be able to:
- Explain the role of cities as interconnected urban systems that concentrate population, infrastructure, economic activity, institutions, and innovation.
- Describe major global urbanization trends and their implications for governance, infrastructure, sustainability, and development.
- Analyze key urban challenges, including housing shortages, informal settlements, infrastructure deficits, spatial inequality, congestion, and environmental stress.
- Apply urban planning concepts and data-driven approaches to understand how cities can manage growth, improve service delivery, and support sustainable development.
- Evaluate the role of AI, digital twins, geospatial data, and predictive analytics in transforming urban planning from reactive management to anticipatory governance.
- Assess the importance of institutional coordination and data interoperability in scaling AI-enabled urban systems across agencies, utilities, and public–private partnerships.
- Explain how cross-sector collaboration supports smart city development, including the roles of governments, private firms, academic institutions, civil society, and international organizations.
- Identify governance, ethical, and institutional risks related to urban AI, including data governance, algorithmic transparency, digital inequality, accountability, and public trust.
- Examine how integrated urban systems strengthen resilience by helping cities anticipate risks, manage cascading infrastructure impacts, and coordinate responses across sectors.
- Connect smart, responsive, and integrated city strategies to global frameworks, including SDG 11, the New Urban Agenda, and broader sustainable development priorities.
Syllabus
Module 1: Understanding Cities and Urbanization
Learning Objectives:
- Define cities as interconnected systems of infrastructure, economic activity, governance, and populations.
- Explain how urbanization transforms demographic, spatial, economic, and institutional systems.
- Compare different types of human settlements, including rural areas, towns, intermediate cities, and cities.
- Describe major global urbanization trends, including regional growth patterns and the role of small and intermediate cities.
- Explain the relevance of SDG 11 and the New Urban Agenda to sustainable urban development.
- Identify how cities function as centers of innovation, productivity, governance, and risk.
Module 2: Urban Problems and Planning in an Urban Century
Learning Objectives:
- Analyze how rapid urbanization creates pressure on housing, infrastructure, land markets, and public institutions.
- Explain the causes and consequences of informal settlements, dual urbanism, and housing shortages.
- Describe how infrastructure deficits and spatial inequality affect mobility, service access, and social inclusion.
- Explain the evolution of urban planning from physical land-use design to integrated, data-driven urban management.
- Assess how urban form, density, transportation, and land-use patterns influence sustainability and equity.
- Describe how AI, digital twins, geospatial data, and predictive analytics support modern urban planning.
Module 3: AI Infrastructure and Multi-Sector Governance Across Urban Institutions
Learning Objectives:
- Explain why integrated infrastructure data is essential for AI-driven urban governance.
- Describe how data interoperability, shared platforms, and digital twins support coordinated city planning.
- Identify key governance challenges of urban AI, including data governance, algorithmic transparency, institutional capacity, and digital inequality.
- Explain the role of institutional coordination in scaling AI across municipal agencies, utilities, and public–private partnerships.
- Analyze how governments, private firms, academic institutions, and international organizations contribute to AI-enabled urban ecosystems.
- Evaluate how ethical oversight, standards, and institutional readiness support sustainable and accountable AI implementation.
Module 4: Integrating Cities for Ecosystem Collaboration
Learning Objectives:
- Explain how integrated urban systems help cities anticipate, absorb, adapt to, and recover from shocks and stresses.
- Describe how cascading risks emerge when failures in one infrastructure system affect other urban systems.
- Analyze the connections among water, food, energy, mobility, housing, public services, and environmental systems.
- Explain how data, AI, IoT, and predictive tools can strengthen urban resilience and service coordination.
- Assess how cross-sector collaboration improves risk management, infrastructure performance, and adaptive capacity.
- Evaluate how ecosystem partnerships can support regenerative urban performance and more resilient city planning.
Content provider
Green Earth University
The United Nations Institute for Training and Research (UNITAR) provides innovative learning solutions to individuals, organizations and institutions.
Curriculum
- 5 Sections
- 44 Lessons
- Lifetime
- Module 1: Understanding Cities and UrbanizationLearning Objectives: Understanding the foundational concepts, dynamics, and global frameworks of cities and urbanization.14
- 1.1Key Terms
- 1.2Session 1.1: Defining Cities: Conceptual Foundations
- 1.3Quiz 1.1: Knowledge Check1 Question
- 1.4Session 1.2: Cities as “Systems of Systems”
- 1.5Session 1.3: New Urban Agenda
- 1.6Session 1.4: Cities as Drivers of Economic Growth
- 1.7Quiz 1.2: Knowledge Check1 Question
- 1.8Session 1.5: Defining and Understanding Urbanization Through a Global Lens
- 1.9Session 1.6: Major Global Trends
- 1.10Session 1.7: World Urbanization Prospects
- 1.11Quiz 1.3: Knowledge Check1 Question
- 1.12Session 1.8: Sustainable Development Goal 11 – Sustainable Cities and Communities
- 1.13Session 1.9: High-Level Political Forum – UNITAR Session
- 1.14Quiz 1.4: Module 1 Assessment7 Questions
- Module 2: Urban Problems and Planning in an Urban CenturyLearning Objectives: Understand the key structural challenges facing cities and how planning, data, and emerging technologies are shaping modern urban management.19
- 2.1Key Terms
- 2.2Session 2.1: Understanding Urban Problems in an Urban Century
- 2.3Quiz 2.1: Knowledge Check1 Question
- 2.4Session 2.2: Housing Pressures and Informal Urbanization
- 2.5Quiz 2.2: Knowledge Check1 Question
- 2.6Session 2.3: Infrastructure Deficits and Service Provision
- 2.7Session 2.4: Urban Inequality and Spatial Segregation
- 2.8Quiz 2.3: Knowledge Check1 Question
- 2.9Session 2.5: Urban Planning: From Spatial Design to Intelligent Urban Systems
- 2.10Session 2.6: Flashback – Early International Perspectives on Urban Planning
- 2.11Session 2.7: Measuring Cities and Urban Systems
- 2.12Quiz 2.4: Knowledge Check1 Question
- 2.13Session 2.8: Urban Form and Spatial Expansion
- 2.14Session 2.9: Artificial Intelligence and the Future of Urban Planning
- 2.15Session 2.10: Data-Driven Urban Planning
- 2.16Session 2.11: AI in Action: Urban Digital Twins
- 2.17Session 2.12: The Future of AI-Enabled Urban Governance
- 2.18Quiz 2.5: Knowledge Check1 Question
- 2.19Quiz 2.6: Module 2 Assessment7 Questions
- Module 3: AI Infrastructure and Multi-Sector Governance Across Urban InstitutionsLearning Objectives: Examine how cities scale AI through ecosystem collaboration and institutional coordination.14
- 3.1Key Terms
- 3.2Session 3.1: Integrating Infrastructure Data Across City Systems
- 3.3Quiz 3.1: Knowledge Check1 Question
- 3.4Session 3.2: Governance Challenges of AI in Cities
- 3.5Session 3.3: Institutional Coordination for Urban AI
- 3.6Session 3.4: Cross-Sector Partnerships and Ecosystem Collaboration
- 3.7Session 3.5: Fragmentation vs. Ecosystem Coordination
- 3.8Session 3.6: Components of Effective Cross-Sector Collaboration
- 3.9Quiz 3.2: Knowledge Check1 Question
- 3.10Session 3.7: Case Study – AI in Action: How IBM is leveraging AI and multi-sector partnership for ecosystem collaboration
- 3.11Session 3.8: Case Study 2: IBM Sustainability Accelerator – A Multi-Sector Innovation Ecosystem
- 3.12Session 3.9: International Organizations and Standards
- 3.13Quiz 3.3: Knowledge Check1 Question
- 3.14Quiz 3.4: Module 3 Assessment2 Questions
- Module 4: Integrating Cities for Ecosystem CollaborationLearning Objectives: Understand how integrated urban systems and cross-sector collaboration enable cities to anticipate risks, manage interdependent infrastructure, and strengthen resilience through data, technology, and ecosystem partnerships.16
- 4.1Key Terms
- 4.2Session 4.1: Urban Risk, Vulnerability, and Cascading Impacts
- 4.3Session 4.2: Sendai Framework for Disaster Risk Reduction
- 4.4Quiz 4.1: Knowledge Check1 Question
- 4.5Session 4.3: Environmental Monitoring, Climate Intelligence, and Urban Sustainability
- 4.6Quiz 4.2: Knowledge Check1 Question
- 4.7Session 4.4: Linking Urban Systems for Integrated Resilience
- 4.8Quiz 4.3: Knowledge Check1 Question
- 4.9Session 4.5: Strengthening Social and Institutional Resilience
- 4.10Session 4.6: Case Study – Urban Nexus Planning in Nairobi and Cape Town
- 4.11Quiz 4.4: Knowledge Check1 Question
- 4.12Session 4.7: AI, Ecosystem Collaboration, and Urban Resource Systems: A multi-part case study into integrated water systems
- 4.13Session 4.8: AI in Action – Case Study 1 – How Poseidon AI is using Artificial Intelligence to integrate aquaculture systems
- 4.14Session 4.9: AI in Action – Case Study 2
- 4.15Session 4.10: Looking to the Future of Integrated Cities and Artificial Intelligence — Floating Cities
- 4.16Quiz 4.5: Module 4 Assessment3 Questions
- Course Assessment1
UNITAR
The United Nations Institute for Training and Research (UNITAR) is a principal training arm of the United Nations, working in every region of the world. We empower individuals, governments and organizations through knowledge and learning to effectively overcome contemporary global challenges. Our training targets two key groups of beneficiaries: the delegates to the United Nations and others who develop intergovernmental agreements establishing global norms, policies, and programmes, and the key national change agents who turn the global agreements into action at the national level.
UNITAR under its 5 pillars offers various training and capacity-development activities in the thematic areas of capacity for the 2030 Agenda, strengthen multilateralism, promote economic development and social inclusion, advance environmental sustainability and green development, promote sustainable peace, and research and technology applications.
Mr. Michael Jacobs
Sustainability & Social Innovation Leader, IBM
Michael Jacobs is the Sustainability and Social Innovation Leader for IBM Corporate Social Responsibility. He is responsible for planning and managing technology-driven social impact efforts spanning IBM business units. Previously, he served as the Superintendent for Capital Projects at MTA New York City Transit and as a Booz Allen Hamilton consultant – both roles in which he focused on technology systems development and deployment for the public sector. Michael holds a dual Master of Public Administration from Columbia University and The London School of Economics and Political Science and a Bachelor of Arts in International Affairs from The George Washington University. Michael lives in New York’s Hudson Valley with his wife Abby and son Tommy. Their shared love for the outdoors and active pursuits in it drive Michael’s passion for protecting the natural world.
Dr. Amaj Rahimi-Midani
Founder and CEO of Poseidon-AI
Dr. Amaj Rahimi-Midani is an Iranian born Costa Rican, graduated from Pukyong National University (PKNU) in Busan, South Korea. He pursued his postdoctoral as a climate change modeler in Tromso, Norway by analyzing the climate change impact on European freshwater species. As CEO of Poseidon-AI in Singapore, he collaborates with international organizations to implement sustainable practices in water, soil, aquaculture, and fisheries worldwide. He has published multiple peer-reviewed publications and has presented at global meetings, conferences, and congresses. His latest book titled “Deep Technology for Sustainable Fisheries and Aquaculture” published by Springer Nature, show cases the use of AI/ML for sustainable development of farmers, indigenous and vulnerable communities.