About this course
Cities are booming and constitute the heart of economic and cultural developments. At the same time, cities face complex challenges, such as growing inequality, higher levels of air pollution and congestion, social segregation and exclusion, and, in general, a lower health and life expectancy. Smart cities might provide a response to these challenges, as technological innovations can promote change in the way we live, work, travel, and interact with one another. In this course, new perspectives offered by emerging technologies and research are addressed. The course considers current issues in creating healthier, livable, inclusive, safe, resilient, and sustainable urban environments and links these issues to new approaches in (big) data collection, urban analysis and decision support.
The course consists of a series of lectures. Each lecture addresses a particular topic and is accompanied by a practical and assignment where the students apply the theory to a case.
The following topics are addressed:
- general introduction smart healthy urban environments;
- green city & Virtual Reality (VR);
- sustainable transportation & GPS data collection and processing;
- vital city - physical and leisure activities & Bayesian Belief Network data mining approach;
- sensing the city & Citizen Science;
- social city - social communities & inclusive smart cities;
- digital twinning for urban planning
After finalizing this course, students:
- have insights in current threats and opportunities in urban systems regarding, health, mobility and quality of life
- understand how a smart green infrastructure provides not only climate change resilience but also many health and wellbeing benefits that improve the quality of life of citizens
- understand how better-quality urban planning and evaluation can exploit the potential of the physical environment to promote physical activity
- understand how smart city solutions can be used to build more livable, inclusive communities and improve equal access to public services.
- are able to identify and analyze the potential of integrated land-use and sustainable transport planning
- have a basic understanding of GPS data collection and processing
- understand the principles of the Bayesian Belief Network (BBN) data mining technique and are able to apply this technique to extract patterns from (big) data for policy analysis.
- understand the basic principles of environmental sensing and understand how the data collected with the smart devices (sensors, IoT, web-based big data) can support decision-making processes
- are aware of the characteristics of projects where citizens are involved (citizen science)
- have a basic understanding of digital twinning and how it can help to improve decision-making and develop more informed solutions to the complex urban challenges