During the Master in City & Technology + Thesis Project, students have the unique opportunity to work for an additional time of 9 months on an Individual Thesis Project, focused on the development of one research or pilot project based on the student’s interest. IAAC supports the student in selecting their Thesis Project topic in order to better orient them according to their future career interests and opportunities. Each student is assigned one or more Thesis Advisors that follow the development of the work throughout the year.
Background Public transportation plays a crucial role in urban planning globally. Extensive research indicates that bus transit has become a highly favored mass mobility system. This preference stems from its cost-effective infrastructure, flexible planning, and discrete architecture that supports incremental growth. Bus transit systems exhibit lower infrastructure costs compared to rail or subway networks, making … Read more
The Environmental Asset Resilience (EAR) Tool is a response to the pressing global challenge of environmental instability, particularly in urban areas. These areas face substantial costs associated with environmental problems and climate change. However, decision-making in addressing these issues is complicated by conflicting priorities, limited resources, and a lack of expertise. The EAR Project aims … Read more
Unraveling Footfall Patterns for Enhanced Urban Planning: A Spatial and Temporal Analysis Introduction Footfall prediction plays a crucial role in urban planning decision-making processes. Understanding the intricate relationship between footfall and its two dimensions—space and time—is essential. In this project, we delve into the workflow and key findings of a project aimed at comprehending the … Read more
Alejandro Aravena, renowned for his role in facilitating the recovery of a city struck by an earthquake and a tsunami, asserts that Participatory design transcends mere inclusivity and offers enhanced efficiency. Despite recognizing the significance of public participation in urban planning, we delve into the persisting factors that hinder its optimal efficiency. Conventional methods may … Read more
INTRODUCTION Urban planning decisions have a significant impact on the development of cities, and using machine learning can provide decision-makers with valuable insights to make informed decisions. By clustering urban areas based on various factors such as population density, built density, POI density, green cover, and build diversity, we can reveal spatial patterns that can … Read more
