Nicholas Martino

Bachelor in Architecture and Urban Planning, Federal University of Rio Grande do Norte, 2016
Master in Architecture and Urban Planning, Federal University of Rio Grande do Norte, 2019

I’m a PhD candidate in Interdisciplinary Studies at The University of British Columbia, driven by a passion for transforming urban spaces into more livable, walkable, and affordable environments. My research centers on exploring how urban regulations, like density allocation, shape the quality of life in cities. Using computational tools such as generative design and machine learning, I’m developing new methods to optimize density distribution in ways that enhance both housing affordability and neighborhood walkability. This work reflects my five years of experience in real estate, architecture, and construction technology, where I’ve honed my skills in software development, problem-solving, and computational design. My goal is to bridge technology and urban design, creating data-driven solutions that can guide cities toward a more sustainable and inclusive future. Through my research, I aim to bring meaningful, practical insights to urban policy, ultimately contributing to cities that serve both people and the planet better.

My research focuses on the intersection of urban design, density allocation, and computational methods to improve the livability and affordability of cities. In urban areas, rules governing density—often determined by floor space ratios (FSRs)—play a significant role in shaping how space is used and experienced. Higher-density neighborhoods are typically associated with benefits such as increased walkability, lower per capita energy use, and higher land values. However, these benefits are not universally distributed, as density itself can influence housing costs, accessibility, and overall quality of life. My work investigates how density allocation can be optimized to foster both walkable, vibrant communities and more affordable housing options.

To address this challenge, I use computational approaches such as generative design and machine learning. I apply these technologies to simulate various density allocation strategies across the City of Vancouver, with the goal of identifying configurations that balance walkability and affordability. Leveraging parametric modeling and genetic algorithms, I can generate diverse urban density scenarios, simulate new building constructions, and assess their potential impacts on neighborhood characteristics. Through these simulations, I aim to measure the feasibility of density patterns and their influence on housing and pedestrian accessibility.

A core component of my research is the application of genetic algorithms—a method used for multi-objective optimization—to refine density allocations that enhance both affordability and walkability. I explore how adjusting input parameters can improve outcomes, providing insights into the potential of density policies to positively influence urban development. By linking spatial data with predictive algorithms, my work aims to bridge policy and design, offering cities a data-informed approach to urban planning.

Ultimately, my research aims to contribute a robust methodology for urban planners to simulate, evaluate, and optimize urban density strategies. By providing practical, data-driven insights, I hope to support more equitable, livable cities that respond to the needs of diverse communities.