The MIT Senseable City Lab has revealed a project, named Favelas 4D, to analyze and understand the urban morphology of one of Brazil’s famous informal settlements in an interactive digital environment. 

"Favelas" in Brazil are known as informal settlements and they come out in the hills of big cities like Rio de Janeiro in dense, multilayered forms that represent a neigborhoud built without a masterplan by urban planners or urban designers. 

The MIT Senseable City Lab, directed by Carlo Ratti - founding partner of Carlo Ratti Associati, in collaboration with Washington Fajardo, have used a 3D laser scanning technology to analyze and understand the architectural logic of the urban morphology of Rocinha, the largest favela in Rio de Janeiro. 

Video of courtesy of MIT Senseable City Lab

The project, named Favelas 4D, uses a LIDAR (Light Detection and Ranging) technology generating a detailed point cloud data. The tool uses scans taken at street level in the favela. 

As the team highlights, the fundamental advantage of LiDAR that it provides "a research medium with its precise representation of the form of the built environment."

Through LIDAR, "researchers are able to quantitatively characterize morphological variation in this complex built environment at very high resolution," said the team in its project description.

"The global trend of urbanization is not limited to shining downtown districts and masterplanned developments: of the roughly 4 billion people who live in cities worldwide, almost 1 billion reside in informal settlements constructed without official urban planning," said the MIT Senseable City Lab.  

"These settlements are built spontaneously and competitively, resulting in a complex urban morphology. People living in informal settlements are largely excluded from city infrastructure and services—exposing them to a variety of public health and security challenges—but they continue to absorb new generations of residents as the world’s exploding urban population outpaces the availability of official affordable housing," added the team. 

"The very spatial complexity that makes them so intriguing also makes them largely impervious to remote sensing-based research: satellite imagery, Google Street View, and open-source mapping only offer a glimpse into their labyrinthine cityscapes."

These maps of the two sample scanning areas reveal morphological features like pinching and widening of streets and clusters of tall buildings.

According to the team, LiDAR technology uses a laser pulse to measure precise distances, "mapping minuscule points to locations in space in order to create a dataset that describes a 3D environment with incredible precision."

The team also added that "form is a productive angle for the study of informal settlements because it reveals patterns in the unregulated construction of the favela, illuminating the tendencies of unfettered urban development and revealing actionable insights that can inform interventions and policies to improve the safety of these environments." 

Moreover, they said that there many other problems in the Favelas that they struggle in infrastructure level, such as "property trading due to the lack of land tenure, landslide risks due to the unstable terrain they occupy, and water and air quality due to the limited infrastructure and services that reach them", and all of these issues are part of the existing urban infrastructure in the shape of the built environment.

Professor Carlo Ratti, director of the MIT Senseable City Lab, said that "despite their challenging circumstances, favela residents have developed ingenious and responsive ways to build their own urban systems." 

"This bottom-up planning process and the complex architectural forms it produces can challenge the standard way of designing cities."

Favelas 4D examines urban form from a quantitative perspective, using data analysis techniques to describe and map the morphological dynamics at work in Rocinha.

The team emphasized that "to analyze the LiDAR data, researchers at the Senseable City Lab have developed a scalable and automated process to measure the morphological qualities of streets in the favela."

Through this LiDAR data it can also help to understand the morphology of a street that can be compared with other streets, which can also help analyzing urban street patterns on a global scale for the future of healthier urbanism, according to the team. 

"The analysis operates at two resolutions, including a global analysis focused on comparing different streets of the favela to one another, and a local analysis unpacking the variation of morphology within streets," the team added. 

"Five morphological factors are considered: street width, street elevation, density of facades, variance in facade height, and street canyon (the ratio of facade height to street width). Using the results of the analysis, it is possible to map morphological information at a resolution of 0.5 meters directly onto the favela’s plan."

"The findings could also be used to develop planning strategies that make the formal city more inclusive to informal settlements, or to spark research into alternative ways of building that make urban environments more accommodating and affordable to their growing populations," said Washington Fajardo, City Planning Commissioner for Rio de Janeiro and collaborator on the project.

"This analysis contributes to the improvement of favela conditions and also raises questions about development practices and methods of city-building overall," stated the team. 

"As informal settlements around the world continue to grow, researchers and planners must integrate them with visions of the global urban future, both by learning from their unique development patterns and by improving their access to the benefits of formalized urbanism." 

"Favelas 4D contributes to this goal by mobilizing the unique capabilities of LiDAR data and the revelations of quantitative analysis, offering an example of how 3D morphological analysis can help us understand and shape the cities of tomorrow."

The web visualization of this project can be found here.

All images courtesy of MIT Senseable City Lab.

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