Evolution-Inspired Algorithm Predicts Skyline Growth
Emily Pollock posted on August 20, 2018 |
Created by a growth algorithm, this map shows the probability that a new high-rise building will be constructed in the area within the next several years. (Image courtesy of Pazos et al., 2018.)
Researchers have developed a genetic algorithm that can predict a city’s vertical growth using economic and historical construction data.
A genetic algorithm is a program inspired by natural selection, the guiding force behind evolution. Much like evolution under natural selection, the evolution and development of cities is driven by a self-organizing process, where order occurs not as a result of being imposed from the top down, but by interaction between local forces. But, while genetic algorithms had previously been used to predict the development of self-organized systems, they had never been used to predict city growth. Ivan Pazos and his team at Spain’s University of A Coruña wanted to change that.
The team looked at Tokyo’s Minato Ward, an area of the city notable for its rapid vertical growth. The researchers started by developing a 3D model of the ward’s current skyline based on photogrammetric data, which included the terrain level as well as any buildings over 15m (49ft). From that, they developed a gradient map of the area to determine the factors that had been used to predict skyscraper growth in the past, in order to use them as inputs into…