Forecasting the urban skyline with extreme value theory
The world's urban population is expected to grow fifty percent by the year 2050 and exceed six billion. The major challenges confronting cities, such as sustainability, safety, and equality, will depend on the infrastructure developed to accommodate the increase. Urban planners have long debated the consequences of vertical expansion—the concentration of residents by constructing tall buildings—over horizontal expansion—the dispersal of residents by extending urban boundaries. Yet relatively little work has predicted the vertical expansion of cities and quantified the likelihood and therefore urgency of these consequences. We regard tall buildings as random exceedances over a threshold and use extreme value theory to forecast the skyscrapers that will dominate the urban skyline in 2050 if present trends continue. We predict forty-one thousand skyscrapers will surpass 150 meters and 40 floors, an increase of eight percent a year, far outpacing the expected urban population growth of two percent a year. The typical tall skyscraper will not be noticeably taller, and the tallest will likely exceed one thousand meters but not one mile. If a mile-high skyscraper is constructed, it will hold fewer occupants than many of the mile-highs currently designed. We predict roughly three-quarters the number of floors of the Mile-High Tower, two-thirds of Next Tokyo's Sky Mile Tower, and half the floors of Frank Lloyd Wright's The Illinois—three prominent plans for a mile-high skyscraper. However, the relationship between floor and height will vary considerably across cities.
|Keywords||Demographic forecasting, Government forecasting, Long term forecasting, Population forecasting, Probability forecasting|
|Persistent URL||dx.doi.org/10.1016/j.ijforecast.2019.09.004, hdl.handle.net/1765/123780|
|Journal||International Journal of Forecasting|
Auerbach, J. (Jonathan), & Wan, P. (Phyllis). (2020). Forecasting the urban skyline with extreme value theory. International Journal of Forecasting. doi:10.1016/j.ijforecast.2019.09.004