Space-time correlations in urban sprawl

TL;DR

This paper uncovers regular patterns in urban sprawl, showing that city growth inertia lasts about 15 years and interactions are mainly within 70 km, supported by a mathematical model predicting population changes.

Contribution

It provides the first comprehensive empirical analysis of space-time correlations in urban growth and introduces a predictive model for city population dynamics.

Findings

City growth inertia has a 15-year characteristic time.

Interactions between cities are primarily within 70 km.

Distance accounts for 60% of the total correlations.

Abstract

Understanding demographic and migrational patterns constitutes a great challenge. Millions of individual decisions, motivated by economic, political, demographic, rational, and/or emotional reasons underlie the high complexity of demographic dynamics. Significant advances in quantitatively understanding such complexity have been registered in recent years, as those involving the growth of cities [Bettencourt LMA, Lobo J, Helbing D, Kuehnert C, West GB (2007) Growth,. Innovation, Scaling, and the Pace of Life in Cities, Proc Natl Acad Sci USA 104 (17) 7301-7306] but many fundamental issues still defy comprehension. We present here compelling empirical evidence of a high level of regularity regarding time and spatial correlations in urban sprawl, unraveling patterns about the inertia in the growth of cities and their interaction with each other. By using one of the world's most exhaustive extant demographic data basis ---that of the Spanish Government's Institute INE, with records covering 111 years and (in 2011) 45 million people, distributed amongst more than 8000 population nuclei--- we show that the inertia of city growth has a characteristic time of 15 years, and its interaction with the growth of other cities has a characteristic distance of 70 km. Distance is shown to be the main factor that entangles two cities (a 60% of total correlations). We present a mathematical model for population flows that i) reproduces all these regularities and ii) can be used to predict the population-evolution of cities. The power of our current social theories is thereby enhanced.