Explaining the Prevalence, Scaling and Variance of Urban Phenomena

TL;DR

This paper introduces a unified theory explaining how urban phenomena's prevalence, scaling, and variance relate to city size, based on economic complexity and cultural evolution, with predictive capabilities across diverse urban data.

Contribution

It develops a novel theoretical framework linking urban phenomena to the number of necessary factors and their diversity, explaining differences in prevalence and scaling across cities.

Findings

Phenomena requiring more factors are less prevalent and scale more superlinearly.

The model accurately predicts urban phenomena prevalence across different cities.

Variance in phenomena prevalence increases with the number of required factors.

Abstract

The prevalence of many urban phenomena changes systematically with population size. We propose a theory that unifies models of economic complexity and cultural evolution to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.