Computational modeling of city formation

Abstract

Although a burst of recent research in economics has examined how industries form, a majority of it considers highly simplified models. In this paper, we use computational modeling techniques to expand from traditional, simple, analytically tractable economic models to more complex two dimensional landscapes. Using the basic theories developed in earlier research, we examine what factors cause cities to emerge, including: transportation costs, the percentage of workers in a population, and the elasticity of substitution. These three factors should cause workers and firms to agglomerate, causing cities to emerge out of a scattered population. Copyright Springer Science+Business Media, LLC 2007Computational modeling, City formation, Evolutionary algorithms,

City formation with complex landscapes

“…While Krugman generated a complex landscape based on pure randomness, I impose more structure so the complex elements can be interpreted as being rivers, mountains or valleys. The version of the NEG model that appears in this paper is taken from DeMaagd and Moore (), henceforth referred to as DM; the equations that describe this model are reproduced in Appendix A. DeMaagd and Moore base their work on a basic version of the NEG model that is fully laid out in Chapter 5 of The Spatial Economy (Fujita, Krugman, Venables, et al, ).…”

“…For example, Fujita, Krugman, and Venables () employed simulations in almost every chapter to solve numerical examples in a simple one‐dimensional landscape. DeMaagd and Moore () used computer‐based simulations to explore the properties of the NEG model in two‐dimensional space. In this paper I extend their work to include complex landscapes.…”