Dataset accompanying the publication "Spatiotemporal methods for analysis of urban system dynamics: An application to Chile" (The Annals of Regional Science 2020, 64, 421–454). This paper presents a methodological procedure to evaluate the influence of spatial proximity on evolution of cities to detect regional differences in their spatiotemporal dynamics. The six-step method based on a set of statistical methods can be computed with a new R package: estdaR. The first step consists of the usual characterization of the cross-sectional distribution of the urban areas by means of nonparametric estimations of density functions for a set of significant years. In the second and third steps, the growth process is modeled as a first-order stationary Markov chain to evaluate the effect of global and local spatial autocorrelation on the transition probabilities with a set of indices based on the spatial version of the standard Markov chain. The fourth, fifth, and sixth steps perform in-depth analysis to detect the existence and interaction of spatial regimes in the movement direction and ranking mobility of urban distribution. We apply this novel strategy for the period 1930–2002 to analyze the entire Chilean urban system—not only the Central Zone, in which most of the population and economic activities are concentrated, but also other urban zones in the country.

How to cite the database (APA style): Vallone A & Chasco C (2020) Spatiotemporal methods for analysis of urban system dynamics: An application to Chile [Data set & code] (doi: 10.23728/b2share.16932abe9ce845fca2e933d085394936).