In this article, we use local indicators of spatial association (LISA) and other spatial analysis techniques to analyze the distribution of centers with high employment density within metropolitan areas. We examine the 359 metropolitan areas across the United States at three points in time (1990, 2000, and 2010) to provide a spatio-temporal panoramic of urban spatial structure. Our analysis highlights three key findings. (1) The monocentric structure persists in a majority of metropolitan areas: 56.5% in 1990, 64.1% in 2000, and 57.7% in 2010. (2) The pattern of employment centers remains stable for most metropolitan areas: the number of centers remained the same for 74.9% of metropolitan areas between 1990 and 2000 and for 85.2% between 2000 and 2010. (3) Compared with monocentric metropolitan areas, polycentric metros are larger and more dense, with higher per-capita incomes and lower poverty rates.

The gravity equation has been frequently and successfully used for nearly thirty years to further understanding of the determinants of bilateral trade flows acr...

The aim of this work is to test empirically the validity of Gibrat’s law on the growth of cities, using data on the complete distribution of cities (without size restrictions) from three countries (the US, Spain and Italy) for the entire 20th century. In order to achieve this, different techniques are used. First, panel data unit root tests tend to confirm the validity of Gibrat’s law in the upper-tail distribution. Secondly, when the entire distribution is considered using non-parametric methods, it is found that Gibrat’s law does not hold exactly in the long term (in general, size affects the variance of the growth process but not its mean). Moreover, the log-normal distribution works well as a description of city size distributions across the whole century when no truncation point is considered.

This paper investigates the determinants of the diffusion and intensity of the COVID-19 at the country level, focusing on the role played by urban agglomeration, measured using three urban variables: percentage of the urban population, population density, and primacy. We estimate the influence of urban agglomeration on two outcome variables: cumulative number of cases and deaths per 100,000 inhabitants up to 31 December 2020, using both parametric and semiparametric models. We also explore possible spatial effects. The non-linear effects of the urban variables on the intensity of the disease reveal non-monotonous relationships, suggesting that it is the size of the urban system that is linked to a stronger incidence.

In this paper, an analysis is made of the evolution of Spanish urban structure during the period 1900-99. It is postulated that the size distribution of the cities follows a Pareto distribution, which is estimated on a yearly basis. At the same time, the hypothesis is adopted that the intradistribution dynamics can be modelled by means of a Markov chain. In this way, are deduced the so-called transition matrix and its associated vector or ergodic probabilities. This methodology allows two main results to be obtained. First, this evolution has not been homogeneous ; there has been a divergent pattern of growth during the period 1900-70; and a convergent pattern for the period 1970-99. Secondly, the intradistribution movements in the Spanish urban hierarchy have been very significant during the course of the 20th century. In addition, the existence of spatial correlation has also been detected for those cities that have either entered or exited from the sample of the 100 largest cities during the course of the century.