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This paper investigates the impact of the city’s urban spatial structure in shaping population density distribution over time. This research question is relevant in Barcelona because urban population grew at a sustained pace in various decades due to intense immigration inflows. When the urban spatial structure fails to behave as the backbone of population density distribution, population distribution can suffer from polarization problems. We conduct our empirical study using an urban monocentric framework, tracking the different spatial distribution patterns of the overall population and a few selected urban communities in light of the degree of attractiveness of the central business district (CBD). To this end, we construct an original database by each district in Barcelona from 1902 to 2011 and perform an econometric analysis. Our results reveal that the urban spatial structure continued to be a crucial determinant over time for shaping the overall population distribution in Barcelona and in almost all selected communities. However, its importance fluctuated over time, bottoming out in the 1950s–1960s, and whose resurgence was mostly driven by the political initiative to create a new centrality in the urban periphery. This policy reinforced the attractiveness of the CBD, resulting in the de-facto avoidance of urban polarization.

The effectiveness of local spatial dependence in shaping the population density distribution is investigated. Individual location preferences are modelled by considering the status-related features of a given spatial unit and its neighbours as well as local random spatial dependence. The novelty is framing such a dependence through conditionally autoregressive (CAR) census random effects that are added to a spatially lagged explanatory variable X (SLX) setting. The results not only confirm that controlling for the spatial dimension is relevant but also indicate that local spatial dependence warrants consideration when determining the population distribution of recent decades. In this respect, the framework turns out to be useful for the analysis of microdata in which individual relationships (in a same spatial unit) enforce local spatial dependence.

This study proposes an empirical analysis to assess the changes recorded by the urban gradient when population size suffers from an important and unexpected shock. To this end, we build an original pseudo-panel covering information for the city of Barcelona from 1902 to 2011. Despite the prediction of canonical theoretical models for monocentric cities, our results identify an increase in the size of the urban gradient due to the reinforcement of the attractiveness of the central business district (CBD) during the period of expansion of the population mostly led by the high-skill and immigrant communities.

This study proposes to investigate the effectiveness of modeling local spatial dependence through a conditionally autoregressive process (CAR) to picture the population distribution across space. Following the current literature, the idea is to model individual location preferences by focusing on a selected sample of location determinants but also taking into account spatial dependence in location choices. By exploiting a Bayesian setting, our novelty is to include spatial CAR census random effects that have been found to be effective in enhancing the role of spatial proximity. Our results indicate that to effectively control for the spatial dimension in location choices, one needs to define both a global indicator for space structure and local spatial dependence which has become increasingly important in recent decades.

Working within a Bayesian parametric framework, we develop a novel approach to studying the distribution of regional population density across space. By exploiting the Gamma distribution, we are able to introduce heterogeneity across space without incurring an a priori definition of territorial units. Our contribution also permits the inclusion of an approximation of individual preferences as a further driving force in location choices. We perform an empirical application to the case of Massachusetts. Our results demonstrate that a subjective measure of distance performs well in replicating the population distribution across Massachusetts.

This study aims to investigate the extent to which history matters in shaping population distribution across space. In the wake of the current literature, the idea is to model individual location preferences by focusing on selected local determinants (neighborhood, education, income, amenities and distance from the CBD), while also taking into account temporal and spatial dependence in location choices. Our preliminary results revel the importance of segregation factors in shaping recent population density distribution.

In this paper, we analyse the geographic concentration of economic activities within the framework of a two countries and four regions model. Trade both between regions and countries entails transport costs which are differentiated according to the interregional or international nature of the flows. Allowing for regional migration of the population, the model configures the equilibra of this systems as key parameters change. The results obtained suggest that both types of transaction costs affect the incentive for firms to cluster geographically. Consequently, processes of integration between nations such as the one taking place through the constitution of the European Union is identified as a factor possibly favoring the emergence of regional economic agglomeration.

This study aims at assessing the evolution of the importance of spatial distance in citizens' location choices across time. We focus on the case of population distribution in Massachusetts. We handle a database including data for the years 1880-1890 and 1930-2010. By adopting a Bayesian strategy, we are able to state that Boston reinforced its attractiveness up to the 1960s, but since then Boston's attractiveness has no longer represented the unique determinant of population distribution. Referring to selected historical evidence, we put forward a few possible interpretations to endorse our results.

This paper develops a novel approach studying the distribution of the regional population density across space. We work in a Bayesian parametric framework. Exploiting the Gamma distribution, we are able to introduce heterogeneity across space without incurring any a priori definition of territorial units. Our contribution also allows the inclusion of an approximation of individual preferences as a further driving force in location choices. We perform an empirical application to the case of Massachusetts. Our results assess that a subjective measure of distance performs quite well in replicating the population distribution across Massachusetts.