On the density distribution across space: a probabilistic approach

• Authors: Ilenia Epifani and Rosella Nicolini.
• BSE Working Paper: 388 | September 15
• Keywords: agglomerations , Bayesian inference , Distance , Gibbs sampling , Kendall's tau index , Population density
• JEL codes: C40, R14
• agglomerations
• Bayesian inference
• Distance
• Gibbs sampling
• Kendall's tau index
• Population density

This paper aims at providing a Bayesian parametric framework to tackle the accessibility problem across space in urban theory. Adopting continuous variables in a probabilistic setting we are able to associate with the distribution density to the Kendall’s tau index and replicate the general issues related to the role of proximity in a more general context. In addition, by referring to the Beta and Gamma distribution, we are able to introduce a differentiation feature in each spatial unit without incurring in any a-priori definition of territorial units.We are also providing an empirical application of our theoretical setting to study the density distribution of the population across Massachusetts.