DeepAI AI Chat
Log In Sign Up

An information-theoretic approach to the analysis of location and co-location patterns

by   Alje van Dam, et al.

We propose a statistical framework to quantify location and co-location associations of economic activities using information-theoretic measures. We relate the resulting measures to existing measures of revealed comparative advantage, localization and specialization and show that they can all be seen as part of the same framework. Using a Bayesian approach, we provide measures of uncertainty of the estimated quantities. Furthermore, the information-theoretic approach can be readily extended to move beyond pairwise co-locations and instead capture multivariate associations. To illustrate the framework, we apply our measures to the co-location of occupations in US cities, showing the associations between different groups of occupations.


page 1

page 2

page 3

page 4


infotheory: A C++/Python package for multivariate information theoretic analysis

This paper introduces infotheory: a package that implements multivariate...

An Information-Theoretic Perspective on Overfitting and Underfitting

We present an information-theoretic framework for understanding overfitt...

Information-Theoretic Confidence Bounds for Reinforcement Learning

We integrate information-theoretic concepts into the design and analysis...

Partial and semi-partial measures of spatial associations for multivariate lattice data

This paper concerns the development of partial and semi-partial measures...

Domain Divergences: a Survey and Empirical Analysis

Domain divergence plays a significant role in estimating the performance...

A Partial Information Decomposition Based on Causal Tensors

We propose a partial information decomposition based on the newly introd...