Constructing multi-level urban clusters based on population distributions and interactions

TL;DR

This paper introduces a data-driven method to define multi-level urban clusters based on population distributions and flows, revealing six distinct spatial phases and the emergence of Zipf's law at higher levels, applicable globally without pre-set boundaries.

Contribution

It presents a novel bottom-up aggregation approach to quantify urban clusters across multiple scales using granular population and flow data, without relying on administrative boundaries.

Findings

Identified six phases in population density-flow diagrams corresponding to different cluster scales.

Zipf's law emerges only after the fifth level, indicating spatial dependence of urban laws.

Method is applicable globally and does not require pre-defined boundaries.

Abstract

A city (or an urban cluster) is not an isolated spatial unit, but a combination of areas with closely linked socio-economic activities. However, so far, we lack a consistent and quantitative approach to define multi-level urban clusters through these socio-economic connections. Here, using granular population distribution and flow data from China, we propose a bottom-up aggregation approach to quantify urban clusters at multiple spatial scales. We reveal six 'phases' (i.e., levels) in the population density-flow diagram, each of which corresponds to a spatial configuration of urban clusters from large to small. Besides, our results show that Zipf's law appears only after the fifth level, confirming the spatially dependent nature of urban laws. Our approach does not need pre-defined administrative boundaries and can be applied effectively on a global scale.