Defining Urban Boundaries by Characteristic Scales

TL;DR

This paper introduces a novel method for objectively defining urban boundaries by identifying a characteristic spatial search radius through an exponential relationship with clustering data, validated on Chinese cities.

Contribution

It proposes a new approach to determine the optimal spatial search radius for urban boundary detection using an exponential model, enhancing objectivity in urban spatial analysis.

Findings

The exponential relationship between search radius and cluster count is validated.

The characteristic length parameter effectively guides urban boundary delineation.

Method tested successfully on Chinese cities.

Abstract

Defining an objective boundary for a city is a difficult problem, which remains to be solved by an effective method. Recent years, new methods for identifying urban boundary have been developed by means of spatial search techniques (e.g. CCA). However, the new algorithms are involved with another problem, that is, how to determine the characteristic radius of spatial search. This paper proposes new approaches to looking for the most advisable spatial searching radius for determining urban boundary. We found that the relationships between the spatial searching radius and the corresponding number of clusters take on an exponential function. In the exponential model, the scale parameter just represents the characteristic length that we can use to define the most objective urban boundary objectively. Two sets of China's cities are employed to test this method, and the results lend support to the judgment that the characteristic parameter can well serve for the spatial searching radius. The research may be revealing for making urban spatial analysis in methodology and implementing identification of urban boundaries in practice.