Ht-Index for Quantifying the Fractal or Scaling Structure of Geographic Features

TL;DR

This paper introduces the ht-index as a new measure to quantify the fractal or scaling complexity of geographic features, demonstrating its effectiveness through case studies and discussing its advantages over traditional fractal dimension.

Contribution

The paper proposes the ht-index as an alternative to fractal dimension for measuring geographic complexity, providing a dynamic perspective and empirical validation.

Findings

Ht-index effectively captures geographic feature complexity

Higher ht-index indicates more intricate structures

Complementary to traditional fractal dimension

Abstract

Although geographic features, such as mountains and coastlines, are fractal, some studies have claimed that the fractal property is not universal. This claim, which is false, is mainly attributed to the strict definition of fractal dimension as a measure or index for characterizing the complexity of fractals. In this paper, we propose an alternative, the ht-index, to quantify the fractal or scaling structure of geographic features. A geographic feature has ht-index h if the pattern of far more small things than large ones recurs (h-1) times at different scales. The higher the ht-index, the more complex the geographic feature. We conduct three case studies to illustrate how the computed ht-indices capture the complexity of different geographic features. We further discuss how the ht-index is complementary to fractal dimension, and elaborate on a dynamic view behind the ht-index that enables better understanding of geographic forms and processes. Keywords: Scaling of geographic space, fractal dimension, Richardson plot, nested rank-size plots, and head/tail breaks