Search arXivSearch

arXiv · 1401.3956

Superlinear and sublinear urban scaling in geographical network model of the city

Abstract

Using a geographical scale-free network to describe relations between people in a city, we explain both superlinear and sublinear allometric scaling of urban indicators that quantify activities or performances of the city. The urban indicator $Y(N)$ of a city with the population size $N$ is analytically calculated by summing up all individual activities produced by person-to-person relationships. Our results show that the urban indicator scales superlinearly with the population, namely, $Y(N)\propto N^β$ with $β>1$ if $Y(N)$ represents a creative productivity and the indicator scales sublinearly ($β<1$) if $Y(N)$ is related to the degree of infrastructure development. These coincide with allometric scaling observed in real-world urban indicators. We also show how the scaling exponent $β$ depends on the strength of the geographical constraint in the network formation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K. Yakubo, S. Saijo, D. Korošak. 2014-01-16. Superlinear and sublinear urban scaling in geographical network model of the city. https://doi.org/10.1103/physreve.90.022803

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Self-Reference in Large Language Models: The Introspection Threshold for Recursive Self-Improvement

The pursuit of self-evolving AI raises a critical question: when is autonomous self-improvement sustainable rather than degenerative? Drawing an analogy to von Neumann's complexity threshold for self-reproducing automata, we argue that sustainable recursive self-improvement in Large Language Models (LLMs) requires a functional analogue: introspection -- the system's capacity to simulate its own operations and target modifications. Grounded in Kleene's Second Recursion Theorem, we demonstrate the theoretical existence of such introspective programs. However, an empirical review reveals that while current LLMs exhibit quasi-introspection (e.g., partial metacognition), they fall short of true introspection due to structural bottlenecks: a lack of complete self-access, the feedforward nature of the Transformer, and computational class constraints that prevent fixed-point iteration. We conclude by outlining architectural paths to cross this complexity threshold and discussing the associated safety implications.

physics.soc-ph

Multilayer Analysis of the Global Trade Network

Global trade is more than a single network of aggregate flows. Beneath the observable exchange of products among economies lies a complex multilayer structure, formed by thousands of product-specific trade relationships that differ in their similarity, interdependence, and temporal evolution. Using the CEPII's BACI database, which records bilateral product-level trade flows between economies, we represent the global trade network from 1995 to 2024 as a temporal multilayer network, with economies as nodes and directed weighted trade flows as edges. To investigate product-level organisation and cross-layer similarity, temporal structural change, and the structural role of individual economies, we introduce a random-walk-based similarity measure that provides a unified framework for comparing weighted and directed trade layers. Our results show that the global trade network remains relatively stable over short periods but undergoes gradual structural change over longer timescales. We also find that similarity-based product communities only partially align with the official product taxonomy, indicating that products assigned to the same official category do not necessarily exhibit similar trade-network structures. Finally, we show that an economy's structural influence is not always determined by its trade volume. These results highlight the value of multilayer network analysis for revealing patterns in global trade that remain hidden at the aggregate level.

physics.soc-ph

Detectability limits of scaling laws

Power law scaling relations between size and output are central to quantitative theories of cities, organisms, and other complex systems. Competing theories predict scaling exponents that differ by small fractions, but there is no existing theory for verifying whether a given dataset can even distinguish exponents at the required resolution to address such discrepancies. Here we derive a resolution limit for scaling exponents, giving the smallest exponent difference that any method of analysis can detect. We find that the Hurst exponents governing the evolution of systems' sizes and deviations from the scaling law determine how long a record of growing systems must be before it can separate competing scaling theories. Empirical results suggest that many available data panels are insufficient for reliable scaling model selection.

physics.soc-ph