Spatial Diffusion Waves of Human Activities: Evidence from Harmonized Nighttime Light Data during 1992–2018 in 234 Cities of China

Abstract

This study investigates whether the intensity of human activities conducted by urban populations and carried by urban land follows a wave-shaped diffusion rule using a harmonized DMSP-like NTL dataset during 1992–2018 in 234 cities of China. The results show that variations in the intensity of human activities are diffused in a wave-shaped manner from the urban center to the periphery in cities of different sizes and structures. The results demonstrate that variations in the intensity of human activity also exhibit a wave-shaped diffusion pattern, which is best modeled by a Gaussian function with an average R2 of 0.79 and standard deviation of 0.36 across all fitted functions. The outward movement of these waves in monocentric cities with an urban population <8 million occurred at a pace of ~0.5–1.0 km per year, reaching an average distance of ~18 km from the urban centers. While the pace decreased to ~0.2–0.6 km per year in larger or polycentric cities, the average distance of the waves from the urban centers increased to ~22–25 km in these larger cities. In addition, a process-pattern link between the distance-decayed rule and the wave-shaped rule of human activity dynamics was established. Moreover, a spatiotemporal Gaussian function was further discussed to enable modelers to forecast future variations in the intensity of human activities. The disclosed wave-shape rule and model can benefit the simulation of urban dynamics if integrated with other simulation technologies, such as agent-based models and cellular automata.

Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing

“…With the paradigm shift brought about by big data and machine learning in the quantification of commercial-street space indicators [13,14], the development and use of data such as those from location-based services [15,16] and social media [17,18] have facilitated a large number of studies on the spatiotemporal activities of crowds. For example, Yang used nighttime light data to represent the intensity of crowd activities in different regions and used Gaussian functions to visualize and model the spatial waves in the change in human activity intensity [19]. Meanwhile, innovations in research methods have deepened our understanding of crowd activity.…”

Spatiotemporal Evolution and Rank–Size Pattern of Chinese Urban Settlements

“…Urban settlements are places of concentrated human habitation, production, and living, and they are projected to house 68 percent of people globally in 2050 [1]. Urban settlements have a wide range of related research topics, including human activities [2,3], ecological environment [4,5], biodiversity conservation [6][7][8], economic development [9,10], and social activities [11]. The research of relevant urban settlements requires a defined spatial scope.…”

Spatial gradients of urban land density and nighttime light intensity in 30 global megacities

“…In terms of quantitative models describing urban land density spatial patterns, previous studies proposed a linear model with two stages, inverse-S function, and Geographic Micro-Process (GMP) models to describe the spatial distribution of urban land (Guérois, Pumain, 2008;Jiao, 2015;Jiao et al, 2021). The spatial distribution of other elements except urban land also plays a key role in the analysis of urban expansion (Cai et al, 2021;Chen et al, 2022, Yang et al, 2023. The spatial distribution of different urban elements determines the internal structure of each city, and the mismatch between them hinders the sustainable development of cities (Xiao et al, 2021).…”