Examining the Density and Diversity of Human Activity in the Built Environment: The Case of the Pearl River Delta, China

Abstract

Rapid urbanization in China has been accompanied by spatial inefficiency in patterns of human activity, of which ‘ghost towns’ are the most visible result. In this study, we measure the density and diversity of human activity in the built environment and relate this to various explanatory factors. Using the Pearl River Delta (PRD) as an empirical case, our research demonstrates the distribution of human activity by multi-source data and then explores its dynamics within these areas. This empirical study is comprised of two parts. The first part explores location information regarding human activity in urbanized areas and shows density and diversity. Regression models are applied to explore how density and diversity are affected by urban scale, morphology and by a city’s administrative level. Results indicate that: 1) cities with smaller populations are more likely to be faced with lower density and diversity, but they derive greater marginal benefits from improving land use efficiency; 2) the compactness of the layout of urban land, an index reflecting the plane shapes of the built environment, is highly correlated with density and diversity in built-up areas; and 3) the administrative importance of a city has a significant and positive impact on the density of human activity, but no obvious influence on its diversity.

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

“…The NTLB k index indicates the total intensity of all-level human activities when DN min = 1, and NTLB k indicates the total intensity of urban-related human activities when DN min = 30. This threshold (DN = 30) is widely adopted in existing studies to extract human activities in urban areas [47] based on DMSP-like NTL data. What can be generally seen in Figure 5a,b is a decreasing trend in the total intensity of human activity from the urban centers to the outside areas in each year, regardless of the lower limit of DN value.…”

“…urban areas [47] based on DMSP-like NTL data. What can be generally seen in Figure 5a is a decreasing trend in the total intensity of human activity from the urban centers to t outside areas in each year, regardless of the lower limit of DN value.…”

Behind the Land Use Mix: Measuring the Functional Compatibility in Urban and Sub-Urban Areas of China

“…Although LUM is identified with two key characteristics, namely diverse categories of land use and functional compatibility between various land uses, they are understood differently in academic literature. Until now, current methodological approaches focus largely on the former factor and form quite a few identifying methods including Shannon entropy [29,36], dissimilarity index [37], Simpson index [38,39], and many other indices. Song et al [5] present a comprehensive review on these indices and regard the concepts of "mix" and "diverse land uses" with a high degree of similarity.…”

“…One of the essential components of LUM is the degree to which "mix" can be captured and utilized. Many scholars mainly focus on such a topic and attempt to link it with the categories [28,29], intensity [30], and proximity of various land uses [6]. Nevertheless, an important pre-condition of "mix" implied by Jacobs and New Urbanists is ignored to some extent [7].…”