The inverted U-shaped effect of urban hotspots spatial compactness on urban economic growth

Abstract

The compact city, as a sustainable concept, is intended to augment the efficiency of urban function. However, previous studies have concentrated more on morphology than on structure. The present study focuses on urban structural elements, i.e. urban hotspots consisting of high-density and high-intensity socioeconomic zones, and explores the economic performance associated with their spatial structure. We use night-time luminosity data and the Loubar method to identify and extract the hotspot and ultimately draw two conclusions. First, with population increasing, the hotspot number scales sublinearly with an exponent of approximately 0.50–0.55, regardless of the location in China, the EU or the USA, while the intersect values are totally different, which is mainly due to different economic developmental level. Secondly, we demonstrate that the compactness of hotspots imposes an inverted U-shaped influence on economic growth, which implies that an optimal compactness coefficient does exist. These findings are helpful for urban planning.

Non-trivial relationship between scaling behavior and the GDP microstructure in Indonesian cities

“…This aligns with Ref. [26], where they define the economic hotspots through night-time light intensity. This way, the hotspots are defined as the local maxima in the surface of GDP density.…”

“…3(a)), we found that the hotspots scale sublinearly (β = 0.964 ± 0.058) with population size, while the non-hotspots obey a superlinear scaling (β = 1.045 ± 0.066). Note that similar to β of urban center area, β of hotspots area in our observation is higher than that was observed in previous research in developed countries [24,26]. The hotspots tend to cover the core of the urban center, while the non-hotspots tend to cover parts of urban center and the commuting zone.…”

“…Ref. [26] analyzed economic hotspots based on night-time light intensity in the United States, European Union, and Chinese cities.…”

Spatial sensitivity analysis for urban hotspots using cell phone traces

“…The last two types of indices focus on the quantification of urban structure (Ewing, 2008; Le Néchet, 2012; Louail et al., 2014; Schwanen et al., 2001). Research in quantitative geography and urban economics has shown the importance of studying urban structure, as it can shape people’s mobility in terms of travel distance, model choice, and car usage (Le Néchet, 2012; Schwanen et al., 2001); the transportation system in terms of energy consumption or air pollution (Ewing, 2008; Le Néchet, 2012); and economic growth performance (Huang et al., 2007; Xu et al., 2019).…”

“…hotspots, defined as regions with high concentration of individuals for a given period of time (Chen et al., 2019; Hoteit et al., 2014; L3Harris, n.d.; Louail et al., 2014; Ratti et al., 2006; Vieira et al., 2010). Hotspot analyses using CDR data are generally carried out in two different scenarios: (1) modeling, with a focus on analyzing the urban structure, such as the quantification of the urban sprawl or compactness of cities (Louail et al., 2014; Xu et al., 2019), or the analysis of the spatio-temporal evolution of popular locations for a given region (Ghahramani et al., 2018; Zuo and Zhang, 2012); and (2) prediction, with a focus on the analysis of the predictive power of dense regions with respect to a given variable; for example, high footfall (number of estimated visits) in a region has been associated to high crime (Bogomolov et al., 2015; Traunmueller et al., 2014), or large numbers of individuals at night or work times have been associated to the identification of home (residential) and work locations (Isaacman et al., 2012). These studies are often carried out at two different spatial scales: intra-city , where researchers focus on spatio-temporal models or predictions for a given city (Ratti et al., 2006; Reades et al., 2007) and inter-city , where researchers focus on comparing static behaviors (one-time snapshots) across cities (Ahas et al., 2015; Louail et al., 2014).…”