The concept of urban intensity and China's townization policy: Cases from Zhejiang Province

Abstract

Urban intensity, in this paper, is measured by four related concepts: compactness, diversity, density, and connectivity. Together they lead to a single idea when considering spatial distributions potentially in a virtuous manner with regard to resource consumption, economic opportunity, social integration and environmental performance. The methodologies applied here included Moran's I, Shannon's index entropy, and accessibility isotimelines, which were then applied to real case scenarios in 20 towns in Zhejiang Province, selected based on their economic performances, population sizes, and geographical locations. Further inspection discovered that density, an outcome of urban form, is highly correlated to compactness, leading to its elimination. The results showed that among the varying spatial arrangements of urban activities, building footprints and infrastructural elements characterized by monocentric centers of use inscribed with welldefined and relatively uniform grids of streets and related networks, alongside of relatively integrated zones of use, seemed to perform best with regard to urban intensity.At the other end of the morphological spectrum, towns with sharp separations of uses and zones of development, often resulting in overall bifurcation of a town's spatial layout, performed less well. Also, linear forms for small towns were less favorable.

Spatial Association of Urbanization in the Yangtze River Delta, China

“…Local autocorrelation analysis and the polarization index were complementary and measured changes in spatial agglomeration or dispersion that indicated different levels of urban development. Moran’s I and LISA agglomeration are commonly used indicators of spatial autocorrelation [ 27 , 28 , 29 ]. The polarization index quantified the degree of polarization of regional development, but it could not indicate the specific regions of polarization in space.…”

“…Previous studies have revealed the internal correlation of urban development within China using comprehensive quantitative methods, e.g., the spatial econometric regression model, entropy method, Moore’s structural change index, and Moran’s I index [ 26 ]. Most studies tend to measure the level of urbanization by calculating the Moran’s I index [ 27 , 28 , 29 ]; however, the Tsui–Wang (TW) index has been applied to analyze the overall polarization trend in related research [ 30 ].…”

In pursuit of a well-balanced network of cities and towns: A case study of the Changjiang Delta Region in China

“…Another useful indicator is ‘urban intensity’ measured by four related concepts: compactness, diversity, density, and connectivity. Together they lead to a single idea when considering spatial distributions potentially in a virtuous manner with regard to resource consumption, economic opportunity, social integration, and environmental performance (Guan and Rowe, 2016). Another group of scholars focused on simulation and projections (Batty and Xie, 1994; Liu, 2009; Samat et al., 2011).…”

“…The modification of parameters did not change how the principles of the Cellular Automata model operated. However, they did provide opportunities to reflect the influences of certain urban policies on the outcome of the urban land growth patterns predictions (Guan and Rowe, 2016). The scenario-based Cellular Automata model used the formula …”

Evaluating Urban Intensity Through a City Information Model – Intermediate Results From an Action Research Project

“…Urban analysis performed by urban planners and architects is key to achieve the objectives set for a given urban project (Ayeni, 2017), as it helps to understand the inherent dynamics of places. Due to the multifaceted reality of urbanisation, urban analyses based on traditional static, monocriteria indicators are insufficient and researchers are turning to dynamic notions such as urban intensity (Fouchier, 1997;Da Cunha and Kaiser, 2009;Sevtsuk et al, 2013;Guan and Rowe, 2016;Chadee and Stoute, 2018;Stonor, 2019). This notion includes a set of characteristics which contribute to the understanding of complex urban systems.…”