Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City

Shi, Ge; Shan, Jie; Ding, Liang; Ye, Peng; Li, Yang; Jiang, Nan

doi:10.3390/ijerph16132318

Cited by 52 publications

(28 citation statements)

References 52 publications

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“…Previous studies even identified a negative relation between tertiary industry development and urban expansion generally [15,41], while a positive correlation was found in the Pearl River Delta region. Because the development level of tertiary industry in the Pearl River Delta region is high and urban development is significantly driven by tertiary industry [36].…”

Section: Discussionmentioning

confidence: 99%

“…Public financial expenditure relates to a government’s investment in public goods and services, such as affordable housing, transportation systems, and public service facilities [29]. Road construction improves accessibility, thus attracting investment and guiding urban expansion [15,16,36]. Therefore, total public finance expenditure (PFX) and change of area of paved roads in cities (ROD) were selected to represent decentralization.…”

Section: Methodsmentioning

confidence: 99%

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Determinants of Urban Expansion and Spatial Heterogeneity in China

Zhang

Ying

et al. 2019

IJERPH

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China is the world’s largest developing country and its regions vary considerably. However, spatial heterogeneity in determinants of urban expansion in prefecture-level cities have not been identified. The present study explored the spatiotemporal characteristics of Chinese urban expansion and adopted a geographically weighted regression (GWR) method to determine this spatial heterogeneity. The results indicated that China experienced massive urban expansion during 1990–2015, with urban areas growing from 4.88 × 104 km2 to 1.06 × 105 km2, 46.42% of which was distributed in the eastern region. The results of the GWR model revealed the spatial heterogeneity in the determinants of urban expansion. Marketization was vital for urban expansion and had a stronger impact in the developed eastern and southern regions than in the less-developed northern and western regions. Globalization and decentralization bi-directionally affected urban expansion. The constraining effects of physical factors were limited and stronger in the developing northern region than in the developed southern region. Identifying the varying determinants of urban expansion is essential for policy-making in various regions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Determinants of Urban Expansion and Spatial Heterogeneity in China

Zhang

Ying

et al. 2019

IJERPH

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“…This city is one of the megacities in the Yangtze River basin and has experienced rapid economic development since the 1970s that is still occurring today [29]. At the end of the 20th century, the urbanization of Nanjing entered an accelerated phase, which led to the rapid increase in population, unreasonable industrial structure, unbalanced land use, high energy consumption, and environmental degradation [30,31]. Over the last two decades, the population has increased from 3 M to 8.5 M, and its GDP has increased from 338.12 billion CNY in 2008 to 1171.51 billion CNY in 2018.…”

Section: Study Areamentioning

confidence: 99%

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

Liu

Wang

et al. 2020

PLoS ONE

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The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socioeconomic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.

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“…Along the rural-urban gradient the number and the density of human inhabitants increases (Montalvo et al, 2019). The urbanization rate is associated with an increase in road density (Shi et al, 2019), area with artificial cover and paved surfaces (Murata & Kawai, 2018;Chen et al, 2020), and air and soil pollution (Duh et al, 2008;Jia et al, 2018). Urban forests are constantly exposed to elevated temperatures, carbon dioxide, nitrogen deposition and ozone (Carreiro & Tripler, 2005).…”

Section: Introductionmentioning

confidence: 99%

Applying plant disturbance indicators to reveal the hemeroby of soil macrofauna species

et al. 2020

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Hemeroby is an integrated indicator for measuring human impacts on environmental systems. Hemeroby has a complex nature and a variety of mechanisms to affect ecosystems. Hemeroby is often used to assess disturbances in different vegetation types but this concept has seldom been evaluated for animals. The role of the hemeroby gradient in structuring the soil macrofauna community was investigated. The experimental polygon was located in Botanical Garden of the Oles Honchar Dnipro National University (Dnipro City, Ukraine). There were 20 sites within the polygon. On each of them at 105 points samples of soil macrofauna were taken, soil penetration resistance, electrical conductivity of soil, depth of litter, height of grasses were measured. Within each site, a description of the vegetation cover was made. Based on the description of the vegetation, an indication of the level of ecosystem hemeroby within the polygons was conducted. In total, 48,457 invertebrate (Annelida, Arthropoda, and Mollusca) individuals of 6 classes, 13 orders, 50 families and 83 species or parataxonomic units were recorded. Phytoindication reveals that the level of hemeroby within the studied polygons varies from 34.9 to 67.2. The model V and VI from the HOFJO-list were the most optimal model of the species response to hemeroby gradient. The weighted average factor value was used to assess the optimal factor level for the species in a symmetrical bell-shaped response model. The optimal factor level of the hemeroby for the soil macrofauna species ranges from 34.9 to 66.0. Species also differ in degree of specialization to the factor of hemeroby. There was a regular change in the soil macrofauna community size and diversity in the hemeroby gradient. The limiting influence of anthropogenic transformation of the environment on the abundance of soil macrofauna community is clearly marked at the level of hemeroby above average. Species diversity of the community is greatest at moderate hemeroby level. Both relatively little transformed habitats and strongly transformed ones are characterized by lower species richness of the soil macrofauna community. The Shannon index shows a clear upward trend with increasing hemeroby. The Pielou index indicates that the main reason for this trend is an increase in community evenness with increasing hemeroby. The intermediate disturbance hypothesis was fully supported with respect to species richness. For the number of species, there is indeed a certain level of heterogeneity at which the number of species is highest. For another aspect of diversity, evenness, this pattern is not true. The evenness increases with increasing habitat disturbance. This result is due to a decrease in the abundance of dominant species.

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Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City

Cited by 52 publications

References 52 publications

Determinants of Urban Expansion and Spatial Heterogeneity in China

Determinants of Urban Expansion and Spatial Heterogeneity in China

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

Applying plant disturbance indicators to reveal the hemeroby of soil macrofauna species

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