Examining spatiotemporally varying effects of urban expansion and the underlying driving factors

Li, Cheng; Zhao, Jie; Xu, Yong

doi:10.1016/j.scs.2016.10.005

Cited by 62 publications

(30 citation statements)

References 37 publications

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“…where x i represents the independent variables, y represents the dependent variables, β 0 is the intercept, β i represents the coefficient of x i , k is the number of independent variables, and ε denotes the error term. The GWR model, which extends traditional OLS regression, has been used widely to identify spatially varying relationships through the generation of a set of local-specific coefficients, which is appropriate for measuring non-stationary variations across space [21,38,57].…”

Section: Geographically Weighted Regressionmentioning

confidence: 99%

“…Moreover, because urbanization is a complex process, entailing both spatial and temporal variation across different cities [20], its characteristics cannot be fully captured through an analysis of spatial changes confined to one time period. It is necessary to explore quantitative relationships between spatial patterns of urban green areas and urbanization relating to spatiotemporal changes [21]. To address this gap, we focused especially on spatial and temporal heterogeneities in an examination of the relationships between spatial patterns of green spaces and urbanization, set against a context of wider planning policies.An assessment of the spatiotemporal effects of urbanization on the fragmentation of urban green spaces requires an analysis of spatial and temporal changes relating urbanization with the fragmentation of urban green spaces.…”

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confidence: 99%

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Urban Green Space Fragmentation and Urbanization: A Spatiotemporal Perspective

Zheng

Wang

et al. 2019

Forests

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Urbanization leads to the occupation of green areas, directly contributing to a high level of fragmentation of urban green spaces, which, in turn, results in numerous socioeconomic and environmental problems. Consequently, an understanding of the relationships between patterns of urban green spaces and urbanization processes is essential. Although previous quantitative studies have examined this relationship, they have not included an exploration of spatial heterogeneities in the effects of urbanization on the spatial patterns of urban green areas. We therefore applied a spatiotemporal perspective to examine the above relationship, while considering the wider planning context. First, we quantified the extent of fragmentation of urban green spaces using landscape metrics comprising the largest patch index (LPI) and landscape shape index (LSI). Next, using the calculated spatial metrics and nighttime light data (NTL) for central Beijing for the period 1992–2016, we applied a geographically weighted regression model to assess variations in the spatiotemporal effects of urbanization on the fragmentation of urban green spaces. The results showed that urbanization initially occurred mainly in the northern parts of Beijing, whereas urbanization of southern urban fringe areas occurred after 2008. The reduction in green spaces along with increasing fragmentation and complex spatial patterns are indicative of issues relating to Beijing’s rapid urbanization and planning policies. This study contributes to an understanding of how urbanization influences fragmentation of urban green spaces and offers insights for the planning of urban green spaces from the perspective of promoting sustainability.

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Section: Geographically Weighted Regressionmentioning

confidence: 99%

mentioning

confidence: 99%

Urban Green Space Fragmentation and Urbanization: A Spatiotemporal Perspective

Zheng

Wang

et al. 2019

Forests

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“…Considering data representative and the availability of prior literature [31,42,48,52,[67][68][69][70], we selected 12 potential determinants for urban expansion in Lhasa that were extracted from the corresponding statistical yearbook: these determinants are total population (TP), urban population (UP), country population (CP), number of foreign travelers (FT), number of domestic tourists (DT), gross domestic product (GDP), GDP in primary industries (GDPPI), GDP in secondary industries (GDPSI), GDP in tertiary industries (GDPTI), tourist income (TI), actual investment (AI), and investment in fixed assets (FA). In addition, the build-up area (BA) was selected to reflect the degree of urban expansion.…”

Section: Determinants and Analysis Methodsmentioning

confidence: 99%

Accelerated Urban Expansion in Lhasa City and the Implications for Sustainable Development in a Plateau City

et al. 2017

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Urbanization challenges regional sustainable development, but a slight expansion mechanism was revealed in a plateau city. We have integrated the urban expansion process and analyzed its determinants in Lhasa (Tibet), and we provide insightful suggestions for urban management and planning for Lhasa. The full continuum of the urban expansion process has been captured using time-series of high-resolution remote sensing data . Four categories of potential determinants involved in economic, demographic, social, and government policy factors were selected, and redundancy analysis was employed to define the contribution rates of these determinants. The results illustrate that considerable urban expansion occurred from 1990 to 2015 in Lhasa, with the area of construction land and transportation land increasing at rates of 117.2% and 564.7%, respectively. The urban expansion in the center of Lhasa can be characterized as temperate sprawl from 1990 through 2008, primarily explained by governmental policies and investment, economic development, tourist growth, and increased governmental investment resulting in faster urban expansion from 2008 to 2015, mainly occurring in the east, south, and west of Lhasa. In contrast with other cities of China, central government investment and "pairing-up support" projects have played an important role in infrastructure construction in Lhasa. The miraculous development of the tourism industry had prominent effects on this economic development and urbanization after 2006, due to the running of the Tibetan Railway. An integrative and proactive policy framework, the "Lhasa development model", having important theoretical, methodological, and management implications for urban planning and development, has been proposed.

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“…In Equation (9), the estimates for the parameters are spatially nonstationary. Parameters for sample unit i in the GWR model can be derived by weighting all samples around sample unit i with respect to distance, which is calculated in terms of the Euclidean distance [32]. The samples closer to sample unit i have a stronger impact on the estimation of the local parameter, and are assigned larger weights than for distant samples.…”

Section: Regression Analysismentioning

confidence: 99%

“…In addition, parameters estimated from the GWR model are also sensitive to the kernel bandwidth. Three methods can be used to determine kernel bandwidth: Bandwidth Parameter (BP), corrected Akaike Information Criterion (AICc), and Cross Validation (CV) [32]. BP can be used when the kernel bandwidth is known.…”

Section: Regression Analysismentioning

confidence: 99%

Investigating the Spatiotemporally Varying Correlation between Urban Spatial Patterns and Ecosystem Services: A Case Study of Nansihu Lake Basin, China

Zhao

2019

IJGI

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Ecosystem services are the benefits obtained from an ecosystem that have great significance in sustainable development. Urbanization has triggered significant changes on urban spatial patterns, which have had a great impact on the ecosystem services. However, studies on the spatiotemporally varying relationship between urban spatial patterns and ecosystem services are lacking. Taking as a case study, the Nansihu Lake Basin in China, this study aimed to explore the spatiotemporally varying relationship between urban spatial patterns and ecosystem services. Urban spatial patterns were derived by integrating remote sensing and spatial metrics. Ecosystem service values were calculated using ecosystem service models. The spatiotemporally varying impact of urban spatial patterns on ecosystem services was quantified using the Geographically Weighted Regression (GWR) model. The findings indicate that urban spatial patterns and ecosystem services have dramatically varied with the urbanization process. The estimated parameters indicate that urban spatial patterns have significant impacts on ecosystem services. The GWR revealed a spatiotemporally varying correlation and improved the explanatory ability in comparison with the Ordinary Least Squares (OLS) model. The investigation of the impact of urban spatial patterns on ecosystem services can provide more practical support for effective urban planning and ecosystem management.

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Examining spatiotemporally varying effects of urban expansion and the underlying driving factors

Cited by 62 publications

References 37 publications

Urban Green Space Fragmentation and Urbanization: A Spatiotemporal Perspective

Urban Green Space Fragmentation and Urbanization: A Spatiotemporal Perspective

Accelerated Urban Expansion in Lhasa City and the Implications for Sustainable Development in a Plateau City

Investigating the Spatiotemporally Varying Correlation between Urban Spatial Patterns and Ecosystem Services: A Case Study of Nansihu Lake Basin, China

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