Impacts of spatial scale on the delineation of spatiotemporal urban expansion

Wang, Rong; Feng, Yang; Tong, Xiaohua; Zhao, Jiajun; Zhai, Shuting

doi:10.1016/j.ecolind.2021.107896

Cited by 12 publications

(6 citation statements)

References 72 publications

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“…granularities, Moran's I decrease steadily with the increase of granularity (e.g., Qi & Wu, 1996;Bu et al, 2003;Xu et al, 2004;Tan et al, 2005); however, at coarser granularities, Moran's I fluctuate greatly with no obvious regularity (e.g., Overmars et al, 2003;Xie et al, 2006;Qiu et al, 2007;Yin et al, 2008). Therefore, the break point of Moran's I changing with granularity is called "intrinsic scale" of spatial autocorrelation in study area (Xu et al, 2007;Chen & Zheng, 2013;Feng et al, 2020;Yuan et al, 2020;Li et al, 2022), and the extreme point of Moran's I is called "optimum scale" (Feng et al, 2016;Wang et al, 2021). The concepts of "intrinsic scale" and "optimum scale" are enlightening, but still remain at a qualitative level and are different from "characteristic scale" in mathematical concepts.…”

Section: Discussionmentioning

confidence: 99%

Scaling Invariance of Spatial Autocorrelation in Urban Landscape

Fang

Chen

2022

Preprint

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Context A large number of studies have shown that measures of spatial autocorrelation in urban landscape bear scale dependence. Scale dependence generally suggests fractals and scaling. But the inherent relationships between spatial autocorrelation index and fractal dimension is not clear yet. Objectives This paper is devoted to revealing the internal relationship between the scale dependence of Moran’s I and fractal scaling. Methods Mathematical reasoning and empirical analysis are employed to derive and test the model on the scale dependence of spatial autocorrelation. The fractal dimension estimation is based on box-counting method, and parameter estimation relies on the least squares regression. Results Based on the locality postulate of spatial correlation and the idea of multifractals, a power law model on Moran’s I changing with measurement scale is derived from the principle of recursive subdivision of space. The power exponent is proved to be a function of fractal dimension. This suggests that the numerical relationship between Moran’s I and fractal dimension can be established through the scaling process of granularity. Taking the cities of Beijing, Shanghai and Guangzhou in China as examples, we made an empirical analysis with observation data, and the results lend support to the theoretical model. Conclusions It can be concluded that spatial autocorrelation of urban landscape has no characteristic scale in many cases. In order to make effective spatial statistical analysis, it is necessary to explore spatial complexity based on fractal scaling behind urban landscape.

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Section: Discussionmentioning

confidence: 99%

Scaling Invariance of Spatial Autocorrelation in Urban Landscape

Fang

Chen

2022

Preprint

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“…In addition, urban planning is also an effective measure of urban expansion. Policymakers must consider urban growth factors under different land cover scenarios, monitor the impacts of urban expansion on urban ecology [ 58 ], develop urban greenery strategy and support urban densification [ 54 ], assess urban ecosystem services at the right scale and resolution [ 59 ], account for the multi-functionality of urban green infrastructure, define strategic objectives, ensure long-term commitment in the implementation phase, and strengthen planning arguments against conflicting interests [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Simulating Urban Expansion Based on Ecological Security Pattern—A Case Study of Hangzhou, China

Yang

Zhu

et al. 2021

IJERPH

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Disordered urban expansion has encroached on a large amount of ecological land, resulting in the steady degradation of urban ecology, which has an adverse effect on the sustainable development of the region. An ecological security pattern can effectively control urban expansion, and it is of great significance to balance urban development and ecological protection. In order to analyze the impact of ecological security patterns on urban expansion, Hangzhou was taken as an example, the CA-Markov model and FLUS model were used to simulate the urban expansion pattern in 2030 under the natural development scenario and the ecological security scenario. The results showed that (1) the ecological source area in the study area is 630.90 km2 and was mainly distributed in the western mountainous area. There are 14 ecological corridors, primarily composed of valleys and rivers. Ecological nodes are mainly distributed on the north and south sides of the main urban area. (2) From 2000 to 2018, the annual increase index (AI) of construction land decreased in the northeast and southeast directions but increased in the northwest and southwest directions, and in the northeast direction the value was always the highest. Except for the southwest direction, the average annual growth rate (AGR) of construction land in the other directions decreased. At a distance from the city center of 30 km, AI was relatively higher and was increasing, while AGR was declining. At a distance of 30–45 km, both AI and AGR were increasing, indicating that the focus of construction land was moving outwards. (3) From 2018 to 2030, under both natural development scenario and ecological security scenario, construction land would keep expanding, but the construction land area, proportion, AI, and AGR of the latter would both be smaller than the former, indicating that the ecological security pattern can effectively curb urban expansion. Because of a large amount area of ecological sources, the expansion of construction land in the southwest direction would be constrained, especially under the ecological security scenario. The methods and results of this study can provide theoretical and application references for urban planning and green development in metropolises.

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“…In previous studies, the selection of data sources was carried out according to grids and statistical data [53], and the statistical data analysis was usually not as accurate as grids in revealing the relationship between LUPs and the influencing factors [54]. In the selection of data sources on the basis of grids, many researchers were committed to refining the resolution to reveal the interaction of LUPs and influencing factors, while some studies found that the resolution was not better when more refined [55]. The XWT spectrum identified the interaction grid scales between LUPs and SEFs, avoiding the lack of artificial grid scales selection in previous studies.…”

Section: Identify the Interaction Grid Scales Between Lups And Sefsmentioning

confidence: 99%

The Interaction Relationship between Land Use Patterns and Socioeconomic Factors Based on Wavelet Analysis: A Case Study of the Black Soil Region of Northeast China

Wang

2021

Land

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Analyzing the interaction between land use patterns (LUPs) and socioeconomic factors (SEFs) could provide a basis for regional land spatial planning and management decisions in the future. In this study, population, gross domestic product (GDP) and land use intensity were selected to explain the relationship between SEFs and LUPs. The study designed a new method of sample line acquisition for wavelet analysis, and identified the interaction grid scales of LUP changes with SEFs in 1991, 2005 and 2019 by using cross wavelet transform analysis (XWT). Wavelet transform coherent analysis (WTC) was used to reveal the interaction direction and impact strength between LUPs and SEFs. The results showed that: (1) There were two ranges of 2978–5008 m and 24,400–29,738 m in which the grid scales showing interaction between LUPs and SEFs (population, GDP and land use intensity) from 1991 to 2019 were overlapping. (2) The interaction direction between LUPs and SEFs from 1991 to 2019 was almost negative on all sample lines, while the interaction directions of the middle sample line of population and GDP from 1991 to 2019, the end sample line of GDP in 2019, and the start sample line of land use intensity in 1991 were positive. (3) Dry land, grassland and construction land were most affected by SEFs, followed by paddy fields, forest land and other land, and the least affected were water areas during 1991 to 2019. The impact of population and GDP on LUPs was reduced, while the impact of land use intensity on LUPs was increased from 1991 to 2019. Overall, population, GDP and land use intensity were the important SEFs in the changes of LUPs, and were important factors for social progress and economic development.

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Impacts of spatial scale on the delineation of spatiotemporal urban expansion

Cited by 12 publications

References 72 publications

Scaling Invariance of Spatial Autocorrelation in Urban Landscape

Scaling Invariance of Spatial Autocorrelation in Urban Landscape

Simulating Urban Expansion Based on Ecological Security Pattern—A Case Study of Hangzhou, China

The Interaction Relationship between Land Use Patterns and Socioeconomic Factors Based on Wavelet Analysis: A Case Study of the Black Soil Region of Northeast China

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