Designing a spatial pattern to rebalance the orientation of development and protection in Wuhan

doi:10.1007/s11442-020-1743-6

Abstract

Abstract:

Patterns of spatial development and protection form a basic category of geoscience, and redesigning them is a popular subject of research in regional sustainable development that is important for ecological civilization construction. The authors here report a case study of Wuhan city using the circuit theory model and minimum cumulative resistance (MCR) model to rebalance its spatial protection and development. The results show the following: (1) Using the density of the gross domestic product (GDP), density of population, rate of urbanization, and access to transportation as evaluation indicators, seven core areas of development in Wuhan were identified, accounting for 59% of the total number of streets, that exhibited a “circular-satellite” spatial structure. (2) According to the importance of ecosystem services, ecological sensitivity, land use type, and slope of the terrain, the resistance surface of spatial development in Wuhan had a stereoscopic spatial form of an “inverted pyramid,” with high surroundings and a low center. The area of low resistance accounted for 6.64% of the total area of Wuhan. (3) Based on coupling analysis using the MCR and spatial morphological characteristics of current, nine axes of spatial development with a total area of 427.27 km2 and eight key strategic points with a total area of 40.02 km2 were identified. Streets that were prioritized for development accounted for 9.63% of Wuhan’s total area. (4) By combining the characterization of the development axis with the structure of the three-level core area, we extracted the structure of spatial development of “one heart, two wings, and three belts” in Wuhan. The research framework and empirical results can provide scientific guidance for the urban spatial layout, the development of regional linkages, and ecological environmental protection in China.

Key words: spatial development and protection, circuit theory model, minimum cumulative resistance model, core area, development axis, Wuhan

JIN Gui, SHI Xin, HE Dawei, GUO Baishu, LI Zhaohua, SHI Xianbin. Designing a spatial pattern to rebalance the orientation of development and protection in Wuhan[J].Journal of Geographical Sciences, 2020, 30(4): 569-582.

Figures/Tables 7

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References 39

[1]	Avon C, Bergès L , 2016. Prioritization of habitat patches for landscape connectivity conservation differs between least-cost and resistance distances. Landscape Ecology, 31(7):1551-1565.
[2]	Azaele S, Maritan A, Cornell S J et al., 2015. Towards a unified descriptive theory for spatial ecology: Predicting biodiversity patterns across spatial scales. Methods in Ecology and Evolution, 6:324-332.
[3]	Blatti C, Sinha S , 2016. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks. Bioinformatics, 32(14):2167-2175.
[4]	Blazquez-Cabrera S, Gastón A, Beier P et al., 2016. Influence of separating home range and dispersal movements on characterizing corridors and effective distances. Landscape Ecology, 31(10):2355-2366.
[5]	Bradburd G S, Ralph P L, Coop G M , 2016. A spatial framework for understanding population structure and admixture. PLoS Genetics, 12(1):e1005703. doi: 10.1371/journal.pgen.1005703
[6]	Carroll C, McRae B H, Brookes A , 2012. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conservation Biology, 26(1):78-87. doi: 10.1111/j.1523-1739.2011.01753.x
[7]	Chen M X, Liu W D, Lu D D et al., 2018. Progress of China’s new-type urbanization construction since 2014: A preliminary assessment. Cities, 78:180-193.
[8]	Correa Ayram C A, Mendoza M E, Etter A et al., 2016. Habitat connectivity in biodiversity conservation: A review of recent studies and applications. Progress in Physical Geography, 40(1):7-37.
[9]	Costanza J K, Terando A J , 2019. Landscape connectivity planning for adaptation to future climate and land-use change. Current Landscape Ecology Reports, 4(1):1-13.
[10]	Dobrynin A A, Kochetova A A , 1994. Degree distance of a graph: A degree analogue of the Wiener index. Journal of Chemical Information and Computer Sciences, 34(5):1082-1086.
[11]	Fan J , 2018. “Territorial system of human-environment interaction”: A theoretical cornerstone for comprehensive research on formation and evolution of the geographical pattern. Acta Geographica Sinica, 73(4):597-607. (in Chinese)
[12]	Ferretti V, Pomarico S , 2013. An integrated approach for studying the land suitability for ecological corridors through spatial multicriteria evaluations. Environment, Development and Sustainability, 15(3):859-885.
[13]	Huang Q, Zeng Y, Jiang Q , 2015. Progress and prospect of the study on “making great efforts to promote ecological civilization construction”. China Population, Resources and Environment, 25(2):111-120. (in Chinese)
[14]	Jin G, Chen K, Wang P et al., 2019. Trade-offs in land-use competition and sustainable land development in the North China Plain. Technological Forecasting and Social Change, 141:36-46.
[15]	Jin G, Deng X Z, Chu X et al., 2017. Optimization of land-use management for ecosystem service improvement: A review. Physics and Chemistry of the Earth, 101:70-77.
[16]	Jin G, Deng X Z, Zhao X D et al., 2018. Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014. Journal of Geographical Sciences, 28(8):1113-1126.
[17]	Jin G, Li Z H, Deng X Z et al., 2019. An analysis of spatiotemporal patterns in Chinese agricultural productivity between 2004 and 2014. Ecological Indicators, 105:591-600.
[18]	Klein D J, Randić M , 1993. Resistance distance. Journal of Mathematical Chemistry, 12(1):81-95.
[19]	Kupfer J A , 2012. Landscape ecology and biogeography: Rethinking landscape metrics in a post-FRAGSTATS landscape. Progress in Physical Geography, 36(3):400-420.
[20]	Leonard P B, Duffy E B, Baldwin R F et al., 2017. Gflow: Software for modelling circuit theory-based connectivity at any scale. Methods in Ecology and Evolution, 8(4):519-526.
[21]	Li S C, Bing Z L, Jin G , 2019. Spatially explicit mapping of soil conservation service in monetary units due to land use/cover change for the Three Gorges Reservoir Area, China. Remote Sensing, 11(4):468.
[22]	Liu H T, Tian L L, Tian Y et al., 2015. Exploring the spatial expression and governance policies of urban sprawl in Wuhan. Economic Geography, 35(4):47-53. (in Chinese)
[23]	Luo F H, Liu Y X, Peng J et al., 2018. Assessing urban landscape ecological risk through an adaptive cycle framework. Landscape and Urban Planning, 180:125-134.
[24]	Martin D M, Mazzotta M, Bousquin J , 2018. Combining ecosystem services assessment with structured decision making to support ecological restoration planning. Environmental Management, 62(3):608-618.
[25]	McRae B H, Beier P , 2007. Circuit theory predicts gene flow in plant and animal populations. Proceedings of the National Academy of Sciences, 104(50):19885-19890.
[26]	McRae B H, Dickson B G, Keitt T H et al., 2008. Using circuit theory to model connectivity in ecology, evolution and conservation. Ecology, 89(10):2712-2724.
[27]	Nunes D M, Tomé A, Pinheiro M D , 2019. Urban-centric resilience in search of theoretical stabilisation? A phased thematic and conceptual review. Journal of Environmental Management, 230:282-292.
[28]	Peng J, Li H L, Liu Y X et al., 2018. Identification and optimization of ecological security pattern in Xiong’an New Area. Acta Geographica Sinica, 73(4):701-710. (in Chinese)
[29]	Peng J, Liu Y X, Wu J S et al., 2015. Linking ecosystem services and landscape patterns to assess urban ecosystem health: A case study in Shenzhen City, China. Landscape and Urban Planning, 143:56-68.
[30]	Peng J, Yang Y, Liu Y X et al., 2018. Linking ecosystem services and circuit theory to identify ecological security patterns. Science of the Total Environment, 644:781-790. doi: 10.1016/j.scitotenv.2018.06.292
[31]	Roopnarine P D, Byars G, Fitzgerald P , 1999. Anagenetic evolution, stratophenetic patterns, and random walk models. Paleobiology, 25(1):41-57.
[32]	Spear S F, Balkenhol N, Fortin M J et al., 2010. Use of resistance surfaces for landscape genetic studies: Considerations for parameterization and analysis. Molecular Ecology, 19:3576-3591.
[33]	Thulasiraman K, Yadav M, Naik K , 2019. Network science meets circuit theory: Resistance distance, Kirchhoff index, and Foster’s theorems with generalizations and unification. IEEE Transactions on Circuits and Systems I: Regular Papers, 66(3):1090-1103.
[34]	Todes A , 2017. Shaping peripheral growth? Strategic spatial planning in a South African city-region. Habitat International, 67:129-136. doi: 10.1016/j.habitatint.2017.07.008
[35]	Vasanen A , 2012. Functional polycentricity: Examining metropolitan spatial structure through the connectivity of urban sub-centres. Urban Studies, 49(16):3627-3644. doi: 10.1177/0042098012447000
[36]	Veneri P , 2013. The identification of sub-centres in two Italian metropolitan areas: A functional approach. Cities, 31:177-185. doi: 10.1016/j.cities.2012.04.006
[37]	Yang J X, Gong J, Gao J et al., 2019. Stationary and systematic characteristics of land use and land cover change in the national central cities of China using intensity analysis: A case study of Wuhan City. Resources Science, 41(4):701-716. (in Chinese) doi: 10.18402/resci.2019.04.08
[38]	Yu B L, Shu S, Liu H X et al., 2014. Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: A case study of China. International Journal of Geographical Information Science, 28(11):2328-2355. doi: 10.1080/13658816.2014.922186
[39]	Zhou D, Xu J C, Wang L et al., 2015. Assessing urbanization quality using structure and function analyses: A case study of the urban agglomeration around Hangzhou Bay (UAHB), China. Habitat International, 49:165-176.

Terms	Geographical significance
Current	Probability of each element flowing through a geographic unit in the pattern of spatial protection and development
Resistance	The resistance to development of the spatial unit; the geographical unit with a tendency toward protection is assigned a high resistance value
Voltage	The probability that the spatial element leaves a node and successfully reaches a specified node is also the probability that two nodes can be connected through the axis
Effective resistance	Resistance distance of different paths between nodes
Ground	Endpoint of the spatial development axis

Code of core area	Code of core area
Code of core area	2	3	4	5	6	7
1	5.06	17.71	6.75	17.95	17.34	26.01
2		21.31	5	15.11	15.37	23.26
3			16.22	29.15	27.77	37.1
4				4.87	5.03	13.72
5					13.29	12.8
6						19.25