Delimitation of Urban Growth Boundary Based on the Coordination of Ecology and Residential Activity Spaces: A Case Study of Jinan, China

doi:10.5814/j.issn.1674-764X.2019.05.007

Abstract

Abstract:

Delimitation of an urban growth boundary (UGB) can effectively curb disorderly urban expansion, optimize urban development space and protect the ecological environment. Eco-environmental sensitivity was evaluated and areas prohibiting construction expansion were extracted by establishing an index system. Point of interest (POI) and microblog data were utilized to analyze the expansion of residential activity space. Urban space expansion potential was calculated using a comprehensive evaluation model, and an urban growth boundary for Jinan in 2020 was delimited combining the predicted urban expansion scale. The results showed that: (1) An evaluation of eco-environmental sensitivity can effectively protect ecological land and provide an ecological basis for urban expansion. Regions with high eco-environmental sensitivities in Jinan are located along the banks of the Yellow River and Xiaoqing River and in southeast mountainous areas, but eco-environmental sensitivities in the central, north and southeast areas are relatively low; (2) The model to evaluate urban residential activity expansion can quantify the spatial distribution of urban residents' activities. Regions with high potential for residential activity space expansion in Jinan are mainly concentrated in the middle of Jinan and most are part of existing built-up areas and surrounding areas; (3) The method that delimits urban growth boundaries based on the coordination of ecology and residential activity space is reasonable. Spatial expansion in Jinan mainly extends towards the east and west wings, and the boundary conforms to the spatial strategy guiding Jinan’s development and is consistent with the overall layout in related plans. Considering both ecological protection and the internal forces driving urban expansion, the method of urban growth boundary delimitation used in this study can provide a reference and practical help for studies and management of urban development in the new era.

Key words: urban growth boundary, eco-environmental sensitivity, residential activity space, Jinan

JIA Kun, ZHANG Chao, YANG Yanzhao, YOU Zhen. Delimitation of Urban Growth Boundary Based on the Coordination of Ecology and Residential Activity Spaces: A Case Study of Jinan, China[J]. Journal of Resources and Ecology, 2019, 10(5): 518-524.

Figures/Tables 8

References 25

[1]	Brown D G, Robinson D T, An L , et al. 2007. Exurbia from the bottom-up: modeling multiple actors and their landscape interactions. Geoforum, 39:805-818.
[2]	Fu L, Hu Y C, Zheng X Q . 2016. The prediction of urban growth boundary based on BP artificial neural networks: An application to Beijing. China Land Science, 30(2):22-30. (in Chinese)
[3]	Hashem I A T, Chang V, Anuar N B , et al. 2016. The role of big data in smart city. International Journal of Information Management, 36(5):748-758.
[4]	He C Y, Jia K J, Xu X L , et al. 2010. Planning method for defining the boundary of the urban and rural construction land expansion based on spatial analysis technology of GIS. China Land Science, 24(3):12-18. (in Chinese)
[5]	He Q S, He W S, Song Y , et al. 2018. The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’. Land Use Policy, 78:726-738.
[6]	Jun M J . 2006. The effects of Portland’s urban growth boundary on housing prices. Journal of the American Planning Association, 72(2):239-243.
[7]	Li Y H . 2011. Method of determining urban growth boundary from the view of ecology: A case study of Hangzhou. City Planning Review, 35(12):83-90. (in Chinese)
[8]	Liu J H, Gao Z X, Ma S , et al. 2015. Comprehensive evaluation of eco-environmental sensitivity in Inner Mongolia, China. China Environmental Science, 35(2):591-598. (in Chinese)
[9]	Liu Y . 2009. Using SLEUTH model to simulate and assess future scenarios of urban expansion. Economic Geography, 29(10):1661-1664. (in Chinese)
[10]	Long Y, Han H Y, Mao Q Z . 2009. Establishing urban growth boundaries using constrained CA. Acta Geographica Sinica, 64(8):999-1008. (in Chinese)
[11]	Long Y, Han H, Lai S K , et al. 2013. Urban growth boundaries of the Beijing Metropolitan Area: Comparison of simulation and artwork. Cities, 31(2):337-348.
[12]	Ma S F, Li X, Cai Y M . 2017. Delimiting the urban growth boundaries with a modified ant colony optimization model. Computers, Environment and Urban Systems, 62:146-155.
[13]	Osman A M S . 2019. A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91:620-633.
[14]	Sinclair S K . 2014. Methods and considerations for determining urban growth boundaries - An evaluation of the Cape Town experience. Urban Forum, 25(3):313-333.
[15]	Su W Z, Yang G S, Chen S , et al. 2012. Analysis method of urban growth boundary of the Changzhou City in the Yangtze River delta. Journal of Natural Resources, 27(2):322-331. (in Chinese)
[16]	Tayyebi A, Pijanowski B C, Tayyebi A H . 2011. An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran. Landscape and Urban Planning, 30:35-44.
[17]	Triantakonstantis D, Mountrakis G, Wang J D . 2011. A spatially heterogeneous expert based (SHEB) urban growth model using model regionalization. Journal of Geographic Information System, 3:195-210.
[18]	Wang Y, Gu C L, Li X J . 2015. Preliminary study on urban growth boundary of Suzhou City. Journal of Urban and Regional Planning, 8(2):1-24. (in Chinese)
[19]	Wang Y G, Yin X L, Li G C . 2012. Delimitation of urban growth boundary based on land ecological suitability evaluation: A case of Shenshan Special Corporation Zone. Urban Development Studies, 19(11):76-82. (in Chinese)
[20]	Wu X P, Chen Z J, Jia C Y , et al. 2015. Urban growth boundary of county level towns: Baisha Li Ethnic Minority County. Planners, 31(9):126-132. (in Chinese)
[21]	Zeng C, Yang L D, Dong J N . 2017. Management of urban land expansion in China through intensity assessment: A big data perspective. Journal of Cleaner Production, 153(1):637-647.
[22]	Zhang Z G, Zhang S W . 2013. The ideas and methods of spatial structure-oriented urban growth boundary delimitation - A case study of Hangzhou City. Urban Planning Forum, 57(4):33-41. (in Chinese)
[23]	Zhou J F, Zeng G M, Huang G H , et al. 2007. The ecological suitability evaluation on urban expansion land based on uncertainties. Acta Ecologica Sinica, 27(2):774-783. (in Chinese)
[24]	Zhu X X, Wang H M . 2011. Research methods of expansion scale of urban construction land based on GIS: A case of Fogang County in Guangdong. Economic Geography, 31(4):660-665. (in Chinese)
[25]	Zhu Z W, Mo B, Xie F R . 2009. Delimitation of urban growth boundary based on land ecological suitability evaluation: A case of Fangchenggang. Planners, 25(11):40-44. (in Chinese)

Factors		Insensitive	Light sensitive	Moderate sensitive	Highly sensitive	Extreme sensitive
Topographic	Elevation (m)	≤100	100-150	150-200	200-400	﹥400
Topographic	Slope (°)	≤5	5-15	15-25	25-35	﹥35
Water	Distance from river (m)	﹥1000	500-1000	200-500	100-200	0-100
Water	Distance from lakes and reservoirs (m)	﹥2000	1000-2000	500-1000	200-500	0-200
Bio-inhabitation	NDVI	0-0.2	0.2-0.4	0.4-0.6	0.6-0.7	0.7-1
Value		1	3	5	7	9

Factors		Insensitive	Light sensitive	Moderate sensitive	Highly sensitive	Extreme sensitive
Topographic	Elevation (m)	≤100	100-150	150-200	200-400	﹥400
Topographic	Slope (°)	≤5	5-15	15-25	25-35	﹥35
Water	Distance from river (m)	﹥1000	500-1000	200-500	100-200	0-100
Water	Distance from lakes and reservoirs (m)	﹥2000	1000-2000	500-1000	200-500	0-200
Bio-inhabitation	NDVI	0-0.2	0.2-0.4	0.4-0.6	0.6-0.7	0.7-1
Value		1	3	5	7	9

Indicators	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ
POI Kernel density	0-6000	6000-30000	30000-80000	80000-160000	﹥160000
Microblog Kernel density	0-20000	20000-200000	200000-500000	500000-1600000	﹥1600000
Emotional attitude	< -15	-15 - -1	-1-1	1-10	>10
Value	1	3	5	7	9

Indicators	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ
POI Kernel density	0-6000	6000-30000	30000-80000	80000-160000	﹥160000
Microblog Kernel density	0-20000	20000-200000	200000-500000	500000-1600000	﹥1600000
Emotional attitude	< -15	-15 - -1	-1-1	1-10	>10
Value	1	3	5	7	9