Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method

Landscape and Urban Planning

Volumn 177, Issue , 2018, Pages 47-63

  Liang, Xun ^a   Liu, Xiaoping ^a   Li, Xia ^a,b   Chen, Yimin ^a,b   Tian, He ^a   Yao, Yao ^c  

^a SUN YAT SEN UNIVERSITY   (China)

^b East China Normal University   (China)

^c CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

Cellular automata; Erosion and dilation; Planning scenarios; UGB FLUS model; Urban growth boundaries (UGBs)

Indexed keywords

CELLULAR AUTOMATA; ECONOMICS; EROSION; LAND USE; PLANNING; REGIONAL PLANNING; ROBOTS; TRANSPORT PROPERTIES;

CELLULAR AUTOMATON MODELS; GROWTH SIMULATION; PEARL RIVER DELTA REGION; PROJECTION PERIOD; SOCIO-ECONOMIC STATUS; SYSTEM DYNAMICS MODEL; TOP DOWN APPROACHES; URBAN GROWTH BOUNDARY;

URBAN GROWTH;

BOTTOM-UP APPROACH; CELLULAR AUTOMATON; COMPLEXITY; MORPHOLOGY; SCENARIO ANALYSIS; SOCIOECONOMIC CONDITIONS; SPATIAL PLANNING; URBAN GROWTH; URBAN PLANNING; URBAN SPRAWL;

EID: 85046794891     PISSN: 01692046     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.landurbplan.2018.04.016     Document Type: Article

References (58)

1. Al-Ahmadi, K., Heppenstall, A., Hogg, J., See, L., A Fuzzy Cellular Automata Urban Growth Model (FCAUGM) for the City of Riyadh, Saudi Arabia. Part 2: Scenario Testing. Applied Spatial Analysis and Policy 2:2 (2009), 85–105.
2. Bhatta, B., Modelling of urban growth boundary using geoinformatics. International Journal of Digital Earth 2:4 (2009), 359–381.
3. Cao, K., Huang, B., Wang, S., Lin, H., Sustainable land use optimization using Boundary-based Fast Genetic Algorithm. Computers Environment & Urban Systems 36:3 (2012), 257–269.
4. Carter, F., The reform of housing in Eastern Europe and the Soviet union by B. Turner; J. Hegedüs; I. Tosics. Geografiska Annaler, 74(2), 1992, 147.
5. Cerreta, M., Toro, P.D., Urbanization susceptibility maps: a dynamic spatial decision support system for sustainable land use. Earth System Dynamics 3:2 (2012), 157–171.
6. Chen, Y., Li, X., Liu, X., Ai, B., Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science 28:2 (2014), 234–255.
7. Chen, Y., Li, X., Liu, X., Ai, B., Li, S., Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata. Landscape & Urban Planning 152 (2016), 59–71.
8. Clarke, K.C., Why simulate cities?. Geo Journal 79:2 (2014), 129–136.
9. Clarke, K.C., Gaydos, L.J., Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science 12:7 (1998), 699–714.
10. Coiacetto, E., Residential Sub-market Targeting by Developers in Brisbane. Urban Policy and Research 25:2 (2007), 257–274.
11. Feng, Y., Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules. International Journal of Geographical Information Systems 31:6 (2017), 1198–1219.
12. Feng, Y., Liu, M., Chen, L., Liu, Y., Simulation of dynamic urban growth with partial least squares regression-based cellular automata in a GIS. Environment, 5(12), 2016, 243.
13. Feng, Y., Liu, Y., Tong, X., Liu, M., Deng, S., Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning 102:3 (2011), 188–196.
14. Gao, J., Wei, Y.D., Chen, W., Chen, J., Economic transition and urban land expansion in Provincial China. Habitat International 44 (2014), 461–473.
15. Gennaio, M., Hersperger, A.M., Burgi, M., Containing urban sprawl—Evaluating effectiveness of urban growth boundaries set by the Swiss Land Use Plan. Land Use Policy 26:2 (2009), 224–232.
16. Gordon, D., Vipond, S., Gross density and new urbanism. Journal of the American Planning Association, 71, 2005.
17. Han, H., Lai, S., Dang, A., Tan, Z., Wu, C., Effectiveness of urban construction boundaries in Beijing: An assessment. Journal of Zhejiang University Science 10:9 (2009), 1285–1295.
18. Hashem, N., Balakrishnan, P., Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar. Geographic Information Sciences 21:3 (2015), 233–247.
19. Hepinstallcymerman, J., Coe, S., Hutyra, L.R., Urban growth patterns and growth management boundaries in the Central Puget Sound, Washington, 1986–2007. Urban Ecosystems 16:1 (2011), 109–129.
20. Hu, Z., Lo, C., Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems 31:6 (2007), 667–688.
21. Inkoom, J.N., Nyarko, B.K., Antwi, K.B., Explicit modeling of spatial growth patterns in shama, ghana: An agent-based approach. Journal of Geovisualization & Spatial Analysis, 1(1–2), 2017, 7.
22. Jun, M.J., The effects of Portland's urban growth boundary on urban development patterns and commuting. Urban Studies 41:7 (2004), 1333–1348.
23. Li, X., Chen, G., Liu, X., Liang, X., Wang, S., Chen, Y., et al. A new global land-use and land-cover change product at a 1-km resolution for 2010 to 2100 based on human-environment interactions. Annals of the American Association of Geographers 4452:April (2017), 1–20.
24. Li, X., Chen, G., Liu, X., Liang, X., Wang, S., Chen, Y., et al. A new global land-use and land-cover change product at a 1-km resolution for 2010 to 2100 based on human-environment interactions. Annals of the American Association of Geographers 107:5 (2017), 1040–1059.
25. Li, X., Yeh, A.G., Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science 16:4 (2002), 323–343.
26. Lin, Y., Chu, H., Wu, C., Verburg, P.H., Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – A case study. International Journal of Geographical Information Science 25:1 (2011), 65–87.
27. Liu, X., Liang, X., Li, X., Xu, X., A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning 168 (2017), 94–116.
28. Liu, X., Ma, L., Li, X., Ai, B., Li, S., He, Z., Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. International Journal of Geographical Information Science 28:1 (2014), 148–163.
29. Liu, X., Ou, J., Li, X., Ai, B., Combining system dynamics and hybrid particle swarm optimization for land use allocation. Ecological Modelling 257 (2013), 11–24.
30. Liu, X.P., Xia, L., Shi, X., Zhang, X.H., Chen, Y.M., Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science 24:5 (2010), 783–802.
31. Long, Y., Gu, Y., Han, H., Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of Beijing. Landscape and Urban Planning 108:2–4 (2012), 103–111.
32. Long, Y., Han, H., Lai, S., Mao, Q., Urban growth boundaries of the Beijing Metropolitan Area: Comparison of simulation and artwork. Cities 31 (2013), 337–348.
33. Long, Y., Han, H., Tu, Y., Shu, X., Evaluating the effectiveness of urban growth boundaries using human mobility and activity records. Cities 46 (2015), 76–84.
34. Lu, C., Wu, Y., Shen, Q., Wang, H., Driving force of urban growth and regional planning: A case study of China's Guangdong Province. Habitat International 40 (2013), 35–41.
35. Ma, S., Li, X., Cai, Y., Delimiting the urban growth boundaries with a modified ant colony optimization model. Computers Environment & Urban Systems 62 (2017), 146–155.
36. Monzón, A., Ortega, E., López, E., Efficiency and spatial equity impacts of high-speed rail extensions in urban areas. Cities 30:30 (2013), 18–30.
37. Narayanan, A. (2006). Fast binary dilation/erosion algorithm using kernel subdivision: 335–342.
38. Nelson, A.C., Moore, T., Assessing urban growth management?: The case of Portland, Oregon, the USA's largest urban growth boundary. Land Use Policy 10:10 (1993), 293–302.
39. Tang, S., Savy, M., Doulet, J.C.C.O., {High speed rail in China and its potential impacts on urban and regional development}. Local Economy 26:5 (2011), 409–422.
40. Tayyebi, A., Perry, P.C., Tayyebi, A.H., Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS. International Journal of Geographical Information Science 28:4 (2014), 639–659.
41. Tayyebi, A., Pijanowski, B.C., Pekin, B., Two rule-based Urban Growth Boundary Models applied to the Tehran Metropolitan Area, Iran. Applied Geography 31:3 (2011), 908–918.
42. Tayyebi, A., Pijanowski, B.C., Tayyebi, A.H., An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran. Landscape and Urban Planning 100:1–2 (2011), 35–44.
43. Tian, L., Shen, T., Evaluation of plan implementation in the transitional China: A case of Guangzhou city master plan. Cities 28:1 (2011), 11–27.
44. van Asselen, S., Verburg, P.H., Land cover change or land-use intensification: simulating land system change with a global-scale land change model. Global Change Biology 19:12 (2013), 3648–3667.
45. Venkataraman, M., Analyzing urban growth boundary effects in the city of Bengaluru. SSRN Electronic Journal 49:48 (2013), 54–61.
46. Verburg, P.H., Simulating feedbacks in land use and land cover change models. Landscape Ecology 21:8 (2006), 1171–1183.
47. Verburg, P.H., Ellis, E.C., Letourneau, A., A global assessment of market accessibility and market influence for global environmental change studies. Environmental Research Letters, 6(3), 2011, 034019.
48. Verburg, P.H., Overmars, K.P., Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology 24:9 (2009), 1167–1181.
49. Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S.S., Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management 30:3 (2002), 391–405.
50. Verburg, P.H., van de Steeg, J., Veldkamp, A., Willemen, L., From land cover change to land function dynamics: A major challenge to improve land characterization. Journal of Environmental Management 90:3 (2009), 1327–1335.
51. Xiang, W., Clarke, K.C., The use of scenarios in land-use planning. Environment and Planning B: Planning and Design 30:6 (2016), 885–909.
52. Yao, Y., Li, X., Liu, X., Liu, P., Liang, Z., Zhang, J., et al. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science, 2016, 1–24.
53. Yao, Y., Liu, X., Li, X., Zhang, J., Liang, Z., Mai, K., et al. Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. International Journal of Geographical Information Science 31:6 (2017), 1220–1244.
54. Yeh, A.G., Li, X., An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta. China 2:2 (1997), 193–210.
55. Yeh, A., & Li, X. (1997). Economic Development and Agricultural Land Loss in the Pearl River Delta. International Symposium on Marketization of Land and Housing in Socialist China, organized by Department of Geography, Hong Kong Baptist University and Centre of Urban Planning and Environmental Management, University of Hong Kong, 31 October – 1 November.
56. Yeh, A.G., Li, X.Y., Sustainable land development model for rapid growth areas using GIS. International Journal of Geographical Information Science 12:2 (1998), 169–189.
57. Yuan, T. (2005). Return to the nature of city: Also on the layout of streets in permeable city, Urban Problems.
58. Zhang, X., Li, Y., Li, J., Zhao, K., & Zhang, T. (2015). Proximate control stream assisted video transcoding for heterogeneous content delivery network, pp. 2552–2555.