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Journal of Geographical Sciences

, Volume 29, Issue 2, pp 197–212 | Cite as

Modelling urban spatial impacts of land-use/ transport policies

  • Fangqu Niu
  • Fang WangEmail author
  • Mingxing Chen
Article
  • 38 Downloads

Abstract

China is now experiencing rapid urbanization. Powerful tools are required to assess its urban spatial policies before implemented toward a more competitive and sustainable development paradigm. This study develops a Land Use Transport Interaction (LUTI) model to evaluate the impacts of urban land-use policies on urban spatial development. The model consists of four sub-models, i.e., transport, residential location, employment location and real estate rent sub-models. It is then applied to Beijing metropolitan area to forecast the urban activity evolution trend based on the land-use policies between 2009 and 2013. The modeling results show that more and more residents and enterprises in the city choose to agglomerate on outskirts, and new centers gradually emerge to share the services originally delivered by central Beijing. The general trend verifies the objectives of the government plan to develop more sub-centers around Beijing. The proposed activity-based model provides a distinct tool for the urban spatial policy makers in China. Further research is also discussed at the end.

Keywords

model/simulation LUTI accessibility urban activity policy scenarios 

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References

  1. Albouy D, Ehrlich G, 2014. Housing demand and expenditures: How rising rent levels affect behaviour and costs-of-living over space and time. NBER. Available at: http://cba.unl.edu/academic-programs/departments/ economics/about/seminar-series/documents/housingexpenditures.pdf (accessed 14 March 2016).Google Scholar
  2. Brandi A, Gori S, Nigro M et al., 2014. Development of an integrated transport-land use model for the activities relocation in urban areas. Transportation Research Procedia, 3: 374–383.CrossRefGoogle Scholar
  3. Chen W, Liu W D, Ke W Q et al., 2018. Understanding spatial structures and organizational patterns of city networks in China: A highway passenger flow perspective. Journal of Geographical Sciences, 28(4): 477–494.CrossRefGoogle Scholar
  4. Chen Y M, Li S Y, Li X, 2010. Simulating compact urban form using cellular automata (CA) and multi-criteria evaluation: A case study in Dongguan. Acta Scientiarum Naturalium Universitatis Sunyatseni, 49(6): 110–114. (in Chinese)Google Scholar
  5. Cobb C W, Douglas P H, 1928. A theory of production. American Economic Review, 18(Suppl.): 139–165.Google Scholar
  6. Dai J C, Li X, 2009. Multi-agent systems for simulating traffic behaviors. Chinese Science Bulletin, 54(21): 3380–3389. (in Chinese)Google Scholar
  7. Dang Y X, Zhang W Z, Wu W J, 2010. Residents housing preferences and consuming behaviors in a transitional economy: New evidence from Beijing, China. Progress in Geography, 30(10): 1203–1209. (in Chinese)Google Scholar
  8. Ding C, Lichtenberg E, 2011. Land and urban economic growth in China. Journal of Regional Science, 51: 299–317.CrossRefGoogle Scholar
  9. Dong G P, Zhang W Z, Wu W J et al., 2011. Spatial heterogeneity in determinants of residential land price: Simulation and prediction. Acta Geographica Sinica, 66(6): 750–760. (in Chinese)Google Scholar
  10. Gao J, Wei Y D, Chen W et al., 2014. Economic transition and urban land expansion in provincial China. Habitat International, 44: 461–473.CrossRefGoogle Scholar
  11. Geurs K T, Wee B V, 2004. Land-use/transport interaction models as tools for sustainability impact assessment of transport investment: Review and research perspectives. European Journal of Transport and Infrastructure Research, 4(3): 333–355.Google Scholar
  12. Hansen W G, 1959. How accessibility shapes land use. Journal of the American Institute of Planners, 25: 73–76.CrossRefGoogle Scholar
  13. Jing W, Jianzhong L, 2011. Study on the urban expansion and model of Lianyungang city based on the multi-temporal remote sensing images. Procedia Environmental Sciences, 10: 2159–2164.CrossRefGoogle Scholar
  14. Kryvobokov M, Chesneau J B, Bonnafous A et al., 2013. Comparison of static and dynamic land use-transport interaction models: Transportation research record. Journal of the Transportation Research Board, 2344(1): 49–58.CrossRefGoogle Scholar
  15. Landis J, 2001. CUF, CUF II and CURBA: A family of spatially explicit urban growth and land-use policy simulation models. In: Brail R K, Klosterman R E eds. Planning Support Systems: Integrating Geographic Information Systems, Models and Visualization Tools. Redlands, CA: ESRI Press.Google Scholar
  16. Li X, Ye J A, Liu X P et al., 2007. Geographical Simulation Systems: CA and MAS. Beijing: Science Press. (in Chinese)Google Scholar
  17. Liao F H F, Wei Y H D, 2014. Modeling determinants of urban growth in Dongguan, China: A spatial logistic approach. Stochastic Environmental Research and Risk Assessment, 28(4): 801–816.CrossRefGoogle Scholar
  18. Liu X P, Li X, Chen Y M et al., 2010. Agent-based model of residential location. Acta Geographica Sinica, 65(6): 695–707. (in Chinese)Google Scholar
  19. 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)Google Scholar
  20. Long Y, Mao Q Z, Dang A R, 2009. Beijing urban development model: Urban growth analysis and simulation. Tsinghua Science and Technology, 14(6): 787–794. (in Chinese)CrossRefGoogle Scholar
  21. Long Y, Mao Q Z, Yang D F, 2011. A multi-agent model for urban form: Transportation energy consumption and environmental impact integrated simulation. Acta Geographica Sinica, 66(8): 1033–1044. (in Chinese)Google Scholar
  22. Lowry I S. A Model of Metropolis RM-4035-RC. Santa Monica CA: Rand Corp, 1964.Google Scholar
  23. Mumtaz B, 1995. Meeting the demand for housing, a model for establishing affordability parameters. The Bartlett Development Planning Unit. Available at: www.ucl.ac.uk/silva/bartlett/dpu/publications/dpu-paper-73 (accessed 14 March 2016).Google Scholar
  24. Niu F Q, 2017. Overview of urban land-use/transport interaction model: Origin, techniques and future. Scientia Geographica Sinica, 37(1): 46–54. (in Chinese)Google Scholar
  25. Niu F Q, Li J, 2017. An activity-based integrated land-use transport model for urban spatial distribution simulation. Environment and Planning B: Urban Analytics and City Sciences, 6: 1–14.Google Scholar
  26. Niu F Q, Liu W D, 2017. Modeling urban housing price: The perspective of household activity demand. Journal of Geographical Sciences, 27(5): 619–630.CrossRefGoogle Scholar
  27. Niu F Q, Wang Z Q, Hu Y et al., 2015. A model of urban spatial evolution process based on economic and social activities. Progress in Geography, 34(1): 30–37. (in Chinese)Google Scholar
  28. Pierlugi C, Angel I, Luigi D et al., 2013. LUTI model for the metropolitan area of Santander. Urban Planning and Development, 139(3): 153–165.CrossRefGoogle Scholar
  29. Ryan P P, Txomin H, Nicholas C C et al., 2015. Remote sensing and object-based techniques for mapping fine-scale industrial disturbances. International Journal of Applied Earth Observation and Geoinformation, 34: 51–57.CrossRefGoogle Scholar
  30. Shan Y H, Zhu X Y, 2011. Multi-agents model for simulation of urban residential space evolution. Progress in Geography, 30(8): 956–966. (in Chinese)Google Scholar
  31. Shen Z J, 2011. Simulating spatial market share patterns for impacts analysis of large-scale shopping centre on downtown revitalization. Environment and Planning B: Planning and Design, 38(1): 142–162.CrossRefGoogle Scholar
  32. Simmonds D, Feldman O, 2011. Alternative approaches to spatial modelling. Research in Transportation Economics, 31(1): 2–11.CrossRefGoogle Scholar
  33. Torrens P M, 2000. How land-use transportation models work. London: Centre for Advanced Spatial Analysis.Google Scholar
  34. Wang H, He S, Xingjian L et al., 2013. Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China. Landscape and Urban Planning, 110: 99–112.CrossRefGoogle Scholar
  35. Wegener M, 2004. Overview of land-use transport models. In: Hensher D A, Button K eds. Transport Geography and Spatial Systems. Oxford: Elsevier, 127–146.CrossRefGoogle Scholar
  36. Wei Y H D, 2012. Restructuring for growth in urban China: Transitional institutions, urban development, and spatial transformation. Habitat International, 36: 396–405.CrossRefGoogle Scholar
  37. Wu S K, Li X, Liu X P, 2008. GeoCA based dynamic site selection model: Shenzhen city as a case study. Scientia Geographica Sinica, 28(3): 314–319. (in Chinese)Google Scholar
  38. Xue L, Yang K Z, 2002. Sciences of complexity and studies of evolutional simulation of regional spatial structure. Geographical Research, 21(1): 79–88. (in Chinese)Google Scholar
  39. Yang Q S, Li X, 2009. Agent-based micro-simulation of urban industrial spatial evolution. Scientia Geographica Sinica, 29(4): 515–522. (in Chinese)Google Scholar
  40. Zhang T, 2000. Land market forces and government’s role in sprawl: The case of China. Cities, 17: 123–135.CrossRefGoogle Scholar

Copyright information

© Science in China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Regional Sustainable Development ModelingInstitute of Geographic Sciences and Natural Resources Research, CASBeijingChina
  2. 2.Collaborative Innovation Center for Geopolitical Setting of Southwest China and Borderland DevelopmentKunmingChina
  3. 3.School of Public Administration of Inner Mongolia UniversityHohhotChina

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