Skip to main content
Log in

Characteristics of neighborhood interaction in urban land-use changes: A comparative study between three metropolitan areas of Japan

  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

Abstract

Local spatial interaction between neighborhood land-use categories (i.e. neighborhood interaction) is an important factor which affects urban land-use change patterns. Therefore, it is a key component in cellular automata (CA)-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan, i.e. Tokyo, Osaka, and Nagoya, were compared using such aids as the neighborhood interaction model and similarity measure function. As a result, urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories, meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas, which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Baba Y, Shibuya M, 2000. Tokyo game-soft cluster: Analysis on firm’s spatial agglomeration. The Japan Society for Science Policy and Research Management, 14(4): 266–278. (in Japanese)

    Google Scholar 

  • Barredo J I, Demicheli L, 2003. Urban sustainability in developing countries’ megacities: Modelling and predicting future urban growth in Lagos. Cities, 20(5): 297–310.

    Article  Google Scholar 

  • Barredo J I, Kasanko M, McCormick N et al., 2003. Modelling dynamic spatial processes: Simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64(3): 145–160.

    Article  Google Scholar 

  • Batty M, 1971. Modeling cities as dynamics systems. Nature, 231: 426–428.

    Article  Google Scholar 

  • Batty M, 1991. Cities as fractals: Simulating growth and form. In:Crilly T, Earnshaw R A, Jones H (eds.). Fractals and Chaos. New York: Springer-Verlag, 41–69.

    Google Scholar 

  • Batty M, 1998. Urban evolution on the desktop: Simulation with the use of extended cellular automata. Environment and Planning A, 30: 1943–1967.

    Article  Google Scholar 

  • Batty M, 2005. Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. Cambridge: The MIT Press.

    Google Scholar 

  • Batty M, Longley P A, 1994. Fractal Cities: A Geometry of Form and Function. London: Academic Press.

    Google Scholar 

  • Batty M, Xie Y, 1994. From cells to cities. Environment and Planning B, 21: s31–s48.

    Article  Google Scholar 

  • Batty M, Xie Y, Sun Z, 1999. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3): 205–233.

    Article  Google Scholar 

  • Benenson I, 2007. Warning! The scale of land-use CA is changing! Computers, Environment and Urban Systems, 31(2): 107–113.

    Article  Google Scholar 

  • Carver S J, 1991. Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information System, 5: 321–339.

    Article  Google Scholar 

  • Clarke K C, Hoppen S, Gaydos L, 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24: 247–261.

    Article  Google Scholar 

  • Couclelis H, 1989. Macrostructure and microbehavior in a metropolitan area. Environment and Planning B, 16: 141–154.

    Article  Google Scholar 

  • Herold M, Couclelis H, Clarke K C, 2005. The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems, 29(4): 369–399.

    Article  Google Scholar 

  • Ida N, 2006. A spatial econometric analysis of manufacturing agglomeration in Osaka. The Doshisha University Economic Review, 57(3): 295–318. (in Japanese)

    Google Scholar 

  • Jusuf S K, Wong N H, Hagen E et al., 2007. The influence of land use on the urban heat island in Singapore. Habitat International, 31(2007): 232–242.

    Google Scholar 

  • Lambin E F, Turner B L, Geist H J et al., 2001. The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change, 11(4): 261–269.

    Article  Google Scholar 

  • Le Q B, Park S J, Vlek P L G et al., 2008. Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system I. Structure and theoretical specification. Ecological Informatics, 3(2): 135–153.

    Article  Google Scholar 

  • Li X, Yeh A G O, 2001. Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment and Planning A, 33: 1445–1462.

    Article  Google Scholar 

  • Murayama Y, 2000. Japanese Urban System. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Palivos T, Wang P, 1996. Spatial agglomeration and endogenous growth. Regional Science and Urban Economics, 26(6): 645–669.

    Article  Google Scholar 

  • Parker D C, Evans T P, Meretsky V, 2001. Measuring emergent properties of agent-based landuse/landcover models using spatial metrics. In: Seventh annual Conference of the International Society for Computational Economics, Yale University.

  • Pauleit S, Ennos R, Golding Y, 2005. Modeling the environmental impacts of urban land use and land cover change: A study in Merseyside, UK. Landscape and Urban Planning, 71(2–4): 295–310.

    Article  Google Scholar 

  • Phipps M, 1989. Dynamic behavior of cellular automata under the constraint of neighborhood coherence. Geographical Analysis, 21: 197–215.

    Article  Google Scholar 

  • Takahashi N, Taniuchi T, 1994. The Three Metropolitan Areas in Japan: Changing Spatial Structures and Future Perspectives. Tokyo: Kokon Syoin. (in Japanese)

    Google Scholar 

  • Tobler W, 1970. A computer movie simulating urban growth in the Detroit region. Geographical Analysis, 46(2): 234–240.

    Google Scholar 

  • Torrens P M, 2006. Simulating sprawl. Annals of the Association of American Geographers, 96(2): 248–275.

    Article  Google Scholar 

  • Torrens P M, Benenson I, 2005. Geographic automata systems. International Journal of Geographical Information Science, 19(4): 385–412.

    Article  Google Scholar 

  • Turner II B L, Kasperson R E, Meyer W B et al., 1990. Two types of global environmental change: Definitional and spatial-scale issues in their human dimensions. Global Environmental Change, 1(1): 14–22.

    Article  Google Scholar 

  • von Neumann J, 1951. The general and logical theory of automata. In: Jeffress L A (ed.). Cerebral Mechanisms in Behavior: The Hixon Symposium, New York: Wiley, 1948, 1–41.

    Google Scholar 

  • Voogd H, 1983. Multicriteria Evaluation for Urban and Regional Planning. London: Pion.

    Google Scholar 

  • White R, Engelen G, 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patters. Environment and Planning A, 25: 1175–1199.

    Article  Google Scholar 

  • White R, Engelen G, 1994. Urban systems dynamics and cellular automata: Fractal structures between order and chaos. Chaos, Solitons & Fractals, 4(4): 563–583.

    Article  Google Scholar 

  • White R, Engelen G, 1997. Cellular automata as the basis of integrated dynamic regional modeling. Environment and Planning B, 24: 235–246.

    Article  Google Scholar 

  • White R, Engelen G, 2000. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 24(5): 383–400.

    Article  Google Scholar 

  • Wu F, 1998. SimLand: A prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules. International Journal of Geographical Information Science, 12(1): 63–82.

    Article  Google Scholar 

  • Yang F, Zhu Y, 2004. An efficient method for similarity search on quantitative transaction data. Journal of Computer Research and Development, 41(2): 361–368. (in Chinese)

    Google Scholar 

  • Yang Y, Billings S A, 2000, Neighborhood detection and rule selection from cellular automata pattern. IEEE Transactions on Systems, Man, and Cybernetics — Part A: Systems and Humans, 30: 840–847.

    Article  Google Scholar 

  • Yeh A G O, Li X, 2001. A constrained CA model for the simulation and planning of sustainable urban forms by using GIS. Environment and Planning B, 28: 733–753.

    Article  Google Scholar 

  • Yeh A G O, Li X, 2002. A cellular automata model to simulate development density for urban planning. Environment and Planning B, 29: 431–450.

    Article  Google Scholar 

  • Zhao Y, Dong F, Zhang H, 2010. Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo. Lecture Notes in Computer Science, 6016: 134–143.

    Article  Google Scholar 

  • Zhao Y, Murayama Y, 2006. Effect of spatial scale on urban land-use pattern analysis in different classification systems: An empirical study in the CBD of Tokyo. Theory and Applications of GIS, 14(1): 29–42.

    Google Scholar 

  • Zhao Y, Murayama Y, 2007. A new method to model neighborhood interaction in cellular automata-based urban geosimulation. Lecture Notes in Computer Science, 4488: 550–557.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bingliang Cui.

Additional information

Foundation: National Natural Science Foundation of China, No.40901090; No.70863014; Foundation of Japan Society for the Promotion of Science (JSPS), No.1907003; Talents Introduced into Universities Foundation of Guangdong Province of China, No.2009-26.

Author: Zhao Yaolong (1974–), Ph.D. and Professor, specialized in GISciences and urban study.

Corresponding author: Cui Bingliang (1970–), Ph.D

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhao, Y., Cui, B. & Murayama, Y. Characteristics of neighborhood interaction in urban land-use changes: A comparative study between three metropolitan areas of Japan. J. Geogr. Sci. 21, 65–78 (2011). https://doi.org/10.1007/s11442-011-0829-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-011-0829-6

Keywords

Navigation