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Improvement of an Urban Growth Model for Railway-Induced Urban Expansion

Varquez Alvin Christopher Galang 董 思繁 花岡 伸也 神田 学 Varquez, A.C.G. Dong, S. Hanaoka, S. Kanda, M. 東京工業大学 DOI:https://doi.org/10.3390/su12176801

2020.08

概要

Increasing population in urban areas drives urban cover expansion and spatial growth. Developing urban growth models enables better understanding and planning of sustainable urban areas. The SLEUTH model is an urban growth simulation model which uses the concept of cellular automata to predict land cover change using six spatial inputs of historical data (slope, land use, exclusion, urban, transportation, and hill-shade). This study investigates the potential of SLEUTH to capture railway-induced urban growth by testing methods that can consider railways as input to the model, namely (1) combining the exclusion layer with a station map; (2) creating a new input layer representing stations in addition to the default six inputs. Districts in Tsukuba, Japan and Gurugram, India which historically showed evidence of urban growth by railway construction are investigated. Results reveal that both proposed methods can capture railway impact on urban growth, while the former algorithm under the right settings may perform better than the latter at finer resolutions. Coarser resolution representation (300-m grid-spacing) eventually reduces the differences in accuracy among the default SLEUTH model and the proposed algorithms.

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参考文献

1. Grimm, N.B.; Faeth, S.H. Global change and the ecology of cities. Science 2008, 319, 756–760. [CrossRef] [PubMed]

2. Lambin, E.F.; Meyfroidt, P. Land use transitions: Social-ecological feedback versus socio-economic change. Land Use Policy 2010, 27, 108–118. [CrossRef]

3. Seto, K.C.; Guneralp, B. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA 2012, 40, 16083–16088. [CrossRef] [PubMed]

4. United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2018 Revision; (ST/ESA/SER.A/420); United Nations: New York, NY, USA, 2019.

5. Lebel, L.; Krittasudthacheewa, C.; Salamanca, A.; Sriyasak, P. Lifestyles and consumption in cities and the links with health and well-being: The case of obesity. Curr. Opin. Environ. Sustain. 2012, 4, 405–413. [CrossRef]

6. Marques, B.; McIntosh, J.; Kershaw, C. Healing spaces: Improving health and wellbeing for the elderly through therapeutic landscape design. Intern. J. Arts Hum. 2019, 3, 20–34.

7. Dietzel, C.; Clarke, K.C. Replication of Spatio-Temporal Land Use Patterns at Three Levels of Aggregation by an Urban Cellular Automata; Sloot, P.M.A., Chopard, B., Hoekstra, A.G., Eds.; Springer: Heidelberg/Berlin, Germany, 2004.

8. Clarke, K.C.; Hoppen, S. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environ. Plan. B Plan. Design 1997, 24, 247–261. [CrossRef]

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. Int. J. Geospat. Inf. Sci. 1998, 12, 699–714. [CrossRef]

10. Dietzel, C.; Clarke, K.C. The effect of disaggregating land use categories in cellular automata during model calibration and forecasting. Comput. Environ. Urban Syst. 2006, 30, 78–101. [CrossRef]

11. Triantakonstantis, D.; Mountrakis, G. Urban Growth Prediction: A Review of Computational Models and Human Perceptions. J. Geogr. Inf. Syst. 2012, 4, 555–587. [CrossRef]

12. Nugroho, F.; Al-Sanjary, O.I. A Review of Simulation Urban Growth Model. Int. J. Eng. Technol. 2018, 7, 17–23. [CrossRef]

13. Zhou, Y.; Varquez, A.C.G.; Kanda, M. High-resolution global urban growth projection based on multiple applcations of the SLEUTH urban growth model. Sci. Data 2019, 6, 34. [CrossRef] [PubMed]

14. Guan, C.; Rowe, P.G. Should big cities grow? Scenario-based cellular automata urban growth modeling and policy applications. J. Urban Manag. 2016, 5, 65–78. [CrossRef]

15. Chaudhuri, C.; Clarke, K.C. The SLEUTH Land Use Change Model: A Review. Int. J. Environ. Resour. Res. 2013, 1, 88–104.

16. Yin, C.; Yu, D.; Zhang, H.; You, S.; Chen, G. Simulation of urban growth using a cellular automata-based model in a developing nation’s region. Int. Soc. Opt. Eng. 2008, 7143, 1–8.

17. Feng, H.-H.; Liu, H.-P.; Lü, Y. Scenario Prediction and Analysis of Urban Growth Using SLEUTH Model. Pedosphere 2012, 22, 206–216. [CrossRef]

18. Sandamali, S.P.I.; Kantakumar, L.N.; Sivanantharajah, S. Remote sensing data and SLEUTH urban growthmodel: As decision support tools for urban planning. Chin. Geogr. Sci. 2018, 28, 274–286.

19. Ruetveld, P.; Nierop, J.V. Urban growth and the development of transport networks: The case of the Dutch railways in the nineteenth century. Flux 1995, 19, 31–43. [CrossRef]

20. Zhang, Y.; Song, R.; van Nes, R.; He, S.; Yin, W. Identifying Urban Structure Based on Transit-Oriented Development. Sustainability 2019, 11, 7241. [CrossRef]

21. Yin, H.; Kong, F.; Hu, Y.; James, P.; Xu, F.; Yu, L. Accessing Growth Scenarios for their Landscape Ecological Security Impact, using the SLEUTH Urban Growth Model. J. Urban Plan. Dev. 2016, 142, 1–13. [CrossRef]

22. Liu, Y.; Li, L.; Chen, L.; Cheng, L.; Zhou, X.; Cui, Y. Urban growth simulation in different scenarios using the SLEUTH model: A case study of Hefei, East China. PLoS ONE 2019, 14, 1–22. [CrossRef]

23. Akın, A.; Clark, K.C.; Berberogolu, S. The impact of historical exclusion on the calibration of the SLEUTH urban growth model. Int. J. Appl. Earth Obs. Geoinf. 2014, 27, 156–158.

24. Jantz, C.A.; Goetz, S.J.; Shelley, M.K. Using the SLEUTH urban growth model to simulate the impacts if future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environ. Plan. B Plan. Design 2003, 30, 251–271.

25. Bihamta, N.; Soffianian, A.; Fakheran, S.; Gholamalifard, M. Using the SLEUTH Urban Growth Model to Simulate Future Urban Expansion of the Isfahan Metropolitan Area, Iran. J. Urban Soc. Remote Sens. 2014, 43, 407–414. [CrossRef]

26. Chaudhuri, G.; Clarke, K.C. Modeling an Indian megalopolis—A case study on adapting SLEUTH urban growth model. Comput. Environ. Urban Syst. 2019, 77, 101358. [CrossRef]

27. Kawada, M.; Okamoto, N.; Ishida, H.; Tsutsumi, M. Effects of Tsukuba Express Project on the Residents’ Travel Behavior. J. East. Asia Soc. Transp. Stud. 2010, 8, 539–547.

28. Ogawa, T. The process and effect of the urban development on the Tsukuba Express Line—A case study of the urban development on the Tsukuba Express Line of Chiba Prefecture. Rep. Plan. Inst. Jpn. 2018, 361–366.

29. Geospatial Information Authority of Japan. Title of the Paper of the Figure Source. Announce Time. Available online: https://www.gsi.go.jp/ENGLISH/index.html (accessed on 10 November 2018).

30. National Land Numerical Information Download Service. Available online: http://nlftp.mlit.go.jp/ksj/index. html (accessed on 10 November 2018).

31. Advanced Land Observing Satellite Research and Application Project. Available online: https://www.eorc. jaxa.jp/ALOS/en/ (accessed on 10 November 2018).

32. Jain, R.K.; Jain, K.; Rehan Ali, S. Remote Sensing Enabled Urban Growth Analysis for Gurgaon from 1995 to 2015. Adv. Comput. Sci. Technol. 2017, 10, 1745–1757.

33. Jain., S.; Kohli, D.; Rao, R.M.; Bijker, W. Spatial Metrics to Analyse the Impact of Regional Factors on Pattern of Urbanisation in Gurgaon, India. J. Indian Soc. Remote Sens. 2011, 39, 203–212. [CrossRef]

34. Government of Haryana, Department of Town and Country Planning. Available online: https://tcpharyana. gov.in/ (accessed on 12 August 2020).

35. ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. 2017. Available online: https://maps.elie.ucl. ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (accessed on 19 August 2020).

36. MapCruzin. Available online: https://mapcruzin.com/ (accessed on 8 June 2020).

37. Humanitarian OpenStreetMap Team. Available online: https://www.hotosm.org (accessed on 8 June 2020).

38. DIVA-GIS. Available online: https://www.diva-gis.org/gdata (accessed on 15 July 2020).

39. BHARAT MAPS, National Informatics Centre, Ministry of Electronics & Information Technology, Government of India. Available online: https://bharatmaps.gov.in/map.aspx (accessed on 15 July 2020).

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