Advertisement

Modeling Urban Sprawl

  • Roland Goetzke
Chapter
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 18)

Abstract

New challenges due to changing climatic and environmental conditions, economic and demographic polarization and new energy concepts require smart tools for decision makers and regional and urban planners especially in the context of growing cities. Urban growth models can be valuable tools in order to define future policy alternatives or to analyze environmental impacts of urban growth. This paper provides a concise introduction into the challenges emerging from urban sprawl and introduces methods and technologies that enable a deeper understanding of the processes of urban sprawl. It is focused specifically on European urban areas and demonstrates how empirical research and remote sensing can contribute to the development of improved urban growth models. An example application is presented in order to provide a summary of current research activities in the field of integrated urban modeling.

Keywords

Multi Agent System Cellular Automaton Urban Growth Urban Sprawl Urban Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This study was carried out in the Remote Sensing Research Group (RSRG, Department of Geography, University of Bonn) with the support of Gunter Menz (RSRG). I am also grateful to Andreas Rienow (University of Bonn) for his competent technical and linguistic assistance. The land-use data used in this study has been created at the Center for Remote Sensing of Land Surfaces (ZFL) at the University of Bonn, Germany, within the project “Visualisierung von Landnutzung und Flächenverbrauch in Nordrhein-Westfalen mittels Satelliten- und Luftbildern” funded by the Ministry of Environment and Nature Conservation, Agriculture and Consumer Protection of the federal state of NRW, Germany.

References

  1. Agarwal C, Green GM, Grove JM, Evans TP, Schweik CM (2002) A review and assessment of land-use change models: dynamics of space, time, and human choice. USDA Forest Service, Newton SquareGoogle Scholar
  2. Antrop M (2004) Rural–urban conflicts and opportunities. In: Jongman RHG (ed) The new dimensions of the European landscape. Springer, Dordrecht, pp 83–91CrossRefGoogle Scholar
  3. Arnold CL, Gibbons CJ (1996) Impervious surface coverage: the emergence of a key environmental indicator. J Am Plan Assoc 62:243–258CrossRefGoogle Scholar
  4. Barredo JI, Kasanko M, McCormick N, Lavalle C (2003) Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata. Landsc Urban Plan 64:145–160CrossRefGoogle Scholar
  5. Batisani N, Yarnal B (2009) Urban expansion in centre county, Pennsylvania: spatial dynamics and landscape transformations. Appl Geogr 29:235–249CrossRefGoogle Scholar
  6. Batty M (2008) The size, scale, and shape of cities. Science 319:769–771CrossRefGoogle Scholar
  7. Batty M, Xie Y (1994) From cells to cities. Environ Plan B Plan Des 21:31–48CrossRefGoogle Scholar
  8. Benenson I, Torrens PM (2004) Geosimulation: object-based modeling of urban phenomena. Comput Environ Urban Syst 28:1–8CrossRefGoogle Scholar
  9. Berling-Wolf S, Wu J (2004) Modeling urban landscape dynamics: a review. Ecol Res 19:119–129CrossRefGoogle Scholar
  10. Bhatta B (2010) Analysis of urban growth and sprawl from remote sensing data. Springer, BerlinCrossRefGoogle Scholar
  11. Blais P (2010) Perverse cities: hidden subsidies, wonky policy, and urban sprawl. UBC Press, VancouverGoogle Scholar
  12. Briassoulis H (2000) Analysis of land use change: theoretical and modeling approaches. Regional Research Institute, University of West Virginia, MorgantownGoogle Scholar
  13. Burchell RW (2005) Sprawl costs: economic impacts of unchecked development. Island Press, Washington, DCGoogle Scholar
  14. Chin N (2002) Unearthing the roots of urban sprawl: a critical analysis of form, function and methodology, vol 47. CASA, LondonGoogle Scholar
  15. Christiansen P, Loftsgarden T (2011) Drivers behind urban sprawl in Europe (No. 1136), TØI Report. Institute of Transport Economics. Norwegian Centre for Transport Research, OsloGoogle Scholar
  16. Clark D (1982) Urban geography: an introductory guide. Taylor & Francis, LondonGoogle Scholar
  17. Clarke KC, Hoppen S, Gaydos LJ (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environ Plan B Plan Des 24:247–261CrossRefGoogle Scholar
  18. Couch C, Karecha J, Nuissl H, Rink D (2005) Decline and sprawl: an evolving type of urban development – observed in Liverpool and Leipzig. Eur Plan Stud 13:117–136CrossRefGoogle Scholar
  19. Couclelis H (1985) Cellular worlds: a framework for modeling micro – macro dynamics. Environ Plan 17:585–596CrossRefGoogle Scholar
  20. Couclelis H (2005) “Where has the future gone?” Rethinking the role of integrated land-use models in spatial planning. Environ Plan 37:1353–1371CrossRefGoogle Scholar
  21. De Nijs TCM, de Niet R, Crommentuijn L (2004) Constructing land-use maps of the Netherlands in 2030. J Environ Manage 72:35–42CrossRefGoogle Scholar
  22. Dieleman F, Wegener M (2004) Compact city and urban sprawl. Built Environ 30:308–323CrossRefGoogle Scholar
  23. Dietzel C, Clarke KC (2007) Toward optimal calibration of the SLEUTH land use change model. Trans GIS 11:29–45CrossRefGoogle Scholar
  24. EEA (2006) Urban sprawl in Europe. The ignored challenge (Report No. 10/2006), EEA Report. European Environment Agency, CopenhagenGoogle Scholar
  25. EEA (2012) Urban adaption to climate change in Europe. Challenges and opportunities for cities together with supportive national and European policies. (Report No. 2/2012), EEA Report. European Environment Agency, CopenhagenGoogle Scholar
  26. Engelen G, White R, de Nijs T (2003) Environment explorer: spatial support system for the integrated assessment of socio-economic and environmental policies in the Netherlands. Integr Assess 4:97–105CrossRefGoogle Scholar
  27. Ettema D, de Jong K, Timmermans H, Bakema A (2007) PUMA: multi-agent modelling of urban systems. In: Koomen E, Stillwell J, Bakema A, Scholten HJ (eds) Modelling land-use change, The GeoJournal library. Springer, Dordrecht, pp 237–258CrossRefGoogle Scholar
  28. Freilich RH, Sitkowski RJ, Mennillo SD (2010) From sprawl to sustainability: smart growth, new urbanism, green development, and renewable energy. American Bar Association, ChicagoGoogle Scholar
  29. Gamba P, Herold M (eds) (2009) Global mapping of human settlement: experiences, datasets, and prospects, Har/Dvdr. ed. CRC Press, Boca RatonGoogle Scholar
  30. Geertman S, Stillwell J (2004) Planning support systems: an inventory of current practice. Comput Environ Urban Syst 28:291–310CrossRefGoogle Scholar
  31. Geertman S, Stillwell JCH (2009) Planning support systems best practice and new methods. Springer, DordrechtCrossRefGoogle Scholar
  32. Gillham O, MacLean AS (2002) The limitless city: a primer on the urban sprawl debate. Island Press, Washington, DCGoogle Scholar
  33. Goetzke R (2011) Entwicklung eines fernerkundungsgestützten Modellverbundes zur Simulation des urban-ruralen Landnutzungswandels in Nordrhein-Westfalen. Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn, BonnGoogle Scholar
  34. Goetzke R, Dosch F, Beckmann G, Hoymann J, Distelkamp M (2012) Wie viel Fläche wird wo und wie verbraucht? Trends, Szenario 2030 und Bewertung. In: Meinel G, Schumacher J, Behnisch M (eds) Flächennutzungsmonitoring IV. Genauere Daten – Informierte Akteure – Praktisches Handeln, IÖR Schriften. RHOMBOS-Verlag, Berlin, pp 185–194Google Scholar
  35. Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319:756–760CrossRefGoogle Scholar
  36. Haase D, Schwarz N (2009) Simulation models on human-nature interactions in urban landscapes: a review including spatial economics, system dynamics, cellular automata and agent-based approaches. Living Rev Landsc Res 3:1–45CrossRefGoogle Scholar
  37. Haase D, Haase A, Kabisch N, Kabisch S, Rink D (2012) Actors and factors in land-use simulation: the challenge of urban shrinkage. Environ Model Softw 35:92–103CrossRefGoogle Scholar
  38. Hägerstrand T (1967) The computer and the geographer. Trans Inst Br Geogr 42:1–19CrossRefGoogle Scholar
  39. Hoymann J, Dosch F, Beckmann G, Distelkamp M (2012) Trends der Siedlungsflächenentwicklung. Status quo und Projektion 2030 (No. 09/2012), BBSR-Analysen Kompakt. Bundesinstitut für Bau-, Stadt- und Raumforschung, BonnGoogle Scholar
  40. Huang B, Xie C, Tay R (2010) Support vector machines for urban growth modeling. GeoInformatica 14:83–99CrossRefGoogle Scholar
  41. Kasanko M, Barredo J, Lavalle C, McCormick N, Demicheli L, Sagris V, Brezger A (2006) Are European cities becoming dispersed? A comparative analysis of 15 European urban areas. Landsc Urban Plan 77:111–130CrossRefGoogle Scholar
  42. Klosterman RE (2008) A new tool for new planning. The what if? Planning support system. In: Brail RK (ed) Planning support systems for cities and regions. Lincoln Institute of Land Policy, Cambridge, pp 85–100Google Scholar
  43. Koomen E, Stillwell J (2007) Theories and methods. In: Koomen E, Stillwell J, Bakema A, Scholten HJ (eds) Modelling land-use change, The GeoJournal library. Springer, Dordrecht, pp 1–21CrossRefGoogle Scholar
  44. Koomen E, Diogo V, Hilferink M, van der Beek M (2010) EU-ClueScanner100m; model description and validation results. VU University Amsterdam, AmsterdamGoogle Scholar
  45. Koomen E, Hilferink M, Borsboom-van Beurden J (2011) Introducing Land Use Scanner. In: Koomen E, Borsboom-van Beurden J (eds) Land-use modelling in planning practice, The GeoJournal library. Springer, Dordrecht, pp 3–22CrossRefGoogle Scholar
  46. Kunstler JH (1994) The geography of nowhere: the rise and decline of America’s man-made landscape. Touchstone, New YorkGoogle Scholar
  47. Landis JD (2001) CUF, CUF II, and CURBA: a family of spatially explicit urban growth and land-use policy simulation models. In: Brail RK, Klosterman RE (eds) Planning support systems. Integrating geographic information systems, models, and visualization tools. ESRI Inc, Redlands, pp 157–200Google Scholar
  48. Lavalle C, Barredo JI, McCormick N, Engelen G, White R, Uljee I (2004) The MOLAND model for urban and regional growth forecast. A tool for the definition of sustainable development paths (No. EUR 21480 EN). Joint Research Centre, IspraGoogle Scholar
  49. Lavalle C, Baranzelli C, Batista e Silva F, Mubareka S, Gomes C, Koomen E, Hilferink M (2011) A high resolution land use/cover modelling framework for Europe: introducing the EU-ClueScanner100 model. In: Murgante B, Gervasi O, Iglesias A, Taniar D, Apduhan B (eds) Computational science and its applications – ICCSA 2011, Lecture notes in computer science. Springer, Berlin/Heidelberg, pp 60–75CrossRefGoogle Scholar
  50. Lee DB (1973) Requiem for large-scale models. J Am Inst Plan 39:163–178CrossRefGoogle Scholar
  51. Li X, Yeh AG-O (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16:323–343CrossRefGoogle Scholar
  52. Ligmann-Zielinska A, Church R, Jankowski P (2008) Spatial optimization as a generative technique for sustainable multiobjective land-use allocation. Int J Geogr Inf Sci 22:601–622CrossRefGoogle Scholar
  53. Mainz M (2005) Ökonomische Bewertung der Siedlungsentwicklung, Beiträge zum Siedlungs- und Wohnungswesen. V&R Unipress, GöttingenGoogle Scholar
  54. Miller EJ, Douglas Hunt J, Abraham JE, Salvini PA (2004) Microsimulating urban systems. Comput Environ Urban Syst 28:9–44CrossRefGoogle Scholar
  55. Pijanowski B, Brown DG, Shellito BA, Manik GA (2002) Using neural networks and GIS to forecast land use changes: a land transformation model. Comput Environ Urban Syst 26:553–575CrossRefGoogle Scholar
  56. Poelmans L, Van Rompaey A (2009) Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: a case study in the Flanders–Brussels region. Landsc Urban Plan 93:10–19CrossRefGoogle Scholar
  57. Pontius RG Jr, Cornell JD, Hall CAS (2001) Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for Costa Rica. Agric Ecosyst Environ 85:191–203CrossRefGoogle Scholar
  58. Pontius RG Jr, Huffaker D, Denman K (2004) Useful techniques of validation for spatially explicit land-change models. Ecol Model 179:445–461CrossRefGoogle Scholar
  59. Rounsevell MDA, Pedroli B, Erb K-H, Gramberger M, Busck AG, Haberl H, Kristensen S, Kuemmerle T, Lavorel S, Lindner M, Lotze-Campen H, Metzger MJ, Murray-Rust D, Popp A, Pérez-Soba M, Reenberg A, Vadineanu A, Verburg PH, Wolfslehner B (2012) Challenges for land system science. Land Use Policy 29:899–910CrossRefGoogle Scholar
  60. Schmitz M, Bode T, Thamm HP, Cremers AB (2007) XULU – a generic JAVA-based platform to simulate land use and land cover change (LUCC). In: Oxley L, Kulasiri D (eds) Proceedings of MODSIM 2007 international congress on modelling and simulation. Modelling and Simulation Society of Australia and New Zealand, pp 2645–2649Google Scholar
  61. Schwarz N (2010) Urban form revisited-selecting indicators for characterising European cities RID A-5409-2011. Landsc Urban Plan 96:29–47CrossRefGoogle Scholar
  62. Siedentop S, Junesch R, Straßer M, Zakrzewski P, Samaniego L, Weinert J (2009) Einflussfaktoren der Neuinanspruchnahme von Flächen, vol 139, Forschungen. BMVBS, BBSR, BonnGoogle Scholar
  63. Sieverts T (2005) Zwischenstadt. Zwischen Ort und Welt, Raum und Zeit, Stadt und Land, 3rd edn. Birkhäuser, BaselGoogle Scholar
  64. Silva EA, Clarke KC (2005) Complexity, emergence and cellular urban models: lessons learned from applying Sleuth to two Portuguese metropolitan areas. Eur Plan Stud 13:93–115CrossRefGoogle Scholar
  65. Tobler WR (1979) Cellular geography. In: Gale S, Olson G (eds) Philosophy in geography. Reidel, Dordrecht, pp 379–386CrossRefGoogle Scholar
  66. Torrens PM (2001) Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait … (Working Paper No. 32), UCL working paper series. UCL Centre for Advanced Spatial Analysis, London, UKGoogle Scholar
  67. Torrens PM, O’Sullivan D (2001) Cellular automata and urban simulation: where do we go from here? Environ Plan B Plan Des 28:163–168CrossRefGoogle Scholar
  68. United Nations Population Division (2010) World urbanization prospects: the 2010 revision. United Nations, New YorkGoogle Scholar
  69. US EPA (2000) Projecting land-use change. A summary of models for assessing the effects of community growth and change on land-use patterns. United States Environmental Protection Agency, Washington, DCGoogle Scholar
  70. Verburg PH, Soepboer W, Veldkamp AT, Limpiada R, Espaldon V, Mastura SSA (2002) Modelling the spatial dynamics of regional land use: the CLUE-s model. Environ Manage 30:391–405CrossRefGoogle Scholar
  71. Verburg PH, Ritsema van Eck JR, de Nijs TCM, Dijst MJ, Schot PP (2004a) Determinants of land-use change patterns in the Netherlands. Environ Plan B Plan Des 31:125–150CrossRefGoogle Scholar
  72. Verburg PH, Schot PP, Dijst MJ, Veldkamp AT (2004b) Land use change modelling: current practice and research priorities. GeoJournal 61:309–324CrossRefGoogle Scholar
  73. Verburg P, Koomen E, Hilferink M, Pérez-Soba M, Lesschen J (2012) An assessment of the impact of climate adaptation measures to reduce flood risk on ecosystem services. Landsc Ecol 27:473–486CrossRefGoogle Scholar
  74. Waddell P (2002) UrbanSim: modeling urban development for land use, transportation, and environmental planning. J Am Plan Assoc 68:297–314CrossRefGoogle Scholar
  75. Warner SB (1972) The urban wilderness: a history of the American city. Harper & Row, New YorkGoogle Scholar
  76. Weng Q (2012) Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sens Environ 117:34–49CrossRefGoogle Scholar
  77. White R, Engelen G (1997) Cellular automata as the basis of integrated dynamic regional modelling. Environ Plan B Plan Des 24:235–246CrossRefGoogle Scholar
  78. Yang Q, Li X, Shi X (2008) Cellular automata for simulating land use changes based on support vector machines. Comput Geosci 34:592–602CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of GeographyUniversity of BonnBonnGermany

Personalised recommendations