Skip to main content
Log in

Mathematical analysis of urban land use change in Xi’an city wall area by using parcel-level data

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Nowadays, more and more interdisciplinary approaches have been applied in urban planning, such as computer, mathematics and geography. However, the sophisticated mathematical methods such as transition matrix, joint-count, Bayes rules and Markov chain have not been deeply utilized in urban land use analysis. Furthermore, the newborn parcel-level urban land use data method has just been tested in a few cases and has not yet been adopted in ancient city area. Based on the above, this paper uses a series of mathematical methods and parcel-level urban land use data for quantification study in the Xi’an city wall area. Digitizing the maps compiled in 1935, 1963, 1995, 2007 and 2017 of the study area leads to the acquisition of the parcel-level urban land use data concerning the following four categories: Residential (R), Service (S), Culture (C) and Other (O). Then five parcel maps of different times will be built up. Through a series of mathematical analysis, the result shows that urban land use change in this area has three kinds of characteristics. For urban land use change speed, the period between 1995 and 2007 is the fastest while the period from 1963 to 1995 is the slowest. For the transition of urban land use, R and S are the main categories, and transition from R to S is the dominant change. Besides, dominated neighbors have positive effects on their transition. C is consistently increasing and has a clustering distribution. For the influence of other factors such as environment and policy, C is a special category that has the highest sensitivity to policies. The result clearly explains the data from the research into the evolution of urban land use in the study area work as a powerful support for land use planning and policy. The mathematical methods would provide a new perspective for the study in ancient Chinese cities.

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

  1. Li Z, Huang J N, Zhang T J. A brief review on the centennial study of urban morphology (in Chinese). New Architecture, 2014, 157: 131–135

    Google Scholar 

  2. Debbage N, Bereitschaft B, Shepherd J M. Quantifying the spatiotemporal trends of urban sprawl among large U.S. metropolitan areas via spatial metrics. Appl Spatial Anal, 2017, 10: 317–345

    Article  Google Scholar 

  3. Tian L, Li Y, Yan Y, et al. Measuring urban sprawl and exploring the role planning plays: A shanghai case study. Land Use Policy, 2017, 67: 426–435

    Article  Google Scholar 

  4. Theobald D M. Land-use dynamics beyond the American urban fringe. Geographical Rev, 2001, 91: 544–564

    Article  Google Scholar 

  5. Rodewald A D. The importance of land uses with in the landscape matrix. Wildlife Soc Bull, 2003, 31: 586–592

    Google Scholar 

  6. Jiang B, Claramunt C. Integration of space syntax into GIS: New perspectives for urban morphology. Trans GIS, 2002, 6: 295–309

    Article  Google Scholar 

  7. Tannier C, Thomas I. Defining and characterizing urban boundaries: A fractal analysis of theoretical cities and Belgian cities. Comput Environ Urban Syst, 2013, 41: 234–248

    Article  Google Scholar 

  8. Chen Y. Derivation of the functional relations between fractal dimension of and shape indices of urban form. Comput Environ Urban Syst, 2011, 35: 442–451

    Article  Google Scholar 

  9. Li X, Yeh A G O. Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS. Landscape Urban Planning, 2004, 69: 335–354

    Article  Google Scholar 

  10. Ma R, Gu C, Pu Y, et al. Mining the urban sprawl pattern: A case study on Sunan, China. Sensors, 2008, 8: 6371–6395

    Article  Google Scholar 

  11. Adams J S. Residential structure of midwestern cities. Ann Assoc Am Geograp, 1970, 60: 37–62

    Article  Google Scholar 

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

  13. Verburg P H, Schot P P, Dijst M J, et al. Land use change modelling: Current practice and research priorities. GeoJournal, 2004, 61: 309–324

    Article  Google Scholar 

  14. Costanza R, Ruth M. Using dynamic modeling to scope environmental problems and build consensus. Environ Manage, 1998, 22: 183–195

    Article  Google Scholar 

  15. Waddell P. UrbanSim: Modeling Urban development for land use, transportation, and environmental planning. J Am Planning Association, 2002, 68: 297–314

    Article  Google Scholar 

  16. Chakir R, Le Gallo J. Predicting land use allocation in France: A spatial panel data analysis. Ecol Economics, 2013, 92: 114–125

    Article  Google Scholar 

  17. Zhu J, Zheng Y, Carroll A L, et al. Autologistic regression analysis of spatial-temporal binary data via monte carlo maximum likelihood. JABES, 2008, 13: 84–98

    Article  MathSciNet  MATH  Google Scholar 

  18. Baker W L. A review of models of landscape change. Landscape Ecol, 1989, 2: 111–133

    Article  Google Scholar 

  19. Lambin E F. Modelling and monitoring landcover change processes in tropical regions. Prog Phys Geography, 1997, 21: 375–393

    Article  Google Scholar 

  20. Theobald D M, Hobbs N T. Forecasting rural land-use change: A comparison of regression and spatial transition-based models. Geograp Environ Model, 1998, 2: 65–82

    Google Scholar 

  21. Landis J D. The California urban futures model: A new generation of metropolitan simulation models. Environ Plann B, 1994, 21: 399–420

    Article  Google Scholar 

  22. Turner M G, Wear D N, Flamm R O. Land ownership and land-cover change in the southern Appalachian highlands and the Olympic peninsula. Ecol Appl, 1996, 6: 1150–1172

    Article  Google Scholar 

  23. Geoghegan J, Wainger L A, Bockstael N E. Spatial landscape indices in a hedonic framework: An ecological economics analysis using GIS. Ecol Economics, 1997, 23: 251–264

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. 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 Geographical Inf Sci, 1998, 12: 699–714

    Article  Google Scholar 

  26. Herold M, Couclelis H, Clarke K C. The role of spatial metrics in the analysis and modeling of urban land use change. Comput Environ Urban Syst, 2005, 29: 369–399

    Article  Google Scholar 

  27. Mas J F, Kolb M, Paegelow M, et al. Inductive pattern-based land use/ cover change models: A comparison of four software packages. Environ Model Software, 2014, 51: 94–111

    Article  Google Scholar 

  28. Kolb M, Mas J F, Galicia L. Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico. Int J Geographical Inf Sci, 2013, 27: 1804–1827

    Article  Google Scholar 

  29. Tepe E, Guldmann J M. Spatial and temporal modeling of parcel-level land dynamics. Comput Environ Urban Syst, 2017, 64: 204–214

    Article  Google Scholar 

  30. Long Y, Shen Y, Jin X. Mapping Block-Level urban areas for all Chinese cities. Ann Am Association Geographers, 2016, 106: 96–113

    Article  Google Scholar 

  31. Waddell P, Wang L, Charlton B, et al. Microsimulating parcel-level land use and activity-based travel: Development of a prototype application in San Francisco. JTLU, 2010, 3: 65–84

    Article  Google Scholar 

  32. Evans T P, Moran E F. Spatial integration of social and biophysical factors related to landcover change. Populat Develop Rev, 2002, 28: 165–186

    Article  Google Scholar 

  33. Mitchell Hess P, Vernez Moudon A, Logsdon M G. Measuring land use patterns for transportation research. Transpation Res Record, 2001, 1780: 17–24

    Article  Google Scholar 

  34. Zhang Y, Li X, Song W. Determinants of cropland abandonment at the parcel, household and village levels in mountain areas of China: A multi-level analysis. Land Use Policy, 2014, 41: 186–192

    Article  Google Scholar 

  35. Evans T P, Manire A, de Castro F, et al. A dynamic model of household decision-making and parcel level landcover change in the eastern Amazon. Ecol Model, 2001, 143: 95–113

    Article  Google Scholar 

  36. Bell K P, Irwin E G, King R L. Spatially explicit micro-level modelling of land use change at the rural-urban interface. Agricult Econom, 2002, 27: 217–232

    Article  Google Scholar 

  37. Biba S, Curtin K M, Manca G. A new method for determining the population with walking access to transit. Int J Geographical Inf Sci, 2010, 24: 347–364

    Article  Google Scholar 

  38. Torrens P M, Alberti M. Measuring sprawl. Centre for Advanced Spatial Analysis. Working Paper. London: University College London. 2000

    Google Scholar 

  39. Weston L M. A methodology to evaluate neighborhood urban form. Planning Forum, 2002, 8: 64–77

    Google Scholar 

  40. Pontius Jr. R G, Shusas E, McEachern M. Detecting important categorical land changes while accounting for persistence. Agriculture EcoSyst Environ, 2004, 101: 251–268

    Article  Google Scholar 

  41. Kane K, Tuccillo J, York A M, et al. A spatio-temporal view of historical growth in Phoenix, Arizona, USA. Landscape Urban Planning, 2014, 121: 70–80

    Article  Google Scholar 

  42. Wang L, Wei H Y, Feng P, et al. Analyzing land-use change of Xi’an region on RS image (in Chinese). Resource Develop Market, 2010, 26: 589–592

    Google Scholar 

  43. Xie X. Study on prediction of land use/cover change-a case study in the Xi’an region. Arid Zone Res, 2008, 25: 125–130

    Article  Google Scholar 

  44. Xue X R, Dang X G. Research on forecast of land use in Xi’an city based on the theory of gray system (in Chinese). Stat Inf Forum, 2009, 24: 40–43

    Google Scholar 

  45. Li F X, Shi H, Feng X G, et al. Study of land use dynamic evolution and driving factors in Xi’an (in Chinese). Bull Survey Mapp, 2015, 374: 41–45

    Google Scholar 

  46. Zhang H L, Jiang J J, Xie X P, et al. Analyzing land use changes and its driving forces in Xi’an region during the past 25 Years (in Chinese). Resource Sci, 2006, 28: 71–77

    Google Scholar 

  47. Jiang Y, Cui L P, Yan S F. A study of the history of protecting the Xi’an city Wall in modern times (in Chinese). Architect Culture, 2014, 119: 60–65

    Google Scholar 

  48. Wang S S. Preliminary analysis of planning methods of Xi’an urban pattern in the beginning of Ming Dynasty (in Chinese). Urban Planning Forum, 2004, 153: 85–88

    Google Scholar 

  49. Xiao L. Study on protection project and protection study of Gulou historic block in Xi’an (in Chinese). Inf China Construct, 2004, 317: 59–62

    Google Scholar 

  50. Bell K P, Irwin E G, King R L. Spatially explicit micro-level modelling of land use change at the rural-urban interface. Agricult Econom, 2002, 27: 217–232

    Article  Google Scholar 

  51. López E, Bocco G, Mendoza M, et al. Predicting land-cover and landuse change in the urban fringe. Landscape Urban Planning, 2001, 55: 271–285

    Article  Google Scholar 

  52. Arsanjani J J, Kainz W, Mousivand A J. Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: The case of Tehran. Int J Image Data Fusion, 2011, 2: 329–345

    Article  Google Scholar 

  53. Zhang R, Tang C, Ma S, et al. Using Markov chains to analyze changes in wetland trends in arid Yinchuan Plain, China. Math Comput Model, 2011, 54: 924–930

    Article  Google Scholar 

  54. Jokar Arsanjani J, Helbich M, Kainz W, et al. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Observation GeoInf, 2013, 21: 265–275

    Article  Google Scholar 

  55. Ren Y Y. Development of urban planning conception in modern Xi’an —A case study of the file documents by the 1927–1947 Republic of China (in Chinese). J Shaanxi Normal Univ (Philosophy Soc Sci), 2009, 38: 105–112

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to ShuSheng Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Zhao, Z., Xu, Y. et al. Mathematical analysis of urban land use change in Xi’an city wall area by using parcel-level data. Sci. China Technol. Sci. 62, 687–697 (2019). https://doi.org/10.1007/s11431-018-9432-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-018-9432-9

Keywords

Navigation