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Mathematical analysis of urban land use change in Xi'an city wall area by using parcel-level data
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  • 英文篇名:Mathematical analysis of urban land use change in Xi'an city wall area by using parcel-level data
  • 作者:WANG ; ShuSheng ; ZHAO ; Zi ; Liang ; XU ; YuQian ; LI ; XiaoLong
  • 英文作者:WANG ShuSheng;ZHAO Zi Liang;XU YuQian;LI XiaoLong;State Key Laboratory of Green Building in Western China, Xi'an University of Architecture and Technology;College of Architecture, Xi'an University of Architecture and Technology;
  • 英文关键词:urban space;;land use;;parcel maps;;transition matrix;;probability;;Markov chain
  • 中文刊名:JEXG
  • 英文刊名:中国科学:技术科学(英文版)
  • 机构:State Key Laboratory of Green Building in Western China, Xi'an University of Architecture and Technology;College of Architecture, Xi'an University of Architecture and Technology;
  • 出版日期:2019-02-25 11:25
  • 出版单位:Science China(Technological Sciences)
  • 年:2019
  • 期:v.62
  • 基金:supported by the National Outstanding Youth Foundation of China(Grant No.51322812)
  • 语种:英文;
  • 页:JEXG201904016
  • 页数:11
  • CN:04
  • ISSN:11-5845/TH
  • 分类号:171-181
摘要
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.
        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.
引文
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