Journal of Geo-information Science, Volume. 22, Issue 3, 517(2020)

Urban Expansion Simulation and Early Warning based on Geospatial Partition and FLUS Model

Linfeng ZHAO1...1,2,2, Xiaoping LIU1,1,2,2,*, Penghua LIU1,1,2,2, Guangzhao CHEN1,1,2,2, and Jialv HE1,1,22 |Show fewer author(s)
Author Affiliations
  • 1School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • 1中山大学地理科学与规划学院,广州 510275
  • 2Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China
  • 2广东省城市化与地理环境空间模拟重点实验室,广州510275
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    Figures & Tables(18)
    The location of Pearl River Delta
    Spatial variables of urban expansion simulation in Pearl River Delta
    Geospatial partition based on township streets
    Geospatial partition based on grid
    The framework of FLUS model
    The structure of CA model in FLUS model
    The geospatial partition result in Pearl River Delta
    Comparison of simulation results in Pearl River Delta in 2015
    Simulation of urban expansion for the years 2025, 2035 and 2045
    Integrated early warning results of urban expansion in Pearl River Delta from 2025 to 2045
    • Table 1. Early warning indicators for urban expansion in Pearl River Delta

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      Table 1. Early warning indicators for urban expansion in Pearl River Delta

      影响层指标层
      城市扩张的形态影响C(紧凑度)
      SHAPE_MN(城市斑块平均形状指数)
      FRAC_MN(城市斑块平均分维数)
      ENN_MN(城市斑块平均最近邻距离)
      PD(平均斑块密度)
      AI(聚集指数)
      城市扩张的生态影响SHDI(香农多样性指数)
      F_ AREA_MN(林地斑块平均面积指数)
      W_AREA_MN(水体斑块平均面积指数)
      C_MNN(耕地斑块平均最近邻距离指数)
      F_MNN(林地斑块平均最近邻距离指数)
      FGW_AR(林地、草地及水体面积占比率)
      U_FGW_AR(侵占林地、草地及水体的总面积占新增城市用地的面积比率)
      FGW_CR(林地、草地及水体面积变化率)
      U_C_AR(侵占耕地面积占新增城市用地面积的比率)
      U_EPZ_AR(侵占生态保护区面积占新增城市用地面积的比率)
      U_PCF_AR(侵占永久基本农田占新增城市用地面积的比率)
      城市扩张的强度影响AREA_MN(城市斑块平均面积指数)
      PERIMETER_MN(城市斑块平均周长指数)
      UEI(城市用地扩张速率指数)
      UH_MN(城市扩张用地平均高程)
      US_MN(城市扩张用地平均坡度)
    • Table 2. Weights of early warning indicators for urban expansion in Pearl River Delta

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      Table 2. Weights of early warning indicators for urban expansion in Pearl River Delta

      影响层权重指标层权重
      城市扩张的形态影响0.3C0.06
      SHAPE_MN0.15
      FRAC_MN0.10
      ENN_MN0.34
      PD0.28
      AI0.07
      城市扩张的生态影响0.5SHDI0.04
      F_ AREA_MN0.05
      W_AREA_MN0.04
      C_MNN0.10
      F_MNN0.09
      FGW_AR0.02
      U_FGW_AR0.13
      FGW_CR0.08
      U_C_AR0.07
      U_EPZ_AR0.24
      U_PCF_AR0.14
      城市扩张的强度影响0.2AREA_MN0.12
      PERIMETER_MN0.21
      UEI0.48
      UH_MN0.07
      US_MN0.12
    • Table 3. Accuracy comparison of simulation results under partitioned and unpartitioned scenarios in Pearl River Delta

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      Table 3. Accuracy comparison of simulation results under partitioned and unpartitioned scenarios in Pearl River Delta

      区域分区未分区
      OA/%KappaFoMOA/%KappaFoM
      分区192.810.88260.161793.370.89150.1466
      分区289.680.85700.328988.470.84070.2625
      分区383.170.75320.382479.970.70440.3466
      分区487.820.83510.362188.240.84100.3231
      分区591.760.86190.128090.550.84160.0899
      分区694.430.82620.072295.050.84120.0735
      分区787.400.81660.141188.110.82620.0858
      分区893.650.85200.114194.360.86740.1011
      分区982.370.74430.241482.800.74930.2505
      分区1091.070.84090.169791.500.84770.1430
      珠江三角洲90.040.84180.232990.320.84630.2127
    • Table 4. Area of land use type in Pearl River Delta in 2025 (km2)

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      Table 4. Area of land use type in Pearl River Delta in 2025 (km2)

      区域耕地林地草地水体城市未利用地
      分区11830.903387.38282.44455.70441.320.20
      分区2584.14792.7117.38301.18500.540.48
      分区31301.88470.406.45473.431921.880.95
      分区41216.811142.5845.45556.39992.840.28
      分区5856.722166.7452.45380.71335.750.00
      分区61401.238739.94306.72172.99208.790.32
      分区71054.231528.1663.32414.65547.311.44
      分区81366.784665.10142.02126.71189.260.67
      分区9863.181678.8074.20407.432593.180.47
      分区101448.894059.79177.35235.21818.440.94
      珠江三角洲11 924.7628 631.601167.793524.408549.335.74
    • Table 5. Area of land use type in Pearl River Delta in 2035 (km2)

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      Table 5. Area of land use type in Pearl River Delta in 2035 (km2)

      区域耕地林地草地水体城市未利用地
      分区11810.133318.92304.97483.88479.930.11
      分区2552.09770.6319.61297.85555.940.31
      分区31229.49450.246.08436.912051.360.91
      分区41184.111116.4944.41526.611082.450.28
      分区5843.892143.3756.27374.93373.900.00
      分区61397.988679.47347.34173.19231.680.33
      分区7988.421505.8762.17421.24630.281.13
      分区81352.804634.83156.18133.51212.580.63
      分区9757.941623.2967.79402.382765.340.52
      分区101384.173996.43176.81244.53937.760.93
      珠江三角洲11 501.0328 239.531241.643495.039321.225.15
    • Table 6. Area of land use type in Pearl River Delta in 2045 (km2)

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      Table 6. Area of land use type in Pearl River Delta in 2045 (km2)

      区域耕地林地草地水体城市未利用地
      分区11794.873256.80323.86510.33511.990.10
      分区2527.24751.3821.45293.51602.620.23
      分区31171.35434.015.79409.462153.510.87
      分区41156.351093.3543.51503.711157.150.28
      分区5834.132121.4559.40370.11407.290.00
      分区61398.028626.52381.33173.46250.330.34
      分区7939.431484.3361.14424.71698.481.02
      分区81343.344607.75168.14139.59231.100.61
      分区9690.541580.6563.09397.122885.300.56
      分区101334.293938.84176.26253.111037.200.92
      珠江三角洲11 189.5627 895.081303.973475.129934.974.92
    • Table 7. Early warning level of urban expansion in each sub-region

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      Table 7. Early warning level of urban expansion in each sub-region

      区域2025年2035年2045年
      分区1中警中警重警
      分区2轻警中警中警
      分区3轻警轻警中警
      分区4轻警轻警中警
      分区5轻警无警中警
      分区6轻警轻警中警
      分区7轻警中警中警
      分区8中警中警重警
      分区9中警中警重警
      分区10无警轻警中警
    • Table 8. Early warning level of urban expansion in each city

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      Table 8. Early warning level of urban expansion in each city

      城市2025年2035年2045年
      广州轻警轻警中警
      佛山无警轻警中警
      肇庆无警无警中警
      深圳轻警轻警中警
      东莞重警重警重警
      惠州无警无警无警
      珠海轻警轻警中警
      中山轻警中警中警
      江门轻警轻警中警
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    Linfeng ZHAO, Xiaoping LIU, Penghua LIU, Guangzhao CHEN, Jialv HE. Urban Expansion Simulation and Early Warning based on Geospatial Partition and FLUS Model[J]. Journal of Geo-information Science, 2020, 22(3): 517

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    Paper Information

    Received: Aug. 29, 2019

    Accepted: --

    Published Online: Sep. 16, 2020

    The Author Email: LIU Xiaoping (liuxp3@mail.sysu.edu.cn)

    DOI:10.12082/dqxxkx.2020.190477

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