• Journal of Geo-information Science
  • Vol. 22, Issue 6, 1202 (2020)
Kezhen YAO and Shuping YUE*
Author Affiliations
  • School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
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    DOI: 10.12082/dqxxkx.2020.190432 Cite this Article
    Kezhen YAO, Shuping YUE. Study on Spatial Distribution of Modern Sweet Diet and its Impact Factors in China based on Big Data from Internet[J]. Journal of Geo-information Science, 2020, 22(6): 1202 Copy Citation Text show less
    Crawling and storing flow chart of merchant's menu data from Ele.me in January 2019
    Fig. 1. Crawling and storing flow chart of merchant's menu data from Ele.me in January 2019
    Data processing flow chart of merchant's menu from Ele.me in January 2019
    Fig. 2. Data processing flow chart of merchant's menu from Ele.me in January 2019
    Distribution map of Sweet Habits in modern China
    Fig. 3. Distribution map of Sweet Habits in modern China
    Distribution map of Sweet Habits in different province of China
    Fig. 4. Distribution map of Sweet Habits in different province of China
    Moran scatter graph and LISA agglomeration graph of polygon data in China
    Fig. 5. Moran scatter graph and LISA agglomeration graph of polygon data in China
    Cold point and hot Point bubble diagrams of sweetness in different cities of China
    Fig. 6. Cold point and hot Point bubble diagrams of sweetness in different cities of China
    Distribution Map of Modern Sweet Habits in China based on grouping analysis
    Fig. 7. Distribution Map of Modern Sweet Habits in China based on grouping analysis
    Relationship between Sweetness and Sunshine Hours in coastal and inland cities
    Fig. 8. Relationship between Sweetness and Sunshine Hours in coastal and inland cities
    插值方法均方根误差(RMS)平均偏差标准均方根误差(标准化RMS)
    反距离加权0.130-0.016
    普通克里格0.1380.0081.366
    Table 1. Comparison of accuracy of different interpolation methods
    城市食甜度Moran's I标准化Z值p关联类型
    上海0.76970.000 0355.1219***0.0000H-H
    杭州0.57240.000 0324.4924***0.0000H-H
    福州0.60000.000 0112.8835***0.0039H-H
    成都0.07940.000 0133.0953***0.0019L-L
    银川0.10000.000 0102.3447**0.0190L-L
    Table 2. LISA of Urban Eating Sweetness
    空间约束类型评价参数
    组数R2孤元个数
    CONTIGUITY面邻接类型30.780
    40.880
    50.871
    DELAUNAY三角测量30.872
    40.903
    50.924
    K_NEAREST邻接类型30.801
    40.883
    50.934
    Table 3. Grouping analysis results of sweetness for different provinces in China
    变量模型1模型2模型3模型4模型5模型6
    βiβiβiβiβiβi
    C-1.67***-6.17***-7.10***-7.49***-6.78***-6.68***
    k×ln p0.12***0.13***0.11***0.72**3.54**3.77***
    ln s0.87***0.88***0.99***0.99***0.99***
    GDP¯0.43**0.35**0.05
    k×ln h-1.01*-3.58**-3.81***
    k×ln t-3.19*-3.45***
    F21.89***22.18**20.70***17.95***16.86***22.18***
    R20.500.680.760.790.820.82
    Table 4. Stepwise regression analysis parameters
    Kezhen YAO, Shuping YUE. Study on Spatial Distribution of Modern Sweet Diet and its Impact Factors in China based on Big Data from Internet[J]. Journal of Geo-information Science, 2020, 22(6): 1202
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