Journal of Geo-information Science >
Research on the Spatial Distribution and Influencing Factors of the Catering Industry in Guangzhou from the Perspective of Spatial Correlation
Received date: 2023-05-25
Revised date: 2023-08-09
Online published: 2023-11-02
Supported by
Guangdong Provincial Social Science Planning Project(GD21CYJ06)
Guangdong Provincial Social Science Planning Project(GD22CYJ05)
Based on the Point of Interest (POI) big data of the catering industry and related service industries in Guangzhou in 2021, this study analyzes the spatial distribution characteristics, influencing factors, and the spatial spillover effects of the overall and subdivided catering industry in Guangzhou based on the methods of HDBSCAN clustering, Collaborative Location Quotient, and Spatial Durbin Error Model. The study mainly explores the overall and local spatial relationships between the catering industry and surrounding service industries. The results show that: 1) Different catering density areas show variations in the spatial distribution of the catering industry in Guangzhou. The catering industry in high-density areas is characterized by a muti-central agglomeration pattern, while the catering industry in low-density areas is characterized by central agglomeration with surrounding radiation. The local distribution of restaurants are related to population features, such as population density, population education level, and so on; 2) There are variations in the spatial correlation between the catering industry and its surrounding service industries across different catering density areas. Areas with high catering density have the strongest spatial correlation, while areas with moderate catering density have the weakest spatial correlation; 3) the influence of surrounding service industries on different types of catering industry also varies. In general, the spatial correlation strength from strong to weak is as follows: fast restaurants, dinner restaurants, snack bars, and cold beverage shops. The local spatial differences are similar but the spatial difference of dining restaurants is the most obvious; 4) The estimation results of the Spatial Durbin Error Model at the street-town scale show that transportation facilities services, shopping services, and population size have the most direct impact on the POI number of local catering industries, with obvious spatial spillover effects. Both the population size and surrounding service industries are the main factors that influence the spatial distribution of fast food restaurants, while dinner restaurants, snack bars, beverage shops, and other catering industries are easily affected by transportation facilities and shopping services. In general, from the perspective of spatial correlation, this study deepens the research on the location theory of service industries. It also provides references for the study of commercial geography and the optimization and adjustment of the spatial pattern of the catering industry in big cities.
WU Xueqin , HU Weiping , WU Xibo . Research on the Spatial Distribution and Influencing Factors of the Catering Industry in Guangzhou from the Perspective of Spatial Correlation[J]. Journal of Geo-information Science, 2023 , 25(11) : 2232 -2248 . DOI: 10.12082/dqxxkx.2023.230293
表1 2021年POI数据类型及数量Tab. 1 Type and quantity of POI data in 2021 |
POI类型 | 类别细分 | 数量/万条 | |
---|---|---|---|
餐饮业 | 正餐服务 | 地方菜、火锅、酒楼等 | 8.95 |
快餐服务 | 中式快餐店、肯德基、麦当劳等 | 1.61 | |
小吃服务 | 面点、粉类、炸串、甜品等 | 4.43 | |
饮料及冷饮服务 | 奶茶店、凉茶店、茶饮店等 | 1.79 | |
周边服务业 | 购物服务 | 便利店、超级市场、体育用品店等 | 8.08 |
商务住宅 | 写字楼、产业园区等 | 1.33 | |
教育服务 | 中学、大学、职业学校等 | 0.34 | |
风景名胜 | 观景点、海滩、纪念馆等 | 0.39 | |
交通设施服务 | 公交车站、地铁站、停车场等 | 6.27 | |
医疗保健服务 | 诊所、专科医院、综合医院 | 0.64 |
表2 基于Queen邻接矩阵的因变量Moran's I指数Tab. 2 Moran's I index of dependent variable based on the Queen adjacency matrix |
指标 | Moran's I | 标准差 | Z值 | P值 |
---|---|---|---|---|
所有餐饮业POI数量 | 0.133 | 0.047 | 2.918 | 0.004 |
基于熵权法的4种餐饮业POI数量之和 | 0.134 | 0.047 | 2.944 | 0.003 |
快餐厅POI数量 | 0.169 | 0.048 | 3.643 | 0.000 |
饮料冷饮POI数量 | 0.159 | 0.048 | 0.871 | 0.002 |
小吃POI数量 | 0.166 | 0.047 | 3.607 | 0.000 |
正餐餐厅POI数量 | 0.124 | 0.047 | 2.737 | 0.006 |
表3 变量及其统计描述Tab. 3 Variables and its statistical description |
变量 | 单位 | 样本数/个 | 均值 | 标准差 | 最小值 | 最大值 | 说明 | |
---|---|---|---|---|---|---|---|---|
因变量Yi | Yall | 十个 | 176 | 95.34 | 84.99 | 3.70 | 552.90 | 4种餐饮业POI数量之和 |
Yewall | 十个 | 176 | 24.60 | 22.00 | 0.96 | 143.24 | 基于熵权法的4种餐饮业POI数量之和 | |
快餐厅POI数量Y1 | 十个 | 176 | 9.12 | 7.79 | 0.20 | 47.60 | 高德地图POI中类代码为:0503 | |
饮料冷饮POI数量Y2 | 十个 | 176 | 10.17 | 9.00 | 0.00 | 53.20 | POI中类代码为:0505、0506、0507 | |
小吃POI数量Y3 | 十个 | 176 | 25.19 | 23.93 | 0.50 | 154.60 | POI中类代码为:0508、0509 | |
正餐餐厅POI数量Y4 | 十个 | 176 | 50.85 | 46.07 | 2.40 | 303.00 | POI中类代码为:0501、1001、1002 | |
自变量Xi | 风景名胜X1 | 十个 | 176 | 2.23 | 2.36 | 0.00 | 13.20 | POI中类代码为:1102 |
购物服务X2 | 十个 | 176 | 45.87 | 47.34 | 1.60 | 359.50 | POI中类代码为:0601、0602、0603、0609 | |
交通设施X3 | 十个 | 176 | 35.61 | 28.03 | 0.70 | 185.50 | POI中类代码为:1505、1507、1508、1509 | |
教育服务X4 | 十个 | 176 | 1.94 | 3.88 | 0.00 | 34.70 | POI中类代码为:1402 | |
医疗服务X5 | 十个 | 176 | 3.66 | 3.03 | 0.10 | 21.10 | POI中类代码为:0901、0902、0903 | |
商务住宅X6 | 十个 | 176 | 7.55 | 7.71 | 0.00 | 55.10 | POI中类代码为:1201 | |
区位X7 | - | 176 | 2.22 | 0.841 | 1.00 | 3.00 | 将广州176个街镇分成中心城区(包括越秀、天河、海珠、荔湾)、近郊区(包括黄埔、白云、番禺)及远郊区(包括从化、增城、花都、南沙)三类,分别赋值3、2、1 | |
人口规模X8 | 万人 | 176 | 10.61 | 6.979 | 0.320 | 39.73 | 基于广州市2020年第七次人口普查数据 |
表4 不同餐饮密度区域餐饮业吸引周边服务业的全局区位熵Tab. 4 GCLQ results of surrounding service industry attracted by catering industry in different catering density areas |
餐饮密度 | 商务住宅 | 教育服务 | 购物服务 | 医疗服务 | 交通设施服务 | 风景名胜 |
---|---|---|---|---|---|---|
高密度 | 0.686 1 | 0.591 6 | 0.788 8 | 0.631 1 | 0.591 6 | 0.506 8 |
中密度 | 0.627 2 | 0.411 4 | 0.805 8 | 0.715 7 | 0.592 4 | 0.469 9 |
低密度 | 0.558 9 | 0.633 9 | 0.847 6 | 0.737 5 | 0.574 1 | 0.465 1 |
注:计算结果在0.01水平上显著。 |
表5 不同餐饮密度区域周边服务业吸引餐饮业的全局区位熵Tab. 5 GCLQ results of catering industry attracted by surrounding service industry in different catering density areas |
餐饮密度 | 商务住宅 | 教育服务 | 购物服务 | 医疗服务 | 交通设施服务 | 风景名胜 |
---|---|---|---|---|---|---|
高密度 | 0.829 0 | 0.730 8 | 0.849 4 | 0.710 5 | 0.730 8 | 0.699 8 |
中密度 | 0.822 3 | 0.621 9 | 0.889 2 | 0.823 4 | 0.747 9 | 0.723 2 |
低密度 | 0.807 7 | 0.904 8 | 0.917 0 | 0.888 3 | 0.750 8 | 0.680 4 |
注:计算结果在0.01水平上显著。 |
表6 不同类型餐饮业吸引周边服务业的全局区位熵Tab. 6 GCLQ results of surrounding service industry attracted by different types catering industry |
餐饮类型 | 商务住宅 | 教育服务 | 购物服务 | 医疗服务 | 交通设施服务 | 风景名胜 |
---|---|---|---|---|---|---|
正餐餐厅 | 0.652 4 | 0.452 0 | 0.806 4 | 0.716 6 | 0.623 9 | 0.493 6 |
快餐厅 | 0.699 9 | 0.522 6 | 0.896 3 | 0.762 7 | 0.754 3 | 0.512 8 |
小吃服务 | 0.564 6 | 0.407 3 | 0.828 8 | 0.668 4 | 0.582 2 | 0.410 9 |
饮料及冷饮服务 | 0.644 2 | 0.487 5 | 0.805 8 | 0.683 4 | 0.708 1 | 0.450 6 |
注:计算结果在0.01水平上显著。 |
表7 周边服务业吸引不同类型餐饮业的全局区位熵Tab. 7 GCLQ results of different types catering industry attracted by surrounding service industry |
餐饮类型 | 正餐餐厅 | 快餐厅 | 小吃服务 | 饮料及冷饮服务 |
---|---|---|---|---|
商务住宅 | 0.795 3 | 0.753 8 | 0.735 4 | 0.729 3 |
教育服务 | 0.614 7 | 0.605 7 | 0.588 7 | 0.588 4 |
购物服务 | 0.863 8 | 0.928 7 | 0.922 3 | 0.844 5 |
医疗服务 | 0.777 0 | 0.775 4 | 0.765 5 | 0.744 0 |
交通设施服务 | 0.743 8 | 0.785 1 | 0.700 4 | 0.779 9 |
风景名胜 | 0.668 2 | 0.592 5 | 0.606 5 | 0.570 3 |
注:计算结果在0.01水平上显著。 |
表8 解释变量共线性检验Tab. 8 Collinearity test of explanatory variables |
变量 | 风景名胜X1 | 购物服务X2 | 交通设施X3 | 教育服务X4 | 医疗服务X5 | 商务住宅X6 | 区位X7 | 人口规模X8 |
---|---|---|---|---|---|---|---|---|
VIF | 1.46 | 7.73 | 5.51 | 1.96 | 3.82 | 4.22 | 1.4 | 4.36 |
表9 空间杜宾误差模型估计结果Tab. 9 The estimation results of SDEM |
指标 | 全类型 | 细分业态 | |||||
---|---|---|---|---|---|---|---|
Yall | Yewall | Y1 | Y2 | Y3 | Y4 | ||
lnX1 | -0.015 | -0.008 | 0.025 | -0.027 | -0.027 | -0.000 | |
(-0.56) | (-0.33) | (0.82) | (-0.56) | (-0.99) | (-0.01) | ||
lnX2 | 0.565*** | 0.524*** | 0.549*** | 0.230* | 0.616*** | 0.636*** | |
(8.50) | (8.29) | (6.95) | (1.89) | (8.74) | (9.92) | ||
lnX3 | 0.353*** | 0.282*** | 0.069 | 0.533*** | 0.078 | 0.351*** | |
(5.68) | (4.91) | (0.93) | (4.77) | (1.15) | (6.05) | ||
lnX4 | 0.038 | 0.050 | 0.142*** | 0.074 | 0.071** | 0.027 | |
(1.17) | (1.56) | (3.60) | (1.23) | (2.06) | (0.85) | ||
lnX5 | -0.027 | 0.012 | -0.066 | 0.130 | -0.017 | 0.027 | |
(-0.49) | (0.23) | (-1.00) | (1.29) | (-0.29) | (0.51) | ||
lnX6 | -0.040 | -0.003 | 0.155*** | 0.054 | 0.070 | -0.089** | |
(-0.94) | (-0.06) | (3.08) | (0.70) | (1.55) | (-2.20) | ||
X7 | 0.032 | 0.043 | 0.001 | 0.043 | 0.072* | 0.024 | |
(0.97) | (1.55) | (0.04) | (0.78) | (1.81) | (0.84) | ||
lnX8 | 0.190*** | 0.183*** | 0.135 | 0.154 | 0.360*** | 0.048 | |
(2.72) | (2.67) | (1.59) | (1.18) | (4.83) | (0.69) | ||
_cons | 0.744*** | -0.321** | -0.688*** | -1.257*** | -0.542*** | 0.226 | |
(4.39) | (-2.22) | (-3.65) | (-4.31) | (-2.85) | (1.52) | ||
空间滞后W: | |||||||
lnX1 | 0.079 | 0.031 | -0.008 | -0.043 | 0.048 | 0.037 | |
(1.37) | (0.60) | (-0.11) | (-0.41) | (0.71) | (0.70) | ||
lnX2 | -0.585*** | -0.492*** | -0.411** | 0.107 | -0.422** | -0.700*** | |
(-3.07) | (-3.35) | (-2.38) | (0.41) | (-2.00) | (-4.33) | ||
lnX3 | -0.521*** | -0.281*** | 0.135 | -0.345* | -0.071 | -0.485*** | |
(-3.98) | (-3.06) | (1.13) | (-1.66) | (-0.60) | (-4.94) | ||
lnX4 | 0.011 | -0.062 | -0.088 | -0.094 | -0.046 | -0.014 | |
(0.16) | (-0.96) | (-0.97) | (-0.74) | (-0.55) | (-0.22) | ||
lnX5 | 0.095 | -0.047 | -0.197 | -0.342* | -0.079 | 0.004 | |
(0.86) | (-0.47) | (-1.56) | (-1.74) | (-0.63) | (0.04) | ||
lnX6 | 0.173** | -0.026 | -0.164 | -0.211 | -0.048 | 0.162** | |
(2.43) | (-0.36) | (-1.58) | (-1.56) | (-0.49) | (2.39) | ||
lnX8 | -0.076 | -0.265* | -0.202 | -0.320 | -0.486*** | -0.040 | |
(-0.49) | (-1.76) | (-1.15) | (-1.23) | (-2.61) | (-0.28) | ||
Spatial rho | 0.818*** | 1.122*** | 0.964*** | 1.113*** | 0.993*** | 1.055*** | |
(4.24) | (6.24) | (5.94) | (5.11) | (4.63) | (6.31) | ||
Spatial lamda | -0.598** | -0.876*** | -0.746*** | -0.835*** | -0.307 | -0.800*** | |
(-2.04) | (-3.10) | (-2.76) | (-2.91) | (-0.98) | (-3.05) | ||
N | 176 | 176 | 176 | 176 | 176 | 176 | |
Wald test of spatial terms | 38.29*** | 70.75*** | 62.70*** | 37.88*** | 39.78*** | 90.46*** |
注:*p<0.1; **p<0.05; ***p<0.01,括号内数据为对应系数的z值; Spatial rho为空间自回归系数; Spatial lamda为误差项的空间自回归系数。 |
表10 空间溢出效应分解Tab. 10 Decomposition of spatial spillover effect |
变量 | 总体效应 | 直接效应 | 间接效应 |
---|---|---|---|
lnX1 | 0.217 | -0.000 5 | 0.218 |
lnX2 | 0.135 | 0.538 0 | -0.403 |
lnX3 | -0.454 | 0.303 0 | -0.757 |
lnX4 | 0.186 | 0.047 0 | 0.138 |
lnX5 | 0.226 | -0.011 0 | 0.237 |
lnX6 | 0.449 | -0.010 0 | 0.458 |
X7 | 0.122 | 0.037 0 | 0.085 |
lnX8 | 0.467 | 0.207 0 | 0.260 |
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