网络大数据下的中国现代食甜习惯空间分布特征及其影响因素研究

doi:10.12082/dqxxkx.2020.190432

Abstract

Abstract:

As the most important element with local characteristics in regional culture, dietary geographical culture presents a new diversified situation under the background of large scale population movement. However, up to now, the domestic research on the distribution characteristics of sweet diet based on traditional cognition is still lack of objective data. Based on the web crawler technology, this paper obtained about 20 million pieces of gourmet consumption data in 31 provincial capitals in mainland China. The degree of sweetness in our diets in urban areas was calculated for traditional dishes, main food dishes, drinks, and dessert dishes. Based on ArcGIS and MySQL softwares, spatial analysis and mathematical statistics were used to understand the spatial distribution characteristics of the modern Chinese sweet diet and identify its affecting factors. The results show that there were dramatically regional differences in the spatial distribution of sweet diet in China, especially in the southeastern coastal areas and the central inland areas, with the evaluation parameter (R ²) of spatial grouping analysis reaching 0.88. The distribution of modern sweet diet generally presented a surrounding pattern of "High East, Middle North, Micro-low West and Low Inside". From either the overall or local point of view, the Moran indexes were positive at 1% significance level, and there was a significant positive spatial autocorrelation for sweet diet habits at different areas in China rather than an obvious trend of dispersion. There were three distinct geographical agglomeration areas: the high-sweetness agglomeration areas along the southeast coast of Jiangsu, Zhejiang, Shanghai, and Fujian, the low-sweetness agglomeration areas in southwest areas of Chongqing, Guizhou, and Sichuan, and the northwest inland low-sweetness agglomeration areas in Shanxi and Ningxia. The accuracy of the stepwise regression model of sweet diet habit distribution was 0.82, and results suggest that meteorological elements such as precipitation, humidity, temperature, and geographical location were important factors that influence the spatial distribution of sweet diet habit in modern China. Moreover, we found that geographical location had a regulating effect on the influence of sunshine duration on sweet diet habit. Specifically, the sweetness of inland cities generally increased with the increase of sunshine duration, while the sweetness of coastal cities usually decreased with the decrease of sunshine duration. This study aims to reveal the regional disparity of sweet culture in modern China, which provides reference for the planning of the category structure in urban catering industry and better understanding of the new trend of the development of modern sweet food consumption.

Key words: sweetness of food in urban, big data, web crawler, spatial autocorrelation analysis, hot spot analysis, spatial grouping analysis, stepwise regression analysis, GIS

YAO Kezhen, YUE Shuping. 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): 1202-1215.DOI:10.12082/dqxxkx.2020.190432

Figures/Tables 12

Fig. 1

Fig. 2

Tab. 1

Fig. 3

Tab. 2

Fig. 4

Fig. 5

Fig. 6

Tab. 3

Fig. 7

Tab. 4

Stepwise regression analysis parameters"

变量	模型1	模型2	模型3	模型4	模型5	模型6
	β_i	β_i	β_i	β_i	β_i	β_i
C	-1.67^***	-6.17^***	-7.10^***	-7.49^***	-6.78^***	-6.68^***
k×ln p	0.12^***	0.13^***	0.11^***	0.72^**	3.54^**	3.77^***
ln s		0.87^***	0.88^***	0.99^***	0.99^***	0.99^***
$GDP ˉ$			0.43^**	0.35^**	0.05
k×ln h				-1.01^*	-3.58^**	-3.81^***
k×ln t					-3.19^*	-3.45^***
F值	21.89^***	22.18^**	20.70^***	17.95^***	16.86^***	22.18^***
R²	0.50	0.68	0.76	0.79	0.82	0.82

Tab. 4

Fig. 8

References 28

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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	0.138	0.008	1.366

城市	食甜度	Moran's I	标准化Z值	p值	关联类型
上海	0.7697	0.000 035	5.1219^***	0.0000	H-H
杭州	0.5724	0.000 032	4.4924^***	0.0000	H-H
福州	0.6000	0.000 011	2.8835^***	0.0039	H-H
成都	0.0794	0.000 013	3.0953^***	0.0019	L-L
银川	0.1000	0.000 010	2.3447^**	0.0190	L-L

空间约束类型		评价参数
空间约束类型	组数	R²	孤元个数
CONTIGUITY面邻接类型	3	0.78	0
	4	0.88	0
	5	0.87	1
DELAUNAY三角测量	3	0.87	2
	4	0.90	3
	5	0.92	4
K_NEAREST邻接类型	3	0.80	1
	4	0.88	3
	5	0.93	4

Study on Spatial Distribution of Modern Sweet Diet and its Impact Factors in China based on Big Data from Internet

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