| 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 |
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| 引用本文： | 姚可桢,岳书平.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[J].地球信息科学,2020,22(6):1202-1215. |
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| 作者姓名： | 姚可桢 岳书平 |
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| 作者单位： | 南京信息工程大学地理科学学院,南京 210044 |
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| 基金项目： | 国家自然科学基金项目(41901355);江苏省自然科学基金青年项目(BK20160953);江苏省一流本科专业项目 |
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| 摘 要： | 饮食地理文化作为地域文化中最具地方特色的重要元素,在现代人口大规模流动背景下呈现出全新的多样化局面,而基于传统认知的"南甜北咸"的地域分异已然不能代表中国现代食甜分布的空间特征。因此,本文采用网络爬虫技术,获取我国大陆31个省会城市共计约2000万条美食消费数据,从传统类菜品、主食类菜品、饮料类和甜品类菜品4个方面计算城市食甜度,在ArcGIS、MySQL软件支持下,借助GIS空间分析和数理统计方法探究我国现代食甜习惯的空间分布特征,分析影响食甜分布的因素。研究发现:①中国食甜在空间分布上存在显著的地域分异特征,聚类分析评价参数R2高达0.88,现代食甜习惯总体呈现"东高北中,西微内低"的包围式格局;②从整体抑或局部角度,在1%显著性水平上莫兰指数均为正,中国食甜分布呈现显著的空间正相关关系,形成特色鲜明的3个地理集聚区,即以苏浙沪闽为主的东南沿海高甜集聚区,以渝黔川为主的西南内陆低甜集聚区和以陕宁为主的西北内陆低甜集聚区;③构建了中国现代食甜习惯分布影响因素模型,其拟合精度为0.82,分析结果显示降水、湿度、气温等气象要素及地理位置是影响现代我国食甜空间分布的重要因素。
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| 关 键 词： | 城市食甜度 大数据 网络爬虫 空间自相关分析 热点分析 空间聚类分析 逐步回归分析 GIS |
| 收稿时间： | 2019-08-08 |
Study on Spatial Distribution of Modern Sweet Diet and its Impact Factors in China based on Big Data from Internet |
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| 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].Geo-information Science,2020,22(6):1202-1215. |
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| Authors: | YAO Kezhen YUE Shuping |
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| Institution: | School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| 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. |
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| Keywords: | sweetness of food in urban big data web crawler spatial autocorrelation analysis hot spot analysis spatial grouping analysis stepwise regression analysis GIS |
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