实体-信息与场景-符号视角下的奶茶消费空间分布特征及影响因素

doi:10.12082/dqxxkx.2023.220804

摘要/Abstract

摘要：

数字经济时代，实体空间与信息空间融合叠加、场景消费与符号消费共同作用的新零售消费空间对城市空间发展的影响不断扩大。以西安市新零售奶茶消费空间为研究对象，通过空间句法模型分析奶茶消费空间分布特征，构建门槛回归、多尺度地理加权回归模型对奶茶消费空间分布影响因素进行解析。研究发现：① 新零售奶茶消费空间区位选择对市场信息高度敏感并能够回应空间调控决策，传统零售业区位论中涉及的消费市场、空间接近性、地价、同行业发展等因素仍对新零售奶茶消费空间分布产生显著影响，但实体与信息在新零售奶茶消费空间中存在尺度博弈，信息作用随着尺度的扩大而更加明显；② 新零售奶茶消费空间分布中蕴含着品牌社群组织行为特征，消费社群的消费主体性增强，不同品牌新零售奶茶消费空间通过多种方式促进消费社群成长；③ 社会文化感知在实体与信息消费空间中均存在价格门槛效应。低价位消费空间场景与符号价值作用机制中信息流与物质流之间相互排斥，文化扩散依靠场景与符号密度、传播速度与频次。高价位消费空间场景与符号价值作用机制中信息流与物质流相互促进，文化扩散依靠场景与符号影响力和传播距离。④ 空间句法模型模拟了实体消费的信息空间延展，门槛回归与多尺度地理加权回归模型揭示了新零售消费空间生产与再生产机制，拓展了消费地理学中新零售消费空间研究。

关键词: 奶茶消费空间, 新零售, 场景消费, 符号消费, 实体空间, 信息空间, 空间分布, 影响因素, 西安市

Abstract:

In the era of digital economy, the influence of new retail consumption space which combines scene consumption and symbol consumption, and physical space and information space, on the development of urban space is constantly expanding. Taking the consumption space of milk-tea in Xi 'an as the research object, space syntax is used to analyze the distribution characteristics of milk-tea consumption space. Threshold regression and multi-scale geographical weighted regression are constructed to analyze the influencing factors of the distribution of milk-tea consumption space. The results show that: (1) Location selection for new retail milk-tea is highly sensitive to market information and can respond to space regulatory decisions. The factors involved in the traditional retail location theory, such as consumer market, spatial proximity, land price, and industrial development, have a significant impact on the spatial distribution of new retail milk-tea consumption. However, there is a game between physical and information entities in new retail milk-tea consumption space, with the role of information growing significantly with the expansion of scale; (2) The spatial distribution of new retail milk-tea consumption reflects the characteristics of organizational behaviors displayed by brand community. The consumption subjectivity of consumer community is enhanced. The new retail milk-tea consumption space of different brands promotes the growth of consumer communities in various ways; (3) Social culture perception has price threshold effect in both physical and information consumption space. Information flow and material flow are mutually exclusive in the action mechanism of low-priced consumption space scene and symbol value, and cultural dissemination depends on the density, propagation rate, and frequency of scene and symbol. In the action mechanism of high-priced consumption space scene and symbol value, information flow and material flow promote each other, and cultural dissemination depends on the influence and propagation distance of scene and symbol; (4) Spatial syntactic model simulates the extension of information in the consumption space. Threshold regression and multi-scale geographical weighted regression reveal the production and reproduction mechanism of new retail consumption space. This study expands the research on new retail consumption space in consumer geography.

Key words: milk-tea consumption space, new retail, scene consumption, symbol consumption, physical space, information space, spatial distribution, influencing factors, Xi 'an City

张宇峥, 鲁岩, 陈晓键. 实体-信息与场景-符号视角下的奶茶消费空间分布特征及影响因素[J]. 地球信息科学学报, 2023, 25(4): 823-837.DOI:10.12082/dqxxkx.2023.220804

ZHANG Yuzheng, LU Yan, CHEN Xiaojian. Distribution Characteristics and Influencing Factors of Milk-tea Consumption Space from the Perspective of Scene-Symbol and Physical-Information[J]. Journal of Geo-information Science, 2023, 25(4): 823-837.DOI:10.12082/dqxxkx.2023.220804

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数据名称	数据来源	数据时间	数据说明
奶茶店铺与餐饮场所数据	高德地图 https://lbs.amap.com	2022年5月	主要包含餐饮场所的名称、类别、地理位置、消费评分、人均消费
居住人口与土地价格数据	链家网 https://lianjia.com	2022年5月	主要包含小区位置、户数、交易价格
路网数据	地理空间数据云 https://www.gscloud.cn	2021年11月	沿道路中心线对路网数据进行矢量化

整合度	R300	R500	R1000	R2000	R3000	R5000	R全局
喜茶	43.104	27.126	68.960	191.368	373.994	902.403	8484.742
蜜雪冰城	37.244	25.323	49.021	152.499	300.671	713.562	8209.701
茶话弄	19.551	23.128	56.554	172.645	337.786	792.430	8219.203

整合度	R300	R500	R1000	R2000	R3000	R5000	R全局
Moran's I	0.044	0.004	0.359	0.483	0.536	0.602	0.365
Z得分	6.798	0.628	38.556	51.705	57.402	64.468	39.240
P值	0.000	0.530	0.000	0.000	0.000	0.000	0.000

变量	VIF
平均消费	1.017
消费评分	1.017
商铺楼层	1.036
土地价格	1.037
周围居住人口密度	1.098
周围餐饮聚集程度	1.116

变量	模型I（OLS）β	模型Ⅱ（OLS）β	模型Ⅲ（门槛回归）β
变量	（标准误）	（标准误）	（标准误）
人均消费	0.005	0.005	—
	（0.003）	（0.003）	—
消费评分	0.145^**	0.197^***	—
	（0.066）	（0.064）	—
商铺楼层	0.008^***	0.008^***	0.008^***
	（0.003）	0.003	（0.002）
土地价格	0.166^***	0.150^***	0.156^***
	（0.041）	0.040	（0.041）
周围居住人口密度	-0.042^**	-0.046^**	-0.041^*
	（0.021）	（0.021）	（0.021）
周围餐饮聚集程度	0.383^***	0.564^***	0.379^***
	（0.022）	（0.021）	（0.022）
消费评分（人均消费≤14）	—	—	0.115^*
	—	—	（0.066）
消费评分（人均消费>14）	—	—	0.143^**
	—	—	（0.065）