文献类型：期刊文献

中文题名：多源大数据下的北京市居民就餐活动与城市空间关系探究

英文题名：STUDY ON THE RELATIONSHIP BETWEEN DINING ACTIVITIES OF BEIJING RESIDENTS AND URBAN SPACE BASED ON MULTI-SOURCE BIG DATA

作者：刘坚[1];孟斌[2];陈思宇[2];湛东升[3];陈喆[1]

第一作者：刘坚

机构：[1]首都师范大学资源环境与旅游学院,北京100048;[2]北京联合大学应用文理学院,北京100191;[3]浙江工业大学管理学院,杭州310023

第一机构：首都师范大学资源环境与旅游学院,北京100048

年份：2021

卷号：36

期号：2

起止页码：63-72

中文期刊名：人文地理

外文期刊名：Human Geography

收录：CSTPCD;;北大核心:【北大核心2020】；CSSCI:【CSSCI2021_2022】；

基金：国家自然科学基金项目(41671165,51878052);北京联合大学科研项目(ZK40202001)。

语种：中文

中文关键词：文本信息挖掘;LDA模型;时空间行为;地理探测器;北京

外文关键词：text information mining;LDA;temporal and spatial behavior;Geodetector;Beijing

摘要：融合了位置和文本信息的社交媒体数据为城市空间研究提供了有力的数据支撑,也为发现其背后隐藏的人类行为时空模式及规律提供了可能。本文构建了文本信息挖掘技术与空间分析有机结合的居民时空行为研究框架,利用2017年微博数据,采用BERT与Fast.AI结合的文本分类模型,结合LDA主题模型进行文本主题挖掘,对北京居民日常就餐活动的空间格局进行分析,并利用空间分析方法与地理探测器,探讨其影响因素。研究发现,北京居民就餐活动可以分为4类主题,即朋友聚餐类、日常餐饮类、普通餐饮类和特色餐饮类;四类主题就餐活动的热点区域大多分布在三环以内,形成了以工体—朝外—CBD商圈为中心的等级分布格局;居民就餐选择与餐饮服务设施的空间分布具有最强的一致性;结合城市POI等数据,发现居民日常就餐活动与城市空间结构之间存在空间同位模式。
This paper constructs a residential spatiotemporal behavior research framework that combines text information mining technology and spatial analysis. By obtaining the Sina Weibo data of Beijing residents in2017, using the text classification model combining BERT and fast.AI, combining with the LDA model for text theme mining. Analyze the spatial pattern of residents’ daily dining activities, and use spatial analysis methods and the Geodetector to explore its influencing factors. The study concluded that the residents’ dining activities can be divided into 4 categories of topics, namely friends gathering, daily catering, general catering and special catering. The spatial analysis found that the four types of theme dining activities are mainly distributed within the Third Ring Road, forming a hierarchical distribution pattern centered on the Workers’ Stadium-Chaowai-CBD business district. Meanwhile, various theme dining activities also have the common characteristics of dense distribution along important business districts, famous blocks, popular attractions, and large shopping malls. Residents’ dining choices have the strongest consistency with the spatial distribution of catering service facilities. It is found that there is a spatial co-location model between residents’ dining activities and the urban spatial structure combined with POI data.