基于位置服务数据的社区生活圈测度方法及影响因素分析
作者:
作者单位:

1.同济大学 建筑与城市规划学院,上海200092;2.上海同济城市规划设计研究院有限公司,上海200092;3.西北农林科技大学风景园林艺术学院,陕西 咸阳712100;4.上海社会科学院,上海200020

作者简介:

杨 辰,副教授,博士生导师,社会学博士,主要研究方向为城乡社区和住房规划。 E-mail:yangchen@tongji.edu.cn

通讯作者:

陈 晨,工学博士,助理研究员,主要研究方向为城市网络联系、城市发展战略。 E-mail: chenchen@sass.org.cn

中图分类号:

TU984.12

基金项目:

国家自然科学基金面上项目(52078351);上海市自然科学基金面上项目(23ZR1468300);国家自然科学基金青年项目(51908354);国家留学基金项目(202206265029)


Analysis of Community Living Circle Measurement Method and Influencing Elements Based on Location-Based Services Data
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Affiliation:

1.College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China;2.Shanghai Tongji Urban Planning and Design Institute Co., Ltd., Shanghai 200092, China;3.College of Landscape Architecture and Arts, Northwest Agriculture and Forestry University, Xianyang 712100, China;4.Shanghai Academy of Social Sciences, Shanghai 200020, China

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    摘要:

    生活圈的边界划定是社区生活圈研究的重点也是难点。现有的划定方法有基于行政边界或控规单元、依据设施服务半径及可达性、居民日常活动行为三种方式,其中基于居民日常活动是最接近社区生活圈本意的测度方法。但目前这一方法的应用主要依靠全球定位系统(GPS)数据,成本高且样本有限,难以描述社区大多数居民的活动规律,更缺乏对大范围不同类型社区生活圈的全貌认识。以成都市为例,基于大样本的手机位置服务数据(LBS),采用复杂网络分析技术(Infomap)对中心城区(549 km2)的社区生活圈进行测度,通过识别其规模和边界特征,进一步探讨空间要素对社区生活圈边界的影响及作用机制。结果表明:通过选取恰当的单元精度和距离约束d值,可以获得较好的网络聚类结果(接近15 min生活圈的实际规模);基于LBS数据划分的社区生活圈面积规模差异较大,但大多数的规模处于1~5 km2之间;三类代表性空间要素中,路网密度与社区生活圈面积规模存在中等程度相关,区位和商业兴趣点(POI)密度与社区生活圈规模并不存在显著的相关性;但社区生活圈的人口密度与三类空间要素存在强相关。基于手机LBS数据的社区生活圈测度方法可以加深对社区生活圈现象的理解,并为社区生活圈规划提供理论与技术支撑。

    Abstract:

    The delimitation of the boundary of the living circle is both the focus and the difficulty in the study of community living circles. There are three existing delimitation methods, administrative boundaries or regulatory planning units, service radius and accessibility of facilities, and daily activities of residents, of which the daily activities of residents are the closest to the original definition of the community living circle. However, the current application of this method mainly relies on the data of the global positioning system (GPS), which is costly and limited in sample size, making it difficult to describe the activity patterns of most residents in a community and lacking a comprehensive understanding of a wide range of different types of community living spheres. In this paper, based on the large sample of the data of location-based services (LBS), a complex network analysis technique is used to measure the community living circle in the central city of Chengdu (549 km2), and by identifying its scale and boundary features, the spatial factors such as location, road network density and point of interest (POI) density on the community living circle are further explored. The results show that better network clustering results (close to the actual size of a 15-minute living circle) can be obtained by choosing carefully the unit resolution and distance constraint d-values. The size of community living circles classified based on LBS data varies widely, but most of the sizes are between 1 and 5 km2. Among the three types of representative spatial elements, road network density and community living circle size, there is a moderate correlation, and location and commercial POI density are not significantly correlated with the size of the community living area. However, there exists a strong correlation between these three spatial elements and population density of community living circle. This paper reveals that community living circle measurement method based on LBS data, will deepen our understanding of the community living circle phenomenon and provide theoretical and technical support for community living circle planning.

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杨辰,辛蕾,马东波,贾姗姗,陈晨.基于位置服务数据的社区生活圈测度方法及影响因素分析[J].同济大学学报(自然科学版),2024,52(2):232~240

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  • 收稿日期:2022-06-11
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  • 在线发布日期: 2024-02-27
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