基于轨迹数据场的热点区域提取及空间交互分析
——以深圳市为例

doi:10.13284/j.cnki.rddl.003097

热带地理 ›› 2019, Vol. 39 ›› Issue (1): 117-124.doi: 10.13284/j.cnki.rddl.003097

基于轨迹数据场的热点区域提取及空间交互分析 ——以深圳市为例

周博^1,2，马林兵^1,2，胡继华³，吴苏杰¹，何桂林¹

（1. 中山大学地理科学与规划学院，广州 510275；2. 广东省城市化与地理环境空间模拟重点实验室，广州 510275； 3. 中山大学公共教学实验中心，广州 510006）

收稿日期:2018-07-20 修回日期:2018-09-27 出版日期:2019-01-05 发布日期:2019-01-05
通讯作者: 马林兵（1968—），男，湖北武汉人，副教授，研究方向为城市地理信息系统，（E-mail）malb@mail.sysu.edu.cn。
作者简介:周博（1993—），男，湖北黄冈人，硕士研究生，主要研究方向为时空数据挖掘，（E-mail）zhoubosysu@163.com；
基金资助:
广东省科技计划项目（2016A010101015）

Extraction of Urban Hotspots and Analysis of Spatial interaction Based on Trajectory Data Field: A Case Study of Shenzhen City

Zhou Bo^1,2, Ma Linbing^1,2, Hu Jihua³, Wu Sujie¹ and He Guilin¹

(1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China; 2. Guangdong Key Laboratory for Urbanization and Geo-Simulation, Guangzhou 510275, China; 3.Public Experiment Teaching Center, Sun Yat-Sen University, Guangzhou 510006, China)

Received:2018-07-20 Revised:2018-09-27 Online:2019-01-05 Published:2019-01-05

摘要/Abstract

摘要： 以深圳市出租车GPS数据为基础，运用时空拓展的轨迹数据场聚类方法提取城市交通热点区域，结合城市POI（Point of Interest）数据和地理实况对热点区域加以理解和分析。基于复杂网络的视角，计算交互分析指标并可视化热点区域的空间交互网络，探究城市交通和居民出行的时空规律。结果表明：1）交通枢纽（机场、火车站和口岸）、综合性商圈、城市重要主干道周边和城市商务中心在节假日和工作日均表现为持续热点区域；2）节假日热点区域分布较“发散”，主要反映了居民个性化出行需求；3）工作日热点区域分布较“收敛”，主要表现为职住分离的通勤模式；4）不同热点区域在空间交互网络中的重要性存在明显差异，其空间交互体现了距离衰减效应和局部抱团现象，居民出行的热点区域网络本身具有小世界效应和无标度特征。

关键词: 出租车轨迹, 热点区域, 数据场, 时空聚类, 空间交互, 深圳市

Abstract: As a kind of important public transportation, taxi trajectory data contain abundant information about urban functions and citizen activities owing to its long service time, wide coverage of city area and freedom of its motion. Based on taxi GPS data of Shenzhen city, in this study, we extract urban traffic hotspots using the clustering method of trajectory data field based on space-time development. The hotspot area spatio-temporal distribution of typical time periods of the two time types of holidays and workdays are selected for visual graphic representation, and analyze hot spots combining POI data and the present situation of urban development. Based on the view of complex network, the interaction analysis index is calculated and the spatial interaction network of hot spots is visualized, a qualitative and quantitative analysis method is used to explore the spatial and temporal rules of urban traffic and residents’ travel. The results are showed as follows. 1) The hotspot area spatio-temporal distribution of urban traffic hotspots in holidays and workdays is significantly different. Transportation hub (like airports, railway stations and ports), a comprehensive business circle, area around urban major road and central business district are continuous hot spots during holidays and workdays, and the locations of other hotspots change over time. 2) The distribution of hot spots in holidays is disperse, mainly reflects the personalized travel demands of residents. Urban tourist attractions and other leisure areas have become hot spots, and it is clear that that outflow of Baoan airport and Shenzhen north station are obviously great than the inflow in the holiday season, which means that they are relatively strong. 3) The distribution of hot spots in workdays is convergent, which reflects the commuting mode of separation of work and residence. Residents' travel shows a tidal movement between the work place and the residence, the synchronism of inflow and outflow in hot spots is stronger than that in holidays, and residents travel more regularly. 4) Different hotspots in space interaction network have obviously difference in their significance, which spatial interaction reflects the distance-decay effect and partial aggregation phenomenon. Shennan avenue has become the dividing line between hot spots with close connections. Moreover, it was found that that space interaction network between Shenzhen north station, Futian district and Luohu district is of high importance in the space interaction network, and the links between the three are frequent. The hotspots network of residents travel have the characteristics of small-world effect and scale-free.

Key words: taxi trajectory data, urban hotspots, data field, spatiotemporal clustering, spatial interaction, Shenzhen City

周博，马林兵，胡继华，吴苏杰，何桂林. 基于轨迹数据场的热点区域提取及空间交互分析 ——以深圳市为例 [J]. 热带地理, 2019, 39(1): 117-124.

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基于轨迹数据场的热点区域提取及空间交互分析 ——以深圳市为例

Extraction of Urban Hotspots and Analysis of Spatial interaction Based on Trajectory Data Field: A Case Study of Shenzhen City

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