面向地理流的时空交叉K函数方法

doi:10.11947/j.AGCS.2024.20230258

测绘学报 ›› 2024, Vol. 53 ›› Issue (8): 1644-1655.doi: 10.11947/j.AGCS.2024.20230258

面向地理流的时空交叉K函数方法

周梦杰¹^,²^,³(), 阳孟杰¹(), 陈慧颖¹, 田雨萌¹, 万义良¹, 夏吉喆⁴^,⁵

^1.湖南师范大学地理科学学院，湖南　长沙　410081
^2.地理空间大数据挖掘与应用湖南省重点实验室，湖南　长沙　410081
^3.湖南师范大学城乡转型过程与效应重点实验室，湖南　长沙　410081
^4.深圳大学建筑与城市规划学院，广东　深圳　518060
^5.广东省城市空间信息工程重点实验室，广东　深圳　518060

收稿日期:2023-06-08 发布日期:2024-09-25
通讯作者: 阳孟杰 E-mail:mengjiezhou@hunnu.edu.cn;mengjiezhou@hunnu.edu.cn;yangmengjie@hunnu.edu.cn
作者简介:周梦杰（1990—），女，博士，副教授，研究方向为地理信息挖掘与可视化。E-mail：mengjiezhou@hunnu.edu.cn
基金资助:
国家自然科学基金(41901314);湖南省自然科学基金(2023JJ40447);湖南省教育厅科学研究项目(23B0093);广东省城市空间信息工程重点实验室开放基金资助项目(GEMlab-2023020)

Spatio-temporal cross K-function for geographical flows

Mengjie ZHOU¹^,²^,³(), Mengjie YANG¹(), Huiying CHEN¹, Yumeng TIAN¹, Yiliang WAN¹, Jizhe XIA⁴^,⁵

^1.School of Geographical Sciences, Hunan Normal University, Changsha 410081, China
^2.Hunan Key Laboratory of Geospatial Big Data Mining and Application, Changsha 410081, China
^3.Key Laboratory for Urban-Rural Transformation Processes and Effects at Hunan Normal University, Changsha 410081, China
^4.School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
^5.Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518060, China

Received:2023-06-08 Published:2024-09-25
Contact: Mengjie YANG E-mail:mengjiezhou@hunnu.edu.cn;mengjiezhou@hunnu.edu.cn;yangmengjie@hunnu.edu.cn
About author:ZHOU Mengjie (1990—), female, PhD, associate professor, majors in geographical information mining and visualization. E-mail: mengjiezhou@hunnu.edu.cn
Supported by:
The National Natural Science Foundation of China(41901314);The Natural Science Foundation of Hunan Province(2023JJ40447);The Scientific Research Project of Hunan Provincial Department of Education(23B0093);The Open Research Fund Program of Guangdong Key Laboratory of Urban Informatics(GEMlab-2023020)

摘要/Abstract

摘要：

地理流表示地理对象在不同地理位置之间有意义的交互或移动。挖掘地理流时空关联模式，对于揭示地理流之间的时空依赖性和异质性，理解地理流的发生机制和时空交互作用具有重要意义。目前，地理流空间关联分析方法日益增多，但鲜有研究考虑地理流的时空耦合特征并关注时间效应对探测模式的影响，准确捕捉地理流之间依赖关系的时空演变趋势仍是当前领域的难点问题。因此，本文扩展点模式时空交叉K函数，提出了地理流的时空交叉K函数，该函数以地理流整体为研究对象，用于探测两类地理流事件之间的时空关联模式。全局时空交叉K函数可探测研究区域中整体地理流关联模式，而局部时空交叉K函数可以识别不遵循全局趋势的在局部尺度上的时空关联情况。本文采用地理流时空交叉K函数的方法对厦门岛巡游车和网约车流数据进行了时空关联分析，揭示了两类车辆流的全局和局部时空竞争格局。全局结果表明，两类车辆呈排斥模式，说明在厦门岛整体上两类车辆之间并未出现激烈的正面竞争；而局部结果显示，在早、午、晚高峰期，巡游车流和网约车流在某些局部区域仍存在竞争，且主要分布在居民区—产业园、居民区—机场、火车站—商圈、商圈—旅游景点等区域之间。

关键词: 地理流, 时空关联模式, 时空交叉K函数, 网约车服务, 巡游车服务

Abstract:

Geographical flows represent meaningful interactions of geographical objects between pairs of locations. Mining the spatio-temporal association patterns of geographical flows is of great significance for uncovering the spatio-temporal dependency and heterogeneity among flows, as well as understanding the underlying flow mechanisms and spatio-temporal interactions. Currently, there is an increasing number of methods for spatial association analysis of geographical flows. However, there is limited research considering the spatio-temporal coupling characteristics of geographical flows and focusing on the impact of time effects on association pattern detection. Accurately capturing the spatio-temporal dynamics of dependencies between geographical flows remains a challenging issue in the flow association analysis field. To address this gap, this paper extends the spatio-temporal cross K-function of point process to the flow spatio-temporal cross K-function. The method takes the geographical flow as the research object, which can be used to detect spatio-temporal association patterns between any two types of geographical flows. Specifically, the global flow spatio-temporal cross K-function can detect the overall association patterns of geographical flows in the study area, while the local flow spatio-temporal cross K-function can identify the spatio-temporal associations at the local scale that does not follow the global pattern. This work utilizes the flow spatio-temporal cross K-function to analyze the spatio-temporal association patterns between taxi flows and ride-hailing flows on Xiamen Island. The global results show an isolated pattern between taxi flows and ride-hailing flows, suggesting that there is no intense positive competition between the two types of vehicles on Xiamen island. Whereas, the local results indicate competition between the two types of vehicles during the morning, afternoon, and evening peak hours. This competition is mainly concentrated in several specific areas, such as residential areas to industrial parks, residential areas to the airport, train stations to business districts, and business districts to tourist attractions.

Key words: geographical flows, spatio-temporal association patterns, spatio-temporal cross K-function, ride-hailing service, taxi service

中图分类号:

P208

周梦杰, 阳孟杰, 陈慧颖, 田雨萌, 万义良, 夏吉喆. 面向地理流的时空交叉K函数方法[J]. 测绘学报, 2024, 53(8): 1644-1655.

Mengjie ZHOU, Mengjie YANG, Huiying CHEN, Yumeng TIAN, Yiliang WAN, Jizhe XIA. Spatio-temporal cross K-function for geographical flows[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(8): 1644-1655.

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数据集	地理流数量	时空模式
案例1	A：600 B：600	随机
案例2	A：600 B：600	相关
案例3	A：600 B：600	排斥

时间带宽/h	不同空间带宽的车辆流数量
时间带宽/h	0.2 km	0.4 km	0.6 km	0.8 km	1.0 km
0.2	65	1341	5300	9696	13 608
0.4	280	3522	10 142	15 219	18 305
0.6	486	5514	13 030	18 098	21 034
0.8	752	7275	14 529	20 001	22 825
1.0	1065	8713	16 611	21 119	23 895

面向地理流的时空交叉K函数方法

Spatio-temporal cross K-function for geographical flows

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