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旅游导刊, 2021, 5(4): 37-57 DOI: 10.12054/lydk.bisu.182

手机基站定位数据在旅游领域的应用综述

郑伟民,, 李梦玲, 庄歆怡, 高胜男

厦门大学管理学院 福建厦门 361005

A Review on the Application of Mobile Phone Positioning Data in Tourism

ZHENG Weimin,, LI Mengling, ZHUANG Xinyi, GAO Shengnan

School of Management, Xiamen University, Xiamen 361005, China

收稿日期: 2020-04-20   修回日期: 2021-05-30  

基金资助: 国家自然科学基金面上项目“基于多源异构时空轨迹数据的城市旅游移动性内在机理及应用研究”(71971179)
福建省自然科学基金面上项目 “基于多源异构时空数据的城市旅游流网络演化机制及应用研究”(2020J01033)

Received: 2020-04-20   Revised: 2021-05-30  

作者简介 About authors

郑伟民(1987—),男,福建漳州人,博士,厦门大学管理学院副教授,博士生导师,研究方向:智慧旅游、旅游大数据与旅游信息化。E-mail:zhengweimin@xmu.edu.cn

李梦玲(1997—),女,四川自贡人,硕士研究生,研究方向:智慧旅游、旅游大数据与旅游信息化。

庄歆怡(1995—),女,福建泉州人,硕士研究生,研究方向:智慧旅游、旅游大数据与旅游信息化。

高胜男(1997—),女,山东烟台人,硕士研究生,研究方向:智慧旅游、旅游大数据与旅游信息化。

摘要

手机基站定位数据凭借其普适性和时空广泛性等优势获得越来越多学者的关注。为系统梳理手机基站定位数据在旅游领域的研究现状并评估其应用潜力,本文在全面回顾现有文献基础上:(1)对手机基站数据的概念及分类、特点,以及研究过程进行介绍;(2)从游客识别和分类、旅游统计、旅游流分析、游客行为研究及旅游活动影响等5个主题总结现有研究;(3)从时间维度、空间维度及研究对象/内容3个方面评述现有研究。最后,本文结合手机基站定位数据热门应用领域的研究状况,讨论其在旅游领域存在的不足,并提出未来研究建议。

关键词: 大数据; 手机基站定位数据; 旅游研究; 综述

Abstract

Scholars have focused increasingly on the use of mobile phone positioning data from base stations due to its spatiotemporal universality. In order to systematically analyze the research status of mobile phone positioning data from base stations and evaluate its application potential in the field of tourism, this article: (1) introduces the concept, classification, data characteristics, and research process of mobile phone positioning data from base stations; (2) identifies the five major categories of tourist identification and classification, tourism statistics, tourism flow analysis, tourist behavior research, and research on the impact of tourism activities; and (3) evaluates existing research from three dimensions, which are temporal scope, spatial scope, and research object/content. In addition, this study combines the research development of other fields in the use of mobile phone positioning data from base stations, discusses its weaknesses in the field of tourism, and concludes with suggestions for future research.

Keywords: big data; mobile positioning data from base stations; tourism research; review

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本文引用格式

郑伟民, 李梦玲, 庄歆怡, 高胜男. 手机基站定位数据在旅游领域的应用综述[J]. 旅游导刊, 2021, 5(4): 37-57 DOI:10.12054/lydk.bisu.182

ZHENG Weimin, LI Mengling, ZHUANG Xinyi, GAO Shengnan. A Review on the Application of Mobile Phone Positioning Data in Tourism[J]. Tourism and Hospitality Prospects, 2021, 5(4): 37-57 DOI:10.12054/lydk.bisu.182

观点与创新:

● 系统性地梳理手机基站定位数据在旅游领域的应用现状并评估其应用潜力

● 手机基站定位数据在旅游领域的应用可总结为游客识别和分类、旅游统计等五大主题

● 手机基站定位数据在旅游领域内的研究集中于中、宏观地理尺度,时间跨度较大,对游客的异质性较为忽略

● 手机基站定位数据在旅游领域的研究仍处于初级阶段,创造性贡献较为缺乏

Highlights:

● This article systematically analyzes the application of mobile phone positioning data from base stations and evaluates its future potential in the field of tourism.

● The application of mobile phone positioning data from base stations in the tourism field can be summarized into five major categories, such as tourist identification, tourist classification, and tourism statistics.

● Research on the use of mobile phone positioning data from base stations in tourism focuses on mesoscopic and macroscopic space with a wide temporal span, while tourist heterogeneity is relatively overlooked.

● Research on the use of mobile phone positioning data from base stations in tourism is still in its initial stages with insufficient creative contributions.

0 引言

随着通信技术和移动互联网技术的发展,海量多源数据被大量采集并被广泛应用于旅游领域,极大提升了旅游研究的广度和深度(Li,Xu & Tang,et al.,2018)。在旅游领域的研究中,手机基站定位数据被认为是一种极具潜力的大数据类型(Chantre-Astaiza,Fuentes-Moraleda & Muñoz-Mazón,et al.,2019)。所谓的手机基站定位数据指由移动运营商自动存储在日志文件中的信息(Ahas,Aasa & Roose,et al.,2008),包括通过基站定位的经纬度信息、时间戳等内容。

自爱沙尼亚学者Ahas开创将手机基站定位数据应用于旅游研究的先河以来(Ahas,Aasa & Mark,et al.,2007;Ahas,Aasa & Roose,et al.,2008),越来越多学者关注到了这个领域。在梳理旅游大数据的研究进展时,有学者开始将手机基站定位数据作为大数据的其中一种类型加以评述(Shoval & Ahas,2016;Li,Xu & Tang,et al.,2018;袁雨果、郑伟民,2019)。然而,目前鲜有学者对手机基站定位数据在旅游领域的研究现状及发展状况进行微观地、专门性地梳理。因此,本文将对手机基站定位数据在旅游领域的研究进行回顾和评述,讨论与其他领域研究相比存在的不足,并提出未来潜在的研究方向,以期推动相关研究的认识、发展与创新。

本文筛选文献的时间截至2020年2月,筛选过程如下:首先,在Web of Science核心合集上,以英文组合关键词“tourism/tourist/trip/tour/journey”及 “mobile phone data/call detail records/mobile tracking data/cellular signaling data/mobile phone positioning data”检索到522篇文献,再在中国知网上,以中文组合关键词“旅游”和“手机数据”检索到63篇文献;其次,根据关键词及摘要进行初筛,剔除非旅游领域或与“手机基站定位数据”相关性低的文献;最后,根据全文内容进行二次筛选,采取滚雪球的方法,挖掘并提取已检索文献中相关性高的施引和被引文献进行补充,最终共保留42篇中英文文献。

一、手机基站定位数据的介绍

本节旨在厘清手机基站定位数据的概念及分类、特点、研究过程,以便更好地理解手机基站定位数据在提供基于位置的服务和应用方面的潜力。

1. 概念及分类

手机基站定位数据指由移动运营商自动存储在日志文件中的信息。它主要包含手机蜂窝网络中的位置坐标信息,可用于客户计费、网络维护和性能监视 (Ahas,Aasa & Roose,et al.,2008)。手机基站定位数据可细分为3种类型:(1)话务量数据,即以手机基站为单元的汇总性手机通话或上网流量数据,能按照小时、天、周等不同的时间尺度进行划分;(2)话单数据(call detail records),通过计费系统运行,是用户在通话和收、发短信时产生的通话详细记录;(3)信令数据(signaling data),即手机用户在切换基站、位置更新等事件时所产生的数据(钮心毅、丁亮、宋小冬,2014;赵莹、张朝枝、金钰涵,2018)。

2. 特点

手机基站定位数据作为手机定位系统产生的数据类型之一,会根据用户手机分配到的发射塔的位置来确定时空信息。与之对应,手机定位系统还可以产生基于不同射频信号的位置数据(见图1),主要包括:(1)内置GPS,即通过手机带有的GPS技术对卫星发射的无线电信号进行处理来获取高分辨率的时空信息数据;(2)Wi-Fi,即根据手机接收到的无线网络信号强度从信号接入点推断出位置;(3)蓝牙,即基于短波长来确定一个手机设备到另一个设备的距离(相对位置间的距离)(Wang,He & Leung,2018)。

图1

图1   手机定位系统

Fig.1   Mobile positioning system


相较于其他3种手机定位数据,手机基站定位数据具有一定的优势(见表1):能够在不干扰游客的前提下(Zhao,Lu & Liu,et al.,2018),进行全天候、全过程的记录(Ratti,Frenchman & Pulselli,et al.,2006);能以简单、低成本的方式获取大样本数据(Cheng,Qiu & Ran,2006);获取的数据不仅时间跨度大(黄潇婷、柴彦威、赵莹等,2010)且地理覆盖面积广(Wu,Shi & Wang,et al.,2016;Vanhoof,Hendrickx & Puussaar,et al.,2017)。因此,手机基站定位数据是目前旅游研究中应用最广的手机定位数据类型(Wang,He & Leung,2018)。

表1   手机定位数据各类型对比

Tab.1  Comparison of different types of mobile phone positioning data

类别
(Category)
项目
(Item)
基站定位
Base Station Positioning
内置GPS
Built-in GPS
蓝牙
Bluetooth
无线网络
Wi-Fi
时间尺度几小时~几年仅使用功能时
几小时~几天
仅使用功能时
几天~十几天
仅使用功能时
几天~几个月
空间尺度中、宏观微、中观微观微观
样本量规模大规模较小受人流密度影响受人流密度影响
精度几十米~几千米几米~十几米几米~几十米几米~几十米
电源能耗较高较高
数据获取运营商打包相关移动应用程序后台蓝牙传感器后端服务器Wi-Fi设备后台接口
限制受基站密度影响(城乡差异大)受天气和位置影响大需外部配备蓝牙传感器、设备成本高需外部配备Wi-Fi基站

新窗口打开| 下载CSV


然而,手机基站定位数据在获取性和数据质量方面面临着一些挑战。目前仅能通过特定许可证和协议从运营商处获取(Bucci & Morton,2014);数据不仅缺乏全面性,如用户社会人口统计学信息(Nour,Hellinga & Casello,2016),而且其质量和精度在很大程度上取决于基站密度(Chung & Kuwahara,2007)、基站位置和采样周期(Wu,Shi & Wang,et al.,2016)。虽然手机基站定位数据存在一定的不足,但相对其他数据源,具有能获取海量位置数据(Ratti,Frenchman & Pulselli,et al.,2006)、反映用户行程的关键特征(Wu,Shi & Wang,et al.,2016)的优势,因此具有广泛的适用性。

3. 研究过程

手机基站定位数据在旅游领域的研究一般遵循3个阶段,即数据处理、数据分析、数据应用(见图2)。在数据处理阶段,通过当地移动通信运营商获取匿名化的手机基站定位数据,保留所需时间跨度和地理范围内的数据,进行数据预处理以剔除无关、冗余、无法读取的无用数据(Saluveer,Raun & Tiru,et al.,2020)。在数据分析阶段,首先从游客轨迹中判断该数据是否为旅游行程,并确定游客属性。接着提取游客的出行信息,如根据停留点检测游客的兴趣点,从大量轨迹中提取出完整的出行链,并根据地图匹配识别出行路线,根据移动速度识别出行交通方式等(Lu & Zhong,2016;Sikder,Uddin & Halder,2016)。最后提取游客目的地过夜数、访问景点类型等行为特征,最终应用于主题研究,并将研究结果与其他来源的数据进行比较以验证结果的有效性(Caceres,Romero & Benitez,2020)。

图2

图2   手机定位数据在旅游领域的研究过程

Fig.2   Research process of mobile phone positioning date in tourism


二、手机基站定位数据的研究主题

手机基站定位数据的应用可追溯到2000年左右,早期研究主要展示了其在居民出行行为(Asakura & Hato,2001)及城市规划和管理中(Ahas & Mark,2005)的应用潜力。手机基站定位数据自2007年开始应用于旅游领域以来,为研究游客时空行为规律、旅游景区时空优化、旅游市场变化提供了科学依据(柴彦威、赵莹、马修军等,2010)。现有研究可划分为5个相互影响的主题,即游客识别和分类、旅游统计、旅游流分析、游客行为及旅游活动影响。

1. 游客识别和分类

从手机基站定位数据中提取和检测游客信息是交通预测、旅游规划和服务管理的重要内容(Mamei & Colonna,2018)。识别游客的主要指标是SIM卡的注册地和到目的地的次数及停留时间(郭旸、胡雅静、林玥,2020)。例如,徐菲菲、王旭和徐俐等(2019)将一天内被不同基站接收到3次/小时以上信号频率的外来用户归类为游客,并根据到访数量、空间距离及到访时间差异,以省份为单位划分南京市的客源市场;郭旸、胡雅静和林玥(2020)则是将持续一个月及以上的在沪漫游用户剔除出游客类型。近期的研究开始尝试以用户行为模式来识别游客,例如,Mamei和Colonna(2018)采取机器学习的方式,通过输入行为特征向量,将手机用户划分成5类,即普通居民、通勤者、过境者、多日游客和一日游游客;Sikder、Uddin和Halder(2016)提出了一种克服话单数据位置跟踪局限性的游客识别方法。

2. 旅游统计

收集有关旅游业的统计数据对于衡量不同地理尺度目的地的旅游业数量、规模、影响和价值至关重要。旅游统计部门的主要目标是获取游客的出行次数、在目的地的过夜天数和最常去的地区等信息(Mamei & Colonna,2018)。常用的传统数据源有住宿统计、过境统计、旅行社数据、交通数据和游客调查(Saluveer,Raun & Tiru,et al.,2020),但是这些数据源难以准确地分析游客在整个旅行期间的移动状况及揭示游客时空行为模式(Batista e Silva,Marín Herrera & Rosina,et al.,2018)。主要原因在于:传统数据源获取成本高、缺乏细节、推广性差(Beaman,Huan & Beaman,2004);覆盖面小,难以统计非正式住所(如民宿)游客人数(De Cantis,Parroco & Ferrante,et al.,2015);作为回顾性的事后统计,难以对游客进行实时监管。手机基站定位数据则能够有效减少传统调查中因抽样、未覆盖、未响应和测量误差等因素产生的干扰(de Dios Ortúzar & Willumsen,2011);Caceres、Romero和Benitez(2020) 通过比较同一地区手机基站定位数据和传统旅行调查数据的差异证实了这一点。

此外,保继刚、王亚娟和汤勇刚等(2020) 指出国家旅游统计存在着由旅游人数重复统计、数据回收质量不高、数据统计口径不合理等原因带来的数据“纵向不可加,横向不可比”的问题。因此,他们以三大通信运营商的数据为基础,建立了一套以实用为导向的大数据旅游统计系统。类似地,Saluveer、Raun和Tiru等(2020)在爱沙尼亚建立了一套调查出入境游客数量的旅游统计框架;Setiadi和Uluwiyah(2017)在印度尼西亚得出的研究结果表明,手机基站定位数据对游客人数的统计值远大于普通调查;Peak、Wesolowski和zu Erbach-Schoenberg等(2018)提出了利用手机基站定位数据评估旅游业经济影响的理论模型。

3. 旅游流分析

旅游流是旅游发展的基础内容,也是旅游研究的核心问题之一(保继刚、楚义芳,2012)。旅游流可分为出发地到目的地的流动、目的地内景区之间的流动及景区内部的流动,涵盖流动方向、连接方式、人流密度等基本要素(Bowden,2003)。利用手机基站定位数据进行旅游流分析,可为目的地流量控制、游客分流、交通疏导、安全管理等提供实时有效的支持(Qin,Man & Wang,et al.,2019)。

在目的地视角下,Vanhoof、Hendrickx和Puussaar等(2017) 探索了法国32个城市间的旅游流;Lu和Zhong(2016)分析了不同时段各区县的游客流量。此外,关注到假期、气候和风景等要素的影响,Silm和Ahas(2010) 研究了爱沙尼亚居民随季节性向第二住所(second-home)流动的情况;顾秋实、张海平和陈旻等(2019)则选取了国内法定节假日期间的游客数据研究南京市的客源空间层次和客流强度差异;Park和Pan(2018)将手机基站定位数据与航空交通数据、酒店接待等方面的数据结合来确定目的地直航航线市场。

在景区视角下,旅游流的统计监测手段主要为入口门禁、视频监控等。出入口的闸机虽能较为精确地获得景区游客的出入情况,但仅能应用于封闭式景区,且无法实时获知客流分布状况;视频监控虽能提供远程监控,但受天气、光线等环境因素的影响较大,且只能作简单的人次统计(陈圣威、万红生、宋逸等,2020)。手机基站定位数据的有效性和便捷性为景区计算各个时段游客的总流量、滞留流量及变化量提供了契机。Qin、Man和Wang等(2019)利用话单数据生成了北京市重点景区的游客出发—目的地矩阵(origin-destination matrix),研究景区内不同时间尺度下的客流量变化规律,为缓解旅游线路交通压力、促进景区运营管理提供依据。郭旸、胡雅静和林玥(2020)则分析了来沪的迪士尼游客在国庆期间对其他热门景点的选择差异。

4. 游客行为研究

游客行为本质上是大量个体行为的总和,手机基站定位数据含有研究游客行为模式和规律所需的大量时空信息,是研究游客空间行为的重要工具,为旅游研究提供了新视角(Birenboim & Shoval,2016)。现有基于手机基站定位数据对游客行为开展的研究主要分为三种类型。

一是研究不同类别游客的行为差异。从客源地的角度,Chen、Xie和Tinn等(2017)研究了不同国家的游客在安道尔的景区偏好和旅游路径差异。从散客和团队的角度,Zhao、Lu和Liu等(2018)将游客团队人数划分为5个规模,研究不同规模下游客团队景点类型偏好、景点转换序列及停留时间的差异。此外,还有研究比较了散客和团队游客在交通方式选择和使用趋势(Hu,Zhu & Hu,et al.,2018),以及景点平均停留时间、行程灵活度和行程距离等方面的差异(Zhu,Sun & Yuan,et al.,2019)。

二是分析特定事件下的游客行为。目的地的大型体育赛事和节日活动能极大影响游客出行模式(Leung,Wang & Wu,et al.,2012)、出游动机(Mohammad,2014)和当地旅游业的发展(Hinch & Holt,2017)。Jia、Cheng和Duan(2013)分析了游客在上海世博会期间的主要活动区域、不同时段的活动范围等信息。Nilbe、Ahas和Silm(2014)比较了节事游客和普通游客在旅行距离上的差异,并综合考虑了季节和访问时间等影响因素。此外,由于交通工具便捷性是影响旅游选择的重要因素,Yamamoto(2018)研究了日本北陆新干线的开通对沿线城市游客访问数量和访问时段的影响。同时,节庆期间巨大的游客量会产生旅游拥挤(Fourie & Santana-Gallego,2011)和交通拥堵(Bao,Xiao & Gao,et al.,2017)等不良后果,因此对客流进行监控和提前预警尤为重要。传统方法时间提前量小、对客流空间聚集程度缺少精细化描述,影响了客流管理效率(方家、王德、谢栋灿等,2016)。手机基站定位数据能在游客来源、时空分布、疏散去向的解析方面提供技术支持,如方家、王德和谢栋灿等(2016)对比了上海顾村樱花节节前与节后的流量,分析了居民在出游意愿、节日游憩行为方面的变化;申卓和王德(2018)探究了大型球赛中球迷的分布区域、圈层结构、活动轨迹特征及对周边商业体的影响。

三是研究不同特征人群的行为差异。年龄(Cass & Faulconbridge,2017)、种族(Dougherty,2003)等人口特征差异会对旅游行为产生重要影响。Masso、Silm和Ahas(2019)将爱沙尼亚居民划分成6组年龄段,以境内外访问的地点数量和访问地点之间的距离为指标,证明了各年龄组间的确存在异质性差异;Silm和Ahas(2014)也指出种族对个人活动空间和长途旅行选择有显著的影响。

5. 旅游活动影响研究

这一主题主要关注了节事活动对目的地的影响。节事活动具有展示某个地区和吸引游客的重要功能(Kuusik,Nilbe & Mehine,et al.,2014)。重复访问经常被视为游客对目的地忠诚度的表达(Hernández-Lobato,Solis-Radilla & Moliner-Tena,et al.,2006)。Tiru、Kuusik和Lamp等(2010)提出根据访问次数、持续时间和地理位置来识别游客对特定地区重复访问的方法。Kuusik、Tiru和Ahas等(2011)进一步研究了具体的回头客细分市场及其活动轨迹。Kuusik、Nilbe和Mehine等(2014)则探究了不同节庆活动产生回头客能力的区别,量化不同活动对目的地的长期影响。这一系列对回头客的研究,有助于政府出台多样化的旅游政策来吸引回头客并延长其重复访问时的逗留时间。了解在大型活动期间各景点的游客人数增长是否失衡,对于缓解人为灾害、有效配置资源具有重要价值(Bao,Xiao & Gao,et al.,2017)。Yang、Zhao和Liu等(2020)发现景区设施便利性、吸引力等级、声誉等属性对国庆节期间的景区游客增长率具有显著影响。Raun、Ahas和Tiru(2016)从游客的时间、空间特征和组成成分3个维度评价了旅游目的地。

三、研究评述

旅游包含三个基本要素:人、时间和空间(Caldeira & Kastenholz,2020)。个体的行为研究包含由人的属性带来的对象差异、空间位置间的移动及停留时间等内容(Masso,Silm & Ahas,2019)。现有文献在时间与空间维度的选择以及研究对象与内容方面都各不相同,从而产生了多样化的研究主题。下文将从3个角度评述现有研究。

1. 空间维度

现有旅游文献研究地的空间分布如图3所示。首先,中国和爱沙尼亚在研究数量上显著领先。其次,研究多聚集于中观和宏观尺度,如城市、省和国家,关注游客在目的地之间及景区之间的流动。最后,热门旅游目的地更受青睐,如海南省和上海市,鲜有研究关注小众和偏远目的地。在全域旅游和自媒体营销快速发展的背景下,各类网红打卡点流行开来,但多数研究仍基于传统兴趣点列表开展。因此,未来研究可将视角转向新兴旅游目的地的挖掘和监测,为当地政府和旅游服务业提供即时信息。

图3

图3   国内外研究空间分布

Fig.3   Research on spatial distribution


2. 时间维度

现有研究时间跨度较大,涵盖以天、月和年为单位的时间维度(见图4):既有学者聚焦在节事活动中的几天之内(Jia,Cheng & Duan,2013),也有学者进行长达6年的研究(Kuusik,Nilbe & Mehine,et al.,2014)。除此以外,游客活动具有特定的节奏,如昼夜、工作日和周末、季节等规律(Ahas,Aasa & Roose,et al.,2008)。虽有研究在对游客进行长时段追踪后,分析了游客行为的季节差异(Ahas,Aasa & Mark,et al.,2007),但仍缺乏聚焦于同一区域长时间的纵向对比、不同区域同一时间的横向对比,以及根据时间维度来划分游客(半日游、一日游等)的研究(Baggio & Scaglione,2018)。

图4

图4   研究时间跨度分布

Fig.4   Research on time span distribution


3. 研究对象/内容

在研究对象上,尽管现有研究已涉及按照客源地、停留时间及是否为散客对游客进行划分,但多数研究仍将游客群体一概而论,忽视了游客对象的异质性。此外,按照年龄和种族等人口统计学信息确定游客特征的研究仍旧是孤例。因此,未来研究可进一步关注更细致和多样化的游客分类,并运用到行为差异研究上。

在研究内容上,手机基站定位数据反映出的游客移动行为特征是所有研究的基础,但现有研究的侧重点还停留在统计有效性、旅游流和游客行为的描述上,较少关注这些游客信息对目的地的影响,以及对目的地运营管理的实际启示。此外,基于目的地视角的研究多聚焦于回头客对目的地忠诚、旅游营销的影响,新的视角还亟待探索。即使是同一目的地数据源,研究视角的差异也会带来丰富成果,例如,Ahas等人在爱沙尼亚开展了外国游客季节性消费特征(Ahas,Aasa & Mark,et al.,2007)、目的地重游者细分(Kuusik,Tiru & Ahas,et al.,2011)、多维度目的地评价(Raun,Ahas & Tiru,2016)等多项主题研究,通过对现有数据的多角度挖掘缓解了数据可获取性的局限。基于以上讨论,可判断出目前手机基站定位数据还处于Shoval和Ahas(2016)所提出的从旅游发展的描述性研究走向旅游问题深入探讨的过渡阶段。

四、研究不足及未来方向

1. 研究不足

手机基站定位数据的应用领域十分广泛,根据Liao、Brown和Fei等(2018)的综述研究,手机基站定位的研究集中在居民出行行为和人口分布等领域。相较于这些领域,旅游领域只是其应用的冰山一角,还处于发展的起步阶段(Birenboim & Shoval,2016)。

首先,为有效解决城市人口快速增长带来的交通拥堵等问题,已有研究利用手机基站定位数据对不同城市间(Isaacman,Becker & Cáceres,et al.,2010)和不同国家间(Amini,Kung & Kang,et al.,2014)的居民人口移动模式差异展开了大量研究,加深了对城市动态的理解,但这些研究均没有区分旅游行程和一般行程,因此未来旅游研究的一项重要任务是设计能有效识别游客及划分不同类别游客的算法,为解决城市旅游中居民和游客之间的矛盾、合理安排生活设施和旅游设施提供科学依据。

其次,大型活动可以根据性质划分为正面事件和负面事件(Yang,Zhao & Liu,et al.,2020),多数旅游文献仅涉及正面事件的讨论,但实际上,手机基站定位数据能有效解决在自然灾害、流行疾病等负面事件发生时缺乏人口迁移信息的困境,如Peak、Wesolowski和zu Erbach-Schoenberg等(2018)检验了埃博拉疫情期间“居家隔离”政策下的居民出行的变化。在新型冠状病毒(COVID-19)肺炎疫情暴发期间,Jia、Lu和Yuan等(2020)通过移动手机数据量化真实的人口流动,预测了新冠病毒的地理分布和传播趋势;Chen、Jyan和Chien等(2020)通过追踪钻石公主号乘客的行动轨迹,对一定范围和时间内可能存在的感染人群进行了预警。未来研究应重视手机基站定位数据在负面事件下游客移动和旅游恢复等方面的应用。

再次,国外研究较早地使用手机基站定位数据研究城市活动强度及其在时空上的演变规律(Ratti,Frenchman & Pulselli,et al.,2006),并评估过法国、葡萄牙和科特迪瓦等国家的人口密度(Sterly,Hennig & Dongo,2013;Deville,Linard & Martin,et al.,2014)。虽然这些研究均证明了手机基站定位数据在人口数据统计上的适用性,但案例地多集中在经济发达、基站密度高的大都市,在欠发达地区的适用性还未得到有效验证。目前,仅爱沙尼亚和印度尼西亚两国使用手机基站定位数据作为国家官方旅游统计的依据(Saluveer,Raun & Tiru,et al.,2020),手机基站定位数据的可靠性认知尚未形成共识。

最后,手机基站定位数据能够较强地适应人口分布每天、每月、每年的频繁动态变化(Deville,Linard & Martin,et al.,2014)。已有研究关注到不同活动类型带来的人口动态变化(钟炜菁、王德、谢栋灿等,2017),如日常通勤行为(Wu,Shi & Wang,et al.,2016),但目前旅游领域还缺乏利用手机基站定位数据对旅游流量进行实时性检测的研究,在实时监控系统和实时地图上还值得进一步深入探究。

2. 未来研究方向

手机基站定位数据具有诸多优点,被认为是旅游领域极具应用潜力的数据源(Chantre-Astaiza,Fuentes-Moraleda & Muñoz-Mazón,et al.,2019)。虽然手机基站定位数据的出现给旅游研究带来了新契机(Wang,He & Leung,2018),使研究者有机会重新思考和更新旅游研究中使用的概念和方法(Raun,Ahas & Tiru,2016),并从根本上改变了基于传统数据的传统旅游研究范式(Li,Xu & Tang,et al.,2018),然而,不可否认的是它仍处于初级阶段,目前的研究仍停留在数据实践上,并没有做出原创性贡献(Liu,Li & Li,et al.,2016),手机基站定位数据的未来还具有较大发展空间。

首先,随着国内三大运营商开始开展旅游大数据的实践和探索,旅游者移动行为的研究将会出现新的契机(袁雨果、郑伟民,2019)。相信在规范使用的前提下,手机基站定位数据的获取难度将会降低,并具备推动城市经济发展等方面的实践意义(Blondel,Esch & Chan,et al.,2013)。

其次,手机基站定位数据研究仅能获取时空信息,研究内容较为局限,可将其与多种数据源结合,产生更大的应用价值(Chantre-Astaiza,Fuentes-Moraleda & Muñoz-Mazón,et al.,2019)。例如,结合第三方平台的住宿信息和支付信息,研究游客消费水平;结合GPS、蓝牙数据,提升局部位置精度;通过UGC数据获得游客情感信息;结合道路卡口、公交IC卡、导航地图等交通数据探究游客移动过程。然而处理这些来源不同、质量不同、结构不同的多源异构数据存在诸多挑战,如何构建融合多源数据的泛化模型,如何实现多个数据源的知识融合,如何发现多源数据间的关联关系等都是未来融合手机基站定位数据与其他数据进行研究时需要重点解决的问题。

再次,对手机基站定位数据的研究需要研究者具有获取数据、管理数据和对数据深度分析的能力,以及拥有可执行算法操作的软硬件平台。显然,让每位研究者都具备这些技能是不现实的,取而代之的应是促进拥有不同专业背景、不同专业技能的研究者开展跨学科和协作性的研究项目(Wang,He & Leung,2018)。

最后,在大数据时代,世界被连接关系而不是因果关系所主导(Zhang,2018),学者们大多注重描述旅游现象和建立相关关系模型,忽略了对传统旅游理论的引用和发展。未来需更加注重研究现象下的因果关系和内在机制,丰富和发展新的理论,做好理论研究。

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This paper studies the network hierarchy and regional differentiation patterns of the source market from a city level. Based on the big data of tourist number monitored in Nanjing, 5short-period festivals are selected including New Year's Day, Qingming Festival, Labor′s Day, Dragon Boat Festival and Mid-Autumn Festival of China. The methods of overall trend analysis based on spatial variables, social space network clustering analysis and spatial regional division model are adopted. Among them, the first method can be used to determine the overall spatial distribution trend of tourist flow intensity. The second one can realize the hierarchical division of the source network nodes, so as to simultaneously examine the hierarchical structure and spatial distribution structure of the network nodes. The division model based on machine learning can be used to distinguish the tourist volume and tourist flow intensity in the source market from the city level. The results are as follows: 1) The intensity of tourist flow shows significant spatial hierarchy characteristics. The high-ranking nodes are mainly located in the most adjacent and sub-adjacent areas of Nanjing, and the exogenous network effect is also obvious. 2) Overall, the source network nodes in five different short-period national holidays show similar hierarchical structures and distribution patterns while the regional differences are apparent. 3) Although there are many differences in the spatial distribution patterns of the high level city nodes among the five short-period national holidays, the basic spatial pattern could be generalized as: the most adjacent area of ??Nanjing as the first cluster, the sub-adjacent area of Nanjing as the second cluster and the adjacent area closed to Beijing and Guangzhou as the third and fourth clusters. Those four clusters constitute the most critical tourist generating areas to Nanjing. 4) The regionalized tourist volume division is characterized by a north-south differentiation pattern, while the regionalized tourist flow intensity division exhibits an east-west differentiation pattern. The analytical results of this paper have important practical implication for deepening the zoning of tourist source in Nanjing and provide references to other destinations. It also could help to conduct accurate marketing strategy in tourism marketing and optimizing the configuration of tourism supporting facilities.

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This paper studies the network hierarchy and regional differentiation patterns of the source market from a city level. Based on the big data of tourist number monitored in Nanjing, 5short-period festivals are selected including New Year's Day, Qingming Festival, Labor′s Day, Dragon Boat Festival and Mid-Autumn Festival of China. The methods of overall trend analysis based on spatial variables, social space network clustering analysis and spatial regional division model are adopted. Among them, the first method can be used to determine the overall spatial distribution trend of tourist flow intensity. The second one can realize the hierarchical division of the source network nodes, so as to simultaneously examine the hierarchical structure and spatial distribution structure of the network nodes. The division model based on machine learning can be used to distinguish the tourist volume and tourist flow intensity in the source market from the city level. The results are as follows: 1) The intensity of tourist flow shows significant spatial hierarchy characteristics. The high-ranking nodes are mainly located in the most adjacent and sub-adjacent areas of Nanjing, and the exogenous network effect is also obvious. 2) Overall, the source network nodes in five different short-period national holidays show similar hierarchical structures and distribution patterns while the regional differences are apparent. 3) Although there are many differences in the spatial distribution patterns of the high level city nodes among the five short-period national holidays, the basic spatial pattern could be generalized as: the most adjacent area of ??Nanjing as the first cluster, the sub-adjacent area of Nanjing as the second cluster and the adjacent area closed to Beijing and Guangzhou as the third and fourth clusters. Those four clusters constitute the most critical tourist generating areas to Nanjing. 4) The regionalized tourist volume division is characterized by a north-south differentiation pattern, while the regionalized tourist flow intensity division exhibits an east-west differentiation pattern. The analytical results of this paper have important practical implication for deepening the zoning of tourist source in Nanjing and provide references to other destinations. It also could help to conduct accurate marketing strategy in tourism marketing and optimizing the configuration of tourism supporting facilities.

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Analysis of the dynamic characteristics of Shanghai's population distribution is an important basis for recognizing people's behaviors, allocating urban infrastructures, and making safety emergency plans. Be short of statistical data of temporal and spatial dynamic distribution of population, research on this topic is limited in China. Due to cell phone is the most popular communication terminal equipment, the distribution of cell phone users is able to reflect the distribution of population accurately. Using datasets of cell phone signaling records from Shanghai, this study builds a framework based on the relationship among population, time, and behavior to analyze dynamic characteristics of Shanghai's population distribution. The results show that: (1) there is a single center where the density of population at daytime and nighttime is the highest; (2) people gather in the center at daytime and flow to the suburbs at nighttime; (3) different types of people's behavior result in dynamic changes of population distribution; (4) spatial mismatch between employment and place of residence, and the dependence on city center cause a large number of people flow to city center; (5) the degree of dependence of leisure consumption behavior on city center is obviously higher than that of employment, especially in the ring areas adjacent to city center.

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Analysis of the dynamic characteristics of Shanghai's population distribution is an important basis for recognizing people's behaviors, allocating urban infrastructures, and making safety emergency plans. Be short of statistical data of temporal and spatial dynamic distribution of population, research on this topic is limited in China. Due to cell phone is the most popular communication terminal equipment, the distribution of cell phone users is able to reflect the distribution of population accurately. Using datasets of cell phone signaling records from Shanghai, this study builds a framework based on the relationship among population, time, and behavior to analyze dynamic characteristics of Shanghai's population distribution. The results show that: (1) there is a single center where the density of population at daytime and nighttime is the highest; (2) people gather in the center at daytime and flow to the suburbs at nighttime; (3) different types of people's behavior result in dynamic changes of population distribution; (4) spatial mismatch between employment and place of residence, and the dependence on city center cause a large number of people flow to city center; (5) the degree of dependence of leisure consumption behavior on city center is obviously higher than that of employment, especially in the ring areas adjacent to city center.

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