Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

Abstract

At present, population mobility for the purpose of tourism has become a popular phenomenon. As it becomes easier to capture big data on the tourist digital footprint, it is possible to analyze the respective regional features and driving forces for both tourism sources and destination regions at a macro level. Based on the data of tourist flows to Nanjing on five short-period national holidays in China, this study first calculated the travel rate of tourist source regions (315 cities) and the geographical concentration index of the visited attractions (51 scenic spots). Then, the spatial autocorrelation metrics index was used to analyze the global autocorrelation of the travel rates of tourist source regions and the geographical concentration index of the tourist destinations on five short-term national holidays. Finally, a heuristic unsupervised machine-learning method was used to analyze and map tourist sources and visited attractions by adopting the travel rate and the geographical concentration index accordingly as regionalized variables. The results indicate that both source and sink regions expressed distinctive regional differentiation patterns in the corresponding regional variables. This study method provides a practical tool for analyzing regionalization of big data in tourist flows, and it can also be applied to other origin-destination (OD) studies.

Areas of Crime in Cities: Case Study of Lithuania

“…Regions can further be used for better understanding the phenomenon, making spatial and temporal predictions and planning with an eye to better problem management [35][36][37][38]. While generally acknowledging the applied value of regional mapping results, most authors emphasise that there is no one-size-fits-all approach and results may vary depending on the method used or its parameters [38,39].…”

Study on the Spatial Pattern of Migration Population in Egypt and Its Flow Field Characteristics from the Perspective of “Source-Flow-Sink”

“…In terms of resource science, Wei Huang analyzed the grain source-sink system of prefecture level cities in China [57]; Wu X D analyzed the source-sink system of the world's arable land [58]; Wu X F analyzed the source and sink systems of world crude oil and coal supply chain [59,60]. In terms of transportation and tourism science, Wu Tong evaluated the landscape pattern of the area around Xiamen's main roads (within 1 km) based on the source-sink landscape influence [61]; Liu Yuliu conducted an identification analysis of the taxi traffic source-sink regions in Shanghai [62]; Qiushi Gu analyzed and mapped the source and sink of the regional tourist flow [63]. In terms of land use science, Felzer Benjamin S. analyzed the source-sink patterns of land use and land cover changes in the United States since 1700 [64]; Qin Menglin analyzed changes in the spatial structure of carbon sources and carbon sinks in the urban fringe [65].…”

Detecting Colocation Flow Patterns in the Geographical Interaction Data

“…Moreover, the spatial interaction analysis of flow data has attracted various research attention (Li et al 2017; Zhu et al 2018). Examples include travel chain pattern mining based on tourist behavior data (Gu et al 2019; Li et al 2018; Wang et al 2019), characteristic analysis for population migration at a large scale (Bailey, Kapetanios, and Pesaran 2016; Ma et al 2019), semantic OD flow construction and its spatial interaction pattern detection (Hu et al 2017; Zhang, Zhou, and Huang 2020), and behavior pattern analysis of residential mobility in inner cities (Kamenjuk, Aasa, and Sellin 2017; Jin et al 2020). Recently, a spatial autocorrelation model based on multivariate analysis has been proposed (Anselin 2019, 2020), and some scholars have introduced this idea into OD flow data (e.g., BiFlowLISA model, which is developed to measure the spatial association for bivariate aggregated flow data (Tao 2020)).…”