Human mobility has been a key concept in geography, transport, and regional planning. Measuring and analyzing the flows of travelers at different scales (spatial and temporal) is a major topic in the mobility literature (
Journal of Geographical Sciences, Volume. 30, Issue 12, 1943(2020)
Exploring temporal heterogeneity in an intercity travel network: A comparative study between weekdays and holidays in China
A largely unexplored application of “Big Data” in urban contexts is using human mobility data to study temporal heterogeneity in intercity travel networks. Hence, this paper explores China’s intercity travel patterns and their dynamics, with a comparison between weekdays and holidays, to contribute to our understanding of these phenomena. Using passenger travel data inferred from Tencent Location Big Data during weekdays (April 11-15, 2016) and National Golden Week (October 1-7, 2016), we compare the spatial patterns of Chinese intercity travel on weekdays and during Golden Week. The results show that the average daily intercity travel during Golden Week is significantly higher than that during weekdays, but the travel distance and degree of network clustering are significantly lower. This indicates temporal heterogeneity in mapping the intercity travel network. On weekdays, the three major cities of Beijing, Shanghai, and Guangzhou take prominent core positions, while cities that are tourism destinations or transportation hubs are more attractive during Golden Week. The reasons behind these findings can be explained by geographical proximity, administrative division (proximity of cultural and policy systems), travel distance, and travel purposes.
1 Introduction
Human mobility has been a key concept in geography, transport, and regional planning. Measuring and analyzing the flows of travelers at different scales (spatial and temporal) is a major topic in the mobility literature (
The time-space compression brought by the development of high-speed transport networks and ICT as well as the continuous regional integration largely promotes the expansion of human activity space in China (
With the increasing focus on questions of regional integration and long-distance mobility, there is a growing body of literature on medium- and long-distance travel and intercity travel (
This paper aims to explore temporal heterogeneity by comparing the differences in intercity travel patterns between weekdays and National Golden Week. To this end, we applied network analysis methods and used migration data obtained from the website of Tencent Location Big Data to conduct this study. The results have important implications for transport infrastructure planning and regional studies.
The remainder of this paper is organized as follows. Section 2 reviews the literature on intercity travel. Section 3 briefly describes the data and methodology for modeling intercity travel. Subsequently, we present and discuss the empirical results in Sections 4 and 5. Finally, we present an overview of our key findings in Section 6.
2 Literature review
2.1 Long-distance and intercity travel
The terms “long-distance travel” and “intercity travel” are usually used interchangeably and long-distance is generally defined on the basis of a physical distance threshold (
At present, the existing literature on intercity travel can be divided into two categories. Firstly, there are those that mainly focus on the question of intercity travel per se. For example,
As one of the key aspects of “the space of flows”, intercity travel is particularly important in studies of urban networks. The main aim of these studies is to uncover patterns of urban networks through the lens of intercity travel. Using air-passenger-flow data,
Previous studies of intercity travel have rarely addressed its temporal heterogeneity. Differences in the travel period and travel time availability have a strong effect on intercity travel patterns. “Big Data” sources offer new opportunities for analysis of the temporal heterogeneity of intercity travel patterns.
2.2 Big data in intercity travel studies
In this era of Big Data, geo-referenced data has increasingly attracted interest from scholars and has been widely used in geographical research (
To the best of our knowledge, relatively few studies have compared intercity travel during different periods in one area using the same type of passenger-flow data. Our research tries to fill this gap by using human mobility data released by Tencent for weekdays and holidays within the national urban system of China.
3 Data and methodology
3.1 Data sources
Passenger intercity travel data from weekdays (April 11-15, 2016) and during National Golden Week (October 1-7, 2016) from the Tencent Location Big Data platform were employed in this study. The data encompasses 362 cities, including 293 prefecture-level cities, four municipalities, and 65 county-level cities in mainland China. Passenger intercity travel data from Tencent Location Big Data provides the top-ten inflow and outflow records for each city. Additional passenger inflow (outflow) records can be appropriately supplemented by the outflow (inflow) records of other cities to establish intercity travel patterns. This data has been applied to reveal the characteristics of China’s urban development during the Spring Festival period (
It should be noted that the Tencent platform provides a relative passenger intercity travel volume, not an absolute volume value. In fact, the relative index of the passenger travel data is better than the attributes reflected by the absolute volume when using Big Data to study the characteristics of residents’ travel (
Descriptive statistics of intercity travel networks for weekdays and Golden Week
Descriptive statistics of intercity travel networks for weekdays and Golden Week
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Figure 1.
3.2 Methodology
3.2.1 City centrality
To reveal the centrality of cities in the network, this paper selects two indexes: the weighted degree centrality index (
where
To reveal hierarchical structure in the network as a whole, the city equilibrium degree coefficient
where
3.2.2 Link connectivity
The intercity travel link connectivity refers to the connectivity of a city pair, indicating the relative strength of a link (
where
3.2.3 Community structure mining and visualization model
Considering that this study constructs a directed weighted network and the network data represents the actual intercity relationship flows, this paper adopts the
3.2.4 City role recognition model
Exploring the role of each city is of particular importance in a large network. In this paper, the within-module degree and participation coefficient proposed by
where
Figure 2.
4 Hierarchical structure and spatial patterns of intercity travel network
4.1 City nodes
4.1.1 Hierarchical size structure
In general, the travel size in Golden Week was higher than those on weekdays. The average degree values on weekdays and during Golden Week were 44.8 and 45.7, respectively. Without considering their weight, the average number of directly related cities associated with each city was nearly equal. The average values of the weighted degree in the two periods were 185 200 and 386 605, respectively. Meanwhile, the average value of
Figure 3.
Statistical characteristics of intercity travel network for weekdays and Golden Week
Statistical characteristics of intercity travel network for weekdays and Golden Week
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4.1.2 Spatial patterns
Figure 4.
Statistical characteristics of the DITi values and their absolute changes for the two periods
Statistical characteristics of the DITi values and their absolute changes for the two periods
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The “space-time compression” effect brought about by high-speed transport development plays an important role in expanding travel distances and activity spaces. The regions with high
With regard to the ranking changes of the cities between the two periods, apart from Beijing, Shanghai, Shenzhen, Guangzhou, Zhengzhou, and Wuhan, there were obvious ranking changes for the other cities between weekdays and Golden Week, as shown in
4.1.3 City role identification
We calculated the within-module degree (
Figure 5.
4.2 Intercity network connections
4.2.1 Comparison of spatial patterns by primary linkage
Figure 6.
4.2.2 Comparison of spatial patterns by link centrality
Figure 7.
To further analyze the different rankings of the links for the two periods, we compared the differences in link centrality values (
Figure 8.
5 Community clusters of cities
5.1 Community clusters
Based on the
Figure 9.
The formation of cross-regional communities is affected by the geographical proximity effect and space-jump effect to overcome distance friction. Specifically, the formation of cross-regional communities was driven by radiation from central cities such as Beijing, Shanghai, Guangzhou, and Shenzhen.
On weekdays, a cross-regional community centered on Beijing and Shanghai was formed, including 62 cities, which can be further divided into five secondary communities: Heilongjiang-Jilin, Beijing-Tianjin-Hebei, Shanghai-Zhejiang, Chongqing, and Hubei. The cross-regional community with Guangzhou and Shenzhen as its core cities included 40 cities, which were divided into two secondary communities of Guangdong and Henan. The Beijing-Shanghai community and the Guangzhou-Shenzhen community are located in the Beijing-Guangzhou, Beijing-Shanghai, Beijing-Harbin, and Shanghai-Chengdu riverside axial regions and were highly coupled with the national development axes, including the coastal area, Beijing-Harbin, Beijing-Guangzhou, and the Yangtze riverside corridor.
During Golden Week, the Beijing-Shanghai community still existed, but the number of cities included in it was reduced from 62 to 27, including three secondary communities, Beijing-Tianjin-Hebei, Shanghai-Zhejiang, and Chongqing. A cross-regional community including 28 cities with Harbin, Changchun, and Shenyang as its core cities was formed in northeastern China. The Guangzhou-Shenzhen cross-regional community has dissolved into the two provincial communities of Guangdong and Henan.
The neighboring communities were composed of cities from neighboring provinces that are geographically adjacent and within the hinterland of the core city. During weekdays, a community with Chengdu as its core city was formed, covering Sichuan and Tibet, including 28 cities. A community with Urumqi as its core city was formed, covering Xinjiang and the western region of the adjacent Gansu province, including 29 cities. A community with Nanjing and Suzhou as its core cities was formed, covering Jiangsu and the eastern part of the adjacent Anhui province, including 17 cities. A community with Xi’an as its core city was formed, covering Shaanxi and Qingyang of Gansu, including 11 cities. During Golden Week, a community with Lanzhou and Xining as its core cities was formed, covering Gansu, Qinghai, and Naqu in Tibet, including 22 cities. A community with Yinchuan as its core city was formed, covering Ningxia and Alashan of Inner Mongolia, including six cities. Compared with weekdays, the scope of the community with Nanjing and Suzhou as its core cities further expanded in Golden Week to cover Jiangsu and Anhui provinces, including 29 cities, and the community with Xi’an as its core city still existed, covering Shaanxi and Qingyang in the adjacent Gansu province.
In addition, the number of communities jointly formed by cities located close to each other and within the same provincial administrative region was the largest, specifically 16 and 17 for weekdays and Golden Week, respectively. The provincial communities remained stable during the two periods, including the Shandong group with Jinan and Qingdao as its core, and the Hainan community with Haikou and Sanya as its core cities.
5.2 Discussion
The formation of each community and its change between the two periods is influenced by a variety of factors, including geographical proximity, administrative divisions (cultural-policy proximity), travel distance, and travel purposes.
Geographical proximity determines the possibility of intercity interaction within a region. The closer the location, the greater the degree of intercity interaction as well as the possibility of being in the same community. For example, among the 22 communities of the two periods, provincial communities were the largest grouping, accounting for 72.73% and 77.27% of the total for the two periods, respectively. It is difficult for cities located in the border areas of provinces to be driven by the radiation of the central cities because they are geographically far from the central cities of their associated provinces. These cities attract people from the economic centers of adjacent provinces in their geographic locations and have strong interactions, thus forming neighboring communities. Administrative divisions (cultural-policy proximity) are also the main factors for the formation of provincial communities. Cultural customs, management policies, resource allocation, factor mobility, and other aspects within the same administrative region are inherently convenient, making the links between cities in one province much greater than those between provinces, while provincial capitals and major central cities in a province assume the status of hubs for inter-provincial links.
Travel distance directly affects the spatial coverage of intercity interactions. The further the travel distance is, the greater the possibility of interaction between distant cities and thus the greater the possibility of forming a cross-regional community. The average daily travel distance during Golden Week was significantly lower than that during weekdays (
Figure 10.
There are significant differences in the purpose of travel on weekdays and holidays. Most of the intercity trips that take place on weekdays are mainly business trips and commuting trips between major central cities, and between cities in the upper and lower reaches of the industrial chain. Therefore, with the background of the space-time compression brought about by the development of high-speed transport, business travel on weekdays presented a cross pattern between distant national central cities (along the Beijing-Guangzhou- Shenzhen, Beijing-Shanghai, and Shanghai-Chengdu-Chongqing routes) and a network pattern between the inner and outer central cities within urban agglomerations (Shenzhen- Dongguan, Guangzhou-Foshan, and Guangzhou-Shenzhen of the Pearl River Delta city group). Most of the intercity trips that take place during holidays (such as Golden Week) are leisure and family visits. As noted earlier, 593 million tourists were received nationwide during the National Day Golden Week in 2016. Nearly half of the people in China chose to travel, which directly resulted in the daily average intercity travel scale during Golden Week being significantly higher than that on weekdays. At the same time, Sanya, Dali, Shanghai, Hangzhou, Xi’an, Xiamen, Beijing, Chengdu, Nanjing, and Guangzhou became the top-10 tourist-destination cities. Leisure and family visits mainly took place between a central city and peripheral secondary cities within its hinterland, showing a radial structure dominated by short distances (Figures 1, 6 and 9).
6 Conclusion
This paper provides a systematic analysis on the temporal heterogeneity in mapping intercity travel network. Using the migration data obtained from the website of Tencent Location Big Data in China for weekdays (April 11-15, 2016) and National Golden Week (October 1-7, 2016), this paper explored spatial patterns in China’s intercity travel network and disclose the differentiated patterns among weekdays and hoilday. Main findings of this paper include:
The total relative volume of the daily average flow during Golden Week was notably higher than that during the weekdays, but the travel distance was clearly lower. On weekdays, the intercity network formed a rhombic dominant-link structure with Beijing, Shanghai, Guangzhou, Shenzhen, Chengdu, and Chongqing as core cities. During Golden Week, the intercity travel network showed a significant proximity effect.
Patterns of network agglomeration during the two periods are of hierarchies and regional tendencies. Under the combined effects of the geographical proximity effect, administrative division (cultural-policy proximity), travel distance, travel purposes, and other factors, three types of communities formed: cross-regional communities, neighboring communities, and provincial communities. With the increasing travel volume and the popularity of shorter-distance travel during Golden Week, the influence of national hub cities significantly declined, while regional and local hubs taking dominant positions.
The level of economic development of a city is directly related to the scale of its centrality, which spatially decreased from east to west. The space-time compression effect brought about by the development of high-speed transport plays an important role in expanding travel distances and activity space.
Due to the daily trips of a large number of users who are not connected to the Tencent platform cannot be recorded, and most of trips are disassembled so that users can not identify the real OD travel, the results of this paper inevitably have some limitations. Moreover, the characteristics of intercity travel network of weekdays and holidays is not the same as the characteristics of intercity travel network dominated by business flows and tourism flows. Seeking the data acquired from questionnaire, ticketing information and other survey data would be valuable for future exploration of the formative mechanism of intercity travel network.
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Tao LI, Jiaoe WANG, Jie HUANG, Xingchuan GAO. Exploring temporal heterogeneity in an intercity travel network: A comparative study between weekdays and holidays in China[J]. Journal of Geographical Sciences, 2020, 30(12): 1943
Category: Research Articles
Received: Sep. 2, 2020
Accepted: Oct. 30, 2020
Published Online: May. 7, 2021
The Author Email: WANG Jiaoe (wangje@igsnrr.ac.cn)