Public Transport Connectivity and Intercity Tourist Flows

Abstract

In this study, we investigate how dyadic air and rail transport connectivity affects domestic tourist flows among 343 Chinese cities. Using geo-tagged Sina Weibo data to track tourists during China’s National Day Golden Week in 2014, we estimate several gravity models with a negative binomial distribution. The estimation results suggest that air transport connectivity generally has a greater influence than rail transport on dyadic tourist flows, while connectivity provided by ordinary trains (compared to other rail types) is most important in the context of rail transport. Also, we find the effects of transport connectivity and intermodal transport competition to depend on the origin-to-destination distance. Different types of railway trains appear to have distinct effective distance ranges: the effect of high-speed rail trains is strongest at travel distances between 1,800 and 2,000 km, whereas bullet trains’ effect is strongest at distances between 400 and 600 km.

Full text

PUBLIC TRANSPORT CONNECTIVITY AND INTER-CITY TOURIST
FLOWS
Yang Yang
Department of Tourism and Hospitality Management, Temple University, Philadelphia, USA
Dong Li
Beijing Tsinghua Tongheng Urban Planning & Design Institute, Beijing, China
Xiang(Robert) Li
Department of Tourism and Hospitality Management, Temple University, Philadelphia, USA
Please cite as:
Yang, Y., Li, D, and Li, X. (in press). Public transport connectivity and inter-city tourist
flows. Journal of Travel Research: doi: 10.1177/0047287517741997.

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Public Transport Connectivity and Inter-City Tourist Flows
Abstract: In this study, we investigate how dyadic air and rail transport connectivity affects
domestic tourist flows among 343 Chinese cities. Using geo-tagged Sina Weibo data to track
tourists during China's National Day Golden Week in 2014, we estimate several gravity models
with a negative binomial distribution. The estimation results suggest that air transport
connectivity generally has a greater influence than rail transport on dyadic tourist flows, while
connectivity provided by ordinary trains (compared to other rail types) is most important in the
context of rail transport. Also, we find the effects of transport connectivity and inter-modal
transport competition to depend on origin-to-destination distance. Different types of railway
trains appear to have distinct effective distance ranges: the effect of high-speed rail trains is
strongest at travel distances between 1,800 and 2,000 km, whereas bullet trains’ effect is
strongest at distances between 400 and 600 km.
Keywords: Sina Weibo; gravity model; air transport; transport connectivity; high-speed rail;
domestic tourist flows
Introduction
In a typical tourist flow system, transport infrastructure plays a vital role in connecting origin and
destination locations while ensuring safe, comfortable, and efficient transportation for tourists.
When evaluating their travel experiences, tourists consistently cite transport as having great
bearing on trip satisfaction (Pritchard and Havitz 2006). Efficient transport connectivity can
significantly reduce travel time and costs for tourists (Peng et al. 2015) while improving
destination access for those in origin markets (McKercher 1998). Additionally, a destination’s
transport infrastructure, particularly its level of sophistication, largely dictates the destination’s

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tourism demand (Khadaroo and Seetanah 2007, 2008) and overall competitiveness compared to
similar regions (Dwyer and Kim 2003; Duval and Schiff 2011). When a destination is accessible
via different modes of transport (e.g., airplane, train, car, boat), tourists can choose the option
that best suits their budget, length of stay, and travel distance (Thrane 2015).
Unfortunately, inadequate infrastructure plagues destinations in emerging travel markets (Li
2016; Sheth 2011). Poor transport infrastructure carries significant financial implications for
destinations and hinders their tourism industry growth (Khadaroo and Seetanah 2007). Many
emerging-market countries (e.g., China) consider improved transport infrastructure a national
priority. Over the last decade, the quantity and quality of transport options in China have grown
significantly. Thanks to the National Trunk Highway Development Program, China’s national
expressway coverage increased from 400 km in 1990 to 111,900 km in 2014 (Qin 2016).
Likewise, government-initiated deregulation efforts and airport construction plans in many tier II
and tier III cities helped China become the second-largest air transport market in the world in
2005 (Zhang et al. 2014). The country’s railways have also benefited greatly from continued
infrastructural development, including upgrades to existing networks and the construction of a
nationwide high-speed rail (HSR) network (Qin 2016). The Chinese Mid- to Long-Term Railway
Network Plan (2016–2025) includes proposals for the construction of eight HSR corridors with
four north-south and four east-west HSR trunk lines (Cao et al. 2013), which will facilitate travel
throughout the country (Wang et al. 2014).

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Much of the relevant literature has used destination-centric transport provision measures to
examine the effects of transport factors on tourism demand (Khadaroo and Seetanah 2007, 2008;
Yang and Wong 2012). However, the role of transport in connecting origin cities has been
largely overlooked. Although many scholars have studied inter-modal transport competition and
investigated the competition/substitution pattern across different types of transport (Jiménez and
Betancor 2012; Dobruszkes, Dehon, and Givoni 2014), empirical exploration of the effect of
transport-mode competition on tourist flows is lacking. To fill this gap, we apply a gravity model
to assess the relationship between inter-city tourist flows and air and rail transport connectivity.
This study aims to estimate (1) the effect of public transport connectivity on inter-city tourist
flows, and (2) the effect of inter-modal transport competition on inter-city tourist flows. We also
intend to investigate how these effects vary with distance. We expect to contribute to current
knowledge of tourism demand and tourism transport in four ways. First, our study represents a
pioneering effort to examine the effect of transport network structure on tourist flows: rather than
relying on destination-side transport infrastructure measures, we focus on transport connectivity
between origins and destinations to better understand how dyadic linkages shape tourist flows.
Second, we incorporate the inter-modal transport competition effect into our gravity model,
which yields critical insights into substitution patterns across different types of transport. Third,
as the first of its kind, our study represents an initial attempt to evaluate how the effect of
transport connectivity varies at different travel distances. Lastly, we use social media data to
track aggregate tourist mobility during China’s National Day Golden Week, thereby highlighting
geo-tagged social media data as an alternative source of large-scale tourist flow data.

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This paper is organized as follows. In the next section, we review relevant literature, namely
studies on the effects of air and rail transport on tourist flows and inter-modal transport
competition. Based on previous research, we then specify and elucidate the empirical model and
define our variables. After presenting and discussing our estimation results, we conclude by
providing policy implications.
Transport and Tourism
The success of the tourism industry is closely tied to transport connectivity between origin and
destination locations. In the tourism literature, transport systems are defined broadly as “the
operation of, and interaction between, transport modes, ways and terminals that support tourists
into and out of destinations and also the provision of transport services within the destination”
(Prideaux 2000). The primary function of transport infrastructure is to provide access to specific
regions. Compared to underperforming networks, effective infrastructure minimizes
transportation costs and attracts more tourists (Khadaroo and Seetanah 2007). Service quality is
similarly important; it influences tourists’ overall assessment of a destination and, by extension,
their revisit intention and word-of-mouth (WOM) communication with other potential tourists
(Ahrholdt, Gudergan, and Ringle 2016).
A destination’s transport infrastructure (i.e., roads, airports, railways, harbors) plays a central
role in shaping a local tourism industry’s overall competitiveness. McKercher (1998)
investigated how tourists’ destination choices are informed by market access, which refers to the

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competitive (dis)advantages of a destination vis-à-vis its proximity to major markets. He found
that transport largely determines market access; that is, greater market access encourages
sustained tourist flows to a destination and creates opportunities to attract visitors by leveraging
pass-through traffic (McKercher 1998). In Prideaux’s (2000) proposed resort development
model, transport access to a resort is considered a key factor in determining its rate and scale of
growth because transport affects the resort’s ability to expand to new markets. Prideaux (2000)
also developed a transport cost model to examine the role of transport in facilitating destination
growth and shaping destination market portfolios. The model delineates transport access costs
into three categories: actual travel fares, time value for the journey, and cost of comfort.
Transport has been found to influence tourist flows in a variety of destinations. For example,
Khadaroo and Seetanah (2007) used destination-side transport capital stocks as a proxy for
transport modes (air, land, sea) to evaluate infrastructure’s impact on international tourist flows
to Mauritius. The effect was estimated to be significant for tourists from Europe, America, and
Asia, in part because they prefer to maintain a baseline level of transport-related comfort when
away from home. Khadaroo and Seetanah (2008) investigated how bilateral tourist flows are
determined by destination-side transport infrastructure, including road, airport, and port
measures, using a gravity model. They found that transport infrastructure is most influential on
tourist flows to African and Asian countries, implying that international tourists value transport
infrastructure highly when traveling to unfamiliar destinations. In another study, Seetanah and
Khadaroo (2009) highlighted the positive short- and long-term effects of transport capital stock
on incoming tourist arrivals.

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Air transport and tourism
Air transport possesses several advantages over other transport modes, namely greater speed,
safety, service quality, and reliability. As suggested in previous literature, air transport
infrastructure plays a significant role in stimulating regional tourism growth. Air travel is
generally reliable (Mukkala and Tervo 2013), which is particularly important for long-haul
travelers and those traveling to remote and peripheral areas with inferior land transport (Mukkala
and Tervo 2013). For long-haul travel, air transport greatly reduces travel time and, in turn,
travel-related opportunity costs. Air transport infrastructure may also allow destinations to
establish and/or strengthen tourism-related economic linkages with other areas to create
competitive advantages. Bieger and Wittmer (2006) proposed a system model to analyze the
inter-connection between air transport and tourism in which air transport supply improves the
quantity, quality (spending power), and structure (length of stay and travel purpose) of incoming
visitors. Based on this system, they also developed an analytical framework to understand
success factors associated with different airline business models.
The accessibility and type of air travel also informs tourist flows. Khadaroo and Seetanah (2007)
looked at the significant impact of airport terminals on inbound tourist flows to 26 island
destinations. Tveteras and Roll (2014) investigated the effects of non-stop international flights on
tourist flows and found that the long-run demand elasticity of long-haul direct flights ranges
from 0.3 to 0.5 in Peru. However, Duval and Schiff (2011) found insignificant effects of non-
stop air services for international visitors from most countries to New Zealand, explaining that
leisure travelers are willing to take indirect flights to secure lower fares. Yang and Wong (2012)

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estimated the impact of destination-side air transport infrastructure on tourist flows to Chinese
cities and found the impact of air transport to be consistently lower than that of road transport.
An exception is Western cities in Chna, where the air transport effect is largest due to poor land
transport accessibility. Lastly, the expansion of low-cost carriers (i.e., airlines that provide flights
at lower prices) not only improves market accessibility for outbound tourists, but also has a
positive impact on the number of inbound tourist arrivals to destinations (Rey, Myro, and Galera
2011).
Rail transport and tourism
Technological advancements have positioned rail transport as an appealing travel option in terms
of safety, convenience, timeliness, flexibility, and affordability (Yan, Zhang, and Ye 2014).
Compared to air transport, rail transport may be more economical for some tourists. Givoni and
Dobruszkes (2013) noted the importance of “door-to-door” travel time when evaluating transport
connectivity. Unlike airports, most train stations are located closer to city centers and urban
activities, allowing tourists to immerse themselves in their destination nearly immediately (Fu,
Zhang, and Lei 2012). Many train stations do not require passengers to undergo thorough (and
time-consuming) security screenings, further reducing tourists’ transport obligations (Pagliara,
Vassallo, and Román 2012). HSR systems in particular have revolutionized tourism by
minimizing travel time and improving passenger comfort (Albalate and Fageda 2016). HSR
availability also influences tourists’ perceptions of a destination’s accessibility and utility
(Pagliara et al. 2015) and continues to change the competitive landscape across destinations
(Masson and Petiot 2009; Wang et al. 2012). Bazin, Beckerich, and Delaplace (2011) identified
several reasons why HSR is preferred by some tourists in Europe. For short-haul urban tourists,
HSR provides an attractive alternative to driving by eliminating problems related to fatigue,

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traffic congestion, and parking. HSR service can also be bundled with other tourism products to
create higher-value promotions. Lastly, HSR provides superior on-board comfort and similar
advantages over other transport modes.
Given its growing popularity, many scholars have investigated the impact of HSR access on
local tourism demand, with Asia being a common region of study. Su and Wall (2009) found that
the opening of the Qinghai–Tibet railway greatly boosted tourism in Tibet, and travelers
identified the railway as a major factor when choosing Tibet as a destination. Yan, Zhang, and
Ye (2014) analyzed the impact of the newly constructed Wuhan–Guangzhou HSR on local
tourist receipts and found significant effects in two of the three provinces served by the railway.
Chen and Haynes (2015) discovered that China’s HSR led to a 29% increase in international
tourism demand from different origin countries.
It is important to note that the benefits of HSR differ across destinations. For example, Pagliara
et al. (2015) found Spain’s HSR system to influence tourists’ destination choices of cities nearby
Madrid, but not travelers’ choice of Madrid itself. Delaplace et al. (2014) discovered that the
French HSR network’s esteemed reputation informed travelers’ decisions to visit Paris. Italy’s
HSR did not exert the same impact in Rome, however, because the country’s system is not as
popular.

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Inter-modal transport competition
The presence of HSR does not necessarily increase travel demand by itself; instead, it can
cannibalize demand for other transport modes, such as air and bus. As mentioned earlier, rail
travel (and particularly HSR) is more competitive at short distances due to higher-frequency
services, cheaper fares, proximity to city centers, and service reliability and safety (Taniguchi
1992). Albalate and Fageda (2016) found that the launch of Spain’s HSR network did not
increase tourist arrivals because some tourist segments use HSR as a substitute for air transport.
Dobruszkes, Dehon, and Givoni (2014) found that the inter-modal competition between air
service and HSR is contingent upon HSR’s travel time and distance; shorter distances come with
fewer air service options. When the HSR network opened in Spain, Jiménez and Betancor (2012)
found that the number of air transport options decreased by 17%, and the market share of major
airlines across different travel modes declined. Based on model estimates, Martín and Nombela
(2007) posited that trains divert travelers from planes and buses for long-distance routes of over
500 km, whereas most rail demand comes from car users traveling shorter routes. Pagliara,
Vassallo, and Román (2012) underscored price and service frequency as shaping the substitution
of HSR for scheduled air service in Spain. Using a supply-side empirical analysis, Albalate, Bel,
and Fageda (2015) found that HSR-air competition depends on HSR network design and length
as well as HSR station locations. When confronted with HSR competition, air services were
reduced more at hub airports than at non-hub airports, and even more so at airports without an
on-site HSR station.

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Analyzing transport mode choice from the demand-side perspective provides useful information
about competition across different modes. Nerhagen (2003) found that for visitors to a major ski
resort in Sweden, those who traveled long distances preferred traveling by train rather than by
car. Likewise, van Goeverden (2009) investigated long-haul German and Dutch tourists’
decisions to travel by train vs. alternative modes of transport; the likelihood of traveling by rail is
highest at travel distances between 600 and 900 km and lowest between 1,400 and 1,500 km.
Thrane (2015) analyzed long-haul Dutch tourists’ transport choices among private car, air, and
other public transit services; long-distance (>400 km) travelers and those with higher household
incomes indicated stronger preferences for air transport.
The foregoing review suggests that the impact of transport connectivity on tourist flows is under-
researched, especially in dyadic contexts that include origin and destination information about
transport connectivity. Furthermore, the results of previous studies fail to illuminate how
transport effects vary at different origin-to-destination travel distances and whether a
competition/substitution pattern across transport modes may shape inter-regional tourist flows.
Research Methods
Data collection
This study evaluates inter-city tourist flows among 343 Chinese prefectural cities during the
2014 National Day Golden Week (October 1–7). First enacted by the Chinese central
government in 1999, the Golden Week vacation policy aims to stimulate domestic tourism and
related consumption to maximize tourism’s multiplier effect on the national economy (Wu et al.

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2012). Following a 2007 amendment to the Golden Week policy, the week coinciding with
National Day (October 1) became the primary time of year when Chinese travel for vacation
(Yan and Zhang 2010). Most employers in China do not offer paid leave; therefore, the seven-
day holiday presents a valuable opportunity for Chinese to take extended vacations, especially to
long-haul domestic destinations (Li and Yang 2017). According to official statistics, 475 million
domestic tourists traveled during Golden Week in 2014, totaling 245.3 billion RMB in tourism
receipts which comprised 8.09% of the annual total (CNTA 2014).
Due to a lack of official statistics on dyadic tourist flows in China’s prefectural cities, we used an
alternative data source, Weibo check-ins, to construct a 343-by-343 tourist flow matrix. Sina
Weibo, a microblogging website similar to Twitter, is one of the most popular Chinese social
media platforms; as of December 2014, there were 175.7 million monthly active users and 80.6
million daily active users. Weibo members can post short text messages and photos, which
include timestamps and optional geo-tagged location information. Many tourism studies have
demonstrated the usefulness and accuracy of geo-tagged social media data in monitoring large-
scale tourist flows (Kádár 2014; Hawelka et al. 2014). Indeed, Weibo data provide an innovative
way to monitor mobility flows on precise temporal and spatial scales (Wu, Wang, and Dai 2016).
Li and Yang (2017) applied Weibo data to model inter-province tourist flows in China, and our
study extends their work by monitoring dyadic tourist flows at a finer geographic scale at the
inter-city level.

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We used the nearby-search function of a public Weibo Application Programming Interface to
collect geo-tagged data by overlapping a fishnet across China (Li and Yang 2017). All geo-
tagged Weibo data were cleaned and processed to obtain destination information at the
prefectural city level using spatial joins. We only retained records related to domestic travel
within mainland China; all other data were discarded. Origin information was obtained based on
users’ geo-tagged check-ins during the seven days prior to Golden Week. We also collected geo-
tagged information from one month before the holiday. If users’ self-reported home cities (as
indicated in their Weibo profile) did not appear in their geo-tagged records, we excluded those
observations. Over 6.67 million geo-tagged Weibo posts appeared during Golden Week,
representing an 18% increase in the daily average number of geo-tagged Weibo posts compared
to the week before. Weibo check-in records with unique user IDs were considered individual
travel records if a geo-tagged destination city differed from a user’s city of origin. Because the
Chinese government regulates public holidays, we assumed there were very few business-related
inter-city trips during Golden Week. We then aggregated individual geo-tagged Weibo posts to
generate a city-to-city tourist flow matrix.
To verify the credibility of Weibo data in monitoring tourist flows, we compared the total
number of geo-tagged Weibo posts in each destination with (1) the announced number of tourist
arrivals during the 2014 Golden Week, and (2) official statistics on tourist arrivals in 2013. As
shown in the log-log scatterplots (Figure 1), a stronger linear association suggests the geo-tagged
social media data we collected from 2014 mimic recorded tourism flows in China. Unlike

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official statistics, however, Weibo data capture origin information and thus depict origin-to-
destination tourist flows dyadically.
(Please insert Figure 1 about here)
Figure 2 presents the flow map of inter-city tourist flows based on geo-tagged Weibo posts. A
diamond shape appears in the map, representing Beijing, Shanghai, Guangzhou, and Chengdu as
four apexes. The diamond covers key tourist hotspot areas previously identified in China (Yang
and Wong 2013). The three largest pair-wise flows are from Beijing to Tianjin, from Chengdu to
Chongqing, and from Guangzhou to Shenzhen. In addition, 69.04% of city-pairs have a value of
0, indicating an absence of Weibo posts in our data.
(Please insert Figure 2 about here)
In addition to tourist flow information, we also collected data on inter-city air and rail transport
connectivity using the number of scheduled passenger air flights and trains based on official
timetables from the Civil Aviation Administration of China and China Railway Corporation
(CRC), respectively. Because some domestic flights are not scheduled daily, we measured air
transport connectivity by the number of flights scheduled per week between two cities. We
excluded code-share flights to avoid double counting, and we merged air flight counts for
multiple airports serving a single destination. We then constructed a 343-by-343 matrix, WA, to
capture air transport connectivity among cities.

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Unlike air transport, the train timetable is based on railway stations instead of cities. We
measured inter-city rail transport connectivity based on (1) Principal and Class I stations served
by the CRC, (2) station names that included the prefectural city’s name, and (3) other stations to
ensure our data included at least one train station in each city. Due to data unavailability, we did
not include some temporary trains that operated exclusively during Golden Week. According to
the CRC, trains can be broadly categorized into three types: (1) HSR trains, with an operational
speed of up to 300 km/h on HSR lines, most of which were built after 2007; (2) bullet trains,
with an operational speed of up to 200 km/h on HSR and conventional rail lines; and (3) ordinary
trains, which include all other types of trains operating on conventional rail lines. We then
constructed a 343-by-343 rail transport connectivity matrix, WR. In total, our sample was made
up of 11,793 scheduled flights and 4,903 scheduled trains, 24.35% of which were HSR trains and
31.49% of which were bullet trains. The supplementary materials include the flow map of air and
rail transport connectivity.
We further investigated the characteristics of the air and rail transport connectivity matrices to
understand how connectivity varies over different distances between cities. The results appear in
Figure 3. First, the distribution of air transport connectivity has a flatter center and a fatter tail
over distance, indicating that compared to rail transport, air transport connectivity dominates in
long-distance city-pairs. Second, by comparing the distributions of various rail connectivity
types, we find that HSR trains’ connectivity has a longer median distance than that of bullet
trains. However, HSR and bullet trains are rarely used to travel to long-distance destinations ≥

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1500 km away. Lastly, we constructed the indirect connectivity matrices such that W*A = WA •
WA and W*R = WR • WR to allow for one stop-over between two cities.
(Please insert Figure 3 about here)
Empirical model
Based on the utility maximizing framework, we assume each domestic tourist is economically
rational and free to travel domestically. By comparing the expected utility of any set of domestic
destinations, a tourist selects the one that appears to have the greatest utility. According to
Morley, Rosselló, and Santana-Gallego (2014), under these assumptions, we can derive a gravity
model for dyadic tourist flows using individual tourists’ aggregate demand. The general gravity
model is written as follows (Sen and Smith 1995):
 =() (1)
where  is the interaction between two entities i and j,  and  are origin- and destination-
specific masses, and () represents the separation between the two entities, which is usually
captured by the distance between them.
The dependent variable in our empirical model is the number of geo-tagged Weibo check-in
posts, denoted by non-negative integers. We used the count data model to establish a gravity
model, which mitigates several drawbacks of the traditional log-linear ordinary least squares
model including (1) estimation bias after logarithmic transformation, (2) violation of the
assumption of equal-variance of error terms, and (3) sensitivity of the results to zero-valued

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flows (Burger, van Oort, and Linders 2009). The Poisson model, the most popular count data
model, suffers from a limitation on the equi-dispersion assumption, which requires equality
between the mean and variance of the dependent variable. Burger, van Oort, and Linders (2009)
recommended using a negative binomial (NB) model to alleviate the equi-dispersion restriction
in gravity modeling; here, the probability that a destination j will attract a domestic tourist from
origin i depends on a set of factors  and the likelihood of observing a count number of  for
the origin-destination pair i and j, represented thusly:
() = 
()

 


 (2)
where () denotes the gamma function. The conditional expectation of , , is specified as a
log-linear function of a set of explanatory variables :
ln=ln
=
+ (3)
where  is a vector of coefficients of  and  follows a gamma distribution with mean 1 and
variance α, a parameter reflecting the degree of dispersion in predictions. The NB model
collapses to the Poisson model if α becomes 0; this can be estimated using the maximum
likelihood method.
Variable definition and description
As explained earlier, our dependent variable y comes from the matrix of inter-city tourist
movements. Exclusion of the diagonal elements in the 343-by-343 matrix yielded a total of
117,306 pair-wise observations. Due to data unavailability, we were unable to obtain water

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transport connectivity data across cities. According to official statistics, less than 1.2% of total
passenger traffic flows in China are associated with water transport (National Bureau of
Statistics of China 2015). Table 1 presents descriptions of our control variables and variables of
interest. We introduced connectivity measures of different transport modes to the model
successively. Endogeneity of connectivity measures is a common issue in econometric modeling;
however, it proved trivial in our research. When transport service providers map out service
routes, tourism is usually not a major concern. For example, as shown in some transport
connectivity maps (see supplementary materials), HSR and bullet train connectivity was largely
determined by economic conditions, population, and city location. This argument is further
supported by the results of auxiliary regressions of air/rail connectivity of each city on the city’s
level of domestic tourism specialization (domestic tourism revenue over GDP). The coefficients
of tourism specification variables are estimated to be insignificant, and these results are available
upon request.
(Please insert Table 1 about here)
As suggested by past studies, we incorporated a large set of control variables into our empirical
gravity model. In the model, lnGDP_O/lnGDP_D measures origin- and destination-specific mass
(Marrocu and Paci 2013). lnMOBILE_O/lnMOBILE_D measures a city’s information and
communication technology (ICT) development, which can influence the penetration rate of
social media tools (including Sina Weibo) (Liu et al. 2014). lnHOTEL_D captures the extent of
tourism infrastructure in a destination (Patuelli, Mussoni, and Candela 2013). lnHIGHWAY_D
reflects the state of a destination’s road transport infrastructure (Yang and Wong 2012). In terms

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of destination attractiveness measures, we considered the three most distinguished tourist
attraction types in China: AAAAA scenic spots, which are designated by the country’s National
Tourism Administration as the highest level of scenic spots in the country (Hong, Ma, and Huan
2015); national parks (Yang and Wong 2012); and world heritage sites (Patuelli, Mussoni, and
Candela 2013; Yang, Lin, and Han 2010). We also included two dummy variables to capture the
city hierarchy in the Chinese political system: MUNICIPALITY_D and PROV_CAPITAL _D.
Cities in upper levels of the hierarchy receive significant resources from the central government,
which likely affects their infrastructure. Lastly, we considered four geo-spatial variables. Among
them, lnDISTANCE, as a classical variable in the gravity model, captures the geographical
separation between origins and destinations (Morley, Rosselló, and Santana-Gallego 2014); it
can also be used as a proxy for personal road transport connectivity (Bilotkach, Fageda, and
Flores-Fillol 2010). Another geo-spatial variable, lnCD_D, measures the competing destination
(CD) effect in the gravity model, which represents spatial competition vs. spatial agglomeration
(Yang, Fik, and Zhang 2013). If d denotes the distance between two cities, the CD index of
destination j relative to origin i is calculated as:
 =
 

 (4)
Additionally, SAME_PROV indicates within-province tourism to control for border/boundary
effects on tourism (Smith and Xie 2003), and NEIGHBOR represents tourism between
neighboring cities (Fourie and Santana-Gallego 2011).

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Table 1 also presents the statistical summary and collinearity diagnostics of independent
variables in the NB gravity model. Large variations are observed for most variables. Regarding
collinearity diagnostics, the largest variance inflation factor (VIF) value is 2.93 across major
independent variables. The VIF values of all variables are much lower than the cutoff value of
10, and their tolerance values are greater than 0.2, suggesting the absence of multi-collinearity
(Dormann et al. 2013).
Empirical Results
In Table 2, we present the estimation results of the NB models. In Model 1, our benchmark
model, we considered two general transport connectivity variables, lnFLIGHTS (air transport
connectivity) and lnTRAINS (rail transport connectivity), and the control variables. The
likelihood ratio test suggests the NB model is superior to the Poisson model in addressing over-
dispersion issues. The coefficient of a naturally logged independent variable can be directly
interpreted as its elasticity (Long and Freese 2006). The coefficients of lnFLIGHTS and
lnTRAINS are estimated to be 0.386 and 0.275, respectively, indicating that for all city-pairs, a
1% increase in air connectivity (as measured by the number of scheduled passenger flights) and
rail connectivity (as measured by the number of scheduled passenger trains) leads to a 0.386%
and 0.275% increase in tourist flows between cities, respectively. Therefore, in general, the data
demonstrate that air transport is more important than rail transport in generating inter-city tourist
flows.
For our control variables, two origin-specific variables, lnGDP_O (origin GDP) and
lnMOBILE_O (mobile user per capita in origin), are estimated to be positive and significant. The
magnitudes of these two coefficients are larger than those of their destination-specific

20
counterparts, lnGDP_D (destination GDP) and lnMOBILE_D (mobile user per capita in
destination), both of which are also statistically significant and positive. Interestingly, the
coefficient of lnMOBILE_O is close to 1. This result can be partially attributed to the social
media data we used to construct our inter-city tourist movement matrix; the ICT penetration rate
on the origin side, proxied by lnMOBILE_O, is highly associated with the popularity of social
media use among origin residents. With regard to other destination-side variables, the significant
coefficient of lnHOTELS_D (number of star-rated hotels) indicates that hotel infrastructure plays
a significant role in explaining dyadic tourist flows.
(Please insert Table 2 about here)
All our attraction variables—lnA5_D (AAAAA attractions), lnNP_D (national parks), and
lnWHS_D (world heritage sites)—are estimated to be significant and positive, suggesting that
domestic tourists travel to certain destinations to see these attractions. Based on the magnitudes
of their coefficients, we find that national parks and world heritage sites (lnWHS_D) are more
attractive than AAAAA scenic spots (lnA5_D) to domestic tourists. For the two city hierarchical
indicators, MUNICIPALITY_D is positive and moderately significant at the 0.10 level, whereas
PROV_CAPITAL_D is positive and significant at the 0.01 level. These results show that
provincial capitals tend to attract twice (exp(0.789)) as many tourists as other cities, all other
variables remaining constant. Moreover, the significant and negative coefficient of lnCD_D
highlights cross-destination competition losses rather than agglomeration gains in inter-city

21
tourist flows. The coefficient of lnDISTANCE, the traditional distance decay coefficient in the
gravity model, is estimated to be -0.866 and statistically significant. The political and physical
linkage variables, WITHIN_PROV and NEIGHBOR, are both positive and significant.
Compared to other city-pairs, tourist flows are six times higher (exp(1.808)) between cities
within the same province and three times higher (exp(1.122)) between bordering cities, all other
variables held constant.
Model 2 includes two additional variables, lnFLIGHTS_INDIRECT and
lnTRAINS_INDIRECT, which allow for one stop-over connection between two cities via air and
rail, respectively. Both variables are estimated to be positive and significant. However, their
magnitudes are considerably smaller than their direct connectivity counterparts. In Model 3, we
include an interaction term, lnFLIGHTS*lnTRAINS, to capture the inter-modal competition
effect between air and rail transport. Its coefficient is estimated to be negative and significant,
highlighting the fierce competition between air and rail transport in shaping domestic tourist
flows in China. In Model 4, we distinguish between two different types of rail transport
connectivity: through en-route trains (lnTRAINS_EN_ROUTE) and through originating and/or
terminating trains (lnTRAINS_OT). Both coefficients are positive and significant; however, that
of lnTRAINS_OT is almost five times larger than that of lnTRAINS_EN_ROUTE,
demonstrating that for each city-pair, trains originating from the origin and/or terminating at the
destination boost tourist flows much more effectively than en-route service between two cities.
One reason for this is that CRC usually assigns many more seats at originating and terminating
stations than at others, therefore limiting en-route stations’ transport capacity.

22
In Model 5, we compare the effects of three major types of trains: ordinary passenger trains
(lnTRAINS_ORDINARY), HSR trains (lnTRAINS_HSR), and bullet trains
(lnTRAINS_BULLET). The coefficient of lnTRAINS_ORDINARY is the largest, indicating
that compared to HSR and bullet trains, the connectivity of ordinary passenger trains dominates
inter-city tourist flows in China. The coefficient of lnTRAINS_BULLET is also positive and
significant. However, that of lnTRAINS_HSR is insignificant, which may lead to questions
regarding the effectiveness of the national HSR network in boosting tourist flows. This result is
consistent with Albalate and Fageda (2016)’s finding about the limited impact of HSR on
tourism demand in Spain. Lastly, the signs, magnitudes, and significance levels of the estimated
coefficients are largely consistent across control variables in Models 1–5, suggesting robustness
of the results with regard to model specifications.
Figure 4 summarizes the overall effects of different types of transport connectivity (estimated
coefficients of measures and their corresponding 95% confidence intervals) on inter-city tourist
flows as depicted in Models 1–5. All effects except for those of HSR trains are statistically
significant and positive. By comparing their magnitudes, we find that (1) the effect from indirect
transport connectivity is consistently small for air flights and trains, and (2) connectivity between
originating/terminating trains has the largest effect in spurring domestic tourist movement
between cities.
(Please insert Figure 4 about here)

23
As noted in previous literature, transport connectivity effects depend heavily on travel distance
and time (Givoni and Dobruszkes 2013; Bilotkach, Fageda, and Flores-Fillol 2010). Therefore,
we estimated the distance-based effects of different transport connectivity measures using their
interactions with a set of distance-interval dummy variables. Due to space limitations, we present
these results in the Appendix. Figure 5 demonstrates how the effects vary over distance. The
distance-based coefficient of lnFLIGHTS generally increases over distance, suggesting that air
transport connectivity becomes more important as travel distance increases, especially for long-
haul travel. Similarly, Figure 5 shows that the effect of rail transport connectivity (lnTRAINS)
increases over distances smaller than 2,600 km, and its magnitude is close to that of air transport
connectivity (lnFLIGHTS). However, for travel distances over 2,800 km, the effect of rail
transport declines sharply, implying that air transport is particularly important for long-haul
domestic tourists. Regarding the distance-based effects of HSR and bullet trains, Figure 5
indicates that the effect of HSR trains is strongest for travel distances between 1,800 and 2,000
km, while that of bullet trains is strongest for distances between 400 and 600 km. These results
corroborate Fu, Zhang, and Lei (2012)’s argument that Chinese HSR services are competitive in
city-pairs of short to medium distances. Results for the distance-based effect of ordinary trains
are similar to those for all types of trains (lnTRAINS), and they are available upon request.
(Please insert Figure 5 about here)

24
We estimated additional models to understand how inter-modal air-rail competition varies over
different distance intervals; these results are presented in the Appendix. Specifically, we
examined the competition effect between transport modes via interactions of inter-transport
competition measures and distance-interval dummy variables. Figure 6 shows that competition
effects change over distance as suggested by our estimation results. Notably, air-rail competition
(lnFLIGHTS*lnTRAINS) generally increases over distance. The most intense air-HSR
competition (lnFLIGHTS*lnTRAINS_HSR) exists for distances between 1,400 and 1,600 km,
whereas the most intense air-bullet train competition (lnFLIGHTS*lnTRAINS_BULLET) occurs
for distances between 1,200 and 1,400 km.
(Please insert Figure 6 about here)
Conclusion
In this study, we used geo-tagged Sina Weibo posts to track inter-city domestic tourist flows
between Chinese cities during the 2014 National Day Golden Week. Using a gravity model, we
investigated how air and rail transport connectivity shapes inter-city tourist flows. Our results
suggest that both types of transport play a significant role in strengthening linkages between
cities in terms of tourist arrivals, with significant indirect connectivity effects that are smaller in
magnitude. Among different connectivity measures by train type, we found that originating
and/or terminating trains are much more influential than en-route trains in boosting tourist flows,
and ordinary trains facilitate inter-city tourist flows more than bullet and HSR trains. The
impacts of transport connectivity and air-rail competition vary based on origin-to-destination
distance. While the effect of air transport connectivity strengthens as city-pair distance increases,
the effects of HSR and bullet trains are significant only for city-pairs with short and medium

25
distances between them. Lastly, the results show that the air-rail inter-modal competition effect
generally becomes more intense as distance increases.
Across different rail transport connectivity measures, we found that connectivity by ordinary
trains had the largest impacts on inter-city tourist flows. This result can be explained by the
substantial scope and size of the ordinary train network in China. Only when a transport network
achieves a certain size can it benefit the local tourism industry. As shown in the supplementary
materials, China’s HSR and bullet train networks remained relatively underdeveloped in 2014,
covering a small number of cities. Our results indicated that the impact of HSR connectivity is
insignificant overall and only moderately significant at a few middle-distance intervals. One
possible explanation is that the HSR system was designed as a node-to-node transport mode to
serve prime urban areas and may not fully address tourists’ transportation needs (Albalate and
Fageda 2016), thereby limiting its impact. Also, high prices may hinder HSR’s recognition as a
competitive transport option because mass tourists tend to be more sensitive to price than to time
(Albalate and Fageda 2016), especially in emerging markets.
Our study made several theoretical contributions to the current knowledge of tourist flows. First
and foremost, we investigated the transport-tourism relationship from a dyadic perspective.
Unlike previous studies capturing transport effects by measuring destination-specific transport
infrastructure investment/stock (e.g., Khadaroo and Seetanah 2007, 2008), we looked into the
transport connectivity between pairwise origin and destination. It has been known that the
investment and improvement of transport infrastructure per se do not necessarily improve the
public transport connectivity. For example, airlines or rail service providers may arrange fewer

26
scheduled services due to regional transport regulations, logistical issues and considerations on
network optimization with origins (Starkie 2012) despite superior destination-specific transport
infrastructure. In many cases, it is the transport connectivity that imposes a more direct and
instant effect on tourist flows. Second, we proposed and empirically confirmed the inter-mode
competition between air and rail transport, which was largely untapped in the previous tourism
literature. This competition effect highlights a substitution pattern between these two public
transport modes from a demand perspective, and an over-investment on transport may lead to a
decreasing return of benefits. Third, our study is among the very few empirical studies
scrutinizing the distance-based transport effects: how the effect of transport connectivity varies at
different travel distances of tourists. Although these effects have been recognized in various
conceptual studies (Prideaux 2000), none has empirically confirmed them. Our results on these
distance-based effects would help academics and practitioners better understand the nature and
geographic scope of transport effect on tourist flows. A one-fit-all specification of transport
effect in empirical models can be problematic, and a spatial-domain should be incorporated when
evaluating the effects of different transport modes.
Another important contribution of this study is that it demonstrated social media’s usefulness in
tracking domestic tourism travel patterns. Compared to traditional tourist flow data gathered
from aggregated, static official statistics and small-scale surveys (Hawelka et al. 2014), social
media data provide more precise and dynamic information (Wu, Wang, and Dai 2016). To date,
many big data analytic applications in tourism are exploratory by nature (Pan and Yang 2016). In
this study, however, we used big data to conduct explanatory research with sound theoretical

27
underpinnings. Using social media data, especially geo-tagged posts, as a digital footprint
measure, future applications of this data at various geographic scales could be quite promising.
Last but not least, our study represents the very first research effort investigating inter-
regional/city domestic tourist flows within China. With the estimates of origin- and destination-
specific independent variables in gravity models, we are able to better understand the underlying
factors shaping the domestic tourist flow pattern. Because of the statistical inter-linkage between
count data models (macro-level) and discrete choice models (micro-level) (Schmidheiny and
Brülhart 2011), our estimates can also be used as tuning parameters for tourist flow simulation at
a micro-level based on individual tourists (Nicholls, Amelung, and Student 2017).
To fully capitalize on the positive effects of transport connectivity, local tourism administrative
units should work closely with public transport service providers to encourage and fulfill tourism
demand. For instance, tourism administrators and stakeholders are recommended to participate
actively at various levels of transport planning projects to improve overall transport connectivity
to origin destinations, particularly those with significant opportunities to expand the market
share. Instead of normal en-route trains, tourism administrators of a destination should schedule
more trains on which the destination is the originating/terminating stop. Second, some tourism
marketing campaigns should be directed specifically to public transport passengers, such as by
playing destination advertisements on trains and planes, providing tourism-themed food and
beverage, and naming trains or planes after major destination attractions.

28
In a similar vein, our results on inter-modal transport competition and the distance-based effects
of transport connectivity have several important implications for local tourism authorities when it
comes to collaborating with transport service providers to improve destination competitiveness.
First, given the competition between air and rail transport, redundant investments should be
avoided in long-term strategic plans related to transport infrastructure development. Second, for
major markets at different distance intervals, diverse transport modes should be promoted to
maximize benefits. Our findings also provide important insights for destinations, railway
companies, and airlines in identifying their target markets and allocating marketing resources
based on geographic locations and distance. For example, markets 1,600 to 2,000 km away from
a destination, which are also accessible via HSR, should be prioritized when setting strategies.
Further, the results of this study suggest that Chinese destinations and tour operators should not
underestimate the role of ordinary trains in encouraging domestic tourism.
For planning purposes, these findings can be used in scenario analyses when transport
connectivity changes. Local tourism authorities may use our estimated parameters to identify the
competitive advantages and disadvantages of new transport plans and to project future tourist
flows in the short and long term. For example, several new HSR routes were proposed in the
Chinese Mid- to Long-Term Railway Network Plan (2016–2025). Destinations can use our
model to evaluate the potential benefits associated with HSR expansion to other markets. Then,
marketing campaigns can be tailored to maximize said benefits. Our results can also be used to
conduct a cost-benefit analysis of airport construction plans from a tourism perspective to
determine appropriate capacity for a new airport. Our findings provide several important

29
parameters for simulation analysis, such as those related to distance decay and the competing
destination effect. It is therefore possible to construct a gravity-type accessibility measure for
geo-marketing (Borodako and Rudnicki 2014) and to use agent-based modeling to monitor an
individual tourist’s behavior as an agent (Nicholls, Amelung, and Student 2017).
Lastly, the estimation results of some control variables have useful implications for local tourism
administrative units and marketing organizations. Our results highlight strong territory effects
associated with provincial borders that impede domestic tourist flows. Minimizing the
disadvantages stemming from these territory effects requires tactical collaboration between
destinations in different provinces. Moreover, for destinations and attractions employing geo-
fencing strategies to lure out-of-province tourists (i.e., delivering targeted messages or
advertisements to mobile users who enter or leave a location), this finding could be instrumental
in radius setting, audience targeting, and content design. Moreover, our results suggest that
national parks and world heritage sites are more attractive than AAAAA scenic spots to domestic
tourists. Therefore, effectively embracing positive spillovers from these two types of attractions
is essential to strengthening an area’s overall competitiveness as a tourist destination.
Our study did have some limitations. Weibo data constraints prevented us from tracking within-
city domestic flows. This issue may be particularly detrimental for municipalities like Beijing
and Shanghai, which have significantly larger land areas and populations than other cities. Also,
due to data unavailability, we were unable to conduct a longitudinal analysis to examine the

30
effects of transport connectivity over time. Moreover, because tourist flows may also partially
determine transport connectivity, endogeneity problems may be a concern in our empirical
model. Lastly, because of spillover effects, tourist flows to a city can be influenced by tourist
flows to neighboring cities (Marrocu and Paci 2013); our gravity model is unable to incorporate
this type of spatial dependence. Therefore, in the future, researchers should collect panel data
sets and apply spatial econometric models to better investigate tourist flows as a type of spatial
interaction.
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Table 1. Descriptive statistics and collinearity diagnostics of independent variables.
Variable
Definition
Mean
Std.
Dev.
VIF
Toler
ance
lnFLIGHTS
log of one plus the number of scheduled passenger flights between origin and destination each week
0.074
0.460
1.19
0.840
lnTRAINS
log of one plus the number of scheduled passenger trains between origin and destination each day
0.296
0.685
1.43
0.698
lnFLIGHTS_INDIRECT
log of one plus the number of indirect scheduled passenger flights (with one stop-over) between
origin and destination each week
1.471
2.886
lnTRAINS_INDIRECT
log of one plus the number of indirect scheduled passenger trains (with one stop-over) between
origin and destination each day
3.860
3.174
lnTRAINS_EN_ROUTE
log of one plus the number of scheduled en-route passenger trains between origin and destination
each day
0.255
0.635
lnTRAINS_OT
log of one plus the number of scheduled passenger trains between origin and destination with the
origin as the originating station and/or the destination as the terminal stop each day
0.068
0.321
lnTRAINS_ORDINARY
log of one plus the number of scheduled ordinary passenger trains between origin and destination
each day
0.260
0.606
lnTRAINS_HSR
log of one plus the number of scheduled HSR passenger trains between origin and destination each
day
0.051
0.329
lnTRAINS_BULLET
log of one plus the number of scheduled bullet passenger trains between origin and destination each
day
0.035
0.263
lnGDP_O
log of origin city GDP (in 10,000 RMB) in 2013
6.982
1.090
1.38
0.726
lnMOBILE_O
log number of registered mobile phone users relative to the total population in origin in 2013
-0.185
0.491
1.29
0.774
lnGDP_D
log of destination city GDP (in 10,000 RMB) in 2013
6.982
1.090
2.93
0.341
lnMOBILE_D
log number of registered mobile phone users relative to the total population in destination in 2013
-0.185
0.491
1.52
0.657
lnHIGHWAY_D
log of a destination’s highway density (km per km2) in 2013
-0.452
0.910
2.79
0.358
lnA5_D
log of one plus a destination’s number of AAAAA scenic spots in 2014
0.307
0.450
1.68
0.596
lnNP_D
log of one plus the destination’s number of national parks in 2014
0.387
0.474
1.36
0.736
lnWHS_D
log of one plus the destination’s number of world heritage sites in 2014
0.118
0.280
1.32
0.759
lnHOTEL_D
log of a destination’s number of star rated hotels in 2013
3.327
0.924
2.31
0.433
MUNICIPALITY_D
dummy indicator of four direct-controlled municipalities in China as destinations; 1=destination cities
Beijing, Shanghai, Tianjin, or Chongqing, and 0= otherwise
0.012
0.107
1.25
0.798
PROV_CAPITAL_D
dummy indicator of provincial capital cities as destinations; 1 = capital city of a province as
destination, and 0 = otherwise;
0.079
0.269
1.34
0.747
lnCD_D
destination’s competing destination (CD) index in 2014
-1.104
0.840
2.78
0.360
lnDISTANCE
log of the geographic distance (in km) between origin and destination
7.049
0.701
2.40
0.417
SAME_PROV
dummy indicator for travel within a single province; 1 = origin and destination cities within the same
province, and 0 = otherwise;
0.038
0.190
1.49
0.671
NEIGHBOR
dummy indicator for travel to a neighboring destination; 1 = neighboring origin and destination cities
sharing a land border, and 0 = otherwise.
0.015
0.123
1.44
0.695

Table 2. Estimation results of NB models with basic specifications.
Model 1
Model 2
Model 3
Model 4
Model 5
lnFLIGHTS
0.386***
0.215***
0.486***
0.353***
0.386***
(0.014)
(0.015)
(0.018)
(0.014)
(0.014)
lnTRAINS
0.275***
0.254***
0.295***
(0.010)
(0.013)
(0.010)
lnFLIGHTS_INDIRECT
0.0749***
(0.003)
lnTRAINS_INDIRECT
0.0187***
(0.004)
lnFLIGHTS*lnTRAINS
-0.104***
(0.010)
lnTRAINS_EN_ROUTE
0.114***
(0.011)
lnTRAINS_OT
0.557***
(0.019)
lnTRAINS_ORDINARY
0.285***
(0.012)
lnTRAINS_HSR
-0.0115
(0.020)
lnTRAINS_BULLET
0.139***
(0.023)
lnGDP_O
0.490***
0.462***
0.490***
0.480***
0.493***
(0.015)
(0.015)
(0.015)
(0.015)
(0.015)
lnMOBILE_O
0.992***
0.942***
0.988***
0.942***
0.998***
(0.022)
(0.023)
(0.022)
(0.022)
(0.022)
lnGDP_D
0.403***
0.361***
0.402***
0.402***
0.406***
(0.015)
(0.017)
(0.015)
(0.015)
(0.015)
lnMOBILE_D
0.226***
0.242***
0.224***
0.229***
0.233***
(0.023)
(0.023)
(0.023)
(0.023)
(0.023)
lnHIGHWAY_D
-0.0812***
-0.0317*
-0.0825***
-0.0828***
-0.0816***
(0.019)
(0.019)
(0.019)
(0.019)
(0.019)
lnA5_D
0.176***
0.157***
0.177***
0.181***
0.177***
(0.028)
(0.031)
(0.029)
(0.028)
(0.028)
lnNP_D
0.270***
0.226***
0.268***
0.275***
0.272***
(0.022)
(0.021)
(0.022)
(0.022)
(0.022)
lnWHS_D
0.235***
0.280***
0.237***
0.234***
0.230***
(0.035)
(0.035)
(0.035)
(0.035)
(0.035)
lnHOTEL_D
0.241***
0.203***
0.241***
0.242***
0.240***
(0.016)
(0.016)
(0.016)
(0.016)
(0.016)
MUNICIPALITY_D
0.142*
0.177**
0.136
0.167**
0.141
(0.086)
(0.090)
(0.086)
(0.084)
(0.086)
PROV_CAPITAL_D
0.789***
0.691***
0.790***
0.814***
0.786***
(0.035)
(0.035)
(0.035)
(0.034)
(0.035)

lnCD_D
-0.190***
-0.181***
-0.188***
-0.193***
-0.188***
(0.020)
(0.019)
(0.020)
(0.020)
(0.020)
lnDISTANCE
-0.866***
-0.873***
-0.863***
-0.883***
-0.872***
(0.020)
(0.020)
(0.020)
(0.020)
(0.020)
SAME_PROV
1.808***
1.817***
1.809***
1.763***
1.809***
(0.041)
(0.041)
(0.041)
(0.040)
(0.041)
NEIGHBOR
1.122***
1.065***
1.120***
1.101***
1.122***
(0.139)
(0.126)
(0.140)
(0.136)
(0.140)
CONSTANT
-1.234***
-0.701***
-1.260***
-1.050***
-1.223***
(0.211)
(0.207)
(0.212)
(0.209)
(0.212)
lnα
1.604***
1.588***
1.602***
1.598***
1.604***
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
N
117306
117306
117306
117306
117306
pseudo R-sq
0.137
0.139
0.137
0.138
0.137
AIC
345013.4
344265.5
344956.9
344742.7
345031.2
BIC
345197.2
344468.6
345150.4
344936.1
345234.3
(Note: *** indicates significance at 0.01, ** indicates significance at 0.05, * indicates significance
at 0.1. Robust standard errors are presented in parentheses.)

(a)
(b)
Figure 1. Log-log scatterplots of aggregate geo-tagged Weibo counts against tourism statistics

Figure 2. Spatial pattern of inter-city tourist flows based on Weibo data

Figure 3. Histogram of scheduled flights and trains for different distances

Figure 4. Effects of different types of public transport connectivity on domestic tourist flows

Figure 5. Effect of different transport connectivity measures by distance

Figure 6. Effect of different inter-modal transport competition measures by distance

Supplementary Materials (online and available in article webpage)
Figure S1. Spatial pattern of inter-city air flight connection in National Day Week, 2014

Figure S2. Spatial pattern of inter-city train connection in National Day Week, 2014

Figure S3. Spatial pattern of inter-city Bullet Train connection in National Day Week, 2014

Figure S4. Spatial pattern of inter-city HSR train connection in National Day Week, 2014