| The study of the temporal-spatial behavior of tourists has been one of the central issues of tourism studies, but the theoretical and empirical research of temporal behavior decision of tourists, the modes of travel trajectories and network structures of tourism flow, are still lacking, especially in terms of tourist path trajectory. The data obtained by the traditional way is difficult to record the information of the temporal and spatial variation and travel trajectory comprehensively and accurately. Geographic information systems and the development of network sharing platform pro vide technical support for tracking and collecting the data of travelling of tourists. Digital cameras have been a travel standing electronic products, to record the scenery along the way While using a digital camera to take pictures, the informations of photographing time, latitude and longitude are recorded simultaneously. The appearing of the photo network sharing platforms, such as Flickr and Panoramio, can show the shared travel photos on a map. These data provided by the travellers comprises a great deal of valuable informations for the study of temporal behavior of tourists.This study determined that the scope of the study was "big Xi’an tourism circle" firstly, then the scope of "big Xi’an tourism circle" had been re-defined on the basis of traffic conditions, travel distance, terrain, scenic distribution considerations and previous research. The data of geotagged photos were drawing from Flickr and Panoramio using the relevant technology, and the scope was greater than the study area. We delimitted the final scope of the data using ArcGIS to ensure the integrity of travel trajectories of tourists.A variety of methods of spatial hotspot analysis, trace analysis, social network analysis, kernel density analysis were used to show the information of geotagged photos of "big xi’an tourism circle" in the way of visualization comprehensively and analysis, to reveal the distribution of spatial, temporal of tourists and the characteristics of path trajectories, providing important references for the measurement and controlling of tourism flow, crisis prevention, tourism marketing, traffic building, circuit design, regional integration. The main conclusions obtained from this study are as follows:(1) The tourist flow in "big xi’an tourism circle" in the seasons and the ordinary weeks were analyzed to draw that the tourism flows showed some seasonal concentration. July, August, and October were the busy seasons, February, March, November and December were the off-seasons, and January, April, May, June and September were the flat seasons. The tourist flow was "buckle spoon" type in ordinary weeks in the whole, in which the Xi’an area was "一" type; The number of visitors in the tourist areas of East and West line shows inverted "N" type; the distribution in the tourist area of Qinling was "M" type. The overall tourist flow in 6:00-11:00 was in the rapid growth phase, reached the peak at 12:00,12:00-13:00 for lunch peak,13:00-17: 00 for stabilization phase,17:00-19:00 for evacuatting,20:00-21:00 for small peak of nightlife. Four tourist areas in a day exhibited different characteristics, and the downtown of Xi’an was affected minimally by seasons, the five-day work system and the change in one day.(2) It was found that the distribution of tourists was closely related to the development of scenic spots though the analysis of the distribution of tourists, and it appeared the trend of expansion. The tourist flow expanded from spring to summer, retracted since autumn, and distributed mainly in the downtown of Xi’an. Because of the distance, the Qinling Mountains reached the peak of a day late. Visitors distributed mainly in known attractions of the downtown of Xi’an for nightlife. Most visitors chose to stay in the downtown of Xi’an for overnight and accommodations gathered near Mt.Lishan and Mt.Huashan.(3) There are three ways of travel path trajectories in "big xi’an tourism circle" single-node path trajectories, intraregional and interregional path trajectories. The single node trajectories were focused on the downtown of Xi’an, the intraregional path trajectories tended to focus on mountains such as the Qinling Mountains and the downtown of Xi’an, while the interregional path trajectories distributed mainly in the scenic spots with intensive heritage and high visibility.(4) It was found that the aggregation ability and the diffusion capacity of tourist flow was balanced for counties/districts though the analysis of the network structure of the inter-regional routes. The gathering centers of tourist flow are often the diffusion centers. The counties/districts were divided into the gathering and diffusion centers, the gathering centers, the general area and the isolated regions. Chang’an District, Beilin District, Huayin city and Huxian played an important role in the control of the diffusion of tourist flows. Beilin District, Lianhu District, Chang’an District, Yanta District, Baqiao District, New Town, Huayin City and Lintong District were more difficult controlled by other areas. The "big Xi’an tourismcircle" formed a clear core-periphery structure:Baqiao District, Beilin District, New City District, Weiyang, Lianhu District, Lintong District and Yanta District located in the core area; Lantian County, Xianyang City area, Huayin City and Chang’an District located in the second core area; and the inter-regional tourism flows formed 11 main tourist paths.The main innovation of this study included three points:1) The definition of "big xi’an tourism circle" referenced to the path trajectories of the geotagged photos as much as possible to retain complete travel trajectories of visitors; 2)Choosing a new way of geotagged photos to track the temporal-spatial behavior of tourists; 3) It showed travel trajectories of tourists using ArcGIS, and we adopted a variety of relatively new theoretical approaches to reveal the spatial and temporal distribution and metastasis of tourist flow, such as spatial hotspot analysis, trace analysis, social network analysis and nuclear density analysis. |
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