TY - JOUR
T1 - Extraction and analysis of city's tourism districts based on social media data
AU - Shao, Hu
AU - Zhang, Yi
AU - Li, WenWen
N1 - Funding Information:
This work was supported by funds from the National Science Foundation of China grants 41625003.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/9
Y1 - 2017/9
N2 - Through the perspective of tourism, a city as a tourist destination usually consists of multiple tourist attractions such as natural or cultural scenic spots. These attractions scatter in city spaces following some specific forms: clustered in some regions and dispersed in others. It is known that users organize their tours in a city not only according to the distance between different attractions but also according to other factors such as time constraints, expenses, interests, and the similarities between different attractions. Hence, users' travel tours can help us gain a better understanding about the relationships among different attractions at the city scale. In this paper, a methodological framework is developed to detect tourists' spatial-temporal behaviors from social media data, and then such information is used to extract and analyze city's tourism districts. We believe that this city space division will make significant contributions to the fields of urban planning, tourism facility providing, and scenery area constructing. A typical tourism city in China—Huangshan—is selected as our study area for experiments.
AB - Through the perspective of tourism, a city as a tourist destination usually consists of multiple tourist attractions such as natural or cultural scenic spots. These attractions scatter in city spaces following some specific forms: clustered in some regions and dispersed in others. It is known that users organize their tours in a city not only according to the distance between different attractions but also according to other factors such as time constraints, expenses, interests, and the similarities between different attractions. Hence, users' travel tours can help us gain a better understanding about the relationships among different attractions at the city scale. In this paper, a methodological framework is developed to detect tourists' spatial-temporal behaviors from social media data, and then such information is used to extract and analyze city's tourism districts. We believe that this city space division will make significant contributions to the fields of urban planning, tourism facility providing, and scenery area constructing. A typical tourism city in China—Huangshan—is selected as our study area for experiments.
KW - Social media
KW - Spatial-temporal behavior
KW - Tourism district
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U2 - 10.1016/j.compenvurbsys.2017.04.010
DO - 10.1016/j.compenvurbsys.2017.04.010
M3 - Article
AN - SCOPUS:85019618886
VL - 65
SP - 66
EP - 78
JO - Computers, Environment and Urban Systems
JF - Computers, Environment and Urban Systems
SN - 0198-9715
ER -