Extraction and analysis of city's tourism districts based on social media data. Shao, H., Zhang, Y., & Li, W. Computers, Environment and Urban Systems, 65:66–78, September, 2017. ZSCC: 0000028![Extraction and analysis of city's tourism districts based on social media data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{shao_extraction_2017,
title = {Extraction and analysis of city's tourism districts based on social media data},
volume = {65},
issn = {01989715},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0198971517302193},
doi = {10/gbt76h},
abstract = {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.},
language = {en},
urldate = {2020-08-06},
journal = {Computers, Environment and Urban Systems},
author = {Shao, Hu and Zhang, Yi and Li, Wenwen},
month = sep,
year = {2017},
note = {ZSCC: 0000028},
pages = {66--78},
}
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