The Missing Parts from Social Media–Enabled Smart Cities: Who, Where, When, and What?

Yuan, Yihong; Lu, Yongmei; Chow, Theodore; Ye, Chao; Alyaqout, Abdullatif; Liu, Yu

doi:10.1080/24694452.2019.1631144

Cited by 44 publications

(22 citation statements)

References 31 publications

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“…Second, significantly unbalanced demographic structures cause deviations. Since young adults have greater presences in mobile networks [59], [60], recorded activity magnitudes tend to increase in regions with greater proportions of young people (Fig. 9 (i)) or rapidly growing cities such as Shenzhen.…”

Section: Constraining Factorsmentioning

confidence: 99%

Mapping Human Activity Volumes Through Remote Sensing Imagery

Xing

Huang

Cheng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The spatial concentration of human activity is a crucial indication of socioeconomic vitality. Accurately mapping activity volumes is fundamental to support regional sustainable development. Current approaches rely on mobile positioning data, which record information about human daily activity but are inaccessible in most cities due to privacy and data sharing concerns. Alternative methods are needed to provide more generalized predictions on extensive areas while maintaining low cost. This study demonstrates how remote sensing imagery can be used through an end-to-end deep learning framework for reliable estimates of human activity volumes. The neighbour effect, representing the inherent nature of spatial autocorrelation in the volumes, is incorporated to improve the network. The proposed model exhibits strong predictive power and demonstrates great explainability of physical environment on variations of activity volumes. Landscape interpretations based on hierarchical features provide both object-based and region-based insights into the co-evolvement of landscape and human activity. Our findings indicate the possibility of extensively predicting activity volumes, especially in areas with limited access to mobile data, and provide support for the promising framework to better comprehend broad aspects of human society from observable physical environments.

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Section: Constraining Factorsmentioning

confidence: 99%

Mapping Human Activity Volumes Through Remote Sensing Imagery

Xing

Huang

Cheng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…In addition, as a typical social media data source, the data bias of geo-located travel blogs should be given more attention in further studies. An ideal data bias evaluation of social media should be carried out from four perspectives, who, where, when, and what, but there is still a lack of solutions to fully address or quantify these perspectives ( Yuan et al, 2019 ). The above shortcoming merits further empirical research and methodological evaluations.…”

Section: Discussionmentioning

confidence: 99%

Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data

Zheng

Mou

Zhang

et al. 2021

Computers, Environment and Urban Systems

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Geo-located travel blogs, a new data source, enable to achieve more detailed analysis of tourists' spatio-temporal behavior. Taking Chinese tourists in Nordic countries as the research object, this paper focuses on their behavior, seasonal patterns and complex network effects by using geo-located travel blog data collected from Qunar.com . The results show that: (1) Chinese tourists visiting Nordic countries are often experienced in traveling. The local climate during the cold season does not prevent them from pursuing the aurora scenery. (2) The travel behavior of Chinese tourists is spatially heterogeneous. The network analysis reveals that Iceland showcases stronger, compared to the other Nordic countries, community independence and small world effect. (3) During the warm season, Chinese tourists choose a variety of destinations, while in cold season, they tend to choose destinations with higher chances for spotting the northern lights. These results provide helpful information for the tourism management departments of Nordic countries to improve their marketing and development efforts directed for Chinese tourists.

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“…As with many social media data sources [59] We have collected Yelp data for the city of Toronto, which we use as a case study for developing methods that should be applicable elsewhere. The dataset includes approximately 24,000 businesses and 500,000 reviews.…”

Section: Datamentioning

confidence: 99%

Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change

Olson

Calderon‐Figueroa

Bidian

et al. 2021

Cities

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This paper develops novel methods for using Yelp reviews as a window into the collective representations of a city and its neighbourhoods. Basing analysis on social media data such as Yelp is a challenging task because review data is highly sparse and direct analysis may fail to uncover hidden trends. To this end, we propose a deep autoencoder approach for embedding the language of neighbourhood-based business reviews into a reduced dimensional space that facilitates similarity comparison of neighbourhoods and their change over time. Our model improves performance in distinguishing real and fake neighbourhood descriptions derived from real reviews, increasing performance in the task from an average accuracy of 0.46 to 0.77. This improvement in performance indicates that this novel application of embedded language analysis permits us to uncover comparative trends in neighbourhood change through the lens of their venues' reviews, providing a computational methodology for reading a city through its neighbourhoods. The resulting toolkit makes it possible to examine a city's current sociological trends in terms of its neighbourhoods' collective identities.

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The Missing Parts from Social Media–Enabled Smart Cities: Who, Where, When, and What?

Cited by 44 publications

References 31 publications

Mapping Human Activity Volumes Through Remote Sensing Imagery

Mapping Human Activity Volumes Through Remote Sensing Imagery

Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data

Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change

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