Applying Big Data Analytics to Monitor Tourist Flow for the Scenic Area Operation Management

Qin, Siyang; Man, Jie; Wang, Xuzhao; Li, Can; Dong, Hui; Ge, Xiaojia

doi:10.1155/2019/8239047

Cited by 35 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advancements in spatial big data and associated analytics have allowed for more granular level consumer behaviour data to be collected and analyzed. The retail, tourism, management, and health care industries have started to experience significant changes in their informatics and decision-making as a result of this data evolution [14,15,16,17,18,19,20,21,22,23]. Spatial big data are becoming a more prominent research area [24,25,26,27].…”

Section: Mobile Location Datamentioning

confidence: 99%

The effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York

Aversa¹,

Quach²,

Hernández³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

The declaration of the COVID-19 pandemic and the resulting lockdowns brought focus on the importance of the retail sector for community well-being. The restrictive government policies that were put into place to curb the spread of COVID-19 virus added pressure on retailers to adapt to the subsequent changes in consumption. This research, using a case study of Erie County in the State of New York (NY), investigates these changes in visitation patterns for a commercial service sector that was deemed 'essential' -food and beverage. This study uses mobile location data to identify variations in shopping patterns for independent and chain stores. The study finds that by comparing the pre-pandemic to pandemic, there were changes to visitation patterns over time and between retail types. While the study highlights the potential to use mobile data to study shifts in consumption behaviours, the paper also reveals several challenges in using such data. Research Context COVID-19 Timeline in Erie County

show abstract

Section: Mobile Location Datamentioning

confidence: 99%

The effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York

Aversa¹,

Quach²,

Hernández³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…and Lunke, E.B. 2018;Qin, S. et al 2019). Therefore, in the methodological part, we do not present the technological background of mobile network systems and only introduce the methodology of our specific research.…”

Section: Analysis Of Urban Mobile Use Based On Cell Datamentioning

confidence: 99%

Urban geographical patterns of the relationship between mobile communication, social networks and economic development – the case of Hungary

Egedy

Ságvári

2021

HunGeoBull

View full text Add to dashboard Cite

In the post-industrial age, the transformation of urban networks and urban regions was fundamentally influenced by the rapid spread of infocommunication technologies (ICT) and the Internet. People share information in their daily lives with the help of various ICT devices and ultimately generate georeferenced data that could obtain important information about people’s use of space, spatial movement and social connections. The main aim of the study is to explore the urban geographical and spatial impacts of ICT and social media networks in Hungarian cities. We focus on drawing territorial and settlement hierarchical patterns and clusters based on the mobile communication and online social network relationship data of Hungarian cities. The paper highlights the relationship between the intensity of mobile communication and the density and expansion of intercity social relations and the settlements’ level of economic development, respectively. The methodology is based on mobile phone call detail record (CDR) analysis and intercity network analysis of social media activities. Our findings suggest that different communication networks follow divergent spatial patterns in Hungary. The traditional East–West dichotomy of the Hungarian spatial divide is still reflected in mobile communication, but intercity clusters based on social media activities are usually aligned to the borders of administrative structures. In several cases, we were able to identify strong intercity links between settlements with a similar level of economic development of the mesolevel spatial structure that traverses over different counties and regional borders. Results on social and demographic issues suggest that ‘generation Z’ could play a key role in dampening the social and economic tensions created by the digital divide in the long run. Using a multidimensional explanatory model, we could demonstrate the growing interconnectedness between digital networks and economic development.

show abstract

“…The integration of big data and IoT technologies has been applied in many areas, including smart environment applications for monitoring, protecting, and improving natural resources 14 and smart tourism for monitoring tourist flow. 15 The catering industry is no exception. Researchers can have instant access to online catering data, for example, locations, reviews, evaluation scores, and transaction data.…”

Section: Data Analytics and Its Implications In The Catering Industrymentioning

confidence: 99%

Big Data Applications in Guangzhou Restaurants Analysis

Chang

2021

Big Data

View full text Add to dashboard Cite

With the development of modern information and communication technologies, such as the internet of things and big data analytics, businesses and users have become more adaptable to rapid changes. Both consumers and merchants have obtained great convenience. Meanwhile, a huge amount of data is generated. However, many businesses lack the ability to process these data, which contain critical business values. Therefore, this article uses data from the Dianping website to show how to use big data analytics techniques to exploit the valuable information from these raw data. First, descriptive analysis is conducted by using kernel density estimation. Then, multilinear regression analysis, Naive Bayes, and J48 are used to predict the level of restaurants. We found that flavor, environment, and service score are essential factors to the restaurant level. Moreover, J48 performs best among the three models with an accuracy of 88.89%.

show abstract

Applying Big Data Analytics to Monitor Tourist Flow for the Scenic Area Operation Management

Cited by 35 publications

References 13 publications

The effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York

The effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York

Urban geographical patterns of the relationship between mobile communication, social networks and economic development – the case of Hungary

Big Data Applications in Guangzhou Restaurants Analysis

Contact Info

Product

Resources

About