scholarly journals Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

2019 ◽  
Vol 8 (7) ◽  
pp. 314 ◽  
Author(s):  
Qiushi Gu ◽  
Haiping Zhang ◽  
Min Chen ◽  
Chongcheng Chen
Keyword(s):  
Big Data ◽  
Concentration Index ◽  
Driving Forces ◽  
Regional Differentiation ◽  
Geographical Concentration ◽  
Source Regions ◽  
Short Period ◽  
Tourist Flows ◽  
Travel Rate ◽  
Source And Sink

At present, population mobility for the purpose of tourism has become a popular phenomenon. As it becomes easier to capture big data on the tourist digital footprint, it is possible to analyze the respective regional features and driving forces for both tourism sources and destination regions at a macro level. Based on the data of tourist flows to Nanjing on five short-period national holidays in China, this study first calculated the travel rate of tourist source regions (315 cities) and the geographical concentration index of the visited attractions (51 scenic spots). Then, the spatial autocorrelation metrics index was used to analyze the global autocorrelation of the travel rates of tourist source regions and the geographical concentration index of the tourist destinations on five short-term national holidays. Finally, a heuristic unsupervised machine-learning method was used to analyze and map tourist sources and visited attractions by adopting the travel rate and the geographical concentration index accordingly as regionalized variables. The results indicate that both source and sink regions expressed distinctive regional differentiation patterns in the corresponding regional variables. This study method provides a practical tool for analyzing regionalization of big data in tourist flows, and it can also be applied to other origin-destination (OD) studies.

scholarly journals Correction: Gu, Q., et al. Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows. ISPRS Int. J. Geo-Inf. 2019, 8, 314

2020 ◽  
Vol 9 (1) ◽  
pp. 35
Author(s):  
Qiushi Gu ◽  
Haiping Zhang ◽  
Min Chen ◽  
Chongcheng Chen
Keyword(s):  
Tourist Flows ◽  
Source And Sink

The authors wish to make the following corrections to their paper [...]

scholarly journals Use cases and challenges in telecom big data analytics

2016 ◽  
Vol 5 ◽  
Author(s):  
Chung-Min Chen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Driving Forces ◽  
Big Data Analytics ◽  
Use Cases ◽  
Telecommunication Industry ◽  
New Research

This paper examines the driving forces of big data analytics in the telecom domain and the benefits it offers. We provide example use cases of big data analytics and the associated challenges, with the hope to inspire new research ideas that can eventually benefit the practice of the telecommunication industry.

scholarly journals Innovation and Development of Rural Leisure Tourism Industry Using Mobile Cloud IoT Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guangwei Wang
Keyword(s):  
Rural Areas ◽  
Rural Economy ◽  
Concentration Index ◽  
Tourism Industry ◽  
Security Requirements ◽  
Management Policy ◽  
Mobile Cloud ◽  
Chinese Market ◽  
Rapid Improvement ◽  
Geographical Concentration

As a promising IoT application, the rural leisure tourism industry can promote the reconstruction of industrial structure in rural areas and realize a sustainable, rapid, and healthy development of rural economy. This paper takes the rural leisure tourism industry in China as an example and aims at building an intelligent and integrated modern IoT use case. Based on the traditional rural leisure tourism, we improve the system by adding the data analysis over a mobile cloud IoT computing platform. In particular, this work investigates the characteristics of the national tourism market under the security requirements from governmental cloud data management policy. Our study shows that the geographical concentration index G of tourists in the Chinese market continues to increase. With the booming of IoT applications in rural leisure tourism, intelligent and integrated tourism guidance and optimized decision-making will provide tourists with better information and thus make rapid improvement of geographical concentration index.

scholarly journals Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data

2020 ◽  
Vol 9 (11) ◽  
pp. 666
Author(s):  
Chengming Li ◽  
Jiaxi Hu ◽  
Zhaoxin Dai ◽  
Zixian Fan ◽  
Zheng Wu
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Smart Cities ◽  
Sampling Rate ◽  
Individual Characteristics ◽  
Mobile Phone Data ◽  
Mobility Pattern ◽  
Individual Mobility ◽  
Short Period ◽  
The Individual

With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers’ behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities.

CONCEPTUAL BIG DATA CASE STUDY TO IDENTIFYING RISKS OF NEW NUCLEAR TECHNOLOGIES

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jaeheum Yeon ◽  
Mark Czarny ◽  
John Walewski ◽  
Julian Kang
Keyword(s):  
Big Data ◽  
Content Analysis ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Reactor ◽  
New Technologies ◽  
Risk Identification ◽  
Visual Content ◽  
Short Period ◽  
Identification Approach

New technologies associated with nuclear power plants are being introduced regularly. However, many of the risks and uncertainties associated with these new nuclear technologies have yet to be identified. In this study, the risks related to newly-developed nuclear technologies were determined through an extensive review of the extant literature. A qualitative visual content analysis was selected as the research method employed to identify words repeatedly occurring in 147 journal articles. Through this conceptual “big data” approach, frequently mentioned words were identified using a co-occurrence map. The analysis results were then grouped into four categories: fuel resources, operational system designs, nuclear reactor cooling systems, and steam generators. Words used repeatedly to reference these four key categories were determined to also represent potential causes of risk factors. Many texts could be analyzed in a short period of time through the use of visual content analysis software. Frequently emphasized correlating words were then identified. This big data approach can also be applied to additional nuclear power practices to identify other uncertainties. Last, the limitations of a visual content analysis employed as a risk identification approach were revealed through this study.

A systemic and cybernetic perspective on causality, big data and social networks in tourism

Kybernetes ◽  
2019 ◽  
Vol 48 (2) ◽  
pp. 287-297
Author(s):  
Miguel Lloret-Climent ◽  
Andrés Montoyo ◽  
Yoan Gutierrez ◽  
Rafael Muñoz Guillena ◽  
Kristian Alonso
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Real Data ◽  
Current Data ◽  
Set Covering ◽  
Invariant Sets ◽  
Content Type ◽  
Tourist Flows ◽  
Relevant Variables ◽  
Orbit Analysis

PurposeThe purpose of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on an algorithm and applying an interpretation of chaos theory developed in the context of General Systems Theory and Big Data.Design/methodology/approachTourism is one of the most digitalized sectors of the economy, and social networks are an important source of data for information gathering. However, the high levels of redundant information on the Web and the appearance of contradictory opinions and facts produce undesirable effects that must be cross-checked against real data. This paper sets out the causal relationships associated with tourist flows to enable the formulation of appropriate strategies.FindingsThe results can be applied to numerous cases, for example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups, as well as analysing tourist behaviour in terms of the most relevant variables.Originality/valueThe technique presented here breaks with the usual treatment of the tourism topics. Unlike statistical analyses that merely provide information on current data, the authors use orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.

An early pliocene pollen assemblage from Lake Tay, south-western Australia, and its phytogeographic implications

1981 ◽  
Vol 29 (3) ◽  
pp. 277 ◽  
Author(s):  
AN Bint
Keyword(s):  
Regional Differentiation ◽  
Pollen Spectrum ◽  
Pollen Assemblage ◽  
Early Pliocene ◽  
The Past ◽  
South West ◽  
Open Forest ◽  
Short Period ◽  
Eastern Australia ◽  
Warm Temperate

Pollen assemblages indicate an Early Pliocene age for sediments in the Lake Tay area, south-west of Norseman, W.A. They also show unexpected similarities to assemblages of the same age from south-eastern Australia and suggest that regional phytogeographic differentiation of the flora of southern Australia was less pronounced in the Early Pliocene than usually supposed. This implies that considerable regional differentiation of southern Australian floras has taken place in a relatively short period, principally during the past 4 or 5 million years. Although the dominant elements in the pollen spectrum indicate a warm temperate open-forest with a lake edge or marsh component, small numbers of the pollen of Nothofagus (brassii-type) and some podocarpaceous conifers are also present. These suggest a wetter climate and may have derived from small stands surviving in refugia on high country to the east or south of Lake Tay.

scholarly journals Big Data as a Creeping Crisis

2021 ◽  
pp. 69-86
Author(s):  
Swapnil Vashishtha ◽  
Mark Rhinard
Keyword(s):  
Big Data ◽  
Driving Forces ◽  
Government Intervention ◽  
Personal Integrity ◽  
Public Attention ◽  
Horizon Scanning ◽  
Main Challenge ◽  
Private Data ◽  
Mass Accumulation

AbstractThis chapter examines the mass accumulation of private data in terms of a creeping crisis. The threat at hand—commonly referred to as “Big Data”—pertains to the direct compromising of personal integrity and safety. The chapter explores the driving forces behind this threat, identifies the precursor events or “flare-ups” of the deeper problem, and documents the varying levels of scientific, political, and public attention given to the problem. Our analysis reveals the breadth of the problem and the main challenge to managing it: societies’ deep dependence on the underlying technologies and systems. Addressing this creeping crisis will require substantial government intervention to regulate privacy and effective horizon scanning to track its many possible costs.

Sign in / Sign up

Export Citation Format

Share Document