RESEARCH ON BIG DATA ANALYSIS OF LOCATION SERVICE AND INTELLIGENT SERVICE PLATFORM OF URBAN SCENIC SPOTS: A CASE STUDY OF GUILIN CITY, GUANGXI, CHINA
Authors: Li B. S. , Liao M. Z. , Huang L. L. 
Published: February 2020

Abstract. In many famous tourist cities, there is a lack of big data analysis and perception of tourist behavior, which reflected in the existence of a large number of basic data in the scenic area. Through the traditional and/or non-special sensors of the Internet of Things, a large amount of special -temporal change data is collected, including video monitors data, RFID, WIFI, temperature and humidity, water depth sensors and other big data of the Internet of Things(IoT)that can perceive the location and environmental resource information of tourists. In addition, the thermodynamic data of the distribution location of tourists' mobile phones, although these data include the behavior data of tourist groups and individuals in the scenic spot, which can be most shown by supplying, lack of in-depth analysis and intelligent service application.As we all know, Guilin, Guangxi is a famous tourist attraction in the word. In this tourism city, the big data processing methods of location service include establishing a data processing framework, information extraction, fusion and batch processing technology. As a result of the location data of tourists in the scenic spot are constantly created over time, the location service data will be processed by stream mode. At the same time, dimension reduction analysis of big data is an important link in processing as it has a large volume but a low-value density. The analysis methods of location service include kernel density analysis, GIS spatial and temporal analysis, artificial intelligence deep learning, two-dimensional mapping, visualization processing results, and extraction of hot spots or scenic spots, etc. Spatial and temporal index technology is used to manage the big data of scenic spot location service and to improve the efficiency of data query and access. At the same time, a reasonable predictive analysis of data processing results is an important research method.The main system structure of an intelligent service platform includes platform, data processing sing, and analysis end, mobile management end. The platform includes a general user service module, location service module, a communication module, etc, which is responsible for providing reasonable service to customers. The data processing and analysis end include a data receiving module and a data processing and analysis module. Responsible for receiving and processing the location service data returned by the client. The mobile management end includes the scenic spot management module. Responsible for providing the manager with the distribution of tourists in the scenic area, the location information of the staff, the location of the emergency situation and other contents, and providing great help for the manager to maintain the order of the scenic area, reasonably dispatch personnel, and launch rescue in time. Development and design to show users good models and development concepts and typical artificial intelligence products, to improve the scenic spot tourist's playing efficiency and humanized experience and reduce the management cost.