Wintour

Tourism Big Data: Tourism Big Data sources utilize mobile internet technology to collect real but anonymized visitor data from specific attractions. This data is cleaned, analyzed, and mined before integrating with third-party big data portrait tags. The analysis encompasses various dimensions such as historical visitor flow, popular routes, duration of stay, frequency of visits, sources of visitors, age distribution, gender distribution, educational background, occupation, marital status, modes of travel, consumer preferences, purchasing power, sensitivity to promotions, brand preferences, and evaluation analysis. Data display methods include:

• Visitor Flow Monitoring: Real-time monitoring and statistical analysis of visitor flow data in the attractions are displayed through data charts and heatmaps, covering both real-time and historical visitor data.
• Visitor Origin Statistics: Analysis of visitors' source locations to identify the main sources of tourists.
• Visitor Duration Analysis: Analysis of the time visitors spend in the attraction. This includes the relationship between the duration of stay and the number of visitors, the days spent by visitors, the time periods of visits, and rankings of attractions by visitor stay.
• Heatmaps: Display varying intensities of visitor concentrations in different areas using colors and brightness, with the most crowded areas typically marked in red. Heatmaps are essential for monitoring visitor density and congestion within regions of the attraction, providing real-time visual representation and alerts for areas with high visitor density.
• Visitor Profiling: In-depth analysis and cross-analysis of multi-source data to delineate key characteristics of visitors, such as transportation modes, age, gender, spending levels, and interests, supporting data-driven decision-making for targeted marketing.
• Sentiment Analysis: Analysis of public sentiment data from third-party platforms, including keyword data, online public opinion volume, key channels of public sentiment, and sentiment tendencies

Hotel Big Data: The Hotel Big Data application experimental teaching system interfaces, collects, cleans, analyzes, and mines relevant data from individual hotels. Specific analyses include:

• Room Management Analysis: Visualization and analysis of room revenue, occupancy rates, average room rates, average revenue per room, and average duration of stay across multiple indices and channels.
• Catering Operation Analysis: Analysis of restaurant revenue, menu items, payment channels and methods, menu pricing, and average customer spending at different meals (breakfast, lunch, dinner).
• Energy Consumption Monitoring: Analysis and visual display of various forms of energy usage in hotels, including electricity, water, and gas consumption across different time periods.
• Consumable Items Analysis: Analysis of room consumables and individual room consumables based on OMS data processing.
• Customer Profiling Analysis: Clustering analysis to segment user profiles, using heatmaps for visual representation, and categorizing guests by value for further profile visualization and analysis.

Big data technology is applied across all segments of a big data system's end-to-end process, including data access, data preprocessing, data storage, data processing, data visualization, data governance, as well as security and privacy protection. In big data application courses, students need to learn and master the entire key steps from data access to data visualization.

1. Data Access: Given the diverse sources, large volumes, and high generation speeds of big data, it is crucial to ensure the reliability and efficiency of data access. Additionally, avoiding duplicate data and standardizing data access can significantly reduce the costs of subsequent maintenance and use.
2. Data Preprocessing: After data collection, the next stage is data preprocessing, which includes data cleaning, standardization, and formatting. Data cleaning primarily addresses inconsistencies, invalid data, or missing data within the dataset; standardization eliminates semantic ambiguities; and formatting involves storing data from various sources and types into a standardized database.
3. Data Processing: The goal of data processing is to transform large amounts of chaotic or complex data into valuable, easy-to-understand information. This process includes the real-time collection and computation of data to quickly produce results that support decision-making.
4. Data Visualization: Data is intuitively displayed through charts, 2D/3D views, and other graphical means to reflect trends across various metrics, supporting user analysis, monitoring, and data mining. Data visualization technology enables users to perform data analysis within a customized visual interface, helping them deduce relationships and causality between data points.

On the Wintour Tourism (Hotel) Big Data Application Platform, all practical training tasks are designed to start from raw data in the tourism or hotel industry. Students are trained step-by-step to handle data access, preprocessing, processing, and visualization for various data types and sources, thus mastering the implementation processes and methodologies of big data technology.