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Dashboard
The project utilizes Python Dash, a web application framework, to develop an interactive dashboard tailored to MFLG's branding and user interface requirements. Dash Bootstrap Components (dbc) are employed to create a user-friendly navigation bar, facilitating seamless navigation between different sections of the application. Dash, built on top of Flask, Plotly.js, and React.js, simplifies the development process, enabling the creation of rich, web-based dashboards using only Python code. This choice was made because Dash allows for easy incorporation of HTML and CSS, empowering us to customize the dashboard while maintaining interactivity and functionality.
We chose a line graph to show the monthly average ridership over time for its clarity and precision in presenting historical data. By including a dropdown feature, users can select multiple years within one graph, enhancing clarity. The solid line represents the average ridership, while dotted lines in various colors show data for individual years, allowing direct comparison and trend identification.
The histogram visualization of the data enables stakeholders to observe the distribution of ridership across different hours, offering valuable insights into visitor behaviour and daily patterns.
The choice of a bar chart for displaying the top 5 nationalities of ridership was made for its ability to clearly present categorical data, allowing easy comparison between different nationalities. This data is crucial as it provides insights into the demographic makeup of our visitors, aiding in tailoring services and marketing efforts to better meet their needs. Overall, the bar chart facilitates quick comprehension, enabling stakeholders to make informed decisions based on a comprehensive understanding of our ridership demographics.
Utilizing a stripped box plot with a slider for dynamic price range filtering, the visualization highlights variability in ticket prices within attractions and aids in comparing average price ranges. This analysis helps identify outliers and assesses competitors' pricing strategies against our own, facilitating informed decisions to remain competitive in the tourism market. Key features include strip chart formatting for clarity, line annotations for context, and a slider for focused price range selection in the dashboard. Additionally, data points are color-coded by attraction for visual coherence.
The visualization depicts the volume of tourist arrivals across months, with a focus on the period from 2008 to 2024. Line plots are used to display continuous variables effectively, making them suitable for demonstrating seasonal trends in visitor numbers. The x-axis indicates the months, while the y-axis represents the average number of visitors. It helps identify peak tourist seasons, providing valuable information for strategic decision-making for SkyHelix.
Demographic breakdowns of tourists by age group are explored for the years 2021 to 2024. Stacked bar plots are employed to compare multiple categories (e.g., youth and adult) across different years while also showing overall trends at a glance. The y-axis represents the years, while the x-axis denotes the average proportions of age groups. This analysis provides insights into shifting demographics and preferences among visitors to Singapore.
Further analysis explores the distribution of visitor arrivals across various countries and years. In this case, a strip plot is used because it excels at displaying individual data points, providing a detailed view of trends over time. Color encoding is also utilized to enhance clarity and help identify countries on the plot. The x-axis represents the years, while the y-axis denotes the volume of visitor arrivals.
Revenue data is obtained from MFLG for the SkyHelix attraction. From the revenue data, we can observe the trends of revenue gained across the years, and also across both B2C (Business to Customer) and OTC (Over The Counter) sales. For the dashboard component of company revenue focusing on the SkyHelix attraction, we incorporated the following elements.
A line graph is suitable for this purpose because it allows us to clearly see the trend of revenue over time. It helps in identifying patterns, such as whether revenue is increasing, decreasing, or remaining relatively stable over the years.
Dropdown
For the graph showing revenue source, the user can use the dropdown to select between B2C and OTC sales, or to observe the total across both categories.
For the segment on year, the user can use the dropdown to select the year to view and therefore compare across different years.