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Background

The Crown Jewel of the Formula One Race Circuit, backdrop of the successful Hollywood Film “Crazy Rich Asian” and the honorable host of the Memorable North Korea-United States Summit, Singapore’s ability to position herself as a neutral yet vibrant destination has led to hordes of visitors setting foot onto her sunny shores. It is no surprise that the tourism sector has been developing into a growth engine for Singapore’s economy. For 2017, Singapore’s tourism sectors attained records highs in both tourists’ arrivals and spending. According to the data released by Singapore Tourism Board, the number of arrivals increased by 6.2 per cent to $17.4 million, while tourism receipts increased by 3.9 per cent to $26.8 million. The increasing affordability of travel, with the prevalence of low-cost carriers globally, as contribute to the opportunistic trend.

Motivation

During our exploratory analysis on the data comprising of the tourism arrival into Singapore, we noticed that the arrival patterns of tourists from respective countries at heterogenous. A keen understanding to the unique of arrival patterns can reveal the travel preference which is essential for businesses to attract more tourism receipts to boost their business revenue. The ability of the analysts to grapple the data and transform the insights into actionable business decision will see their businesses flourishes.

With the recent completion of Marina Cruise Centre and ongoing construction of Jewel Changi Airport, the tourism receipts are expected to continue to grow steady for the next decade, barring any black swans.

Objectives

We aim to build an interactive platform to illustrate the trends and seasonality within given time-series data on Singapore tourism sector. Users can have a better understanding of the Singapore tourism situation over the last ten years.

Through this project, we hope that the tourism industry business, especially the small and medium business (check with the SME contribution to Singapore economy or tourism industry) can make optimal marketing solutions and business decision. We attempt to create the platform that assist the business owners and analysts to detect some useful insights from the relationship between travelling revenue and expenditure to promote the economic growth.

• The platform can give us the overview on the visitors’ arrivals pattern by country, age and different transportation methods.

• It also provides the geographic map to illustrate the visitor density among different countries.

• Tourism demand forecasting

Data Source

The Consumer Price Index (CPI) data is extracted from data.gov.sg^[1] in a monthly format which reveals the figures from January 1961 to August 2017, while the index reference period is 2014. The data has an overall index representing changes in the price level of the whole basket with all items considered, and can also be drilled down to sub-indices and sub-sub-indices for different categories and sub-categories of goods and services. For our system analysis, we plan to use filtered data from 1990 onwards.

Methodology

Exploratory Analysis

We will explore the different trends of time-series data provided by the various economic data sets (Period cyclicity and seasonality). Different interactions of identified attributes might provide certain data insights that we can use for our analysis.

Explanatory Analysis

Relationships between our data will be explained based on our understanding of possible real-world events or causes. Using our CPI use-case as an example, the difference in CPI between the months of June and December can be explained as a result of the holiday seasons causing an increase of demand for clothing in December.

Predictive Analysis

We can use analytics techniques such as Exponential Smoothing and ARIMA to predict future trends of our time-series data, due to the data's cyclical and seasonal nature.

Application

The proposed system would have three major functions: